Low-Earth-Orbit (LEO) satellite constellations are becoming integral to 6G infrastructure, but increasing in-orbit computation accelerates battery degradation and raises sustainability concerns. Meanwhile, renewable-heavy regions worldwide experience persistent energy curtailment due to transmission bottlenecks, leaving substantial clean energy stranded near generation sites. We identify a satellite-grid co-design opportunity: adaptively offloading task-critical data from satellite to data centers co-located with renewable power plants. However, realizing this vision requires jointly considering intermittent and capacity-limited communication windows, as well as time-varying electricity budgets. In this paper, we propose SQSO, a Sustainable and QoS-aware Satellite Offloading framework that models per-interval task offloading as a constrained optimization over dynamic topology and electricity prices. Under this framework, we design $\text{AO}^2$, an adaptive offloading orchestration algorithm to solve the formulated optimization problem. Using Starlink-scale simulations and real-world electricity price traces, $\text{AO}^2$ reduces energy consumption by up to 76.03% and battery life consumption by up to 76.85% compared to state-of-the-art schemes, while also lowering task delay. This work highlights that sustainable scaling of LEO constellations requires co-design of space networking and renewable energy infrastructure, while our solution promotes renewable-aware task offloading and cross-domain collaboration for space-energy integration in the 6G era.

Multimodal large language models (MLLMs) are increasingly integrated into video conferencing, but mostly for speech-centric tasks such as transcription and summarization. Conferencing adaptation remains dominated by signal-driven congestion control that reacts to bandwidth changes without understanding why certain moments or streams will soon become quality-critical. This separation wastes predictive structure in conversation: text often reveals impending role shifts and interaction-mode transitions—for example, taking the floor or initiating screen sharing—seconds before the corresponding media and bandwidth demands materialize.

Real-time imaging sonar is crucial for underwater monitoring where optical sensing fails, but its use is limited by low uplink bandwidth and severe sonar-specific artifacts (speckle, motion blur, reverberation, acoustic shadows) affecting up to 98% of frames. We present SCOPE, a self-supervised framework that jointly performs compression and artifact correction without clean–noise pairs or synthetic assumptions. SCOPE combines (i) Adaptive Codebook Compression (ACC), which learns frequency-encoded latent representations tailored to imaging sonar, with (ii) Frequency-Aware Multiscale Segmentation (FAMS), which decomposes frames into low-frequency structure and sparse high-frequency dynamics while suppressing rapidly fluctuating artifacts. A hedging training strategy further guides frequency-aware learning using low-pass proxy pairs generated without labels. Evaluated on months of in-situ ARIS sonar data, SCOPE achieves a structural similarity index (SSIM) of 0.77, representing a 40% improvement over prior self-supervised denoising baselines, at bitrates down to ≤ 0.0118 bpp. It reduces uplink bandwidth by more than 80% while improving downstream detection. The system runs in real time, with 3.1 ms encoding on an embedded GPU and 97 ms full multi-layer decoding on the server end. SCOPE has been deployed for months in three Pacific Northwest rivers to support real-time salmon enumeration and environmental monitoring in the wild. Results demonstrate that learning frequency-structured latents enables practical, low-bitrate sonar streaming with preserved signal details under real-world deployment conditions.

Low Earth Orbit (LEO) satellite networks such as Starlink and Project Kuiper are increasingly integrated with cloud infrastructures, forming an important internet backbone for global web services. By extending connectivity to remote regions, oceans, and disaster zones, these networks enable reliable access to applications ranging from real-time WebRTC communication to emergency response portals. Yet the resilience of these web services is threatened by space radiation: it degrades hardware, drains batteries, and disrupts continuity, even if the space-cloud integrated providers use machine learning to analyze space weather and radiation data. Specifically, conventional fixes like altitude adjustments and thermal annealing consume energy; neglecting this energy use results in deep discharge and faster battery aging, whereas sleep modes risk abrupt web session interruptions. Efficient network-layer mitigation remains a critical gap. We propose RALT (Radiation-Aware LEO Transmission), a control-plane solution that dynamically reroutes traffic during radiation events, accounting for energy constraints to minimize battery degradation and sustain service performance. Our work shows that unlocking space-based web services' full potential for global reliable connectivity requires rethinking resilience through the lens of the space environment itself.

WiFi-based passive indoor localization has gained prominence owing to its high accuracy and ease of deployment in GPS-denied environments. However, Channel State Information (CSI)-based systems face challenges, including high data acquisition requirements, significant computational overhead, and limited transferability. In this paper, we introduce DBLoc, a WiFi localization system that leverages beamforming feedback information (BFI), a novel attribute provided by modern WiFi hardware. BFI’s clear-text transmission and stable characteristics make it an ideal choice for localization tasks. We prove that BFI provides a lightweight alternative to CSI, significantly reducing both data acquisition and storage requirements. Compared with traditional deep learning frameworks using convolutional networks, DBLoc employs a pruning-based residual architecture to reduce computational overhead, achieving an inference cost of only 175.7 MFLOPs, thus optimizing performance within an edge-deployment budget. To enable transferability that surpasses current meta-learning approaches, DBLoc incorporates a virtual-domain-based meta-learning algorithm, ensuring robust performance with minimal target-domain data. Additionally, a spatial-encryption mechanism is proposed to safeguard the BFI-based model from eavesdropping. Extensive evaluations demonstrate that DBLoc achieves a median localization error of approximately 0.5 m, while significantly reducing localization accuracy for unauthorized attackers.

Federated Learning (FL) is a promising learning paradigm that allows for training a shared model by coordinating multiple distributed devices, namely, clients, without exposing their raw data. To mitigate excessive communication overhead and enhance practicality, a variant known as Hierarchical FL (HFL) has been introduced, which integrates edge servers between the cloud server and clients. In HFL, the number of potential clients is typically large, making full client participation impractical due to various resource constraints. As a result, it is essential to develop a sampling strategy that effectively selects suitable clients for federated optimization. While several methods have been proposed to protect the privacy of communicated models, we argue that the outcomes of client sampling are closely tied to the local data of clients, thereby raising privacy concerns, like the risk of differential attacks. Motivated by this, we propose a Two-step Privacy-Preserving client Sampling framework (TPPS) designed to protect against both attacks on communicated models and potential vulnerabilities in client sampling outcomes. Initially, we consider the diverse privacy requirements of clients by presenting a double-layer noise mechanism. We then conduct a thorough analysis of the impact of this noise mechanism, proposing a novel client sampling strategy that seeks to balance the trade-off between privacy and training performance. The insight lies in maintaining a real-time sampling probability for each client, which can be acutely tuned based on personalized privacy needs and previous training feedback. We provably show that TPPS achieves local differential privacy, a bounded sampling regret, and a privacy-related convergence rate. Furthermore, we conduct extensive simulations based on open datasets, showing the robustness and applicability of TPPS in enhancing privacy while optimizing HFL performance.

Domainadaptation has proven effective for suppressing the inter-subject variability problem in cross-subject EEG classification tasks in which labeled data is available for source subjects while only unlabeled data is provided for target subjects. Existing domain adaptation methods typically reduced the distribution discrepancy between source and target domains by directly utilizing source domain samples or features. To safeguard the privacy of source domain data, we propose to construct a Proxy Domain by simultaneously considering the prediction Consistency and Confidence (PDCC) of locally trained source models on target EEG samples, serving as the substitute to the source domain. The framework commences with the augmentation and alignment of the source domain data to enhance feature generalizability, after which source models are trained independently on each source subject's data in a decentralized manner. Knowledge transfer from source to target domains is achieved exclusively through accessing to the source domain model, enabling the PDCC-based proxy domain construction that encapsulates the source knowledge. Finally, domain adaptation is performed using the proxy domain and target domain. As a result, PDCC eliminates the need to access source domain data while effectively leveraging source knowledge. Experimental results on four benchmark EEG datasets demonstrate that PDCC consistently outperforms eleven existing methods, including several advanced transfer learning and source-free methods. Especially, the effectiveness of the proxy domain is extensively investigated.

With the proliferation of mobile sensing technology, substantial time series data have been generated and accumulated in various distributed domains, providing the basis for practical applications. Federated Learning (FL) has emerged as an essential framework for machine learning on decentralized data, especially with its potential for privacy-preserving. However, deployed with distributed and various edge devices, existing FL frameworks struggle to address the statistical heterogeneity of spatial feature distribution shifts from heterogeneous sensors. Due to the complex temporal dynamics of real-world time series data, temporal feature heterogeneity may result in suboptimal model adaptation performance. To this end, we propose a novelFederated learning approach withOrthogonal regularization forSpatial-Temporal heterogeneity (FedOST) on time series classification. It is featured in three aspects: (1) In the local training phase, we utilize an orthogonal projection to disentangle and align the shared and personalized features, as well as complementary information from different views of the time series data to formulate a robust multi-view training. (2) In the global aggregation phase, we adopt trainable global prototypes to improve feature space separation through orthogonal constraint, to serve as refined global knowledge in the local training. (3) In the testing phase, we leverage an uncertainty-aware test-time adaptation scheme to tackle the temporal feature shifts of unlabeled test data. We conduct extensive evaluations on real-world datasets, where FedOST outperforms existing state-of-the-art baselines with significant advantages.

While 3D Gaussian Splatting (3DGS) has revolutionized static scene representation, the extension to dynamic scene, i.e., 3DGS video (GSV), faces challenges related to reconstruction quality, rendering speed, and storage requirements. The substantial data volume of current GSV poses significant hurdles for streaming applications, particularly in the realm of AR, VR and MR. To tackle these challenges, we introduce 4DGStream, a novel framework that integrates an efficient GSV compression method, Light4D, and a bitrate adaptation streaming strategy, QoSmooth, to ensure smooth playback while maintaining high visual quality. Light4D employs a binarizationassisted spatiotemporal deformation network to model the deformation of Gaussian primitive attributes over time, while a spatiotemporal-aware masking module prunes trivial Gaussians, further enhancing long-term reconstruction quality. To reduce storage, Light4D uses a binary hash grid to model the entropy of attributes for arithmetic coding, with its binary nature allowing efficient entropy modeling via a Bernoulli distribution. These components enable Light4D to improve the FPS/Storage metric by up to 12.4× over SpacetimeGS and 26.4× over 4DGS on the Neu3D dataset, with performance gains exceeding 3× orders of magnitude compared to other NeRF-based state-of-the-art (SOTA) methods. Here, FPS/Storage reflects the balance between rendering speed and data storage. Despite significant model size reductions, Light4D maintains or surpasses the reconstruction quality of 4DGS. Furthermore, QoSmooth provides effective rate control to enhance playback smoothness, reducing bitrate level switches by 61.6% and increasing time-average utility by 26.2%. All these improvements make 4DGStream highly suited for GSV streaming, improving QoE by 36.7% compared to SOTA methods.

The widespread integration of the Internet of Things with sensors like depth-of-field cameras, LiDAR scanners, and eye-tracking infrared sensors, in head-mounted devices, has ushered in a new era of immersive digital experiences. Full-scene volumetric video (VV), a key innovation in this integration, provides a deeply immersive experience by capturing the richness and detail of the 3D world. However, its massive data volume presents significant streaming challenges. While 3D tile-based viewport approaches have been proposed, they struggle to full-scene VV given the small video buffer limitation, high tile segmentation overhead, and lack of full-scene consideration. In this work, inspired by the advancements of implicit neural radiance field (NeRF), we present \({\mathsf{V}^{2}\mathsf{NeRF}}\) , a novel full-scene VV streaming system featured by layered representation. It harmonizes the NeRF with explicit point clouds to represent the static background and dynamic foreground, thereby avoiding large data transfers and achieving photorealistic content representation. To tackle the issues of intensive computation requirements and multiscale adaptation scheduling within \({\mathsf{V}^{2}\mathsf{NeRF}}\) system, we propose a lightweight non-visible background removal method and a two-stage decoupled architecture. In addition, an efficient buffer-aware simulated annealing algorithm is developed, alongside the utilization of a perceptually learned metric, to enhance user experience. We further discuss the concerns about practical development and deployment. Extensive prototype evaluations demonstrate \({\mathsf{V}^{2}\mathsf{NeRF}}\) ’s superior streaming and viewing performance on a wide variety of networks, viewing motions, and scenes. For instance, compared to state-of-the-art approaches, it achieves a 24% increment in perceptual quality, an 83% reduction in rebuffering time, and a 54% enhancement in user experience on average.

Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates with a parameter server over the Internet. To accelerate the communication speed, it has been explored to deploy a programmable switch (PS) in lieu of the parameter server to coordinate clients. The challenge to deploy the PS in FL lies in its scarce memory space, prohibiting running memory consuming aggregation algorithms on the PS, like TopK. To overcome this challenge, we propose Federated Learning In-Network Aggregation with Global TopK Model Updates (Fed-GTopK) algorithm, consisting of two phases: voting and aggregating. In the voting phase, clients efficiently upload their votes for top model updates to the PS for selecting global top ones. Note that the voting phase consumes little memory or communication resources by only exploring the sparsity of top model updates without transmitting any model update values. In the aggregating phase, clients can reach the consensus to upload global top model updates such that the PS can swiftly aggregate global top model updates in a streamline manner without consuming much memory cost. Compared with existing works, our study is the first one accelerating in-network aggregation for FL by sparsifying model updates, and hence achieving the highest compression rate and the best learning performance. Finally, we conduct extensive experiments by using public datasets to demonstrate that Fed-GTopK remarkably surpasses the state-of-the-art baselines in terms of both model accuracy and communication traffic.

Vision Language Models (VLMs) can enhance Internet of Things (IoT) applications by efficiently extracting valuable information from excessively long videos captured by IoT cameras. Due to the large volume of video data and the high computation overhead of VLMs, a practical deployment strategy is to transmit the video to the cloud only on demand and also deploy the VLMs on the cloud for video analytics. Yet, the interaction experience between humans and VLMs is degraded by the high latency in such cloud-edge collaboration applications. The latency is caused by both the video transmission process and the heavy VLM inference process. We propose SwiftReTaKe, a two-round transmission framework coupled with a low-latency pre-pruning strategy to reduce both network and inference latency. By first sending keyframes for relevance estimation and then adaptively transmitting informative frames, SwiftReTaKe minimizes data transfer and LLM computation. Compared to the state-of-the-art (SOTA) long video processing method, SwiftReTaKe reduces the latency by 6 times with only 3.33% accuracy drop.

Immersive video serves as a key component of eXtended Reality (XR) that aims to create and interact with simulated virtual or hybrid environments. Such a technology allows users to experience immersive sensations that transcend time and space, and meanwhile continuously providing training data for emerging technologies like Embodied AI. Thanks to the advancements in sensing, computing, and display, recent years have witnessed many excellent works for XR and related hardware or software systems. However, challenges like high creation cost, lack of immersion, and limited scalability hinder the practical application of immersive video services. Whilst recently emerged generative artificial intelligence (GenAI) provides us with new insights in tackling existing challenges. In this paper, we conduct a comprehensive survey into the recent advances and future opportunities on how GenAI can benefit immersive video services. By introducing a systematic taxonomy, we meticulously classify the pertinent techniques and applications into three well-defined categories aligned with the pipeline of immersive video service: content creation, network delivery, and client-side display. This categorization enables a structured exploration of the diverse roles on how GenAI can benefit immersive video service, providing a framework for a more comprehensive understanding and evaluation of these technologies. To the best of our knowledge, this work is the first systematic survey of GenAI in XR settings, laying a foundation for future research in this interdisciplinary domain.

Wild salmon are essential to the ecological, economic, and cultural sustainability of the North Pacific Rim. Yet climate variability, habitat loss, and data limitations in remote ecosystems that lack basic infrastructure support pose significant challenges to effective fisheries management. This project explores the integration of multimodal foundation AI and expert-in-the-loop frameworks to enhance wild salmon monitoring and sustainable fisheries management in Indigenous rivers across Pacific Northwest. By leveraging video and sonar-based monitoring, we develop AI-powered tools for automated species identification, counting, and length measurement, reducing manual effort, expediting delivery of results, and improving decision-making accuracy. Expert validation and active learning frameworks ensure ecological relevance while reducing annotation burdens. To address unique technical and societal challenges, we bring together a cross-domain, interdisciplinary team of university researchers, fisheries biologists, Indigenous stewardship practitioners, government agencies, and conservation organizations. Through these collaborations, our research fosters ethical AI co-development, open data sharing, and culturally informed fisheries management.

Recent breakthroughs in generative artificial intelligence (AI) are transforming multimedia communication. This paper systematically reviews key recent advancements across generative AI for multimedia communication, emphasizing transformative models like diffusion and transformers. However, conventional information-theoretic frameworks fail to address semantic fidelity, critical to human perception. We propose an innovative semantic information-theoretic framework, introducing semantic entropy, mutual information, channel capacity, and rate-distortion concepts specifically adapted to multimedia applications. This framework redefines multimedia communication from purely syntactic data transmission to semantic information conveyance. We further highlight future opportunities and critical research directions. We chart a path toward robust, efficient, and semantically meaningful multimedia communication systems by bridging generative AI innovations with information theory. This exploratory paper aims to inspire a semantic-first paradigm shift, offering a fresh perspective with significant implications for future multimedia research.

Multimedia systems underpin modern digital interactions, facilitating seamless integration and optimization of resources across diverse multimedia applications. To meet growing personalization demands, multimedia systems must efficiently manage competing resource needs, adaptive content, and user-specific data handling. This paper introduces Generative Flow Networks (GFlowNets, GFNs) as a brave new framework for enabling personalized multimedia systems. By integrating multi-candidate generative modeling with flow-based principles, GFlowNets offer a scalable and flexible solution for enhancing user-specific multimedia experiences. To illustrate the effectiveness of GFlowNets, we focus on short video feeds, a multimedia application characterized by high personalization demands and significant resource constraints, as a case study. Our proposed GFlowNet-based personalized feeds algorithm demonstrates superior performance compared to traditional rule-based and reinforcement learning methods across critical metrics, including video quality, resource utilization efficiency, and delivery cost. Moreover, we propose a unified GFlowNet-based framework generalizable to other multimedia systems, highlighting its adaptability and wide-ranging applicability. These findings underscore the potential of GFlowNets to advance personalized multimedia systems by addressing complex optimization challenges and supporting sophisticated multimedia application scenarios.

Over the past decade, adaptive video streaming technology has witnessed significant advancements, particularly driven by the rapid evolution of deep learning techniques. However, the black-box nature of deep learning algorithms presents challenges for developers in understanding decision-making processes and optimizing for specific application scenarios. Although existing research has enhanced algorithm interpretability through decision tree conversion, interpretability does not directly equate to developers' subjective comprehensibility. To address this challenge, we introduce ComTree, the first bitrate adaptation algorithm generation framework that considers comprehensibility. The framework initially generates the complete set of decision trees that meet performance requirements, then leverages large language models to evaluate these trees for developer comprehensibility, ultimately selecting solutions that best facilitate human understanding and enhancement. Experimental results demonstrate that ComTree significantly improves comprehensibility while maintaining competitive performance, showing potential for further advancement. The source code and appendix are available at https://github.com/thu-media/ComTree.

Neural enhancement through super-resolution (SR) deep neural networks (DNNs) opens up new possibilities for ultra-high-definition (UHD) live streaming. Yet, the heavy SR DNN inference overhead leads to severe deployment challenges. To reduce the overhead, existing systems propose to apply DNN-based SR only on carefully selected anchor frames while upscaling non-anchor frames via the lightweight reusing-based SR approach. However, frame-level scheduling is coarse-grained and fails to deliver optimal efficiency. In this work, we propose Palantír, the first neural-enhanced UHD live streaming system with fine-grained patch-level scheduling.

Control algorithms in production environments typically require domain experts to tune their parameters and logic for specific scenarios. However, existing research predominantly focuses on algorithmic performance under ideal or default configurations, overlooking the critical aspect of Tuning Potential. To bridge this gap, we introduce \texttt{Crucible}, an agent that employs an LLM-driven, multi-level expert simulation to turn algorithms and defines a formalized metric to quantitatively evaluate their Tuning Potential. We demonstrate \texttt{Crucible}'s effectiveness across a wide spectrum of case studies, from classic control tasks to complex computer systems, and validate its findings in a real-world deployment. Our experimental results reveal that \texttt{Crucible} systematically quantifies the tunable space across different algorithms. Furthermore, \texttt{Crucible} provides a new dimension for algorithm analysis and design, which ultimately leads to performance improvements. Our code is available at https://github.com/thu-media/Crucible.

Traditional optimization methods based on system-wide Quality of Service (QoS) metrics have approached their performance limitations in modern large-scale streaming systems. However, aligning user-level Quality of Experience (QoE) with algorithmic optimization objectives remains an unresolved challenge. Therefore, we propose LingXi, the first large-scale deployed system for personalized adaptive video streaming based on user-level experience. LingXi dynamically optimizes the objectives of adaptive video streaming algorithms by analyzing user engagement. Utilizing exit rate as a key metric, we investigate the correlation between QoS indicators and exit rates based on production environment logs, subsequently developing a personalized exit rate predictor. Through Monte Carlo sampling and online Bayesian optimization, we iteratively determine optimal parameters. Large-scale A/B testing utilizing 8% of traffic on Kuaishou, one of the largest short video platforms, demonstrates LingXi's superior performance. LingXi achieves a 0.15% increase in total viewing time, a 0.1% improvement in bitrate, and a 1.3% reduction in stall time across all users, with particularly significant improvements for low-bandwidth users who experience a 15% reduction in stall time.

Recent advancements in 3D Gaussian Splatting (3DGS) have demonstrated its potential for efficient and photorealistic 3D reconstructions, which is crucial for diverse applications such as robotics and immersive media. However, current Gaussian-based methods for dynamic scene reconstruction struggle with large inter-frame displacements, leading to artifacts and temporal inconsistencies under fast object motions. To address this, we introduce TrackerSplat, a novel method that integrates advanced point tracking methods to enhance the robustness and scalability of 3DGS for dynamic scene reconstruction. TrackerSplat utilizes off-the-shelf point tracking models to extract pixel trajectories and triangulate per-view pixel trajectories onto 3D Gaussians to guide the relocation, rotation, and scaling of Gaussians before training. This strategy effectively handles large displacements between frames, dramatically reducing the fading and recoloring artifacts prevalent in prior methods. By accurately positioning Gaussians prior to gradient-based optimization, TrackerSplat overcomes the quality degradation associated with large frame gaps when processing multiple adjacent frames in parallel across multiple devices, thereby boosting reconstruction throughput while preserving rendering quality. Experiments on real-world datasets confirm the robustness of TrackerSplat in challenging scenarios with significant displacements, achieving superior throughput under parallel settings and maintaining visual quality compared to baselines. The code is available at https://github.com/yindaheng98/TrackerSplat.

With the rapid development of big data, Federated learning (FL) has found numerous applications, enabling machine learning (ML) on edge devices while preserving privacy. However, FL still faces crucial challenges, such as single point of failure and poisoning attacks, which motivate the integration of blockchain-enabled FL (BeFL). Beyond that, the efficiency issue still limits the further application of BeFL. To address these issues, we propose a novel decentralized framework: Accelerating Blockchain-Enabled Federated Learning with Clustered Clients (ABFLCC), who utilize actual training time for clustering clients to achieve hierarchical FL and solve the single point of failure problem through blockchain. Additionally, the framework clusters edge devices considering their actual training times, which allows for synchronous FL within clusters and asynchronous FL across clusters simultaneously. This approach guarantees that devices with a similar training time have a consistent global model version, improving the stability of the converging process, while the asynchronous learning between clusters enhances the efficiency of convergence. The proposed framework is evaluated through simulations on three real-world public datasets, demonstrating a training efficiency improvement of 30% to 70% in terms of convergence time compared to existing BeFL systems.

Federated learning (FL) is a distributed learning paradigm that enables multiple clients to train a unified model without sharing their private data. However, recent works demonstrate that FL models are vulnerable to membership inference attacks (MIAs), which can infer whether a data sample was used to train a given FL model. Existing countermeasures either require far-reaching modifications of FL training process or enforce extra processing in prediction phase, yielding them unlikely to be applied well in practice. In this paper, we design a post-protection mechanism, dubbedP$^{2}$-Protection, which degrades the inference performance of MIAs by simultaneously poisoning the prediction and gradient of the target FL model to reduce the privacy leakage of training data while keeping the model prediction accuracy.P$^{2}$-Protectiononly involves one additional training round to embed the poisoned prediction and gradient into the target FL model, without requiring model retraining or training process modification. We evaluateP$^{2}$-Protectionand compare it with two state-of-the-art defenses against three MIAs on five realistic datasets. Experimental results show thatP$^{2}$-Protectionoutperforms the existing defenses by offering limited implement overhead and improved utility-privacy trade-off.

To preserve the data privacy, the federated learning (FL) paradigm emerges in which clients only expose model gradients rather than original data for conducting model training. To enhance the protection of model gradients in FL, differentially private federated learning (DPFL) is proposed which incorporates differentially private (DP) noises to obfuscate gradients before they are exposed. Yet, an essential but largely overlooked problem in DPFL is the heterogeneity of clients' privacy requirement, which can vary significantly between clients and extremely complicates the client selection problem in DPFL. In other words, both the data quality and the influence of DP noises should be taken into account when selecting clients. To address this problem, we conduct convergence analysis of DPFL under heterogeneous privacy, a generic client selection strategy, popular DP mechanisms and convex loss. Based on convergence analysis, we formulate the client selection problem to minimize the value of loss function in DPFL with heterogeneous privacy, which is a convex optimization problem and can be solved efficiently. Accordingly, we propose the DPFL-BCS (biased client selection) algorithm. The extensive experiment results with real datasets under both convex and non-convex loss functions indicate that DPFL-BCS can remarkably improve model utility compared with the SOTA baselines.

As a fundamental tool for sequential decision-making, the Combinatorial Multi-Armed Bandits model (CMAB) has been extensively analyzed and applied in various online applications. However, the privacy concerns in budgeted CMAB are rarely investigated thus far. Few bandit algorithms have adequately addressed the privacy-preserving budgeted CMAB setting. Motivated by this, we study this setting using differential privacy as the formal measure of privacy. In this setting, playing an arm yields both a random reward and a random cost, and these values are kept private. In addition, multiple arms can be played in each round. The objective of the decision-maker is to minimize regret while subject to a budget constraint on the cumulative cost of all played arms. We demonstrate an exploration-exploitation-balanced bandit policy, which preserves the privacy of both rewards and costs under budgeted CMAB settings. This policy is proven differentially private and achieves an upper bound on regret. Furthermore, to provide incentives for the differentially private bandit policy so as to ensure that the reported costs are truthful, we introduce the concept of truthfulness and incorporate a payment mechanism that has been proven to be$\sigma$-truthful. Numerical simulations based on multiple real-world datasets validate the theoretical findings and demonstrate the effectiveness of our policy compared to state-of-the-art policies.

We study an intriguing and practical scenario of online Spatial Crowdsourcing (SC), in which workers have the flexibility to perform tasks using various methods, such as walking, driving, or utilizing remote aerial vehicles (RAVs). This results in workers having heterogeneous, arbitrary, and non-stationary utilities over time. We refer to this scenario as integrated SC. Unfortunately, existing studies are limited in addressing integrated SC settings due to two aspects: (1) these studies are based on the assumption that workers’ utilities are independently and identically distributed and follow a stationary distribution like Gaussian, which does not hold in integrated SC; (2) their approaches fail to provide personalized privacy preservation for different workers. Motivated by these limitations, we closely investigate the heterogeneous utility and personalized privacy requirement in integrated SC and propose an Online Personalized Privacy-preserving Selection framework (OPPS). In this framework, we present an online selection policy that balances the exploration-exploitation trade-off given heterogeneous utilities and develop a built-in privacy policy that ensures differential privacy guarantee. We then demonstrate that our framework effectively addresses the trade-off by deriving a sublinear, privacy-related upper bound on regret that scales as$O(\sqrt{T})$. Extensive numerical simulations based on real-world drone datasets are conducted to validate the effectiveness of our framework compared with state-of-the-art approaches.

Physical layer steganography plays a key role in physical layer security. Yet most works are strongly modulation-sensitive and have to modify the modulation at the baseband. However, these methods cannot work with wireless devices whose baseband modulations cannot be software-defined. To overcome these drawbacks, we propose an analog solution that uses a symbiotic hardware component designed, called Pluggable Cloak, connecting to the radio frequency front end (RFFE) to establish a steganographic symbiotic channel (SSC) over constant envelope physical layer (CE-PHY) in 2.4GHz ISM band, such as Bluetooth, ZigBee and 802.11b Wi-Fi, to hide information. The advantage lies in enabling secure transmission of the deployed devices that are not software-defined with this pluggable hardware. Specifically, Pluggable Cloak analogously modulates the amplitude of CE-PHY, so that sensitive information can be securely sent to a customized receiver without being detected by regular CE receivers. To further protect hidden information from the detection of a malicious adversary, we propose methods to randomize the SSC. We develop a lightweight prototype to evaluate symbiosis, undetectability, and throughput. The results show that the symbol error rates (SERs) of the sensitive data received and regular CE data are lower than$10^{-5}$at the customized receiver. In contrast, the SER of the sensitive data is close to 1 in the adversary, confirming the effectiveness of the SSC technique.

Tile-based streaming and super resolution (SR) are two representative technologies adopted to improve bandwidth efficiency of 360° video streaming. The former allows selective downloading of contents in the user viewport by splitting the video into multiple independently decodable tiles. The latter leverages client-side computation to enhance the received video to higher quality using advanced neural network models. In this work, we propose a Collaborated Streaming and Enhancement (CSE) adaptation framework for mobile 360° videos, which integrates super resolution with tile-based streaming to optimize the user experience with dynamic bandwidth and limited computing capability. To effectively enhance the tile-based video streaming through SR, we propose to adaptively group the tiles for quality enhancement adapting to the content similarity. We also identify and address several key design issues to integrate SR into tile-based video streaming including unified video quality assessment, computational complexity model for super resolution, and buffer analysis considering the interplay between transmission and enhancement. We further formulate the quality-of-experience (QoE) maximization problem for mobile 360° video streaming and propose a rate adaptation algorithm to make the best decisions for download and for enhancement based on the Lyapunov optimization theory. Extensive evaluation results validate the superiority of our proposed approach, which demonstrates stable performance with considerable QoE improvement, while enabling a trade-off between playback smoothness and video quality.

The development of artificial intelligence (AI) enables vehicular edge computing (VEC) servers to be able to provide more intelligent services. However, the limited storage resources of VEC servers constrain the deployment of intelligent service contents, which greatly restricts the intelligence level of the VEC network. To resolve this problem, we first design a novel vehicle-assisted VEC network architecture and further propose VaCo, aVehicle-assistedCollaborative caching system. VaCo allows VEC servers to download the cached service content from any vehicle in the VEC network to support task offloading. VaCo mainly considers the real-time scheduling problem of vehicle storage resources under the dynamic VEC network and the benefit problem caused by invoking vehicle resources under the highly dynamic load environment. VaCo models the vehicle storage resources as an independent resource pool and deploys a cross-VEC server content retrieval mechanism to achieve unified and efficient management of the storage resources of the vehicle cluster and the VEC server cluster. Then, we propose a multi-swarm collaborative optimization scheme to jointly optimize the service failure rate and cost, and further propose a Pareto-based optimization scheme to ensuring that VaCo can correctly evaluate the benefits of invoking vehicle resources in a dynamic VEC network. Finally, we implement VaCo and conduct extensive evaluations on real-world dataset. The experimental results on the real trajectory dataset show that VaCo can effectively utilize vehicle resources and ensure the benefits of both vehicles and VEC servers simultaneously.

Virtual Reality (VR) has emerged as a transformative platform for remote collaboration, but its adoption for video conferencing is hindered by challenges related to facial expression reconstruction and computational resource constraints, especially on economical mobile VR headsets. This paper introduces a novel system for VR video conferencing that addresses these challenges through two key modules: Predictive Generation and Collaborative Computation. Predictive Generation leverages multimodal inputs, including voice, head motion, and eye blinks, to synthesize realistic facial animations with low latency, eliminating the need for high-precision hardware. Collaborative Computation enhances computational efficiency by employing a game-theoretic framework for resource sharing among users. Experimental evaluations demonstrate that our system delivers immersive and realistic VR video conferencing experiences with superior facial expression reconstruction and efficient resource utilization. Our approach makes VR video conferencing more accessible and practical for a broader audience across mobile headsets.

The development of Internet of Things (IoT) technology has led to the emergence of a large number of Intelligent Sensing Devices (ISDs). Since their limited physical sizes constrain the battery capacity, wireless powered IoT networks assisted by Unmanned Aerial Vehicles (UAVs) for energy transfer and data acquisition have attracted great interest. In this paper, we formulate an optimization problem to maximize system energy efficiency while satisfying the constraints of UAV mobility and safety, ISD quality of service and task completion time. The formulated problem is constructed as a Constrained Markov Decision Process (CMDP) model, and a Multi-agent Constrained Deep Reinforcement Learning (MCDRL) algorithm is proposed to learn the optimal UAV movement policy. In addition, an ISD-UAV connection assignment algorithm is designed to manage the connection in the UAV sensing range. Finally, performance evaluations and analysis based on real-world data demonstrate the superiority of our solution.

Viewport prediction is the crucial task for adaptive 360-degree video streaming, as the bitrate control algorithms usually require the knowledge of the user's viewing portions of the frames. Various methods are studied and adopted for viewport prediction from less accurate statistic tools to highly calibrated deep neural networks. Conventionally, it is difficult to implement sophisticated deep learning methods on mobile devices, which have limited computation capability. In this work, we propose an advanced learning-based viewport prediction approach and carefully design it to minimize transmission and computation overhead for mobile terminals. To improve viewport prediction accuracy, we utilize both spatial information through a saliency prediction model and temporal information through a modified LSTM model. Different computations introduced by the neural network models are distributed across the network to keep the computation light on mobile devices. To better adapt to the content dynamics in live streaming, we employ the model-agnostic meta-learning (MAML) method for video saliency prediction. The learned saliency prediction model with optimized initialization via offline meta-training can be fast fine-tuned online using a few samples. We further discuss how to integrate this mobile-friendly viewport prediction (MFVP) approach into a typical 360-degree video live streaming system by formulating and solving the bitrate adaptation problem. Extensive experiment results demonstrate that our approach achieves real-time prediction for live video streaming and surpasses existing methods in prediction accuracy on mobile terminals, which, together with our bitrate adaptation algorithm, significantly improves the streaming QoE from various aspects. Compared to baseline methods, MFVP achieves a 4.7–28.7% improvement in accuracy and demonstrates faster adaptability to dynamic content changes, enabling rapid fine-tuning and adjustment. When integrated into a streaming system and paired with our adaptive bitrate allocation algorithm, MFVP enhances overall video quality by 5.6–12.9% and reduces quality fluctuations by 33.3–50.9%.

Networked 360$^\circ$video has become increasingly popular. Despite the immersive experience for users, its sheer data volume, even with the latest H.266 coding and viewport adaptation, remains a significant challenge to today's networks. Recent studies have shown that integrating deep learning into video coding can significantly enhance compression efficiency, providing new opportunities for high-quality video streaming. In this work, we conduct a comprehensive analysis of the potential and issues in applying neural codecs to 360$^\circ$video streaming. We accordingly present$\mathsf {NETA}$, a synergistic streaming scheme that merges neural compression with traditional coding techniques, seamlessly implemented within an edge intelligence framework. To address the non-trivial challenges in the short viewport prediction window and time-varying viewing directions, we propose implicit-explicit buffer-based prefetching grounded in content visual saliency and bitrate adaptation with smart model switching around viewports. A novel Lyapunov-guided deep reinforcement learning algorithm is developed to maximize user experience and ensure long-term system stability. We further discuss the concerns towards practical development and deployment and have built a working prototype that verifies$\mathsf {NETA}$’s excellent performance. For instance, it achieves a 27% increment in viewing quality, a 90% reduction in rebuffering time, and a 64% decrease in quality variation on average, compared to state-of-the-art approaches.

Recently, Low Earth orbit Satellite Networks (LSNs) have been suggested as a critical and promising component toward high-bandwidth and low-latency global coverage in the upcoming 6G communication infrastructure. SpaceX’s Starlink is arguably the largest and most operational LSN to date. There have been practical uses of Starlink across diverse networked applications, including those with stringent demands, such as multimedia applications. Given the mixed and inconsistent feedback from end users, it remains unclear whether today’s LSNs, in particular Starlink, are ready for realtime multimedia. In this article, we present a systematic measurement study on realtime multimedia services over Starlink, seeking insights into their operations and performance in this new generation of networking. Our findings demonstrate that Starlink can handle most video-on-demand (VoD) and live-streaming services with properly configured buffers but suffers from video pauses or audio cut-offs during interactive videoconferencing. We identify the key factors that impact the performance of LSN, particularly for multimedia services, including satellite switching, routing strategies, and weather conditions. Our findings offer valuable hints into future enhancements for multimedia services over LSNs. Specifically, we further propose a Weather Aware Buffer Based Rate Adaption algorithm based on our observations on weather impacts, which is capable of maximizing the quality of experience for VoD applications with seamless integration of dynamic weather conditions.

It is known that federated learning (FL) incurs heavy communication overhead for model training by exchanging model updates between clients and the parameter server (PS) over the Internet for multiple rounds. Compressing model updates is an effective approach to alleviating communication overhead in FL. Yet the tradeoff between compression and model accuracy in the networked environment remains unclear and, for simplicity, most implementations adopt a fixed compression rate only during the entire learning process. In this paper, we for the first time systematically examine this tradeoff, explicitly quantifying the relation between the compression error, the final model accuracy and the learning rate. Specifically, we factor the compression error of each global iteration into the convergence rate analysis under non-convex loss for both unbiased and biased compression algorithms. We then present an adaptation framework to maximize the final model accuracy by strategically adjusting the compression rate in each iteration. We further discuss key implementation issues of our framework in practical networks with classical compression algorithms. Experiments over the most representative MNIST, CIFAR-10 and CIFAR-100 datasets demonstrate that our solutions effectively shrink network traffic volume while maintain high model accuracy in FL.

Inter-Satellite Links (ISLs) are pivotal for delivering global connectivity services and optimizing resource utilization in 6 G and beyond. However, delivering effective topology control services through ISL provisioning faces critical challenges insustainabilityandreliability. Reducing ISLs can conserve energy and extend satellite battery life for Low-Earth-Orbit (LEO) satellites where replacing batteries is impractical. Conversely, increasing ISLs can enhance service reliability but may lead to uneven traffic distribution, overloading nodes, and accelerating battery degradation, ultimately degrading the quality of 6 G services. To tackle this dilemma, we propose TASRI—a service-oriented framework forTraffic-Aware, Sustainable, and Reliable ISL provisioning. TASRI provides a dynamic topology control service by partitioning network topologies into logical zones, enabling flexible ISL activation and deactivation to adapt to varying service demands, ensuring efficient resource utilization and dynamic service orchestration. Using a sustainability-oriented weight model, we formulate the topology control service optimization problem and introduce a scalable on-demand topology evolving algorithm with a bounded approximation ratio. Extensive real-world deployment-based simulation results show that, compared to the state-of-the-art, our TASRI can substantially reduce battery life consumption, while achieving comparable reliability and excellent scalability with considerably fewer ISLs or ISL handovers.

The rise of 5G communication has transformed the telecom industry for critical applications. With the widespread deployment of 5G base stations comes a significant concern about energy consumption. Key industrial players have recently shown strong interest in incorporating energy storage systems to store excess energy during off-peak hours, reducing costs and partic-ipating in demand response. The fast development of batteries opens up new possibilities, such as the transportation area. An effective method is needed to maximize base station battery utilization and reduce operating costs. In this trend towards next-generation smart and integrated energy-communication-transportation (ECT) infrastructure, base stations are believed to play a key role as service hubs. By exploring the overlap between base station distribution and electric vehicle charging infrastructure, we demonstrate the feasibility of efficiently charging EVs using base station batteries and renewable power plants at the Hub. Our model considers various factors, including base station traffic conditions, weather, and EV charging behavior. This paper introduces an incentive mechanism for setting charging prices and employs a deep reinforcement learning-based method for battery scheduling. Experimental results demonstrate the effectiveness of our proposed ECT-Hub in optimizing surplus energy utilization and reducing operating costs, particularly through revenue-generating EV charging.

Aggregating geospatial data plays a crucial role in location-based services. However, collecting such sensitive data raises concerns about location privacy leakage. Local Differential Privacy (LDP), as a de facto privacy paradigm, has been widely employed to ensure individual location privacy. Nonetheless, existing approaches for aggregating geospatial data under LDP either suffer from compromised accuracy or involve complex computations. In this work, we propose a history-aware geospatial data aggregation framework to enhance both accuracy and efficiency while guaranteeing LDP. To this end, we first investigate an efficient aggregation method, namely General Randomized Response (GRR), and find that its variance of aggregation error follows the sum of two zero-mean binomial distributions. This reveals that multiple aggregations can boost the accuracy of GRR. To obtain multiple aggregations without compromising privacy, we adopt a Markov transition model to complement current aggregations from historical ones. However, learning the Markov transition matrix on perturbed data is challenging. Accordingly, we propose a privacy-aware Markov Transition Matrix Estimation (MTME) algorithm. Finally, we introduce a truth discovery-based refinement algorithm to iteratively derive an accurate aggregated result from multiple inaccurate aggregations. We evaluate our proposed method on two real-world trajectory datasets, and thorough experiments demonstrate its superior accuracy and very low time overhead compared to competitors.

In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model distribution over multiple rounds, without touching private data owned by individual clients. FL is appealing in preserving data privacy; yet the communication between the PS and scattered clients can be a severe bottleneck. Model compression algorithms, such as quantization and sparsification, have been suggested but they generally assume a fixed code length, which does not reflect the heterogeneity and variability of model updates. In this paper, through both analysis and experiments, we show strong evidences that variable-length is beneficial for compression in FL. We accordingly present Fed-CVLC (Federated Learning Compression with Variable-Length Codes), which fine-tunes the code length in response of the dynamics of model updates. We develop optimal tuning strategy that minimizes the loss function (equivalent to maximizing the model utility) subject to the budget for communication. We further demonstrate that Fed-CVLC is indeed a general compression design that bridges quantization and sparsification, with greater flexibility. Extensive experiments have been conducted with public datasets to demonstrate that Fed-CVLC remarkably outperforms state-of-the-art baselines, improving model utility by 1.50%-5.44%, or shrinking communication traffic by 16.67%-41.61%.

Inter-Satellite Links (ISLs) are key for worldwide communication and efficient use of space networks in the future 6G network. However, they face challenges in sustainability and reliability. Reducing ISLs saves energy and extends battery life, which is critical since satellite batteries are hard to replace. More ISLs, however, can make the system more reliable but at the cost of higher energy use, especially problematic when traffic is uneven, speeding up battery wear. To tackle this dilemma, we for the first time develop a Traffic-Aware, Sustainable and Reliable ISL provisioning (TASRI) framework for LEO satellite constellation networks. In TASRI, ISLs can be flexibly switched on and off to better accommodate various traffic conditions as well as reliability and sustainability. We formulate the ISL provisioning problem based on the sustainability-oriented weight model and then propose an on-demand topology evolving algorithm. Extensive real-world deployment-based simulation results show that, compared to the state-of-the-art, our TASRI can substantially reduce battery life consumption, while achieving comparable reliability with considerably fewer ISLs.

Satellite networks are believed to become an indispensable component in the forthcoming 6G network and beyond. The surging demands attract numerous satellite network operators into this market to compete, yet also cooperate via resource sharing for cost and performance improvement, which is similar to the growth trajectory of how the Internet becomes the network of networks. Hence, we envision a federated network of satellite networks (shortened as federated satellite network) in this paper, where satellite network operators will eventually federate with each other to achieve a win-win situation. However, the yet-to-come federated satellite network faces two unique challenges: sustainability and dynamic topology. As such, we propose a sustainable and service-differentiable framework named Federated Cross-orbit Satellite Network (FCSN). Different from most existing solutions which focused on the Internet or simple cooperation among satellites, the FCSN orchestrates network resources in the dynamic topology to improve sustainability, through service-differentiable offloading in the resource-limited scenario. We formulate the sustainability-oriented federated offloading problem based on the utility and cost models tailored for the FCSN and propose an efficient hardware-budget constrained auction algorithm with a bounded approximation ratio. Finally, we design a truthful and rational payment scheme to motivate the construction of the FCSN. Extensive simulation results based on real-world deployments show that our solution significantly improves sustainability and delay, making it one step further toward the vision of the federated network of satellite networks.

Large Language Models (LLMs) have become increasingly popular, transforming a wide range of applications across various domains. However, the real-world effectiveness of their query cache systems has not been thoroughly investigated. In this work, we for the first time conducted an analysis on real-world human-to-LLM interaction data, identifying key challenges in existing caching solutions for LLM-based chat services. Our findings reveal that current caching methods fail to leverage semantic connections, leading to inefficient cache performance and extra token costs. To address these issues, we propose SCALM, a new cache architecture that emphasizes semantic analysis and identifies significant cache entries and patterns. We also detail the implementations of the corresponding cache storage and eviction strategies. Our evaluations show that SCALM increases cache hit ratios and reduces operational costs for LLMChat services. Compared with other state-of-the-art solutions in GPTCache, SCALM shows, on average, a relative increase of 63% in cache hit ratio and a relative improvement of 77% in tokens savings.

The rapid expansion of AI technology, driven by high-speed networks and high-performance mobile devices, enables personalized and cross-content recommendations across diverse applications. Recent methods consider a large number of textual content keywords or topics as labels for recommendations, transforming the problem into Extreme Multi-label Learning (XML). However, addressing the XML problem in AI-driven recommendation systems that handle extensive user-generated data while ensuring Quality of Service (QoS) faces two main challenges: significant computational costs and inferior tail label prediction performance. We propose SAT, a semantic-aware framework with a tree architecture that effectively tackles these challenges and demonstrates improved performance compared to well-established approaches.

Live streaming has experienced significant growth recently. Yet this rise in popularity contrasts with the reality that a substantial segment of the global population still lacks Internet access. The emergence of Low Earth orbit Satellite Networks (LSNs), such as SpaceX's Starlink and Amazon's Project Kuiper, presents a promising solution to fill this gap. Nevertheless, our measurement study reveals that existing live streaming platforms may not be able to deliver a smooth viewing experience on LSNs due to frequent satellite handovers, which lead to frequent video rebuffering events. Current state-of-the-art learning-based Adaptive Bitrate (ABR) algorithms, even when trained on LSNs' network traces, fail to manage the abrupt network variations associated with satellite handovers effectively. To address these challenges, for the first time, we introduce Satellite-Aware Rate Adaptation (SARA), a versatile and lightweight middleware that can seamlessly integrate with various ABR algorithms to enhance the performance of live streaming over LSNs. SARA intelligently modulates video playback speed and furnishes ABR algorithms with insights derived from the distinctive network characteristics of LSNs, thereby aiding ABR algorithms in making informed bitrate selections and effectively minimizing rebuffering events that occur during satellite handovers. Our extensive evaluation shows that SARA can effectively reduce the rebuffering time by an average of 39.41% and slightly improve latency by 0.65% while only introducing an overall loss in bitrate by 0.13%.

Given the truly immersive viewing experiences, full-scene volumetric videos have received increasing attention from both academia and industry. Their vast data volumes, however, present significant challenges for real-time streaming over today's bandwidth-limited Internet. Considering the vast amount of full-scene volumetric data to be streamed and the limited bandwidth on the Internet, achieving adaptive full-scene volumetric video streaming over the Internet presents a significant challenge. Inspired by the advantages offered by neural fields, especially the feature grid method, we propose FSVFG, a novel full-scene volumetric video streaming system integrated feature grids as the representation of volumetric content. FSVFG employs an incremental training approach for feature grids and stores the features and residuals between adjacent grids as frames. To support adaptive streaming, we delve into the data structure and rendering processes of feature grids and propose bandwidth adaptation mechanisms. The mechanisms involve a coarse ray-marching for the selection of features and residuals to be sent, and achieve variable bitrate streaming by Level-of-Detail (LoD) and residual filtering. Based on these mechanisms, FSVFG achieves adaptive streaming by adaptively balancing the transmission of feature and residual according to the available bandwidth. Our preliminary results demonstrate the effectiveness of FSVFG, demonstrating its ability to improve visual quality and reduce bandwidth requirements of full-scene volumetric video streaming.

Streaming videos from resource-constrained front-end devices over networks to resource-rich cloud servers has long been a common practice for surveillance and analytics. Most existing live video analytics (LVA) systems, however, have been built over terrestrial networks, limiting their applications during natural disasters and in remote areas that desperately call for real-time visual data delivery and scene analysis. With the recent advent of space networking, in particular, Low Earth Orbit (LEO) satellite constellations such as Starlink, high-speed truly global Internet access is becoming available and affordable. This paper examines the challenges and potentials of LVA over modern LEO satellite networking (LSN). Using Starlink as the testbed, we have carried out extensive in-the-wild measurements to gain insights into its achievable performance for LVA. The results reveal that the uplink bottleneck in today's LSN, together with the volatile network conditions, can significantly affect the service quality of LVA and necessitate prompt adaptation. We accordingly develop StarStream, a novel LSN-adaptive streaming framework for LVA. At its core, StarStream is empowered by a Transformer-based network performance predictor tailored for LSN and a content-aware configuration optimizer. We discuss a series of key design and implementation issues of StarStream and demonstrate its effectiveness and superiority through trace-driven experiments with real-world network and video processing data.

360° videos are gaining popularity, but immersive analytics, particularly in object detection, confront challenges from complex scenes and high data volume. This imposes significant burdens on individual users and resource-limited edge devices. Fortunately, Machine Learning as a Service (MLaaS) offers an economical solution for quick deployment without specific hardware or expertise. However, current MLaaS are mostly 2D image-designated and not optimized for the distinctive characteristics of raw 360° video frames. In this paper, we propose a novel MLaaS-based system to address this challenge. Our solution partitions 360° frames into distortion-free 2D regions with dynamic region of interest prediction. We then present an image-stitching algorithm featuring Skyline representation, seamlessly combining all the 2D regions into a unified frame. This frame is then transmitted to the MLaaS platform, with the detected objects being back-projected to yield the final results. Our experiments demonstrate the superiority of this system over baselines, proving its effectiveness in 360° video object detection tasks.

Immersive full-scene volumetric video (VV) showcases the richness and detail of the 3D world, yet poses significant streaming challenges given its massive data volume. Existing 3D tile-based viewport approaches struggle to effectively adapt to full-scene VV owing to their small video buffer limitation, high tile segmentation overhead, and lack of full-scene consideration.

Sonar radar captures visual representations of underwater objects and structures using sound wave reflections, making it essential for exploration, mapping, and continuous surveillance in wild ecosystems. Real-time analysis of sonar data is crucial for time-sensitive applications, including environmental anomaly detection and in-season fishery management, where rapid decision-making is needed. However, the lack of both relevant datasets andpre-trained DNN models, coupled with resource limitations in wild environments, hinders the effective deployment and continuous operation of live sonar analytics.

Neural-based super-resolution (SR) has achieved great success in enhancing image or video quality, creating new opportunities for building bandwidth-efficient and high-accuracy video analytics (VAs) systems. Intuitively, with the help of SR techniques, cameras only need to send downsampled low-quality frames to the server in a canonical edge-assisted VAs framework. The server-side SR model then upscales the quality of received frames for the subsequent VAs tasks, incurring thus substantially reduced bandwidth consumption. Nonetheless, as revealed by our measurement results on real-world video clips, higher delivery quality does not necessarily lead to higher analysis accuracy. This motivates us to study the content-adaptive downsampling and upscaling ratio selection problem for VAs streaming. We propose an SR-based VAs framework, named AdaDSR that can dynamically select the optimal downsampling and upscaling ratios so that the system utility can be maximized. AdaSDR is configured to balance the tradeoffs among accuracy, network cost, and computational cost. It further leverages the temporal consistency of videos to skip trivial decisions so that the camera’s processing overhead can be reduced. Experiments on real-world video data sets demonstrate that AdaDSR can improve the average utility by 7.2%–18.4% when compared with state-of-the-art approaches under diverse video scenes.

Existing IoT systems suffer from restricted communication distances, high deployment costs, and frequent battery replacements, making them ineffective for managing healthcare data. This paper presents Prometheus, a self-powered wristband for reporting personal health status over long distances and intelligently managing healthcare data. Prometheus backscatters ambient BLE and ZigBee signals for low-power communication while incorporating a multi-source energy harvester to convert ambient RF, light, and heat into electricity. It also features a biochemical sensor array for monitoring sweat biochemical markers. Prototyped on a flexible PCB, Prometheus demonstrates impressive efficiency, consuming only 5.8 mW for sweat sensing, with BLE and ZigBee transmission energies significantly lower than standard electrochemical workstations and commercial alternatives. Our experiments show consistent signal quality at distances up to 20 meters. In summary, Prometheus emerges as a convenient, efficient, and self-powered wristband, promising to provide ubiquitous healthcare data management in our lives.

Self-service libraries need self-service book collection and monitoring of book quality to improve user experience This article proposes a privacy-preserving alternative RFbook, a book classification and moisture sensing system formed from an array of passive commercial RFID tags. We have three key observations in designing RFbook for such benefits. The first observation is that when tags are in the vicinity, their interrogation currents can alter each other’s circuit properties, based on which unique phase and amplitude signatures can be obtained from the backscattered signal. The second observation is that books with different thicknesses and sizes of material will have different signal features. Finally, we found that changes in book humidity are reflected in the reader’s received signal strength (RSS). To turn the high-level idea into a practical system, we built a prototype of RFbook and conducted comprehensive experiments to evaluate the system’s performance. The experimental results show that RFbook can distinguish different types of books with an average accuracy rate higher than 96% and monitor the humidity change of the book.

Modern smartphones hold massive amounts of private and potentially sensitive user data (e.g., identity and messages). User authentication is the key measure to protect such sensitive data from adversaries. In this article, we explore a novel authentication mechanism, LipAuth, leveraging the unique spatial-temporal features (i.e., both static physiological and dynamic behavioral characteristics) of human lips biometrics for secure and convenient user authentication, without requiring any special sensors on smartphones. The key principle behind LipAuth is that the geometric structure of lips is unique across different users while consistent and stable for the same user, which is dependent on three types of static features, i.e., lip width, thicknesses, and the joint characteristic of the former two, and the dynamic features in smiling process, i.e., the bending processes of the boundary lines between the upper and lower lips. On that basis, LipAuth can accurately identify legal users by actively extracting the spatial-temporal features on the lips’ profile changes, while also remaining fast and easy to use. We have implemented the prototype of LipAuth on Android platforms and comprehensively evaluated its performance by recruiting 50 volunteers. The experimental results show that LipAuth can achieve an overall 99.24% accuracy for user authentication and can resist potential intrusion from video replaying and mimic attacks.

The evolution of Internet-of-Things (IoT) is fostering the use of intelligent controls for energy conservation. Yet, the efficacy of these strategies is largely tied to diverse load forecasting algorithms. Given the significant contribution of heating, ventilation, and air-conditioning (HVAC) systems to global energy consumption, accurate forecasting of HVAC power usage is crucial for improving overall energy efficiency. However, real-world HVAC load forecasting, bolstered by various IoT devices, is complicated by multiple factors: data variability, power load fluctuations, electronic phenomena (e.g., zero drifts), and the increased time complexity and larger model sizes required to manage accumulating historical data. To address these challenges, we first present an in-depth measurement study on the characteristics of HVAC load at a minute scale based on HVAC data collected in six locations. We propose HALO, a transformer-based framework specifically designed for forecasting HVAC load. HALO incorporates an adaptive data pre-processing stage and a local-global-scale transformer-based load forecasting stage, enabling precise forecasting of HVAC load and optimization of energy utilization. Evaluation based on real-world data traces from a prototype application demonstrates that the proposed framework significantly outperforms existing models.

In recent years, crowdsourced livecast has seen remarkable progress due to the interactivity and real-time nature, playing an essential role in multimedia applications in the post-epidemic era. Given the delay sensitivity, large viewing volumes, and heterogeneous viewing patterns, the traditional video streaming methods fail to provide the optimized quality of experience (QoE) for viewers using the minimum system cost over an edge-assisted service architecture. The emerging technology of mobile edge computing (MEC) offers a new perspective of reducing user latency and enhancing the quality of dispatched videos in a promising way. In this paper, we propose Proffler, an integrated framework that addresses this problem through effective stream caching at the network edge server. We first examine the underlying correlations in viewing patterns across different regions and propose a novel transformer-based algorithm, Chili-TF, that achieves accurate viewer request prediction, even for regions with insufficient data. We then design a scalable algorithm, U2VR, that achieves near-optimal video stream allocation as well as viewer scheduling. Extensive real-data-driven experiments further confirm that Proffler can achieve improvements of 20%-55% in average QoE compared to state-of-the-art solutions.

Wi-Fi signals contain information about the surrounding propagation environment and have been widely used in various sensing applications such as gesture recognition, respiratory monitoring, and indoor position. Nevertheless, this information can also be easily stolen by eavesdroppers to obtain private information. In this paper, we propose WiShield, a new framework that protects legitimate users using Wi-Fi sensing applications while preventing unauthorized privacy attacks. The implementation of WiShield is based on a simple principle of physically encrypting Wi-Fi channel status information (CSI) to prevent eavesdroppers from inferring sensitive information through stolen CSI. To achieve a balance between encryption strength, sensing accuracy, and communication quality, we design an efficient multi-objective optimization framework that can safely deliver decryption keys to legitimate users and prevent illegal eavesdropping by eavesdroppers. We implemented the WiShield prototype on an SDR platform and conducted extensive experiments to verify its effectiveness in common Wi-Fi sensing applications. We believe that the implementation of WiShield can improve the privacy standards of Wi-Fi sensing applications, and it is also an important step towards making the integration of Integrated Sensing and Communications (ISAC).

Accurate head movement tracking is crucial for virtual reality and Metaverse in ubiquitous human-computer interaction (HCI) applications. Existing works for head tracking with wearable VR kits and wireless signals require expensive devices and heavy algorithmic processing. To resolve this problem, we propose HeadTrack, a low-cost, high-precision head motion tracking system that uses commercially available wireless earphones to capture the user’s head motion in real-time. HeadTrack uses smartphones as ‘sound anchors’ and emits inaudible chirps picked up by the user’s wireless earphones. By measuring the time-of-flight of these signals from the smartphone to each microphone on the earphone, we can deduce the user’s face orientation and distance relative to the smartphone, enabling us to accurately track the user’s head movement. To realize HeadTrack, we use the cross-correlation method to optimize the Frequency Modulated Continuous Wave (FMCW) based acoustic ranging method, which solves the problem of insufficient wireless earphone bandwidth. Moreover, we solve the problems of asynchronous startup time between devices and the existence of sampling frequency offset. We conduct excessive experiments in real scenarios, and the results prove that HeadTrack can continuously track the direction of the user’s head, with an average error under 6.3° in pitch and 4.9° in yaw.

Smartphones are widely used as portable data collectors for wearable and healthcare sensors that can passively collect data streams related to the environment, health status, and behaviors. Recent research shows that the collected data can be used to monitor not only the physical states but also the mental health of individuals. However, extracting the features of digital phenotypes that characterize major depressive disorder (MDD) is technically challenging and may raise significant privacy concerns. Addressing such challenges has become the focus of many researchers. This article provides a comprehensive analysis of several key issues related to ubiquitous sensing to aid in detecting MDD. Specifically, this article analyzes existing methodologies and feature extraction algorithms used to detect possible MDD through digital phenotyping from smartphone data. In particular, five types of features are summarized and explained, namely, location, movement, rhythm, sleep, and social and device usage. Finally, related limitations and challenges are discussed to provide paths for further research and engineering.

In this article, we explore intercity mobility in urban agglomerations by surveying people traveling across cities based on private car trajectory data. Specifically, we first adopt the statistical analysis method to mine the intercity mobility in terms of various metrics of travel trips, so as to gain a preliminary understanding of intercity mobility in urban agglomeration. Then, we utilize the tensor decomposition method to conduct in-depth study on the intercity mobility pattern from the perspectives of complexity and multidimensionality. We construct a 4-D tensor based on private car trajectory and point-of-interest (POI) datasets and define the functional similarity and geographic adjacency between regions. Finally, we design an alternating proximal gradient (APG)-based method to resolve the core tensor and factor matrix, leading to the fine-grained discovery of intercity mobility patterns on administrative divisions in the urban agglomeration. Extensive experiments are conducted to evaluate the analysis of intercity mobility, using a real-world dataset containing one-year private car trajectories from five cities in the selected urban agglomeration. The experiments show that the proposed method successfully captures 20 intercity mobility patterns, in which the factor matrices retrieve the patterns from different dimensions with core tensors characterizing correlations between patterns in factor matrices. Besides, the extracted intercity mobility patterns not only cover administrative areas with frequent intercity interactions, but also contain areas with less intercity interactions. It validates that the intercity mobility is consistent with the regional functions in urban agglomeration.

We study defending strategies against adversarial attacks onCombinatorial Multi-Armed Bandits(CMAB) algorithms. CMAB is an effective sequence decision making tool that has been broadly applied in online real-world applications. We consider a realistic CMAB setting, budgeted CMAB, in which multiple arms associated with pulling costs and unknown rewards are pulled per round, aiming to maximize the cumulative reward under a budget constraint. However, the adversarial attack against budgeted CMAB is rarely studied, posing a very important security issue. Specifically, a suboptimal arm that is not pulled (i.e., attacker) can hijack the budgeted CMAB algorithm's behavior, forcing itself to be pulled frequently by manipulating other arms' rewards. Existing strategies cannot prevent such attacks. Motivated by this, we closely study the adversarial attack against a popular budgeted CMAB algorithm, exposing a significant security threat to real-world applications. The attack extends to other algorithms with certain customization. To address this, we incorporate a truthful pricing-based defending strategy that prevents such attacks effectively and ensures arms share pulling costs truthfully. Extensive simulations have illustrated the proposed attack strategy can hijack the algorithm efficiently, while the defending strategy provides attack prevention, individual rationality, and asymptotic truthfulness guarantees.

Head motion tracking is a promising research field with vast applications in ubiquitous human-computer interaction (HCI) scenarios. Unfortunately, solutions based on vision and wireless sensing have shortcomings in user privacy and tracking range, respectively. To address these issues, we propose IA-Track, a novel head motion tracking system that combines inertial measurement units (IMU) and acoustic sensing. Our wireless earphone-based method balances flexibility, computational complexity, and tracking accuracy, requiring only an earphone with an IMU and a smartphone. However, we still face two challenges. First, wireless earphones have limited hardware resources, making acoustic Doppler effect-based method unsuitable for acoustic tracking. Second, traditional Kalman filter-based trajectory restoration methods may introduce significant cumulative errors. To tackle these challenges, we rely on IMU sensor data to recover the trajectory and use smartphones to emit ”inaudible” acoustic signals that the earphone receives to adjust the IMU drift track. We conducted extensive experiments involving 50 volunteers in various potential IA-Track usage scenarios, demonstrating that our well-designed system achieves satisfactory head motion tracking performance.

Blink detection is essential for various human-computer interaction scenarios, such as virtual reality and driving state detection. It has gained significant attention from industry and academia alike in recent years. Existing non-contact detection systems (cameras, acoustics, etc.) have made significant progress, but various issues have prevented their widespread adoption, including privacy concerns, line-of-sight requirements, and cost issues. Therefore, there is a critical need for a simple and robust system that can detect eye blinks using common commercial equipment. In this paper, we propose BlinkRadar, which uses a low-cost customized impulse-radio ultra- wideband (IR-UWB) radar for non-contact and fine-grained blink detection. BlinkRadar can reliably detect driver blinks in driving conditions, making it possible to infer drowsy driving. To effectively extract the eye blink signal, we analyzed real experimental data to study the characteristics of the eye blink pattern and successfully used the multi-sequence variational mode decomposition (MS-VMD) algorithm to separate the blink signal from the noise signal. We conducted extensive experiments in two different environments (a quiet room and moving vehicles) and found that BlinkRadar had an average blink detection accuracy of over 96.2%. Our results demonstrate the feasibility of using UWB radar for non-contact eye blink detection.

Passenger counting is crucial for many applications such as vehicle scheduling and traffic capacity assessment. However, most of the existing solutions are either high-cost, privacy invasive or not suitable for passengers the vehicle scenarios. In this work, we propose thePa-Count, an effective real-timePassengerCounting system deployed inside the vehicle via using Wi-FiCSI(Channel State Information). Specifically, in Pa-Count, we design a set of combined filters to eliminate environmental interference and enhance CSI quality. In so doing, we can identify the fluctuation of weak CSI caused by passengers’ subtle movement, i.e., the fidgeting, and then obtain the distribution of fidgeting period and silent period. Following that, we describe the subtle movements of passengers via power law with exponential cutoff distribution and establish a counting model based on the queuing theory. A mathematical inference method with a priori probability is devised to calculate the number of real-time passengers through CSI. We evaluate the performance of the Pa-Count by conducting a set of experiments in real-world vehicle scenarios (including private car and subway). Experimental results show that Pa-Count can achieve robust performance with an average accuracy of over 92$\%$.

Due to the revolution of communication technology, the rapidly increasing number of mobile devices in edge networks generates various real-time service requests, requiring a considerable volume of heterogeneous resources all the time. However, edge devices with limited resources cannot afford substantial learning cost, while migrating services requires heterogeneous resources, especially for dynamic networks. To address these issues, we first establish a cooperative service migration framework and formulate a bi-objective optimization problem to optimize service performance and cost. By analyzing the optimal migration ratio of service cooperative migration, we propose an offline expert policy based on global states to provide optimal expert demonstrations. To realize real-time service migration based on observable states, we design a lightweight online agent policy to imitate expert demonstrations and leverage meta update to accelerate the model transfer. Experimental results show that our algorithm is exceptional in training cost and accuracy, and has significant superiors in multiple metrics such as the service latency and payment under different workloads, compared to other representative algorithms.

Federated learning (FL) has emerged as a prominent distributed learning paradigm, enabling collaborative training of neural network models across local devices with raw data stay local. However, FL systems often encounter significant challenges due to data heterogeneity. Specifically, the non-IID dataset in FL systems substantially slows down the convergence speed during training and adversely impacts the accuracy of the final model. In our paper, we introduce a novel client selection framework that judiciously leverages correlations across local datasets to accelerate training. Our framework first employs a lightweight locality-sensitive hashing algorithm to extract client features while respecting data privacy and incurring minimal overhead. We then design a novel Neural Contextual Combinatorial Bandit (NCCB) algorithm to establish relationships between client features and rewards, enabling intelligent selection of client combinations. We theoretically prove that our proposed NCCB has a bounded regret. Extensive experiments on real-world datasets further demonstrate that our framework surpasses state-of-the-art solutions, resulting in a 50% reduction in training time and a 17% increase in final model accuracy, closing to the performance in the ideal IID case.

Spatial Crowdsourcing (SC) has been an indispensable Location-based Service where the SC server assigns tasks to workers based on the locations of task requesters and workers, raising strong privacy concerns. Limited by the computational and time complexity, existing works prefer differential privacy-based methods to protect location privacy. However, most differential privacy-based works ignore the road network, perturbing locations on two-dimensional plane, resulting in more failures in tasks and moreover extensive privacy disclosure in practice. This paper aims to implement a multi-task assignment with both high utility and efficiency while protecting the location privacy of both task requesters and workers on road networks. Specifically, we design a Road Network-aware Exponential Mechanism and propose an Obfuscated Locations Selection algorithm to guarantee location privacy of all participants and extensive privacy. Then, we propose region distance. Based on this, we further formulate multi-task assignment as a Binary Linear Programming problem and a utility-aware optimization problem. We solve the first problem to obtain optimal efficiency and then propose a utility-aware optimization algorithm for the second problem to improve the utility. Our experiments demonstrate sufficient and stable privacy guarantee and the well-performance on both utility and efficiency of our framework.

With the popularity of mobile devices and the continuous advancement of mobile network technology, running online augmented reality (AR) on lightweight mobile devices is much more desirable than on heavy and expensive head-mounted devices that are difficult to satisfy users. Mobile edge computing can assist in supporting AR applications running on mobile devices, which copes with compute-intensive and delay-sensitive requirements. However, subject to the limited and heterogeneous edge resources, offloading tasks to edge devices is not easy, especially if the application requires multi-party interaction. It is challenging to develop a credible task placement scheme that satisfies user experience with flexible use of edge resources. This article focus on the task offloading placement problem for AR overlay rendering in multi-party mobile augmented reality system. We first present our observations about performance bottlenecks of edge devices and explain the necessity of splitting the AR overlay rendering pipeline. We then formulate a joint optimization problem of task placement decisions, aiming to maximize the user experience of quality and minimize the service cost. We develop a novel decision approach based on deep reinforcement learning (DRL) to address this complex problem. Finally, we verify the effectiveness and superiority of the proposed method through extensive evaluation experiments.

The use of controlled excitation makes pervasive backscatter communication difficult to achieve and the redundant modulation severely limits the performance of the system. We present a novel WiFi backscatter system that can take uncontrolled OFDM WiFi signals as excitations and efficiently embed tag data at the single-symbol rate. Specifically, we are the first to discover the fundamental reason why the previous systems have to rely on multi-symbol modulation, which makes it possible to demodulate tag data on the single-symbol level. Further, we design deinterleaving-twins decoding that can reuse any uncontrolled WiFi signals as carriers to backscatter tag data. Moreover, we present how to robustly handle high-order excitations, including different demapping rules for diverse excitations and three different bit-translation methods for decoding. To verify the effectiveness of our proposal, we prototype our solution using various FPGAs and SDRs. Comprehensive evaluations show that our solution’s maximum throughput is 3.92x and 1.97x better than FreeRider and MOXcatter. In addition, with 16QAM excitations, the decoding BERs of majority voting are around 5%, which is 10x better than subsequence matching and jaccard similarity methods. Meanwhile, the throughput of deinterleaving-level demodulation is 2x better than payload-level demodulation with 16QAM ambient traffic.

To ensure high performance for HTTP adaptive streaming (HAS), it is critical to provide accurate prediction of end-to-end network bandwidth. Low Latency Live Streaming (LLLS), which has been gaining popularity, faces even greater challenges in this regard. Unlike Video-on-Demand (VOD) streaming, which only needs long-term bandwidth prediction and can tolerate some prediction errors, LLLS demands precise short-term bandwidth predictions. These challenges are amplified by the fact that short-term bandwidth experiences both large abrupt changes and uncertain fluctuations. Furthermore, obtaining valid bandwidth measurement samples in LLLS poses difficulties due to the on-off traffic pattern. In this work, we present DeeProphet, a system designed to enhance the performance of LLLS by achieving accurate bandwidth prediction. DeeProphet collects valid bandwidth samples by identifying intervals of packet continuous sending leveraging TCP state information, estimates the segment-level bandwidth robustly by filtering out noisy samples, and predicts both significant changes and uncertain fluctuations in future bandwidth by combining both time series and learning-based models. Experimental results demonstrate that DeeProphet effectively enhances the overall Quality of Experience (QoE) by 39.5% to 464.6% compared to state-of-the-art LLLS Adaptive Bitrate (ABR) algorithms.

Middle ear effusion is a common symptom of otitis media, the reactive physical manifestation of otitis media (OM) in children's middle ear. However, diagnosing MEE for little children at home is troublesome due to their difficulty cooperating and the caregiver's lack of medical knowledge. To this end, we propose EarSonar, a novel acoustic-based MEE diagnostic system. The principle behind EarSonar is that the acoustic absorption effect exists in ear scenarios, and the volume of middle ear fluid can markedly affect the absorbed spectrum energy. By automatically eliminating the impact of potential interference factors and identifying the representative frequency range with the typical reaction of acoustic absorption, EarSonar captures fine-grained signal features on absorbed spectrum energy and models the intrinsic relationship between acoustic absorption and the volume of the filler fluid in the eardrum. On that basis, EarSonar extracts the features of the MEE signal segment and uses k-means clustering to classify middle ear effusion status. We conducted a test on 112 adolescents aged 4–6. We divided the degree of middle ear effusion into three grades. The final average detection accuracy rate exceeds 92%, which is 8 % higher than the previous method. We have implemented a proof-of-concept prototype of EarSonar by building upon earphones embedded with a microphone and speaker. Experimental results demonstrate a feasible and effective way to turn earphones into potential home-use MEE screening tools.

With the reduced hardware costs of omnidirectional cameras and the proliferation of various extended reality applications, more and more 360° videos are being captured. To fully unleash their potential, advanced video analytics is expected to extract actionable insights and situational knowledge without blind spots from the videos. In this paper, we present OmniSense, a novel edge-assisted framework for online immersive video analytics. OmniSense achieves both low latency and high accuracy, combating the significant computation and network resource challenges of analyzing 360° videos. Motivated by our measurement insights into 360° videos, OmniSense introduces a lightweight spherical region of interest (SRoI) prediction algorithm to prune redundant information in 360° frames. Incorporating the video content and network dynamics, it then smartly scales vision models to analyze the predicted SRoIs with optimized resource utilization. We implement a prototype of OmniSense with commodity devices and evaluate it on diverse real-world collected 360° videos. Extensive evaluation results show that compared to resource-agnostic baselines, it improves the accuracy by 19.8% – 114.6% with similar end-to-end latencies. Meanwhile, it hits 2.0× – 2.4× speedups while keeping the accuracy on par with the highest accuracy of baselines.

Today's electrical grid is experiencing a fast transition toward a smart infrastructure. Modern smart grid is expected to integrate Artificial Intelligence of Things (AIoT)-empowered energy management systems (EMS) to sense, analyze, and optimize the power consumption and QoS of diverse end users. Non-Intrusive Load Monitoring (NILM) plays a key role in this transition, particularly considering that many legacy devices/appliances may not have built-in sensors. Yet most of the NILM solutions rely on large (often impractical) datasets for training. In this paper, we address this challenge through a meta learning-inspired approach, which implements a hierarchical architecture with a “meta-learner” to supervise the training of each appliance. Current EMS also relies on a central controller to access long-term information across all participants, which mismatches their distributed nature, and so often with slow responses. To this end, we develop a deep reinforcement learning based controller to make dynamic decisions for each component in the system. The experiment results based on real-world data sets and simulation data show that applying the meta learning approach can greatly improve the performance of NILM and the QoS of the whole system.

Recently, video/image intelligent analytics has been widely used in industrial Artificial Intelligence (AI) applications, such as defect detection, face recognition, and security monitoring. To provide better applicability and compatibility in such applications, the embedded AI models must be developed, compiled, and deployed under different development frameworks, such as cuDNN, RKNN, etc. Unfortunately, these frameworks are supported by various Graphic Processing Unit (GPU) hardware vendors, resulting in different model parameter structures and increased development costs. To address these issues, we propose LiGo, a low cost cross-platform video/image process framework, that simplifies and accelerates video intelligent processing in practical heterogeneous hardware systems. LiGo1 provides video processing pipeline, cross-platform development environments, and unified model serving structures. We demonstrate LiGo's efficiency and flexibility in model generation and deployment through its use in supporting multiple real-world commercial industrial systems.

Recently, Low Earth orbit Satellite Networking (LSN) has been suggested as a critical and promising component toward high-bandwidth and low-latency global coverage in the upcoming 6G communication infrastructure. SpaceX's Starlink is arguably the largest and most operable LSN to date. There have been practical uses of Starlink with diverse networked applications, including multimedia applications of stringent demands. Given the mixed and inconsistent feedbacks from end users, it remains unclear whether today's LSNs, in particular, Starlink, have been ready for realtime multimedia. In this paper, we present a systematic measurement study on realtime multimedia services over Starlink, seeking insights into their operations and performance in this new generation networking. Our findings demonstrate that Starlink can effectively handle most video-on-demand (VoD) and live-streaming services with properly configured buffers, but suffer from video pauses or audio cut-offs during interactive video conferencing, especially in extreme weather. We also examine the impact of satellite switching and evolution of satellite routing strategies, offering hints into the future enhancements for multimedia services and for LSNs.

Voice assistants support contactless smart device control and thus act as a holy grail of human–computer interaction. However, recent studies reveal that an adversary can manipulate devices by vicious voice commands. This security risk is caused by only executing one-time liveness detection and lacking safeguard modules after service activation. Therefore, identifying speaker type (i.e., human articulators or loudspeakers) is critical in protecting voice-driven services during an entire interaction session. In this article, we propose a continuous voice liveness detection approach LiveProbe, leveraging unique energy response patterns in frequency bands induced by distinct voice generation mechanisms. The rationality behind LiveProbe is presented in two aspects: human articulator reshapes initial voices by exquisitely coordinated movements of vocal organs, which act as band-pass filters generating unique energy responses; nevertheless, the internal modules of loudspeakers are position fixed and cannot reproduce this response characteristic. To that end, we first work on voice generation mechanisms behind two-type speakers that cause spectrum differences. Then, we elaborately construct signal processing and deep-learning modules to extract liveness features. Especially, our approach does not interfere with normal voice interaction and need not to carry customized sensors. The experiment presents its effectiveness against potential attacks with a false acceptance rate of 0.51%.

Continuous identity authentication is critical for privacy protection throughout an entire user login session. In this article, we propose a continuous user authentication mechanism, namely, HandPass, which employs the vibration responses from hand biometrics and is passively activated by natural user-device interaction. Hand vibration responses are embedded in the mechanical vibration of a force-bearing body consisting of one mobile device and one user hand. A built-in accelerometer of the device can capture hand-dependent vibration signals. Considering the concealment of vibration generation and the nonreplicability of hand structure, it is difficult for attackers to counterfeit user identity. Moreover, for ensuring the robustness of authentication performance to tapping behavior interference, we construct a data augmentation module jointly leveraging a signal processing and learning-based pipeline. It can generate enough vibration responses representing hand structure biometrics under various behaviors, thereby making HandPass comprehensively understand vibration response variation. We prototype HandPass on smartphones, and extensive experiments demonstrate that HandPass can achieve satisfactory authentication accuracy.

Many countries around the world are facing a shortage of healthcare resources, especially during the post-epidemic era, leading to a dramatic increase in the need for self-detection and self-management of diseases. The popularity of smart wearable devices, such as smartwatches, and the development of machine learning (ML) bring new opportunities for the early detection and management of various prevalent diseases, such as cardiovascular diseases, Parkinson’s disease, and diabetes. In this survey, we comprehensively review the articles related to specific diseases or health issues based on small wearable devices and ML. More specifically, we first present an overview of the articles selected and classify them according to their targeted diseases. Then, we summarize their objectives, wearable device and sensor data, ML techniques, and wearing locations. Based on the literature review, we discuss the challenges and propose future directions from the perspectives of privacy concerns, security concerns, transmission latency and reliability, energy consumption, multimodality, multisensor, multidevices, evaluation metrics, explainability, generalization and personalization, social influence, and human factors, aiming to inspire researchers in this field.

Heart disease has now become a very common and impactful disease, which can actually be easily avoided if treatment is intervened at an early stage. Thus, daily monitoring of heart health has become increasingly important. Existing mobile heart monitoring systems are mainly based on seismocardiography (SCG) or photoplethysmography (PPG). However, these methods suffer from inconvenience and additional equipment requirements, preventing people from monitoring their hearts in any place at any time. Inspired by our observation of the correlation between pupil size and heart rate variability (HRV), we consider using the pupillary response when a user unlocks his/her phone using facial recognition to infer the user’s HRV during this time, thus enabling heart monitoring. To this end, we propose a computer vision-based mobile HRV monitoring framework-PupilHeart, designed with a mobile terminal and a server side. On the mobile terminal, PupilHeart collects pupil size change information from users when unlocking their phones through the front-facing camera. Then, the raw pupil size data is preprocessed on the server side. Specifically, PupilHeart uses a 1-D convolutional neural network (1-D CNN) to identify time series features associated with HRV. In addition, PupilHeart trains a recurrent neural network (RNN) with three hidden layers to model pupil and HRV. Employing this model, PupilHeart infers users’ HRV to obtain their heart condition each time they unlock their phones. We prototype PupilHeart and conduct both experiments and field studies to fully evaluate effectiveness of PupilHeart by recruiting 60 volunteers. The overall results show that PupilHeart can accurately predict the user’s HRV.

In the 5G era (and the upcoming 6G), mobile edge computing (MEC) has been advocated to serve the massive amount of Internet of Things (IoT) devices by base stations (BSs) and edge data centers (EDCs). Geo-distributed EDCs are generally of much smaller scales as compared to mega data centers and hence of much lower costs, but can have fast response to their users so as to satisfy the demands of real-time applications. As their reliability and availability heavily depend on the electrical power supply, most EDCs are equipped with battery groups as backup power in case of power grid load shedding or outage. In a heterogeneous geo-distributed environment, the QoS of heavily loaded EDCs however can be severely impacted by limited backup power while lightly loaded EDCs may simply waste such precious resources. Moreover, a heavily loaded EDC may suffer from deep discharge of its battery group, which will cause a significant reduction of battery capacity and lifetime. This further aggravates the aforementioned situations should load shedding/outage happen again. In this article, we carefully analyze the workloads in EDCs and classify them into interactive workloads and batch workloads, respectively. We then develop a novel battery allocation framework with smart workload migration for EDCs, which simultaneously protects interactive workloads from being interrupted and minimizes the waiting time of batch workloads. Our extensive evaluations show that our strategies can optimize all the objectives within a limited overall cost as compared to state-of-the-art practical allocation.

Federated learning (FL) is a privacy-preserving machine learning paradigm that enables multiple clients to train a unified model without disclosing their private data. However, susceptibility to membership inference attacks (MIAs) arises due to the natural inclination of FL models to overfit on the training data during the training process, thereby enabling MIAs to exploit the subtle differences in the FL model’s parameters, activations, or predictions between the training and testing data to infer membership information. It is worth noting that most if not all existing MIAs against FL require access to the model’s internal information or modification of the training process, yielding them unlikely to be performed in practice. In this paper, we present with TEAR the first evidence that it is possible for an honest-but-curious federated client to perform MIA against an FL system, by exploring the Temporal Evolution of the Adversarial Robustness between the training and non-training data. We design a novel adversarial example generation method to quantify the target sample’s adversarial robustness, which can be utilized to obtain the membership features to train the inference model in a supervised manner. Extensive experiment results on five realistic datasets demonstrate that TEAR can achieve a strong inference performance compared with two existing MIAs, and is able to escape from the protection of two representative defenses.

Mobile edge computing (MEC) and device-to-device (D2D) offloading are two promising paradigms in the industrial Internet of Things (IIoT). In this article, we investigate task co-offloading, where computing-intensive industrial tasks can be offloaded to MEC servers via cellular links or nearby IIoT devices via D2D links. This co-offloading delivers small computation delay while avoiding network congestion. However, erratic movements, the selfish nature of devices and incomplete offloading information bring inherent challenges. Motivated by these, we propose a co-offloading framework, integrating migration cost and offloading willingness, in D2D-assisted MEC networks. Then, we investigate a learning-based task co-offloading algorithm, with the goal of minimal system cost (i.e., task delay and migration cost). The proposed algorithm enables IIoT devices to observe and learn the system cost from candidate edge nodes, thereby selecting the optimal edge node without requiring complete offloading information. Furthermore, we conduct simulations to verify the proposed co-offloading algorithm.

In enterprise management systems (EMS), augmented Intelligence of Things (AIoT) devices generate delay-sensitive and energy-intensive tasks for learning analytics, articulate clarifications, and immersive experiences. To guarantee effective task processing, in this work, we present a cloud-assisted fog computing framework with task offloading and service caching. In the framework, tasks make offloading decisions to determine local processing, fog processing, and cloud processing with the goal of minimal task delay and energy consumption, conditioned on dynamic service caching. To this end, we first propose a distributed task offloading algorithm based on noncooperative game theory. Then, we adopt the 0–1 knapsack method to realize dynamic service caching. At last, we adjust the offloading decisions for the tasks offloaded to the fog server but without caching service support. In addition, we conduct extensive experiments and the results validate the effectiveness of our proposed algorithms.

Over the past decade, backscatter nodes have received booming interest for many emerging mobile applications, such as sports analytics and interactive gaming. However, backscatter networks are not ready to provide a high-throughput and stable communication platform for billions of such mobile nodes due to two main factors in rate adaptation. First, the common mapping paradigm that chooses the optimal rate based on RSSIs is hardly adaptable to hardware diversity. Second, the current probing processes are not optimized for mobile scenarios due to inefficient probing trigger, inaccurate channel estimation, and unique self-interference. To address those issues, we propose MobiRate, a mobility-aware rate adaptation link-layer that fully exploits the mobility hints from PHY information to deliver a high-throughput link-layer for mobile backscatter networks. The key insight is that mobility-hints can greatly benefit link-layer design, including rate selection and channel probing. Specifically, we introduce a novel velocity-based loss-rate estimation module, a mobility-assisted probing trigger, a selective probing module, and a robust self-interference detection module, significantly saving probing time and improving probing accuracy. As MobiRate is fully compatible with the current standard, we prototype it using COTS RFID readers and commercial tags. Our extensive experiments demonstrate that MobiRate can successfully identify self-interference with detection accuracy over 90% for tags of different velocities. Moreover, it achieves up to 3.8x throughput gain over the state-of-the-art methods across a wide range of mobility, channel conditions, and tag types.

In device to device (D2D) aided mobile edge computing (MEC) networks, by implementing content caching and D2D links, the edge server and nearby mobile devices can provide task offloading platforms. For parallel tasks, proper decisions on content caching and task offloading help reduce delay and energy consumption. However, what is often ignored in the previous works is the joint optimization of parallel task offloading and content caching. In this paper, we aim to find optimal content caching and parallel task offloading strategies, so as to minimize task delay and energy consumption. The minimization problem is formulated as a multi-objective optimization problem, concerning both content caching and parallel task offloading. The content caching is formulated as an integer knapsack problem (IKP). To solve the IKP problem, an enhanced Binary Particle Swarm Optimization algorithm is proposed. The parallel task offloading problem is formulated as a constrained multi-objective optimization problem, an improved multi-objective bat algorithm is proposed to address the problem. Experimental results show that our algorithm can decrease delay and energy cost by at most 45% and 56%, respectively. In addition, the parallel task offloading ratio remains over 91% even with large number of mobile devices (MDs).

Given the rapid growth of user-generated videos, internet traffic has been heavily dominated by online video streaming. Caching videos on edge servers in close proximity to users has been an effective approach to reduce the backbone traffic and the request response time, as well as to improve the video quality on the user side. Video popularity, however, can be highly dynamic over time. The cost of cache replacement at edge servers, particularly that related to service interruption during replacement, is not yet well understood. This paper presents a novel lightweight video caching algorithm for edge servers, seeking to optimize the hit rate with real-time decisions and minimized cost. Inspired by recent advances in deep Q-learning, our DQN-based online video caching (DQN-OVC) makes effective use of the rich and readily available information from users and networks. We decompose the Q-value function as a product of the video value function and the action function, which significantly reduces the state space. We instantiate the action function for cost-aware caching decisions with low complexity so that the cached videos can be updated continuously and instantly with dynamic video popularity. We used video traces from Tencent, one of the largest online video providers in China, to evaluate the performance of our DQN-OVC and to compare it with state-of-the-art solutions. The results demonstrate that DQN-OVC significantly outperforms the baseline algorithms in the edge caching context.

In today's Internet, bandwidth dynamics are inevitable, and hence, the bitrate for live streaming applications should also be dynamically adjusted. However, in existing HTTP-based adaptive streaming (HAS), bitrate switching can only be performed at segment boundaries, making decisions unresponsive and often inaccurate. In this paper, we start from a close investigation on the impact of the segment length in HAS and accordingly presentVHAS, an extension towards intelligent variable-length segmentation, which makes client-side decisions based on the massive amount of real-time information from the network and viewers. VHAS implements a smart trigger mechanism that balances accuracy and overhead for variable-length segmentation. We further develop an adaptive bitrate switching algorithm with data-driven I-frame prediction, which is tailored to individual viewers to minimize bitrate mismatches. We evaluate VHAS via extensive trace-driven simulations, and our results demonstrate that compared with state-of-the-art solutions, VHAS achieves 15%–49% gains in QoE, with a noticeable bandwidth reduction of 37%–57%.

Recently, federated learning (FL) has received tremendous attention in both academia and industry, in which decentralized clients collaboratively complete model training by exchanging model updates with a parameter server through the Internet. Its distributed nature well utilizes the localized data and preserves clients’ privacy, but also incurs heavy communication overhead. Existing studies on model update have mostly focused on the bandwidth constraint of the communication channels. Today's Internet however is highly unreliable. Simply using Transmission Control Protocol (TCP) would lead to low network utilization under frequent losses. In this paper, we closely examine the optimal transmission strategies in FL over the realistic lossy Internet. We systematically integrate model compression, forward error correction (FEC) and retransmission towards Federated Learning with Lossy Communications (FedLC). We derive the convergence rate of FedLC under non-convex loss with the optimal transmission. We then decompose this non-convex problem and present effective practical solutions. Public datasets are exploited for performance evaluation by varying the packet loss rate from 10% to 50%. In a fixed training time budget, FedLC can improve model accuracy by 3.91% on average or reduce the communication traffic by 34.27%-47.57% in comparison with state-of-the-art baselines.

Even though conventional wisdom claims that in order to improve viewer engagement, the cloud-edge providers should serve the viewers with the nearest edge nodes, however, we show that doing this for crowdsourced live streaming (CLS) services can introduce significant costs inefficiency. In this paper, we first carry out large-scale measurement analysis by using the real-world service data from Huawei Cloud, a representative cloud-edge provider in China. We observe that the massive number of channels has proposed great burdens to the operating expenditure of the cloud-edge providers, and most importantly, unbalanced viewer distribution makes the edge nodes suffer significant costs inefficiency. To tackle the above concerns, we proposeAggCast, a novel CLS scheduling framework to optimize the edge node utilization for the cloud-edge provider. The core idea ofAggCastis to aggregate some viewers that are initially scattered on different regions, and assign them to fewer pre-selected nodes, thereby reducing bandwidth costs. In particular, by integrating the useful insights obtained from our large-scale measurement,AggCastcan not only ensure that quality of experience (QoS) does not suffer degradation, but also satisfy the systematic requirements of CLS services.AggCasthas been A/B tested and fully deployed. The online and trace-driven experiments show that, compared to the most prevalent method,AggCastsaves over 16.3%back-to-source(BTS) bandwidth costs while significantly improving QoS (startup latency, stall frequency and stall time are reduced over 12.3%, 4.57% and 3.91%, respectively).

We consider the problem of dependent task offloading in edge computing with unknown system-side information (e.g., edge transmission rate and computation resources). In this problem, tasks have complicated dependency relationships and have no prior knowledge of system-side information to assist offloading decision-making. Although existing learning-based approaches can help to address unknown system-side information, the impact of inherent task dependency on such approaches has not been formally explored. To bridge the gap, we first use a breadth-first-search (BFS) method to decouple task dependency, and then leverage the Lyapunov optimization technique to transfer the long-term offloading problem to an online optimization problem. Furthermore, we employ the multi-armed bandit (MAB) theory to develop theonlinelearning-baseddependenttaskoffloading algorithm, called OL-DTO. The algorithm can address the unknown system-side information and is augmented with task dependency awareness. We present a rigorous theoretical analysis to evaluate the performance of this algorithm in terms of application delay and UD energy consumption. Our extensive experimental results demonstrate that the OL-DTO algorithm significantly reduces application delay while satisfying the long-term energy budget constraint of the UD.

Mobile edge computing (MEC) has emerged as a promising computing paradigm that provides flexible and responsive local services for mobile user equipment at the network edge. Software instances for user equipment tasks are typically deployed as Virtualized Network Functions (VNFs) at resource-constrained edge nodes. Task data exchanged across the VNFs in serial can incur high task completion latency. It is therefore desirable to deploy certain VNFs in parallel. However, deciding where to deploy VNFs depends on which VNFs are parallel, and conversely, their deployment also affects their parallel execution. In this paper, for the first time, we jointly consider the parallelization and deployment strategies for VNFs at edge nodes. We closely examine the complexity of the joint optimization problem and introduce an Improved Service Function Graph (I-SFG) that reflects the coordination and dependency relations among the VNFs to provide parallel services for each piece of user equipment. We first propose an approach based on integer linear programming to find optimal solutions in small-scale scenarios and then present an effective solution through cascading I-SFG construction and VNF deployment approximation to solve large-scale problems. Theoretical analyses and experimental results show the superiority of our joint design and the proposed practical solution.

The eye-blink pattern is crucial for drowsy driving diagnostics, which has become an increasingly serious social issue. However, traditional methods (e.g., with EOG, camera, wearable, and acoustic sensors) are less applicable to real-life scenarios due to the disharmony between user-friendliness, monitoring accuracy, and privacy-preserving. In this work, we design and implement BlinkRadar as a low-cost and contact-free system to conduct fine-grained eye-blink monitoring in a driving situation using a customized impulse-radio ultra-wideband (IR-UWB) radar which has superior spatial resolution with the ultra-wide bandwidth. BlinkRadar leverages an IR-UWB radar to achieve contact-free sensing, and it fully exploits the complex radar signal for data augmentation. BlinkRadar aims to single out the eye-blink induced waveforms modulated by body movements and vehicle status. It solves the serious interference caused by the unique characteristics of blinking (i.e., subtle, sparse, and non-periodic) and from the human target itself and surrounding objects. We evaluate BlinkRadar in a laboratory environment and during actual road testing. Experimental results show that BlinkRadar can achieve a robust performance of drowsy driving with a median detection accuracy of 92.2% and eye blink detection of 95.5%.

Recent years have seen booming development of realtime streaming services, highly improving user experience in remote work, online education, and entertainment. Unlike video-on-demand (VoD) or live services, realtime streaming service has extremely stringent delay requirements, rendering the TCP-based transmission no longer applicable. Existing works based on UDP (or its variants) either suffer from the packet loss problem or only focus on improving several QoS metrics, which cannot achieve satisfactory user QoE. Our insight is to slightly sacrifice the bitrate and video quality to trade for the most significant delay to maximize the overall QoE. We propose Oppugno‡‡Oppugno is a spell in Harry Potter that makes magical creatures attack the caster. It is a metaphor that we use an additional mechanism to mitigate the influence of packet loss., an integrated framework that achieves joint loss adaptation and bitrate adaption towards maximized QoE in realtime streaming services. Oppugno leverages existing UDP mechanisms and employs an advanced deep reinforcement learning algorithm Proximal Policy Optimization (PPO), to adaptively select optimal actions based on network conditions. Trace-driven experiments demonstrate the superiority of our framework, which outperforms the SOTA work by 3.9% ∼ 11.6%.

Viewport prediction is the crucial task for viewport-adaptive 360-degree video streaming. Various viewport prediction methods are studied and adopted from less accurate statistic tools to highly calibrated deep neural networks. Conventionally, it is difficult to implement sophisticated deep learning methods on mobile devices, which have limited computation capability. In this work, we propose an advanced learning-based viewport prediction approach and carefully design it to introduce minimal transmission and computation overhead for mobile terminals. We further discuss how to integrate this mobile-friendly viewport prediction (MFVP) approach into the adaptive 360-degree video live streaming by formulating and solving the bitrate adaptation problem. Extensive experiment results show that our prediction approach can work in real-time for live streaming and can achieve higher accuracies compared to other existing prediction methods on mobile clients, which, together with our proposed bitrate adaptation algorithm, significantly improves the streaming Quality-of-Experience (QoE) from various aspects.

Federated Learning (FL) incurs high communication overhead, which can be greatly alleviated by compression for model updates. Yet the tradeoff between compression and model accuracy in the networked environment remains unclear and, for simplicity, most implementations adopt a fixed compression rate only. In this paper, we for the first time systematically examine this tradeoff, identifying the influence of the compression error on the final model accuracy with respect to the learning rate. Specifically, we factor the compression error of each global iteration into the convergence rate analysis under both strongly convex and non-convex loss functions. We then present an adaptation framework to maximize the final model accuracy by strategically adjusting the compression rate in each iteration. We have discussed the key implementation issues of our framework in practical networks with representative compression algorithms. Experiments over the popular MNIST and CIFAR-10 datasets confirm that our solution effectively reduces network traffic yet maintains high model accuracy in FL.

The advent of high-accuracy and resource-intensive deep neural networks (DNNs) has fulled the development of live video analytics, where camera videos need to be streamed over the network to edge or cloud servers with sufficient computational resources. Although it is promising to strike a balance between available bandwidth and server-side DNN inference accuracy by adjusting video encoding configurations, the influences of fine-grained network and video content dynamics on configuration performance should be addressed. In this paper, we propose CASVA, a Configuration-Adaptive Streaming framework designed for live Video Analytics. The design of CASVA is motivated by our extensive measurements on how video configuration affects its bandwidth requirement and inference accuracy. To handle the complicated dynamics in live video analytics streaming, CASVA trains a deep reinforcement learning model which does not make any assumptions about the environment but learns to make configuration choices through its experiences. A variety of real-world network traces are used to drive the evaluation of CASVA. The results on a multitude of video types and video analytics tasks show the advantages of CASVA over state-of-the-art solutions.

Video streaming plays a critical role in the video analytics pipeline and thus its adaptation scheme has been a focus of optimization. As machine learning algorithms have become main consumers of video contents, the streaming adaptation decision should be made to optimize their inference performance. Existing video streaming adaptation schemes for video analytics are usually designed to adapt to bandwidth and content variations separately, which fail to consider the coordination between transmission and computation. Given the nature of batch transmission in video streaming and batch processing in deep learning-based inference, we observe that the choices of the batch sizes directly affects the bandwidth efficiency, the response delay and the accuracy of the deep learning inference in video analytics. In this work, we investigate the effect of the batch size in transmission and processing, formulate the optimal batch size adaptation problem, and further develop the deep reinforcement learning-based solution. Practical issues are further addressed for Implementation. Extensive simulations are conducted for performance evaluation, whose results demonstrate the superiority of our proposed batch adaptive streaming approach over the baseline streaming approaches.

For the recent two years, companies such as Starlink, Kuiper, and Telesat are launching low earth orbit (LEO) satellites to form LEO satellite mega-constellations. Unfortunately, the LEO satellite mega-constellations are not sustainable in the long term since their large size makes LEO congested, causing issues such as satellite brightness, satellite conjunction, and space debris. The issues become worse as LEO satellites have shorter battery lifespans and experience drag force, which shortens the satellite life and produces more space debris objects when LEO satellites reach the end of life. To address the issues, we propose deploying higher-orbit satellites to form a satellite-based sustainable multi-tier space network (SMTSN) instead of launching a massive number of LEO satellites. In this paper, we model the costs and gains for routing traffic with our SMTSN framework. We propose a solution to find the optimal routing paths and an efficient distributed coverage-aware (EDCA) algorithm to predict the number of skipped LEO satellites when the traffic is routed through a higher-orbit satellite. We run extensive simulations to compare the LEO satellite constellations with and without our SMTSN framework, and the results show a significant improvement in the battery cell cycle life consumption with the SMTSN framework.

Conventional wisdom claims that in order to improve viewer engagement, the cloud-edge providers should serve the viewers with the nearest edge nodes, however, we show that doing this for crowdsourced live streaming (CLS) services can introduce significant costs inefficiency. We observe that the massive number of channels has greatly burdened the operating expenditure of the cloud-edge providers, and most importantly, unbalanced viewer distribution makes the edge nodes suffer significant costs inefficiency. To tackle the above concerns, we propose AggCast, a novel CLS scheduling framework to optimize the edge node utilization for the cloud-edge provider. The core idea of AggCast is to aggregate some viewers who are initially scattered on different regions, and assign them to fewer pre-selected nodes, thereby reducing bandwidth costs. In particular, by leveraging the insights obtained from our large-scale measurement, AggCast can not only ensure quality of experience (QoS), but also satisfy the systematic requirements of CLS services. AggCast has been A/B tested and fully deployed in a top cloud-edge provider in China for over eight months. The online and trace-driven experiments show that, compared to the common practice, AggCast can save over 15% back-to-source (BTS) bandwidth costs while having no negative impacts on QoS.

Applications that allow sharing of user-created short videos exploded in popularity in recent years. A typical short video application allows a user to swipe away the current video being watched and start watching the next video in a video queue. Such user interface causes significant bandwidth waste if users frequently swipe a video away before finishing watching. Solutions to reduce bandwidth waste without impairing the Quality of Experience (QoE) are needed. Solving the problem requires adaptively prefetching of short video chunks, which is challenging as the download strategy needs to match unknown user viewing behavior and network conditions. In our work, we first formulate the problem of adaptive multi-video prefetching in short video streaming. Then, to facilitate the integration and comparison of researchers' algorithms towards solving the problem, we design and implement a discrete-event simulator, which we release as open source. Finally, based on the organization of the Short Video Streaming Grand Challenge at ACM Multimedia 2022, we analyze and summarize the algorithms of the contestants, with the hope of promoting the research community towards addressing this problem.

Missing item event detection is one of the most important radio-frequency identification (RFID)-enabled functions. Yet it is largely unaddressed how to fast and reliably detect missing item event in multitagged RFID systems where multiple tags are tagged on one item. The canonical methods can only solve tag-level detection problem where each item is associated with one tag, and applying them to detect the missing multitagged items would falsely alarm and is time inefficient. To bridge the gap, this article formulates and analyzes the missing multitagged item detection problem. Our key idea is to search the proper seeds so that the reader only needs to probe a subset of the tags each being selected from different items instead of the entire tag set for the missing item detection. By employing the computation-communication tradeoffs, we design two protocols named M2ID and M2ID+ that classifies the tags before the segmentation compared to the former to improve time efficiency. With the derived optimum parameters, our protocols can achieve up to$4\times$performance gain in terms of time efficiency compared with the state-of-the-art solution.

As mobile shopping has gradually become the mainstream shopping mode, recommendation systems are gaining an increasingly wide adoption. Existing recommendation systems are mainly based on explicit and implicit user behaviors. However, these user behaviors may not directly indicate users’ inner feelings, causing erroneous user preference estimation and thus leading to inaccurate recommendations. Inspired by our key observation on the correlation between pupil size and users’ inner feelings, we consider using the change of pupil size when browsing to model users’ preferences, so as to achieve targeted recommendations. To this end, we propose PupilRec as a computer-vision-based recommendation framework involving a mobile terminal and a server side. On the mobile terminal, PupilRec collects users’ pupil size change information through the front camera of smartphones; it then preprocesses the raw pupil size data before transmitting them to the server. On the server side, PupilRec utilizes the Tsfresh package and Random Forest algorithm to figure out the key time-series features directly implying user preferences. PupilRec then trains a neural network to fit a user preference model. Using this model, PupilRec predicts user preference to obtain a user–product matrix and further simplifies it by singular value decomposition. Finally, the real-time recommendation is achieved by a collaborative filtering module that retrieves recommended contents to users smartphones. We prototype PupilRec and conduct both experiments and field studies to comprehensively evaluate the effectiveness of PupilRec by recruiting 67 volunteers. The overall results show that PupilRec can accurately estimate users’ preference and can recommend products users interested in.

Unmanned Aerial Vehicle (UAV) Base Station (BS) placement optimization is an essential operational task to improve the Quality of Service (QoS) in UAV-aided wireless cellular networks. The existing approaches are almost zeroth order methods, and the few first order methods mainly ignore the allocation fairness, computational efficiency, and backhaul constraints. In this paper, we formulate the UAV placement problem as a constrained optimization problem, with the objective of maximizing the fair coverage versus energy consumption while satisfying the backhaul constraints at different time nodes. To guarantee fair QoS allocation, we introduce a novel fairness index to ensure fair communication opportunity and the novel region coverage ratio to avoid excess QoS on covered spots. An accurate and efficient proximal stochastic gradient descent based alternating algorithm that iteratively executes two optimization steps is proposed to optimize the UAV locations, which enables the fast single point-based first order methods to solve the complex problems with constraints. Experiment results manifest that the proposed algorithm performs well both in synthetic data scenario and in real city scenario. Furthermore, the proposed first order algorithm is more efficient than the existing zeroth order algorithm, typically referring to the meta-heuristic method.

In the Cognitive Internet of Vehicles (CIoV), vehicles, road side units (RSU) and other key nodes have been equipped with more and more software to support intelligent transportation system (ITS), vehicle automatic control and intelligent road information services. Additionally, technological innovation forces the software in the CIoV to update and upgrade in time. However, escalation is critical to the safety, stability, and maintenance cost of transportation systems. It can be assumed that when the intelligent services supporting CIoV can realize self-perception and escalation, the cognitive ability and coordination ability of the entire CIoV will be greatly improved. To address this, we first propose a deep learning-based method for Software Escalation Prediction (SEP) in CIoV. Specifically, the pretraining mechanism of transformers in the field of natural language processing is combined with software upgrade-related events to dynamically model software sequence activities. To capture the event association in the software activities, we use graph modeling software’s state log and utilize a graph neural network (GNN) to learn the complex life activity rule of software. Finally, the above characteristics are deeply integrated. The proposed method has a 6%–8% improvement over the RoBERTa methods.

A variety of novel mobile applications are developed to attract the interests of potential users in the emerging 5G-enabled vehicular networks. Although computation offloading and task scheduling have been widely investigated, it is rather challenging to decide the optimal offloading ratio and perform adaptive task scheduling in high-dynamic networks. Furthermore, the scheduling policy made by the network operator may be violated, since vehicular users are rational and selfish to maximize their own profits. By considering the incentive compatibility and individual rationality of vehicular users, we present POETS, an efficient partial computation offloading and adaptive task scheduling algorithm to maximize the overall system-wide profit. Specially, a two-sided matching algorithm is first proposed to derive the optimal transmission scheduling discipline. After that, the offloading ratio of vehicular users can be obtained through convex optimization, without any information of other users. Furthermore, a non-cooperative game is constructed to derive the payoff of vehicular users that can reach the equilibrium between users and the network operator. Theoretical analyses and performance evaluations based on real-world traces of taxies demonstrate the effectiveness of our proposed solution.

Video processing plays an essential role in a wide range of cloud-based applications. It typically involves multiple pipelined stages, which well fits the latest fine-grained serverless computing paradigm if properly configured to match the cost and delay constraints of video. Existing configuration tools, however, are primarily developed for traditional virtual machine clusters with general workloads. This paper presents CharmSeeker, an automated configuration tuning tool for serverless video processing pipelines. We first carefully examine the key steps and the performance bottlenecks for video processing over modern serverless platforms. Then, we identify the configuration space for processing pipelines and leverage a carefully designed Sequential Bayesian Optimization search scheme to identify promising configurations. We further address the practical challenges toward integrating our solution into real-world systems and develop a prototype with AWS Lambda. Evaluation results show that CharmSeeker can find out the optimal or near-optimal configurations that improve the relative processing time up to 408.77%. It is also more robust and scalable to various video processing pipelines compared with state-of-the-art solutions.

Wireless sensor networks have enabled smart infrastructures and novel applications. With the recent roll-out of Narrowband IoT (NB-IoT) cellular radio technology, wireless sensors can be widely deployed for data collection in cities around the world. However, empirical evidence regarding the coverage and connectivity of NB-IoT in dense urban areas is limited. This article presents an empirical study that focuses on evaluating the coverage and connectivity of NB-IoT in a dense urban environment. We have designed an NB-IoT sensor node and deployed over 100 of them in high-rise apartment buildings in Hong Kong. These sensor nodes utilize a commercial NB-IoT network to collect high-resolution water flow data for machine learning model training and provide timely feedback to users. We collect and analyze the empirical NB-IoT signal measurements from the sensor nodes deployed in various challenging outdoor and indoor environments for over three months. These empirical measurements reveal correlations between NB-IoT connectivity and sensor installation environments. We also observe that inter-cell interference, as a result of coverage by multiple neighboring NB-IoT cells in a dense urban environment, is a source of connectivity degradation. We discuss potential issues that IoT application designers and system integrators might encounter in practical NB-IoT devices deployment, and we propose a transmission decision algorithm based on signal measurements for mitigating energy wasted due to transmission failures. Finally, we demonstrate the results and the benefits of using high-resolution water flow data collected by our purpose-built NB-IoT sensor nodes for studying the patterns of domestic water consumption in Hong Kong.

Non-IID data present a tough challenge for federated learning. In this paper, we explore a novel idea of facilitating pairwise collaborations between clients with similar data. We propose FedAMP, a new method employing federated attentive message passing to facilitate similar clients to collaborate more. We establish the convergence of FedAMP for both convex and non-convex models, and propose a heuristic method to further improve the performance of FedAMP when clients adopt deep neural networks as personalized models. Our extensive experiments on benchmark data sets demonstrate the superior performance of the proposed methods.

Federated learning (FL) has recently emerged as a promising distributed machine learning (ML) paradigm. Practical needs of the "right to be forgotten" and countering data poisoning attacks call for efficient techniques that can remove, or unlearn, specific training data from the trained FL model. Existing unlearning techniques in the context of ML, however, are no longer in effect for FL, mainly due to the inherent distinction in the way how FL and ML learn from data. Therefore, how to enable efficient data removal from FL models remains largely under-explored. In this paper, we take the first step to fill this gap by presenting FedEraser, the first federated unlearning method-ology that can eliminate the influence of a federated client’s data on the global FL model while significantly reducing the time used for constructing the unlearned FL model. The basic idea of FedEraser is to trade the central server’s storage for unlearned model’s construction time, where FedEraser reconstructs the unlearned model by leveraging the historical parameter updates of federated clients that have been retained at the central server during the training process of FL. A novel calibration method is further developed to calibrate the retained updates, which are further used to promptly construct the unlearned model, yielding a significant speed-up to the reconstruction of the unlearned model while maintaining the model efficacy. Experiments on four realistic datasets demonstrate the effectiveness of FedEraser, with an expected speed-up of 4× compared with retraining from the scratch. We envision our work as an early step in FL towards compliance with legal and ethical criteria in a fair and transparent manner.

Data centers are essential components in the current digital world. The number and scales of data centers have both increased a lot in recent years. The distributed data centers are standing out as a promising solution due to the development of modern applications which need a massive amount of computation resource and strict response requirement. However, compared to centralized data centers, distributed data centers are more fragile when the power supply is unstable. Power constraints or outages because of electrical load shedding or other reasons will significantly affect the service performance of data centers and damage the quality of service (QoS) for customers. Moreover, unlike conventional data centers, distributed data centers are often unattended, so we need a system that can automatically calculate the best workload schedule to maximize profit in such situations. In this paper, we closely investigate the influence of electrical load shedding in distributed data centers and construct a physical model to estimate the relationship among power, heat and workload. We then use queueing theory to approximate the tasks’ response time and aim to minimize the overall response time of tasks by migration. Our extensive evaluations show that our method can improve the response time with more than 9% reduction.

Recent years have seen increasing attention on building High-Speed Railways (HSR) in many countries. Trains running on the railways have a top velocity of up to over 300 km/hour. This makes it become a scenario with unstable connection qualities. In this paper, we propose a novel model that can accurately estimate the mobility status on HSR based on the changing patterns of network latency. Though various impact factors make the prediction complex, we however argue that the recent advance of deep learning applies well in our context, and further we design a neural network model that can estimate the moving velocity based on monitoring network latency’s changing patterns in a short period. In this model, we use a new variable called Round Difference Time (RDT) to describe latency’s changing patterns. We also use the Fourier Transform to extract the hidden time-frequency and use the generated spectrum for estimation. Our data-driven evaluations show that with suitable parameters, this model can get an accuracy of up to 94% on all three lines.

360° videos are becoming one of the major media in recent years, providing immersive experience for viewers with more interactions compared to traditional videos. Most of today’s implementations rely on bulky Head-Mounted Displays (HMDs) or require touch screen operations for interactive display, which are not only expensive but also inconvenient for viewers. In this paper, we demonstrate that interactive 360° video streaming can be done with hints from gaze movement detected by the front camera of today’s mobile devices (e.g., a smartphone). We design a lightweight real-time gaze point tracking method for this purpose. Using only the front camera, our solution detects the users’ faces by a lightweight Haar-like cascaded classifier, measures the user’s face-to-screen distance and sight angle, and then derives the location of the user’s gaze point following a customized triangularity model. We integrate it with streaming module and apply a dynamic margin adaption algorithm to minimize the overall energy consumption for battery-constrained mobile devices. Our experiments on state-of-the-art smartphones show the feasibility of our solution and its energy efficiency toward cost-effective real-time 360° video streaming.

Tile-based approach is widely adopted in adaptive 360\textdegree~video streaming systems. Existing QoE-driven streaming approaches usually obtain the tile selection and adjust the bitrate based on the viewport prediction with a fixed tiling, which fail to consider the unstable prediction performance. However, varying the tiling of the video can produce different number of tiles with different sizes, and thus can have distinct impacts on error tolerance for viewport prediction and on decoding complexity for resource-constrained mobile client. In this work, we introduce adaptive tiling into the conventional bitrate adaptation for mobile 360degree~video streaming. We first analyze the impacts of tilings on tile selection and decoding time, which verify the benefit of tiling adaptation in various practical aspects. We then formulate the QoE optimization problem for adaptive tiling and bitrate streaming and discuss the design details of our adaptation algorithm, which can adapt to the performance of viewport prediction and the decoding capabilities of mobile clients in addition to the conventional influencing factors. Finally, the superiority of our proposed approach compared with the state-of-the-art methods is evaluated through extensive trace-driven simulations.

Delay-sensitive multimedia streaming applications require their data to be delivered before a deadline to be useful. The data transmitted by these applications can usually be partitioned into blocks with different priorities, assigned based on the impact of a block on the Quality of Experience (QoE) if it misses its delivery deadline. Meet their deadline requirements is challenging due to the dynamics of the network and these applications' high demand on network resources. To encourage the research community to address this challenge, we organize the "Meet Deadline Requirements" Grand Challenge at ACM Multimedia 2021. This grand challenge provides a simulation platform onto which the participants can implement their block scheduler and bandwidth estimator and then benchmark against each other using a common set of application traces and network traces.

The ever-growing deployment scale of surveillance cameras and the users' increasing appetite for real-time queries have urged online video analytics. Synergizing the virtually unlimited cloud resources with agile edge processing would deliver an ideal online video analytics system; yet, given the complex interaction and dependency within and across video query pipelines, it is easier said than done. This paper starts with a measurement study to acquire a deep understanding of video query pipelines on real-world camera streams. We identify the potentials and practical challenges towards cloud-edge collaborative video analytics. We then argue that the newly emerged serverless computing paradigm is the key to achieve fine-grained resource partitioning with minimum dependency. We accordingly propose CEVAS, a Cloud-Edge collaborative Video Analytics system empowered by fine-grained Serverless pipelines. It builds flexible serverless-based infrastructures to facilitate fine-grained and adaptive partitioning of cloud-edge workloads for multiple concurrent query pipelines. With the optimized design of individual modules and their integration, CEVAS achieves real-time responses to highly dynamic input workloads. We have developed a prototype of CEVAS over Amazon Web Services (AWS) and conducted extensive experiments with real-world video streams and queries. The results show that by judiciously coordinating the fine-grained serverless resources in the cloud and at the edge, CEVAS reduces 86.9% cloud expenditure and 74.4% data transfer overhead of a pure cloud scheme and improves the analysis throughput of a pure edge scheme by up to 20.6%. Thanks to the fine-grained video content-aware forecasting, CEVAS is also more adaptive than the state-of-the-art cloud-edge collaborative scheme.

Modern acoustic wearables with microphone arrays are promising to offer rich experience (e.g., 360° sound and acoustic imaging) to consumers. Realtime multi-track audio streaming with precise synchronization however poses significant challenges to the existing wireless microphone array designs that depend on complex digital synchronization as well as bulky and power-hungry hardware.

In order to have low-latency real-time response to applications such as Vehicle-to-everything (V2X) communications in Intelligent Vehicle System, edge computing as a paradigm has been proposed to put computing resources near the data origin. The limited computing resources in edge devices results in degraded object recognition results. To resolve this problem, high-level sensor fusion is a promising solution, which make uses of object-level information from multiple sensors to increase the accuracy. However, general high-level camera-radar fusion method does not work well in street information collection scenario. In this paper, we identified the key challenges in low-latency street information collection scenario and developed a multipath-resistant camera-radar sensor fusion method to increase the performance of sensor fusion method in such a scenario. Extensive experiments have shown that our system can increase 45% of detection rate and reduce 13% of error on edge devices comparing with a state-of-the-art method.

The explosive deployment of the Internet of Things (IoT) brings a massive number of light-weight and energy-limited IoT devices, challenging stable wireless access. Energy-efficient, Frame Slotted Aloha (FSA) recently emerged as a promising MAC protocol for large-scale IoT systems such as Machine to Machine (M2M) and Radio Frequency Identification (RFID). Yet the stability of FSA and how to stabilize it, despite of its fundamental importance on the effective operation in practical systems, have not been systematically addressed. In order to bridge this gap, we devote this paper to designing stable FSA-based access protocol (SFP) to stabilize IoT systems. We first design an additive active node population estimation scheme and use the estimate to set frame size and participation probability for throughput optimization. We then carry out theoretical analysis demonstrating the stability of SFP in the sense of geometric ergodicity of Markov chain derived from dynamics of the active node population and its estimate. Our central theoretical result is a set of closed-form conditions on the stability of SFP. We further conduct extensive simulations whose results confirm our theoretical analysis and demonstrate the effectiveness of SFP.

Virtualization has attained mainstream status in enterprise IT industry. Despite its widespread adoption, it is known that virtualization also introduces non-trivial overhead when tasks are executed on a virtual machine (VM). In particular, a combined effect from device virtualization overhead and CPU scheduling latency can cause performance degradation when computation intensive tasks and I/O intensive tasks are co-located on a VM. Such an interference also causes extra energy consumption. In this paper, we present Hylics, a novel solution that enables efficient data traverse paths for both I/O and computation intensive workloads. This is achieved with the provision of in-memory file system and network service at the hypervisor level. Several important design issues are pinpointed and addressed during our prototype implementation, including efficient intermediate data sharing, network service offloading, and QoS-aware memory usage management. Based on our real-world deployment on KVM, we show that Hylics can significantly improve computation and I/O performance for hybrid workloads. Moreover, this design also alleviates the existing virtualization overhead and naturally optimizes the overall energy efficiency.

Automobiles have become one of the necessities of modern life, but also introduced numerous traffic accidents that threaten drivers and other road users. Most state-of-the-art safety systems are passively triggered, reacting to dangerous road conditions or driving maneuvers only after they happen and are observed, which greatly limits the last chances for collision avoidances. Timely tracking and predicting the driving maneuvers calls for a more direct interface beyond the traditional steering wheel/brake/gas pedal. In this paper, we argue that a driver's eyes are the interface, as it is the first and the essential window that gathers external information during driving. Our experiments suggest that a driver's gaze patterns appear prior to and correlate with the driving maneuvers for driving maneuver prediction. We accordingly present GazMon, an active driving maneuver monitoring and prediction framework for driving assistance applications. GazMon extracts the gaze information through a front-camera and analyzes the facial features, including facial landmarks, head pose, and iris centers, through a carefully constructed deep learning architecture. Both our on-road experiments and driving simulator based evaluations demonstrate the superiority of our GazMon on predicting driving maneuvers as well as other distracted behaviors. It is readily deployable using RGB cameras and allows reuse of existing smartphones towards more safely driving.

In modern distributed cyber-physical systems (CPS), information fusion often plays a key role in automate and self-adaptive decision making process. However, given the heterogeneous and distributed nature of modern CPSs, it is a great challenge to operate CPSs with the compromised data integrity and unreliable communication links. In this paper, we study the distributed state estimation problem under the false data injection attack (FDIA) with probabilistic communication networks. We propose an integrated ”detection + fusion” solution, which is based on the Kullback-Leibler divergences (KLD) between local posteriors and therefore does not require the exchange of raw sensor data. For the FDIA detection step, the KLDs are used to cluster nodes in the probability space and to partition the space into secure and insecure subspaces. By approximating the distribution of the KLDs with a general$\chi ^2$distribution and calculating its tail probability, we provide an analysis of the detection error rate. For the information fusion step, we discuss the potential risk of double counting the shared prior information in the KLD-based consensus formulation method. We show that if the local posteriors are updated from the shared prior, the increased number of neighbouring nodes will lead to the diminished information gain. To overcome this problem, we propose a near-optimal distributed information fusion solution with properly weighted prior and data likelihood. Finally, we present simulation results for the integrated solution. We discuss the impact of network connectivity on the empirical detection error rate and the accuracy of state estimation.

This paper studies the problem of tag localization using RFID-augmented robots, which is practically important for promising warehousing applications, e.g., automatic item fetching and misplacement detection. Existing RFID localization systems suffer from one or more of following limitations: requiring specialized devices; only 2D localization is enabled; having blind zone for mobile localization; low scalability. In this paper, we use Commercial Off-The-Shelf (COTS) robot and RFID devices to implement a Mobile RF-robot Localization (MRL) system. Specifically, when the RFID-augmented robot moves along the straight aisle in a warehouse, the reader keeps reading the target tag via two vertically deployed antennas ( Z1 and Z2) and returns the tag phase data with timestamps to the server. We take three points in the phase profile of antenna Z1 and leverage the spatial and temporal changes inherent in this phase triad to construct an equation set. By solving it, we achieve the location of target tag relative to the trajectory of antenna Z1. Based on different phase triads, we can have candidate locations of the target tag with different accuracy. Then, we propose theoretical analysis to quantify the deviation of each localization result. A fine-grained localization result can be achieved by assigning larger weights to the localization results with smaller deviations. Similarly, we can also calculate the relative location of target tag with respect to the trajectory of antenna Z2. Leveraging the geometric relationships among target tag and antenna trajectories, we eventually calculate the location of target tag in 3D space. We perform various experiments to evaluate the performance of the MRL system and results show that the proposed MRL system can achieve high accuracy in both 2D and 3D localization.

In-car human activity recognition opens a new opportunity toward intelligent driving behavior detection and touchless human-car interaction. Among the many sensing technologies (e.g., using cameras and wearable sensors), radio frequency identification (RFID) exhibits unique advantages given its low cost, easy deployment, and less privacy concerns. Existing RFID-based solutions for activity recognition are mostly confined to working in stable indoor spaces. The inside space of a car however is much more compact and complex, not to mention the fast-changing driving conditions. All these introduce non-negligible noises that pollute the activity-related information, and the existence of various car models in the market further complicates the problem. In this article, we for the first time closely examine the distinct factors that affect the RFID-based in-car activity recognition. We present RF-CAR, a novel RFID-based tag-free solution that well adapts to different in-car environments. RF-CAR smartly filters the domain-specific features in RF signals and retains activity-related features to the maximum extent. It then integrates a deep learning architecture and an advanced multi-adversarial domain adaptation network for training and prediction. With only one-time pre-training, RF-CAR can adapt to new data domains such as new driving conditions, car models, and human subjects for robust activity recognition. We also demonstrate that it is readily deployable in cars with commercial off-the-shelf (COTS) RFID devices. Our extensive experiments suggest that RF-CAR achieves an overall recognition accuracy of around 95 percent, which significantly outperforms the state-of-the-art solutions.

The pervasive penetration of mobile smart devices has significantly enriched Internet applications and undoubtedly reshaped the way that users access Internet services. Different from traditional desktop applications, mobile Internet applications require users to input via touch screens and view outputs on the displays with considerably limited size. The significant conflict between the limited-size of touch screens and the richness of online media contents widely exists in dynamic-viewport mobile applications, a class of mobile Internet applications that download contents beyond the user's viewing region (referred to as viewport). As dynamic-viewport mobile applications usually use HTTP for content downloading, to improve their quality of experience (QoE) and cost efficiency, in this paper, we present a Mobile-Friendly HTTP middleware (MF-HTTP), which can interpret user touch screen inputs and optimize the HTTP downloading of media objects for such applications. We first demystify screen scrolling in mobile operating systems and precisely break down the viewport moving process. We identify the key influential factors for media object downloading and develop an optimal download scheme. Towards building a practical middleware, we further discuss and address the implementation issues in detail. We implement a MF-HTTP prototype based on Android platforms and evaluate the performance of MF-HTTP by conducting concrete case studies on two representative dynamic-viewport mobile applications, namely, web browsing and 360-degree video streaming.

Recently backscatter communication with commodity radios has received significant attention since specialized hardware is no longer needed. The state-of-the-art BLE backscatter system, FreeRider, realizes ultra-low-power BLE backscatter communication entirely using commodity devices. It, however, suffers from several key reliability issues, including unreliable two-step modulation, productive-data dependency, and lack of interference countermeasures. To address these problems, we propose RBLE, a robust BLE backscatter system that works with an excitation BLE device and a single BLE receiver. First, it uses BLE signals with partial single tones as excitations, making single-bit modulation much more robust. Then it designs dynamic channel configuration that enables channel hopping to avoid interfered channels. Moreover, it presents BLE packet regeneration that uses adaptive encoding to further enhance reliability for various channel conditions. The prototype is implemented using TI BLE radios, iPhones, Android phones, and customized tags with FPGAs. Empirical results demonstrate that RBLE achieves more than 17x uplink goodput gains over FreeRider under indoor LoS, NLoS, and outdoor environments. We also show that RBLE can realize uplink ranges of up to 25 m for indoors and 56 m for outdoors.

This paper intends to study the energy efficiency in caching based UAV networks, where fog radio access network (FRAN) and non-orthogonal multiple access (NOMA) are considered meanwhile. Taking full account of the impact of caching, subchannel assignment, and power allocation in UAV enabled wireless networks, we formulate the problem of maximizing energy efficiency. In order to better solve the proposed non-convex problem, we propose a subchannel assignment algorithm and a power allocation algorithm applying alternating direction method of multipliers (ADMM). The final simulation section verifies the fast convergence of the algorithm and compares the advantages with existing algorithms.

Physical layer authentication based on channel state information is an effective solution to identifying transmitters and is light weight which is friendly to resource limited terminals in industrial cyber physical systems. Although existing researches have proved the feasibility of the threshold based physical layer authentication under industrial environment, the performance is far from satisfying when transmitter is not static. This article applies residual network to identify legitimate transmitter and illegitimate transmitter by channel state information. The proposed method is applied to measurement from an industrial wireless communication scenario, where the transmitter is mobile. The authentication accuracy is significantly improved and all the numerical results allows to derive meaningful insights on the improvements of such a method to industrial wireless cyber physical systems.

Recently, data-driven prediction strategies have shown the potential of shepherding the optimization strategies for end viewer's Quality-of-Experience in practical streaming applications. The current prediction-based designs have largely focused on optimizing the last-mile, i.e., viewer-side, which 1) need the real-time feedback from viewers to improve the prediction accuracy; and 2) need quick responses to guarantee the effectiveness of optimization strategies in the future. Thanks to the emerged crowdsourced livecast services, e.g., Twitch.tv, we for the first time exploit the opportunity to realize the long-term prediction and optimization with the assistance derived from the first-mile, i.e., source broadcasters.In this paper, we propose a novel framework CastFlag, which analyzes the broadcasters' operations and interactions, predicts the key events (i.e., highlights), and optimizes the transcoding stage in the corresponding live streams, even before the encoding stage. Taking the most popular eSports gamecast as an example, we illustrate the effectiveness of this framework in the game highlight prediction and transcoding workload allocation. The trace-driven evaluation shows the superiority of CastFlag as it: (1) improves the prediction accuracy over other learning-based approaches by up to 30%; (2) achieves an average of 10% saving of the transcoding latency at less cost.

Today's explosively growing Internet video traffics and viewers' ever-increasing quality of experience (QoE) demands for video streaming bring tremendous pressures to the backbone network. As a new network paradigm, mobile edge caching provides a promising alternative by pushing video content closer at the network edge rather than the remote CDN servers so as to reduce both content access latency and redundant network traffic. However, our large-scale trace analysis shows that different from CDN based caching, edge caching environment is much more complicated with massively dynamic and diverse request patterns, which renders that existing rule-based and model-based caching solutions may not well fit such complicated edge environments. Moreover, although cooperative caching has been proposed to better afford limited storage on each individual edge server, our trace analysis also shows that the request similarity among neighboring edges can be highly dynamic and diverse, which is drastically different from CDN based caching environment, and can easily compromise the benefits from traditional cooperative caching mostly designed based on CDN environment. In this paper, we propose MacoCache, an intelligent edge caching framework that is carefully designed to afford the massively diversified and distributed caching environment to minimize both content access latency and traffic cost. Specifically, MacoCache leverages a multi-agent deep reinforcement learning (MADRL) based solution, where each edge is able to adaptively learn its own best policy in conjunction with other edges for intelligent caching. The real trace-driven evaluation further demonstrates that MacoCache is able to reduce an average of 21% latency and 26% cost compared with the state-of-the-art caching solution.

With the dramatic increase of video traffic on the Internet, video quality of experience (QoE) measurement becomes even more important, which provides network operators with an insight into the quality of their video delivery services. The widespread adoption of end-to-end encryption protocols such as SSL/TLS, however, sets a barrier to QoE monitoring as the most valuable indicators in cleartext traffic are no longer available after encryption. Existing studies on video QoE measurement in encrypted traffic support only coarse-grained QoE metrics or suffer from low accuracy. In this paper, we propose DeepQoE, a new approach that enables real-time video QoE measurement from encrypted traffic. We summarize critical fine-grained QoE metrics, including startup delay, rebuffering, and video resolutions. In order to achieve accurate and real-time inference of these metrics, we build DeepQoE by employing Convolutional Neural Networks (CNNs) with a sophisticated input and architecture design. More specifically, DeepQoE only leverages packet Round-Trip Time (RTT) in upstream traffic as its input. Evaluation results with real-world datasets collected from two popular content providers (i.e., YouTube and Bilibili) show that DeepQoE can improve QoE measurement accuracy by up to 22% over the state-of-the-art methods.

Virtualized datacenters are often designed from scratch with multiple, redundant paths. Yet the majority of the existing congestion control schemes for virtual machines or containers are variants based on single-path TCP design. Lacking the flexibility of leveraging underlying paths, these schemes cannot further improve the utilization of datacenter networks, or mitigate hotspot links. In this paper, we examine the performance of multipath congestion control design on typical hypervisor and container virtualization platforms. We observe that, the involvement of virtual switch, together with the multi-tenancy nature on these platforms, poses new challenges when handling multipath traffic. Through realworld experiments with production-grade applications, we further reveal that, while multipath congestion control increases per-connection throughput and achieves better traffic balancing, it experiences performance degradation when the number of connections is abruptly increased or there exist path-sharing subflows. These issues are due to the enforced QoS policies and interface mapping schemes applied by virtual switch. To this end, we present vMCC, a practical solution which incorporates explicit congestion notification (ECN) support on virtual switches and ECN-aware multipath congestion control algorithms. We show by comprehensive evaluations that vMC-C improves throughput, round trip time, fairness, and energy efficiency for cloud datacenter traffic, and subsequently benefits typical cloud workloads.

This paper presents a systematic study on adaptive streaming over MPTCP. We start from realworld experiments with Dynamic Adaptive Streaming over HTTP (DASH) and analysis on its performance over MPTCP. We show that DASH can greatly benefit from the improved aggregated throughput by MPTCP; yet the inter-path throughput difference and the intra-path throughput fluctuation have noticeable (negative) impact, too. Without a proper design of path selection and adaptation in MPTCP, they can easily confuse the adaptation logic of DASH, resulting in low bitrates or frequent rebuffering even if high-bandwidth paths are available. We present MPTCP+, an extended multipath TCP solution to offer high quality and smooth playback for adaptive HTTP streaming. MPTCP+ incorporates a path use decision algorithm that smartly disables/enables a path to minimize the inter-path difference, and a novel congestion control algorithm that smooths congestion window evolution with multiple paths. We have implemented MPTCP+ in the MPTCP Linux kernel, with minimum change on the server-side MPTCP module only. It is fully compatible with the existing MPTCP clients and requires no change on the upper-layer protocols, too. Our experiments suggest that MPTCP+ increases the quality of experience (QoE) of DASH by up to 50%.

Livecast streaming has received great success in recent years. Although many prior efforts have suggested that dynamic viewer scheduling according to the quality of service (QoS) can improve user engagement, they may suffer inefficiency due to their ignorance of viewer heterogeneity in how the QoS impact quality of experience (QoE).

Backscatter communication, in which data is conveyed through reflecting excitation signals, has been advocated as a promising green technology for Internet of Things (IoT). Existing backscatter solutions however are mostly centralized, relying on a single excitation source, typically within one hop. Though recent works have demonstrated the viability of multi-hop backscatter, the excitation signal remains centralized, which attenuates quickly and fundamentally limits the communication scope. For long-range and high-quality communication, distributed excitations are expected and also naturally available as ambient signals (WiFi, BLE, cellular, FM, light, sound, etc.), albeit not being explored for boosting nearby tags for relaying.

With the rapid development of radio-frequency identification (RFID) technology, the ever-increasing research effort has been dedicated to devising various RFID-enabled services. The missing event detection, the functionality of detecting missing objects, is one of the most important services in many Internet-of-Things applications such as inventory management. Prior detection protocols only work in single-tagged RFID systems and would waste much time on repeated checks on one object in the emerging multitagged systems where each object is attached by multiple tags, leaving efficient detection in the new scenario unaddressed. To bridge the gap, this article is devoted to detecting missing multitagged objects. The key technicality is to build a filter from a subset of tags instead of whole in prior works to avoid repeated detections of one object and reduce detection time. Specifically, we first provide a basic solution based on the Bloom filter which can specify only tags in the chosen subset to participate in the final detection. To further improve time efficiency, we propose an advanced protocol that exploits tag ID knowledge and sparsity of slots mapped by only tags in the chosen subset to build a more compact compressive filter. Moreover, a composite vector is used to efficiently coordinate tags to report its presence. We conduct theoretical analysis on optimum protocol parameters and extensive simulations to verify the feasibility of the protocols. The results show that the advanced protocol achieves more than$2\times $performance gain in terms of time efficiency over the Bloom filter-based basic protocol.

The explosion of online shopping brings great challenges to traditional logistics industry, where the massive parcels and tight delivery deadline impose a large cost on the delivery process, in particular the last mile parcel delivery. On the other hand, modern cities never lack transportation resources such as the private car trips. Motivated by these observations, we propose a novel and effective last mile parcel delivery mechanism through car trip sharing, to leverage the available private car trips to incidentally deliver parcels during their original trips. To achieve this, the major challenges lie in how to accurately estimate the parcel delivery trip cost and assign proper tasks to suitable car trips to maximize the overall performance. To this end, we develop Car4Pac, an intelligent last mile parcel delivery system to address these challenges. Leveraging the real-world massive car trip trajectories, we first build up a 3D (time-dependent, driver-dependent and vehicle-dependent) landmark graph that accurately predicts the travel time and fuel consumption of each road segment. Our prediction method considers not only traffic conditions of different times, but also driving skills of different people and fuel efficiencies of different vehicles. We then develop a two-stage solution towards the parcel delivery task assignment, which is optimal for one-to-one assignment and yields high-quality results for many-to-one assignment. Our extensive real-world trace driven evaluations further demonstrate the superiority of our Car4Pac solution.

Efficient event stream dissemination is a challenging problem in large-scale Online Social Network (OSN) systems due to the costly inter-server communications caused by the per-user view data storage. To solve the problem, previous schemes mainly explore the structures of social graphs to reduce the inter-server traffic. Based on the observation of high cluster coefficients in OSNs, a state-of-the-art social piggyback scheme can save redundant messages by exploiting an intrinsic hub-structure in an OSN graph for message piggybacking. Essentially, finding the best hub-structure for piggybacking is equivalent to finding a variation of the densest sub-graph. The existing scheme computes the best hub-structure by iteratively removing the node with the minimum weighted degree. Such a scheme incurs a worst computation cost of O(n2), making it not scalable to large-scale OSN graphs. Using alternative hubstructure instead of the best hub-structure can speed up the piggyback assignment. However, they greatly sacrifice the communication efficiency of the assignment schedule. Different from the existing designs, in this work, we propose a QuickPoint algorithm, which removes a fraction of nodes in each iteration in finding the best hub-structure. We mathematically prove that QuickPoint converges in O(logαn)(α > 1) iterations in finding the best hub-structure for efficient piggyback. We implement QuickPoint in parallel atop Pregel, a vertex-centric distributed graph processing platform. Comprehensive experiments using large-scale data from Twitter and Flickr show that our scheme is 38.8× more efficient compared to existing schemes.

With rapid development of radio frequency identification (RFID) technology, ever-increasing research effort has been dedicated to devising various RFID-enabled services. The missing tag identification, which is to identify all missing tags, is one of the most important services in many Internet-of-Things applications such as inventory management. Prior work on missing tag detection all rely on hash functions implemented at individual tags. However, in reality hash functions are not supported by commercial off-the-shelf (COTS) RFID tags. To bridge this gap between theory and practice, this paper is devoted to detecting missing tags with COTS Gen2 devices. We first introduce a point-to-multipoint protocol, named P2M that works in an analog frame slotted Aloha paradigm to interrogate tags and collect their electronic product codes (EPCs). A missing tag will be found if its EPC is not present in the collected ones. To reduce time cost of P2M resulted from tag response collisions, we further present a collision-free point-to-point protocol, named P2P that selectively specifies a tag to reply with its EPC in each slot. If the EPC is not received, this tag is regarded to be missing. We develop two bitmask selection methods to enable the selective query while reducing communication overhead. We implement P2M and P2P with COTS RFID devices and evaluate their performance under diverse settings.

Ever-increasing research efforts have been dedicated to radio frequency identification (RFID) systems, such as finding top-k, elephant groups, and missing-tag detection. While group labeling, which is how to tell tags their associated group data, is the common prerequisite in many RFID applications, its efficiency is not well optimized due to the transmission of useless data with only one seed used. In this paper, we introduce a unified protocol called GLMS which employs multiple seeds to construct a composite indicator vector (CIV), reducing the useless transmission. Technically, to address Seed Assignment Problem (SAP) arising during building CIV, we develop an approximation algorithm (AA) with a competitive ratio 0.632 by globally searching for the seed contributing to the most useful slot. We then further design two simplified algorithms through local searching, namely c-search-I and its enhanced version c-search-II, reducing the complexity by one order of magnitude while achieving comparable performance. We conduct extensive simulations to demonstrate the superiority of our approaches.

Recently backscatter networks have received booming interest because, they offer a battery-free communication paradigm using propagation radio waves as opposed to active radios in traditional sensor networks while providing comparable sensing functionalities, ranging from light and temperature sensors to recent microphones and cameras. While sensing data on backscatter nodes has been seen on a clear path to increasing in both volume and variety, backscatter communication is not well prepared and optimized for transferring such continuous and high-volume data. To bridge this gap, we propose a high-throughput rate adaptation scheme for backscatter networks by exploring the unique characteristics of backscatter links and the design space of the ISO 18000-6C (C1G2) protocol. Our key insight is that while prior work has left the downlink unattended, we observe that the quality of downlink is affected significantly by multipath fading and thus can degrade the uplink and overall throughput considerably. Therefore, we introduce a novel rate mapping algorithm that chooses the best rate for both the downlink and uplink. Also, we design an efficient channel estimation method fully compatible with the C1G2 protocol and a reliable probing trigger, substantially saving probing overhead. To combat interference, we further design an interference detector using clusters and lightweight countermeasures to make rate adaptation more robust. Our scheme is prototyped using commercial RFID readers and tags. The results show that we can achieve up to 2.6× throughput gain over state-of-the-art approaches across various mobility, channel, network-size, and interference conditions.

Unknown RFID tags appear when tagged items are not scanned before being moved into a warehouse, which can even cause serious security issues. This paper studies the practically important problem of unknown tag detection. Existing solutions either require low-cost tags to perform complex operations or beget a long detection time. To this end, we propose the Collision-Seeking Detection (CSD) protocol, in which the server finds out a collision-seed to make massive known tags hash-collide in the last $N$ slots of a time frame with size $f$ . Thus, all the leading ${f-N}$ pre-empty slots become useful for detection of unknown tags. A challenging issue is that, computation cost for finding the collision-seed is very huge. Hence, we propose a supplementary protocol called Balanced Group Partition (BGP), which divides tag population into $n$ small groups. The group number $n$ is able to trade off between communication cost and computation cost. We also give theoretical analysis to investigate the parameters to ensure the required detection accuracy. The major advantages of our CSD+BGP are two-fold: (i) it only requires tags to perform lightweight operations, which are widely used in classical framed slotted Aloha algorithms. Thus, it is more suitable for low-cost tags; (ii) it is more time-efficient to detect the unknown tags. Simulation results reveal that CSD+BGP can ensure the required detection accuracy, meanwhile achieving $1.7\times $ speedup in the single-reader scenarios and $3.9\times $ speedup in the multi-reader scenarios than the state-of-the-art detection protocol.

Today’s anywhere and anytime broadband connection and audio/video capture have boosted the deployment of crowdsourced livecast services (orcrowdcast). Bridging a massive amount of geo-distributed broadcasters and their fellow viewers, such representatives as Twitch.tv, Youtube Gaming, and Inke.tv, have greatly changed the generation and distribution landscape of streaming content. They also enable rich online interactions among the crowd, and strive to offer personalized Quality-of-Experience (QoE) for individual viewers. Given the ultra-large scale and the dynamics of the crowd, personalizing QoE however is much more challenging than in early generation streaming services. The rich interactions among the broadcasters, viewers, and the network system, on the other hand, also offer invaluable data that could be utilized towards informed management. This paper presentsDeepCast, an edge-assisted crowdcast framework that explores the sheer amount of viewing data towards intelligent decisions for personalized QoE demands. DeepCast seamlessly integrates cloud, CDN, and edge servers for crowdcast content distribution, and advocates a data-driven design that extracts the hidden information from the complex interactions among the system components. Through deep reinforcement learning (DRL), it automatically identifies the most suitable strategies for viewer assignment and transcoding at edges. We collect multiple real-world datasets and evaluate the performance of DeepCast with trace-driven experiments. The results demonstrate its flexibility and effectiveness towards better personalized QoE and lower cost for crowdcast systems.

Multipath TCP (MPTCP) has recently been suggested as a promising transport protocol to boost the utilization of underlaying datacenter networks, yet it also increases the host CPU power consumption. It remains unclear whether datacenters can indeed benefit from using MPTCP from the perspective of energy efficiency. Through realworld measurement of MPTCP, we show that the energy efficiency of MPTCP is largely related to the flow completion time and the existence of link-sharing subflows. In particular, we find that the link-sharing subflows in MPTCP will significantly elevate the CPUs' power consumption on hosts. To make the matter worse, it will also reduce the transmission efficiency for both throughput-sensitive long flows and latency-sensitive short flows. To address such a problem, we present MPTCP-D, an energy-efficient enhancement of MPTCP in datacenter networks. MPTCP-D incorporates a novel congestion control algorithm that improves energy efficiency by minimizing the flow completion time. It also has a build-in subflow elimination mechanism that precludes link-sharing subflows from increasing the host CPU power consumption. We implement MPTCP-D in the Linux kernel, analyze the parameter selection in the algorithm and study its performance through packet-level simulation and on Amazon EC2. Our results show that, without degrading the performance of the long flow throughput and the short flow completion time, MPTCP-D reduces the long flow energy consumption by up to 72% compared to DCTCP for data transfers, and reduces the short flow power consumption by up to 46% compared to MPTCP with link-sharing subflows.

This paper aims to explore the problem of energy economy of mobile devices in the Mobile Crowdsensing (MCS) scenario. The neighbor scanning mechanism of mobile devices usually consumes most of the energy in multi-hop message transmission. Traditional mechanisms such as chaotic neighbor detection and continuous neighbor discovery, can easily exhaust the limited energy. Since they are actually unnecessarily considering the strong correlation between the transmission opportunity and the social characteristics. Therefore, diminishing energy consumption is of key importance toward energy-efficient MCS. Sleeping strategy stands out as a promising approach to improve energy efficiency and hold the the network metrics in MCS applications, while challenges still lie in achieving effective scheduling given the changing environment. In this study, we proposed a novel self-adapt sleeping scheduling approach based on the correlation between the pedestrian's own historical trajectory and geographic grid information for energy saving (ESGeo). As the grid-based method can well record the encounter characteristics of nodes, ESGeo is able to pick flexible duty-cycling strategies for mobile devices in each distinguishable grid. This enables, mobile devices to avoid excessive scanning in low-probability encounter areas. Extensive simulation results further demonstrated that the proposed approach can significantly outperform the typical routing approaches in terms of energy-efficiency, without largely affecting the overall networking performance.

Recently, the fusion of 360° video and multi-viewpoint video, called multi-viewpoint (MVP) 360° interactive video, has emerged and created much more immersive and interactive user experience, but calls for a low latency solution to request the high-definition contents. Such viewing-related features as head movement have been recently studied, but several key issues still need to be addressed. On the viewer side, it is not clear how to effectively integrate different types of viewing-related features. At the session level, questions such as how to optimize the video quality under dynamic networking conditions and how to build an end-to-end mapping between these features and the quality selection remain to be answered. The solutions to these questions are further complicated given the many practical challenges, e.g., incomplete feature extraction and inaccurate prediction.This paper presents an architecture, called iView, to address the aforementioned issues in an MVP 360° interactive video scenario. To fully understand the viewing-related features and provide a one-step solution, we advocate multimodal learning and deep reinforcement learning in the design. iView intelligently determines video quality and reduces the latency without pre-programmed models or assumptions. We have evaluated iView with multiple real-world video and network datasets. The results showed that our solution effectively utilizes the features of video frames, networking throughput, head movements, and viewpoint selections, achieving at least 27.2%, 15.4%, and 2.8% improvements on the three video datasets, respectively, compared with several state-of-the-art methods.

Recent years have seen booming development and great success in interactive crowdsourced livecast (i.e., crowdcast). Different from traditional livecast services, crowdcast is featured with tremendous video contents at the broadcaster side, highly diverse viewer side content watching environments/preferences as well as viewers' personalized quality of experience (QoE) demands (e.g., individual preferences for streaming delays, channel switching latencies and bitrates). This imposes unprecedented key challenges on how to flexibly and cost-effectively accommodate the heterogeneous and personalized QoE demands for the mass of viewers. In this paper, we propose DeepCast, an edge-assisted crowdcast framework, which makes intelligent decisions at edges based on the massive amount of real-time information from the network and viewers to accommodate personalized QoE with minimized system cost. Given the excessive computation complexity in this context, we propose a data-driven deep reinforcement learning (DRL) based solution that can automatically learn the best suitable strategies for viewer scheduling and transcoding selection. To our best knowledge, DeepCast is the first edge-assisted framework that applies the advance of DRL to explicitly accommodate personalized QoE optimization for crowdcast services. We collect multiple real-world datasets and evaluate the performance of DeepCast using trace-driven experiments. The results demonstrate the superiority of our DeepCast framework and its DRL-based solution.

In-car human activity recognition is playing a critical role in detecting distracted driving and improving human-car interaction. Among multiple sensing technologies, WiFi-based in-car activity recognition exhibits unique advantages since it does not rely on visible light, avoids privacy leaks and is cost-efficient with integrated WiFi signals in cars. Existing WiFi-based recognition systems mostly focus on the relatively stable indoor space, which only yield reasonably good performance in limited situations. Based on our field studies, the in-car activity recognition, however, is much more complicated suffering from more impact factors. First, the external moving objects and the surrounding WiFi signals can cause various disturbances to the in-car activity sensing. Second, considering the compact in-car space, different car models can also lead to different multipath distortions. Moreover, different people can also perform activities in different shapes. Such extraneous information related to specific driving conditions, car models and human subjects is implicitly contained for training and prediction, inevitably leading to poor recognition performance for new environment and people.

Live video streaming delivery over Dynamic Adaptive Video Streaming (DASH) is challenging as it requires low end-to-end latency, is more prone to stall, and the receiver has to decide online which representation at which bitrate to download and whether to adjust the playback speed to control the latency. To encourage the research community to come together to address this challenge, we organize the Live Video Streaming Grand Challenge at ACM Multimedia 2019. This grand challenge provides a simulation platform onto which the participants can implement their adaptive bitrate (ABR) logic and latency control algorithm, and then benchmark against each other using a common set of video traces and network traces. The ABR algorithms are evaluated using a common Quality-of- Experience (QoE) model that accounts for playback bitrate, latency constraint, frame-skipping penalty, and rebuffering penalty.

Viewer scheduling among different CDN providers in crowdsourced live streaming (CLS) service is especially challenging due to the large-scale dynamic viewers as well as the time-variant performance of the content delivery network. A practical scheduling method should tackle the following challenges: 1) accurate modeling of viewer patterns and CDN performance; 2) intelligent workload offloading to save costs while guaranteeing the quality of service (QoS); 3) and ease of integration with practical CDN infrastructure in CLS platforms.

Mobile augmented reality (MAR) augments a real-world environment (probably surrounding or close to the mobile user) by computer-generated perceptual information. Utilizing the emerging edge computing paradigm in MAR systems can reduce the power consumption and computation load for the mobile devices and improve responsiveness of the MAR service. Different from existing studies that mainly explored how to better enable the MAR services utilizing edge computing resources, our focus is to optimize the video generation stage of the edge-based MAR services-efficiently using the available edge computing resources to render and encode the augmented reality as video streams to the mobile clients. Specifically, for multi-party AR applications, we identify the advantages and disadvantages of two encoding schemes, namely colocated encoding and spilt encoding, and examine the trade-off between performance and scalability when the rendering and encoding tasks are colocated or split. Towards optimally placing AR video rendering and encoding in the edge, we formulate and solve the rendering and encoding task assignment problem for multi-party edge-based MAR services to maximize the QoS for the users and the edge computing efficiency. The proposed task assignment scheme is proved to be superior through extensive trace-driven simulations and experiments on our prototype system.

The growing demand for video processing and the advantages in scalability and cost reduction brought by the emerging serverless computing have attracted significant attention in serverless computing powered video processing. However, how to implement and configure serverless functions to optimize the performance and cost of video processing applications remains unclear. In this paper, we explore the configuration and implementation schemes of typical video processing functions deployed to the serverless platforms and quantify their influence on the execution duration and monetary cost from a developer's perspective. Our measurement reveals that memory configuration is non-trivial. Dynamic profiling of workloads is necessary to find the best memory configuration. Moreover, compared with calling external video processing APIs, implementing these services locally in serverless functions can be competitive. We also find that the performance of video processing applications could be affected by the underlying infrastructure. Our work provides guidelines for further function-level optimization and complements the existing measurement studies for both serverless computing and video processing.

Indoor localization has witnessed a rapid development in the past few decades. Tremendous solutions have been put forwarded in the literature and the localization accuracy has reach an unprecedent centimeter-level. Among the available approaches, acoustic-enabled solutions have attracted much attention. They customarily achieve decimeter-level localization accuracy with affordable infrastructure costs. However, there still exist several open issues for the acoustic-based approaches which prohibit their wide-scale adoptions. First, although extra infrastructures (i.e., beacons) are economical, deployment, and maintenance can incur excessive labor cost. Second, current approaches have much latency to obtain a location fix, making it infeasible for mobile target tracking. Third, the localization performance of current solutions degrades easily by the near-far problem, multipath effect, and device diversity. To address these issues, this paper presents an asynchronous acoustic-based localization system with participatory sensing. We leverage the collaborative efforts of the participatory users who are relatively stationary in indoor environments as virtual anchors (VAs) to eliminate the predeployment and post-maintenance costs incurred in traditional anchor-based solutions. To mitigate the latency to obtain a location fix, we design an orthogonal ranging mechanism to enable concurrent beacon message transmission, which is $2\boldsymbol \times $ faster than previous work in obtaining a location fix. Moreover, we propose a robust method to address the near-far problem and device diversity, and we conquer the multipath problem via a genetic algorithm-based approach. Our VA-based system is self-deployable, cost-effective, and robust to environmental dynamics. We have implemented and evaluated a system prototype, demonstrating a median accuracy of 0.98 m in typical indoor settings.

The enabling Internet-of-Things technology has inspired many innovative sensing mechanisms by repurposing the onboard sensors. Leveraging the built-in acoustic sensors for ranging is among one of the interesting applications. However, among the few studies on acoustic ranging, the one-way sensing method suffers from synchronization errors and requires cumbersome kernel modifications; the other two-way approaches overcome these shortcomings, but they are sensitive to system delays. In this case, this paper proposes a novel lightweight one-way sensing paradigm without the above drawbacks. The key insight of this paper is to perform ranging by estimating the propagation time of acoustic signals via linear frequency modulation signal mixing. Such a signal mix operation can translate range estimation into fine-grain frequency estimation, thereby enhancing ranging accuracy. In addition, our system can have multiple receivers co-exist and thus the measurement dimensions are boosted. We have implemented and evaluated our system prototype in real-world settings. The prototype demonstrated centimeter-level ranging performance.

In recent decades, unmanned aerial vehicles (UAVs, also known as drones) equipped with multiple sensors have been widely utilized in various applications. Nevertheless, constrained by limited battery capacities, the hovering time of UAVs is quite limited, prohibiting them from serving a wide area. To cater with remote sensing applications, people often employ vehicles to transport, launch, and recycle them. The so-called vehicle-drone cooperation (VDC) benefits from both the far driving distance of vehicles and the high mobility of UAVs. Efficient routing and scheduling can greatly reduce time consumption and financial expenses incurred in VDC. However, previous works in vehicle-drone cooperative sensing considered only one drone, thus unable to simultaneously cover multiple targets distributed in an area. Using multiple drones to sense different targets in parallel can significantly promote efficiency and expand service areas. Therefore, we propose a novel problem, referred to as vehicle-assisted multidrone routing and scheduling problem. To tackle the problem, we contribute an efficient algorithm, referred to as vehicle-assisted multi-UAV routing and scheduling algorithm (VURA). In VURA, we maintain and iteratively update a memory containing candidate UAV routes. VURA works by iteratively deriving solutions based on UAV routes picked from the memory. In every iteration, VURA jointly optimizes anchor point selection, path planning, and tour assignment via nested optimization operations. To the best of our knowledge, we are the first to tackle this novel yet challenging problem. Finally, performance evaluation is presented to demonstrate the effectiveness and efficiency of our algorithm when compared with existing solutions.

The deeply penetrated WiFi signals not only provide fundamental communications for the massive Internet of Things devices but also enable cognitive sensing ability in many other applications, such as human activity recognition. State-of-the-art WiFi-based device-free systems leverage the correlations between signal changes and body movements for human activity recognition. They have demonstrated reasonably good recognition results with a properly placed transceiver pair, or, in other words, when the human body is within a certain sweet zone. Unfortunately, the sweet zone is not ubiquitous. When the person moves out of the area and enters a dead zone, or even just the orientation changes, the recognition accuracy can quickly decay. In this paper, we closely examine such spatial diversity in WiFi-based human activity recognition. We identify the dead zones and their key influential factors, and accordingly present WiSDAR, a WiFi-based spatial diversity-aware device-free activity recognition system. WiSDAR overshadows the dead zones yet with only one physical WiFi sender and receiver. The key innovation is extending the multiple antennas of modern WiFi devices to construct multiple separated antenna pairs for activity observing. Profiling activity features from multiple spatial dimensions can be more complicated and offer much richer information for further recognition. To this end, we propose a deep learning-based framework that integrates the hidden features from both temporal and spatial dimensions, achieving highly accurate and reliable recognition results. WiSDAR is fully compatible with commercial off-the-shelf WiFi devices, and we have implemented it on the commonly available Intel WiFi 5300 cards. Our real-world experiments demonstrate that it recognizes human activities with a stable accuracy of around 96%.

Crowdsourced live streaming (CLS), such as Twitch.tv and Inke.tv, has emerged as an important multimedia application in recent years. Video delivery in such CLS service involves two steps: 1) video uploading-video streaming (i.e., a live channel) generated from a broadcaster is uploaded to the server, which we call the “first mile” network and 2) video distribution-the video streaming is then delivered to viewers in the channel. Today's CLS services usually use conventional content delivery network solutions to address the video distribution problem, while little attention has been paid to improve the video uploading quality. Our measurement study shows that the first mile network causes 17% viewer rebuffers, and some viewers quit the channel once encountering rebuffer. In this paper, we propose a content harvest network (CHN) architecture to address the uploading problem in the CLS service. Specifically, the CHN architecture employs edge devices in the network as relays to receive the streaming uploaded by broadcasters and then forward to the central servers. On one hand, we need to reduce the latency since it is live streaming; on the other hand, we must provide sustainable upload bandwidth. It is challenging to achieve both at the same time, especially in such high dynamic system as CLS. In order to provide global optimal and real-time assignment, we propose a hybrid solution, i.e., centralized and distributed assignment. Specifically, we formulate the centralized relay assignment problem as an optimization problem to achieve both low latency and sustainable bandwidth. To cope with the frequent channel establishments, we use a multi-armed bandit method to characterize the time-variant network condition. Experiment results on a large-scale trace provided by Inke.tv show that our solution can reduce the overall viewer cost by 40% compared to state-of-the-art solutions. The viewers' rebuffer can also be reduced by 50%.

With the multi-antenna design of WiFi interfaces, phased array has become a promising mechanism for accurate WiFi localization. State-of-the-art WiFi-based solutions using Angle-of-Arrival (AoA), however, face a number of critical challenges. First, their localization accuracy degrades dramatically due to low Signal-to-Noise Ratio (SNR) and incoherent processing. Second, they tend to produce outliers when the available number of packets is low. Moreover, the prior phase calibration schemes are not multipath robust and accurate enough. All of the above degrade the robustness of localization systems. In this paper, we present ROArray, a RObust Array based system that accurately localizes a target even with low SNRs. The key insight of ROArray is to use sparse recovery and coherent processing across all available domains, including time, frequency, and spatial domains. Specifically, in the spatial domain, ROArray can produce sharp AoA spectrums by parameterizing the steering vector based on a sparse grid. Then, to expand into the frequency domain, it jointly estimates the Time-of-Arrival (ToAs) and AoAs of all the paths using multi-subcarrier OFDM measurements. Furthermore, through a novel multi-packet fusion scheme, ROArray is enabled to perform coherent estimation over multiple packets. Such coherent processing not only increases the virtual aperture size, which enlarges the number of maximum resolvable paths but also improves the system robustness to noise. In addition, ROArray includes an online phase calibration technique that can eliminate random phase offsets while keeping communication uninterrupted. Our implementation using off-the-shelf WiFi cards demonstrates that, with low SNRs, ROArray significantly outperforms state-of-the-art solutions in terms of localization accuracy; when medium or high SNRs are present, it achieves comparable accuracy.

Base stations have been widely deployed to satisfy the service coverage and explosive demand increase in today's cellular networks. Their reliability and availability heavily depend on the electrical power supply. Battery groups are installed as backup power in most of the base stations in case of power outages due to severe weathers or human-driven accidents, particularly in remote areas. The limited numbers and capacities of batteries, however, can hardly sustain a long power outage without a well-designed allocation strategy. As a result, the service interruption occurs along with an increasing maintenance cost. Meanwhile, a deep discharge of a battery in such case can also accelerate the battery degradation and eventually contribute to a higher battery replacement cost. In this paper, we closely examine the base station features and backup battery features from a 1.5-year dataset of a major cellular service provider, including 4,206 base stations distributed across 8,400 square kilometers and more than 1.5 billion records on base stations and battery statuses. Through exploiting the correlations between the battery working conditions and battery statuses, we build up a deep learning based model to estimate the remaining lifetime of backup batteries. We then develop BatAlloc, a battery allocation framework to address the mismatch between the battery supporting ability and diverse power outage incidents. We present an effective solution that minimizes both the service interruption time and the overall cost. Our real trace-driven experiments show that BatAlloc cuts down the average service interruption time from 4.7 hours to nearly zero with only 85 percent of the overall cost compared to the current practical allocation.

The ubiquitous luminaries provide a new dimension for indoor navigation, as they are often well-structured and the visible light is reliable for its multipath-free nature. However, existing visible light-based technologies, which are generally frequency-based, require the modulation on light sources, modification to the device, or mounting extra devices. The combination of the cost-extensive floor map and the localization system with constraints on customized hardwares for capturing the flashing frequencies, no doubt, hinders the deployment of indoor navigation systems at scale in, nowadays, smart cities. In this paper, we provide a new perspective of indoor navigation on top of the virtual graph representation. The main idea of our proposed navigation system, named PILOT, stems from exploiting the peak intensities of ubiquitous unmodulated luminaries. In PILOT, the pedestrian paths with enriched sensory data are organically integrated to derive a meaningful graph, where each vertex corresponds to a light source and pairwise adjacent vertices (or light sources) form an edge with a computed length and direction. The graph, then, serves as a global reference frame for indoor navigation while avoiding the usage of pre-deployed floor maps, localization systems, or additional hardwares. We have implemented a prototype of PILOT on the Android platform, and extensive experiments in typical indoor environments demonstrate its effectiveness and efficiency.

Tag search, which is to find a particular set of tags in a radio frequency identification (RFID) system, is a key service in such important Internet-of-Things applications as inventory management. When the system scale is large with a massive number of tags, deterministic search can be prohibitively expensive, and probabilistic search has been advocated, seeking a balance between reliability and time efficiency. Given a failure probability$\frac {1}{\mathcal {O}(K)}$, where$K$is the number of tags, state-of-the-art solutions have achieved a time cost of$\mathcal {O}(K \log K)$through multi-round hashing and verification. Further improvement, however, faces a critical bottleneck of repetitively verifying each individual target tag in each round. In this paper, we present an efficient tree-based tag search (TTS) that approaches$\mathcal {O}(K)$through batched verification. The key novelty of TTS is to smartly hash multiple tags into each internal tree node and adaptively control the node degrees. It conducts bottom–up search to verify tags group by group with the number of groups decreasing rapidly. Furthermore, we design an enhanced tag search scheme, referred to as TTS+, to overcome the negative impact of asymmetric tag set sizes on time efficiency of TTS. TTS+ first rules out partial ineligible tags with a filtering vector and feeds the shrunk tag sets into TTS. We derive the optimal hash code length and node degrees in TTS to accommodate hash collisions and the optimal filtering vector size to minimize the time cost of TTS+. The superiority of TTS and TTS+ over the state-of-the-art solution is demonstrated through both theoretical analysis and extensive simulations. Specifically, as reliability demand on scales, the time efficiency of TTS+ reaches nearly 2 times at most that of TTS.

Dynamic Adaptive Streaming over HTTP (DASH) has been widely adopted to deal with such user diversity as network conditions and device capabilities. In DASH systems, the computation-intensive transcoding is the key technology to enable video rate adaptation, and cloud has become a preferred solution for massive video transcoding. Yet the cloud-based solution has the following two drawbacks. First, a video stream now has multiple versions after transcoding, which increases the network traffic traversing the core network. Second, the transcoding strategy is normally fixed and thus is not flexible to adapt to the dynamic change of viewers. Considering that mobile users, who normally experience dynamic network conditions from time to time, have occupied a very large portion of the total users, adaptive wireless transcoding is of great importance. To this end, we propose an adaptive wireless video transcoding framework based on the emerging edge computing paradigm by deploying edge transcoding servers close to base stations. With this design, the core network only needs to send the source video stream to the edge transcoding server rather than one stream for each viewer, and thus the network traffic across the core network is significantly reduced. Meanwhile, our edge transcoding server cooperates with the base station to transcode videos at a finer granularity according to the obtained users' channel conditions, which smartly adjusts the transcoding strategy to tackle with time-varying wireless channels. In order to improve the bandwidth utilization, we also develop efficient bandwidth adjustment algorithms that adaptively allocate the spectrum resources to individual mobile users. We validate the effectiveness of our proposed edge computing based framework through extensive simulations, which confirm the superiority of our framework.

Deep learning has been applied in many recent advanced applications in the field of transportation, finance and medicine. These applications require significant computation resources and large-scale training samples. Cloud becomes a natural choice for conducting these learning tasks due to its abundant resources. However, deeper penetration of deep learning techniques in mission critical applications, like driverless car, calls for stricter time requirement to guarantee its interaction and larger amount of dataset for training to guarantee its accuracy, which cannot be easily satisfied by the cloud and makes the network transmission become the bottleneck. Edge learning emerges to be a promising direction to reduce data transmission time by processing and compressing the raw data at the edge of the network, while brings the concern of accuracy reduction at the meantime. To balance this tradeoff under cloud-edge architecture, we study a task scheduling problem for reducing weighted transmission time which takes learning accuracy into consideration. We also propose efficient scheduling algorithms which are able to achieve up to 50% reduction in makespan with extensive trace-driven simulations.

RFID-based human activity identification has become a key component in today's Internet-of-Things applications. State-of-the-art solutions mostly focus on the simple scenario with a single person in the open space. Extension to the more realistic realworld scenarios with multiple persons however is non-trivial. Given the much richer interactions among them, the backscattered signals will inevitably mixed, obscuring the information of individual activities. This is further complicated with multi-path in a common indoor environment. In this paper, we however argue that, though often considered harmful, the rich interactions combined with multi-path indeed offer more observable data. After careful processing the raw signals, critical information about the activities can be unveiled through modern learning tools. We present M2AI, which for the first time accommodates both multi-path and multi-object for activity identification. M2AI incorporates a phase calibration mechanism to automatically eliminate the frequency hopping offsets, and a novel decoupling mechanism for the periodogram and pseduospectrum in the raw signal mixture. The refined data are then fed into an advanced deep-learning engine that integrates a Convolutional Neural Network and a Long Short Term Memory network, which examines both spatial and temporal information in realtime for activity identification. Our M2AI is readily deployable using off-the-shelf RFID readers. We have implemented an M2AI prototype with Impinj UHF passive tags and a Speedway R420 reader. Experiments with multiple objects in a multipath-rich indoor environments report an activity identification accuracy of 97%, a significant gain (27%) over state-of-art solutions.

The pervasive penetration of mobile smart devices has significantly enriched Internet applications and undoubtedly reshaped the way that users access Internet services. Different from traditional desktop applications, mobile Internet applications require users to input via touch screens and view outputs on the displays with considerably limited size. The significant conflict between the limited-size of touch screens and the richness of online media contents requires the mobile Internet applications to download contents way beyond the user's viewing region (referred as viewport). In this paper, we present a Mobile-Friendly HTTP middleware (MF-HTTP), which interprets user touch screen inputs and optimize the HTTP downloading of media objects to improve quality of experience (QoE) and cost efficiency. We first demystify screen scrolling in mobile operating systems and precisely break down the viewport moving process. We identify the key influential factors for media object downloading and develop an optimal download scheme. Towards building a practical middleware, we further discuss and address the implementation issues in detail. We implement a MF-HTTP prototype based on Android platforms and evaluate the performance of MF-HTTP by conducting concrete case studies on two representative applications, namely, web browsing and 360-degree video streaming.

Multihop wireless networking is a key enabling technology for interconnecting a vast number of IoT devices. Measurement is fundamental to various network operations including management, diagnostics, and optimization. Out-of-band measurement approaches use external sniffers to monitor the network traffic passively, and they provide detailed information about the network. However, existing approaches do not carefully consider lossy and correlated links which are common in low-power wireless networks, resulting in unsatisfactory packet capture ratio and low measurement quality. In this paper, we present NetVision, a practical out-of-band measurement system with special consideration for sniffer deployment. By explicitly considering link quality and link correlation, we are able to achieve a high measurement quality while minimizing the deployment cost. We formulate the sniffer deployment problem as an optimization problem and propose efficient algorithms for solving this problem. We further design a set of instructions and APIs to simplify a variety of common measurement tasks. We implement NetVision on the TinyOS/TelosB platform and evaluate its performance extensively both in simulation and an indoor testbed with 80 TelosB nodes. Results show that NetVision is accurate, generic, and robust. Three typical case studies demonstrate that NetVision can facilitate various measurement and debugging tasks.

Recent years have seen various acoustic applications on mobile devices, e.g. range finding, gesture recognition, and device-to-device data transport, which use near-ultrasound signals at frequencies around 18-24 kHz. Due to the fixed low sound sample rate and hardware limitation, the highest detectable sound frequency on commercial-off-the-shelf (COTS) mobile devices is capped at 24 kHz, presenting a daunting barrier that prevents high-frequency ultrasounds from benefiting acoustic applications. To bridge this gap, we present iChemo, a technology that enables COTS mobile devices to sense high-frequency ultrasound signals. Specifically, we demonstrate how to detect the power spectral density (PSD) of a high-frequency ultrasound signal by customizing the coprime sampling algorithm on COTS devices. Through our prototype and evaluation on extensive mobile devices, we demonstrate that iChemo can sense the PSD of ultrasound at frequency of 60 kHz, which is over twice of the current sensible frequency threshold.

In the past few years, various backscatter nodes have been invented for many emerging mobile applications, such as sports analytics, interactive gaming, and mobile healthcare. Backscatter networks are expected to provide a high-throughput and stable communication platform for those interconnected mobile nodes. Yet, through experiments, we find state-of-the-art rate adaptation methods for backscatter networks share a fundamental limitation of accommodating the hardware diversity of nodes because the common mapping paradigm that chooses the optimal rate based on the radio signal strength indicator (RSSI) or the like is hardly adaptable to hardware-dependent RSSIs. To address this issue, we propose MobiRate (Mobility-aware Rate adaptation) that fully exploits the mobility hints from PHY information and the characteristics of backscatter systems. The key insight is that mobility-hints, like velocity and position, can greatly benefit rate selection and channel probing. Specifically, we introduce a novel velocity-based loss rate estimation method that dynamically re-weighs packets based on time and mobility. In addition, we design a mobility-assisted probing trigger and a new selective-probing mechanism, significantly saving probing time. As MobiRate is fully compatible with the current standard, it is prototyped using a COTS RFID reader and a variety of commercial tags. Our extensive experiments demonstrate that MobiRate achieves up to 3.8x throughput gain over the state-of-the-art methods across a wide range of mobility, channel conditions, and tag types.

Searching for a particular group of tags in an RFID system is a key service in such important Internet-of-Things applications as inventory management. When the system scale is large with a massive number of tags, deterministic search can be prohibitively expensive, and probabilistic search has been advocated, seeking a balance between reliability and time efficiency. Given a failure probability [1/(O(K))], where K is the number of tags, state-of-the-art solutions have achieved a time cost of O(K log K) through multi-round hashing and verification. Further improvement however faces a critical bottleneck of repetitively verifying each individual target tag in each round. In this paper, we present a novel Tree-based Tag Search (TTS) that approaches O (K) through batched verification. TTS smartly hashes multiple tags into each internal tree node and adaptively controls the node degrees. It conducts bottom-up search to verify tags group by group with the number of groups decreasing rapidly. We derive the optimal hash code length and node degrees to accommodate hash collisions, and demonstrate the superiority of TTS through both theoretical analysis and extensive simulations. In particular, we show that, with increasing reliability demand and system size, TTS achieves an even higher performance gain, making it a highly scalable solution.

Many efforts have been made on sensing ultrasound with commercial-off-the-shelf (COTS) mobile devices in the recent literature. Yet due to the limited sound sample rate, current COTS mobile devices can not directly capture any sound at the frequency over 24 kHz. This issue prevents true ultrasound, of which the frequency is typically over 40 kHz, from benefiting the existing sound sensing applications. In this work, we show that by subtly customizing the sampling process, we can make COTS mobile devices hear the true ultrasound that is typically beyond their capability to fully capture. Particularly, we present a system that enable COTS mobile devices to sense the power spectrum density (PSD) of true ultrasounds, of which the frequency can be as high as 60 kHz.

Automobiles have become one of the necessities of modern life and deeply penetrated into our daily activities. They unfortunately also introduce numerous social problems, among which traffic accidents are most notoriously threatening automobile drivers and other road users. Advanced driver-assistance systems (ADAS) are under rapid development in recent years, which can necessarily reduce or even eliminate the driver errors, significantly relieving on drivers suffering or stress. These state-of-the-art ADAS mainly rely on built-in cameras, radars and ultrasound sensors to provide road sensing services for object detection, which are further advanced by recent explosion of vision and neural network technologies.

The emerging 5G mobile networking promises ultrahigh network bandwidth and ultra-low communication latency (100ms), due to its store-and-forward design and the physical barrier from signal propagation speed, not to mention congestion that frequently happens. Caching is known to be effective to bridge the speed gap, which has become a critical component in the 5G deployment as well. Besides storage, 5G base stations (BSs) will also be powered with strong computing modules, offering mobile edge computing (MEC) capability. This paper explores the potentials of edge computing towards improving the cache performance, and we envision a learning-based framework that facilitates smart caching beyond simple frequency- and time-based replace strategies and cooperation among base stations. Within this framework, we develop DeepCache, a deep-learning-based solution to understand the request patterns in individual base stations and accordingly make intelligent cache decisions. Using mobile video, one of the most popular applications with high traffic demand, as a case, we further develop a cooperation strategy for nearby base stations to collectively serve user requests. Experimental results on real-world dataset show that using the collaborative DeepCache algorithm, the overall transmission delay is reduced by 14%~22%, with a backhaul data traffic saving of 15%~23%.

Ensuring high video quality of experience (QoE) on the broadcaster side is critical for interactive live streaming. However, measurements on multiple live streaming platforms show that they all suffer from broadcaster-side video quality degradation in the presence of transient bandwidth fluctuations. This paper presents Greedy Variable Bitrate (GVBR), a suite of solutions that optimizes the QoE through an approriate keyframe interval that trades cross-frame compression for lowered inter-frame interdependency, a simple-yet-efficient frame dropping strategy to prevent excessive frame drops, and a bitrate adaptation strategy customized for broadcasters who have shallow buffer. We compare GVBR with state-of-art algorithms in different network conditions, and find that GVBR can cut video interruption incidents by 90%, while achieving comparable bitrate.

Nowadays online shopping has become explosively popular and the vast numbers of generated packages have brought great challenges to the traditional logistics industry, especially the last mile package delivery. Traditional delivery approaches rely on dedicated couriers for package dispatch, while the labor cost is quite expensive and the quality is hard to guarantee due to the diverse delivery addresses and tight deadlines. On the other hand, modern cities are full of available transportation resources such as private car trips. The mobile crowdsourcing through 4G/5G and vehicle-related communications enables the vehicle resources to be connected as an intelligent transportation system. As such, we believe ridesharing will be a core service for connected vehicles, which we refer to as Ridesharing as a Service (RaaS). In this paper, we focus on the quality of service (QoS) of RaaS in the last mile package delivery. Mining from real-world car trips, we build up a citywide routing graph and conduct a personalized travel cost prediction considering both the travel time of each driver and the fuel consumption of each vehicle. We then design an online algorithm to assign proper package delivery tasks to the submitted car trips, aiming to maximize the utility of the ridesharing service provider. Our extensive real-world trace-driven evaluations further demonstrate the superiority of our RaaS based package delivery.

With rapid development of radio frequency identification (RFID) technology, ever-increasing research effort has been dedicated to devising various RFID-enabled services. The key tag monitoring, which is to detect anomaly of key tags, is one of the most important services in such important Internet-of-Things applications as inventory management. Yet prior work assumes that all tags are armed with hashing functionality and a reader would report channel states in every slot, which is not supported by commercial off-the-shelf (COTS) RFID tags and readers. To bridge this gap, this paper is devoted to enabling key tag monitoring service with COTS devices. In particular, we introduce two anomaly monitoring protocols to detect whether there is any key tag absent from the system. The first protocol employs Q-query that works in an analog frame slotted Aloha paradigm to interrogate tags and collect tag IDs. An anomaly event will be found if at least one key tag ID is not present in the collected ones. To reduce time cost of the first protocol resulted from tag collisions, we present a collision-free method that uses select-query to specify a key tag to reply in each slot. Once there is no response in a slot, the specified key tag is regarded as a missing tag. We conduct experiments to evaluate two protocols.

Recent years have witnessed an explosion of crowdsourced livecast (i.e., live broadcast) services, in which any Internet users can act as broadcasters to publish livecasts to fellow viewers. To help grow broadcasters' channels, crowdsourced livecast services provide a past-broadcast saving service, allowing viewers to watch the replays they may have missed. Our real-trace measurement and questionnaire survey show that (1) the duration of most of livecasts is extremely long; (2) a much longer duration largely affects the viewers' Quality-of-Experiences (QoE) when watching the replays. To address this issue and improve viewers' QoE, we propose a crowdsourced framework HighCast based on the interactive messages contributed by the viewers in crowdsourced livecast services. According to a highlight-aware detection module, HighCast can exploit the detection results to schedule the content placement by considering the importance of the predicted streaming highlights. The trace-based evaluations illustrate that the proposed framework improves the prediction accuracy and reduces the viewing latency.

Enabling users to interact with broadcasters and audience, the crowd-interactive livecast greatly improves viewer's quality of experience (QoE) and attracts millions of daily active users recently. In addition to striking the balance between resource utilization and viewers' QoE met in the traditional video streaming service, this novel service needs to take supererogatory efforts to improve the interaction QoE, which reflects the viewer interaction experience. To tackle this issue, we conduct measurement studies over a large-scale dataset crawled from a representative livecast service provider. We observe that the individual's interaction pattern is quite heterogeneous: only 10% viewers proactively participate in the interaction, and the rest viewers usually watch passively. Incorporating the insight into the emerging cloud-edge architecture, we propose a framework PIECE, which optimizes the Personalized Interaction Experience with Cloud-Edge architecture (PIECE) for intelligent user access control and livecast distribution. In particular, we first devise a novel deep neural network based algorithm to predict users' interaction intensity using the historical viewer pattern. We then design an algorithm to maximize the individual's QoE, by strategically matching viewer sessions and transcoding-delivery paths over cloud-edge infrastructure. Finally, we use trace-driven experiments to verify the effectiveness of PIECE. Our results show that our prediction algorithm outperforms the state-of-the-art algorithms with a much smaller mean absolute error (40% reduction). Furthermore, in comparison with the cloud-based video delivery strategy, the proposed framework can simultaneously improve the average viewers QoE (26% improvement) and interaction QoE (21% improvement), while maintaining a high streaming bitrate.

The past decade has witnessed significant breakthroughs in the world of computer vision. Recent deep learning-based computer vision algorithms exhibit strong performance on recognition, detection, and segmentation. While the development of vision algorithms elicits promising applications, it also presents immense computational challenge to the underlying hardware due to its complex nature, especially when attempting to process the data at line-rate.

Backscatter communication offers a cost- and energy-efficient means for IoT sensor data exchange. The IoT vision for ubiquitous interconnection, in practice, demands multi-hop connectivity for robust and scalable sensor networks, as well as compatibility with such prevailing wireless technologies as WiFi. Today's backscatter solutions however typically follow a single-hop paradigm, i.e., tags do not relay for each other. This paper presents X-Tandem, a multi-hop backscatter system that works with commodity WiFi devices. For the first time, we demonstrate that sensing tags can not only work as relays for each other but also modulate their sensing data into a single backscatter packet, which remains a legit WiFi packet that can be decoded with any commercial WiFi NICs. We discuss the design details of X-Tandem and have built a prototype with FPGAs and off-the-shelf WiFi devices. The prototype demonstrates a two-hop implementation, achieving a throughput up to 200 bps with tag-to-tag distances up to 0.4 m and communication ranges up to 8 m. Compared to single-hop solutions, X-Tandem can improve backscatter throughput by more than 10x in challenging indoor environments with obstacles.

Backscatter WiFi offers a novel low-cost and low-energy solution for RFID tags to communicate with existing WiFi devices. State-of-the-art backscatter WiFi solutions have seldom explored advanced features in the latest WiFi standards, in particular, spatial multiplexing, which has been the cornerstone for 802.11n and beyond. In this paper, we present MOXcatter, a WiFi backscatter communication system that works with spatial streams using commodity radios, while keeping the ongoing data communication unaffected. In MOXcatter, a backscatter tag can embed its sensing data on ambient spatial-stream packets, and both the sensing data and the original packets can be decoded by commodity WiFi devices. We have built a MOXcatter prototype with FPGAs and commodity WiFi devices. The experiments show that MOXcatter achieves up to 50 Kbps throughput for a single stream and up to 1 Kbps for double streams with a communication range (tag-to-RX) up to 14 m. We discuss the tradeoffs therein and possible enhancements, and also showcase the applicability of our design through a sensor communication system.

Cellular data sponsoring (CDS) is a traditional data sponsor scheme widely used in cellular video delivery networks, where content providers (CPs) bear the cellular data downloading cost for mobile video users (MUs), so as to attract more MUs and achieve higher revenue (e.g., via more attached advertisements). Edge caching sponsoring (ECS) is a novel data sponsor scheme recently introduced in the emerging 5G network, where CPs cache popular video contents on the edge network in advance and deliver them to local MUs directly. Thus, it can not only achieve the benefits of CDS (i.e., attracting more MUs and achieving higher revenue), but also reduce the congestion of backhaul network. In this work, we will perform a competitive analysis of CDS and ECS for mobile video streaming. Specifically, we consider a mobile video delivery network with two CPs who adopt CDS and ECS, respectively. MUs can choose one or neither of these two sponsor schemes (from the corresponding CPs) for his video content requests. We formulate the interaction of CPs and MUs as a two-stage Stackelberg game, where CPs act as leaders determining the efforts of their adopted sponsor schemes in the first stage, and MUs act as followers choosing the best sponsor schemes for their content requests in the second stage. We analyze the sub-game perfect equilibrium systematically for both cooperative and competitive scenarios (depending on whether two CPs cooperate or compete with each other). Numerical results show that in the competitive scenario, the joint sponsor of ECS and CDS can increase the total MU payoff by 36% ~ 140%, comparing with that with only one sponsor scheme. Moreover, the CPs can benefit more from ECS than from CDS when the revenue is higher.

The Internet of Things (IoT) enables environmental monitoring by collecting data from sensing devices, including cameras and microphones. The popularity of smartphones enables mobile users to communicate and collect data from their surrounding sensing devices. The mobile devices can obtain useful environmental data from nearby sensors through short-range communication such as Bluetooth. Nevertheless, the limited contact time and the wireless capacity constrain the amount of data to be collected. With the increasing amount of multimedia big data such as videos and pictures from cameras, it is crucial for mobile users to collect prioritized data that can maximize their data utility. In this paper, we propose a distributed algorithm to provide information-centric ubiquitous data collection of multimedia big data by mobile users in the IoT. The algorithm can handle transmissions of multimedia big data recorded by the surrounding cameras and sensors, and prioritize the transmissions of the most important and relevant data. The mobile users construct data collection trees adaptively according to their dynamic moving speeds and the value of information carried by the multimedia and sensor data. The distributed algorithm can support smooth data collection and coordination of multiple mobile users. We provide both numerical analysis and extensive simulations to evaluate the information value, energy efficiency and scalability of our solution. The results showed that our distributed algorithm can improve the value of information up to 50% and reduce energy consumption to half compared with existing approach. Our algorithm also scales perfectly well with increasing number of mobile users and dynamic moving speeds.

MapReduce effectively partitions and distributes computation workloads to a cluster of servers, facilitating today's big data processing. Given the massive data to be dispatched, and the intermediate results to be collected and aggregated, there have been a significant studies on data locality that seeks to co-locate computation with data, so as to reduce cross-server traffic in MapReduce. They generally assume that the input data have little dependency with each other, which however is not necessarily true for that of many real-world applications, and we show strong evidence that the finishing time of MapReduce tasks can be greatly prolonged with such data dependency. In this paper, we present Dependency-Aware Locality for MapReduce (DALM) for processing the real-world input data that can be highly skewed and dependent. DALM accommodates data-dependency in a data-locality framework, organically synthesizing the key components from data reorganization, replication, placement. Beside algorithmic design within the framework, we have also closely examined the deployment challenges, particularly in public virtualized cloud environments, and have implemented DALM on Hadoop 1.2.1 with Giraph 1.0.0. Its performance has been evaluated through both simulations and real-world experiments, and compared with that of state-of-the-art solutions.

Today's multimedia content generation and sharing have been dramatically boosted by the deep penetration of broadband wireless accesses and the much improved processing power of smart mobile terminals. Mobile users can now instantly capture and share short video clips (usually of several seconds) anywhere and anytime, and consume them with convenient touch screen operations. Theinstant video clip sharinghas emerged as a mainstream application; such pioneers as Twitter's Vine, Miaopai, Instagram, and Snapchat have seen great acceptance, particularly by the youth community. In this paper, we present an initial study on instant video clip sharing. Taking Twitter's Vine as a representative, we systematically investigate its distinct mobile interface, service framework, and user watching behaviors, revealing how this mainstream multimedia service type differentiates from its traditional counterparts. Our trace measurement and analysis demonstrate that instant mobile video clips have a much shorter lifespan and highly skewed popularity that quickly decays over time. This is further aggravated by the unique screen scrolling operation for video browsing. As such, the download-and-watch scheduling used by existing platforms can hardly achieve quality user experience and cost efficiency. We closely investigate and model the input user gestures for scrolling, including drag and fling, and analyze the scheduling policy, partitioning it into prefetching scheduling and watch-time download scheduling. We develop effective solutions toward both subproblems as well as their integration with screen scrolling. The superiority of our enhancement is demonstrated by extensive trace-driven evaluation.

Mobile personal livecast (MPL) services are emerging and have received great attention recently. In MPL, numerous and geo-distributed ordinary people broadcast their video contents to worldwide viewers. Different from conventional social networking services like Twitter and Facebook, which have a tolerance for interaction delay, the interactions (e.g., chat messages) in a personal livecast must be in real-time with low feedback latency. These unique characteristics inspire us to: (1) investigate how the relationships (e.g., social links and geo-locations) between viewers and broadcasters influence the user behaviors, which has yet to be explored in depth; and (2) explore insights to benefit the improvement of system performance. In this article, we carry out extensive measurements of a representative MPL system, with a large-scale dataset containing 11M users. In the current costly and limited cloud-based MPL system, which is faced with scalability problem, we find: (1) the long content uploading distances between broadcasters and cloud ingesting servers result in an impaired system QoS, including a high broadcast latency and a frequently buffering events; and (2) most of the broadcasters in MPL are geographically locally popular (the majority of the views come from the same region of the broadcaster), which consume vast computation and bandwidth resources of the clouds and Content Delivery Networks. Fortunately, the emergence of edge computing, which provides cloud-computing capabilities at the edge of the mobile network, naturally sheds new light on the MPL system; i.e., localized ingesting, transcoding, and delivering locally popular live content is possible. Based on these critical observations, we propose an edge-assisted MPL system that collaboratively utilizes the core-cloud and abundant edge computing resources to improve the system efficiency and scalability. In our framework, we consider a dynamic broadcaster assignment to minimize the broadcast latency while keeping the resource lease cost low. We formulate the broadcaster scheduling as a stable matching with migration problem to solve it effectively. Compared with the current pure cloud-based system, our edge-assisted delivery approach reduces the broadcast latency by about 35%.

With the prevalence of broadband network and wireless mobile network accesses, distributed interactive applications (DIAs) such as online gaming have attracted a vast number of users over the Internet. The deployment of these systems, however, comes with peculiar hardware/software requirements on the user consoles. Recently, such industrial pioneers as Gaikai, Onlive, and Ciinow have offered a new generation of cloud-based DIAs (CDIAs), which shifts the necessary computing loads to cloud platforms and largely relieves the pressure on individual user's consoles. In this paper, we aim to understand the existing CDIA framework and highlight its design challenges. Our measurement reveals the inside structures as well as the operations of real CDIA systems and identifies the critical role of cloud proxies. While its design makes effective use of cloud resources to mitigate client's workloads, it may also significantly increase the interaction latency among clients if not carefully handled. Besides the extra network latency caused by the cloud proxy involvement, we find that computation-intensive tasks (e.g., game video encoding) and bandwidth-intensive tasks (e.g., streaming the game screens to clients) together create a severe bottleneck in CDIA. Our experiment indicates that when the cloud proxies are virtual machines (VMs) in the cloud, the computation-intensive and bandwidth-intensive tasks may seriously interfere with each other. We accordingly capture this feature in our model and present an interference-aware solution. This solution not only smartly allocates workloads but also dynamically assigns capacities across VMs based on their arrival/departure patterns.

Presents corrections to the paper, “Modeling, analysis, and implementation of universal acceleration platform across online video sharing sites,” (Xu, K. et al), IEEE Trans. Serv. Comput., vol. 11, no. 3, pp. 534–548, May/Jun. 2018.

User-generated video sharing service has attracted a vast number of users over the Internet. The most successful sites, such as YouTube and Youku, now enjoy millions of videos being watched every day. Yet, given limited network and server resources, the user experience of existing video sharing sites (VSSes) is still far from being satisfactory. To mitigate such a problem, peer-to-peer (P2P) based video accelerators have been widely suggested to enhance the video delivery on VSSes. In this paper, we find that the interference of multiple accelerators will lead to a severe bottleneck across the VSSes. Our model analysis shows that a universal video accelerator can naturally achieve better performance with lower deployment cost. Based on this observation, we further present the detailed design of Peer-to-Peer Video Accelerator (PPVA), a real-world system for universal and transparent P2P accelerating. Such a system has already attracted over 180 million users, with 48 million video transactions every day. We carefully examine the PPVA performance from extensive measurements. Our trace analysis indicates that it can significantly reduce server bandwidth cost and accelerate the video download speed by 80 percent.

Link utilization has received extensive attention since datacenters become the most prevalent platform for data-parallel computing applications. A specific job of such applications involves communication among multiple machines. The coflow abstraction depicts such communication and captures application performance through corresponding network requirements. Existing techniques to improve link utilization, however, either restrict themselves to work conservation, or merely focus on flow-level metrics and ignore coflow-level performance. In this paper, we address the coflow-aware scheduling problem with the objective of maximizing link utilization. Through theoretic analyses, we formulate the coflow-aware scheduling problem as a NP-hard open shop scheduling problem with heterogeneous concurrency. Despite the hardness of this problem, we design Maluca, a hierarchical scheduling framework to conduct both inter- and intra-link scheduling. Maluca's algorithm is not only starvation-free and work-conserving, but also 2-approximate in terms of link utilization. Extensive simulation results demonstrate that Maluca outperforms both per-flow and coflow schemes in terms of link utilization, and achieves similar coflow performance in comparison with the state-of-art coflow scheduling schemes.

Energy consumption has become a key issue for running large-scale data center networks (DCN) nowadays. Previous studies mainly focus on energy saving through reducing the number of active servers or network switches with traffic consolidation. However, since this mechanism benefits from the routing flexibility, both the gained energy savings and flow performance are limited by the conventional static network topology. Recent advances in DCN architecture propose to implement an adaptive network topology with reconfigurable optical/wireless links (i.e., topology-adaptive DCNs), which has shown a great potential to improve the transmission performance of existing static wired network. In this paper, we propose GreenWay, an energy-efficient solution to jointly optimize the virtual machine (VM) placement and flow transmissions under the new paradigm of topology adaption. Based on the VM traffic demands, it can construct a proper run-time network topology to lower both the energy cost and communication cost among VMs. We first formulate the energy optimization problem in topology-adaptive DCNs. After showing the its NP-hardness, we then develop heuristic algorithms to address the VM placement and topology adaption effectively. Extensive trace-based simulations show that GreenWay consumes much less energy than other state-of-the- art solutions while ensuring better flow performance. We finally implement an OpenvSwitch- based testbed and demonstrate the efficiency of our solution.

With the multi-antenna design of WiFi interfaces, phased array has become a promising mechanism for accurate WiFi localization. State-of-the-art WiFi-based solutions using AoA (Angle-of-Arrival), however, face a number of critical challenges. First, their localization accuracy degrades dramatically when the Signal-to-Noise Ratio (SNR) becomes low. Second, they do not fully utilize coherent processing across all available domains. In this paper, we present ROArray, a Robust Array based system that accurately localizes a target even with low SNRs. In the spatial domain, ROArray can produce sharp AoA spectrums by parameterizing the steering vector based on a sparse grid. Then, to expand into the frequency domain, it jointly estimates the ToAs (Time-of-Arrival) and AoAs of all the paths using multi-subcarrier OFDM measurements. Furthermore, through multi-packet fusion, ROArray is enabled to perform coherent estimation across the spatial, frequency, and time domains. Such coherent processing not only increases the virtual aperture size, which enlarges the number of maximum resolvable paths, but also improves the system robustness to noise. Our implementation using off-the-shelf WiFi cards demonstrates that, with low SNRs, ROArray significantly outperforms state-of-the-art solutions in terms of localization accuracy; when medium or high SNRs are present, it achieves comparable accuracy.

Recent years have witnessed a new content delivery paradigm named crowdsourced CDN, in which devices deployed at edge network can prefetch contents and provide content delivery service. Crowdsourced CDN offers high-quality experience to end-users by reducing their content access latency and alleviates the load of network backbone by making use of network and storage resources at millions of edge devices. In such paradigm, redirecting content requests to proper devices is critical for user experience. The uniqueness of request redirection in such crowdsourced CDN lies that: on one hand, the bandwidth capacity of the crowdsourced CDN devices is limit, hence devices located at a crowded place can be easily overwhelmed when serving nearby user requests; on the other hand, contents requested in one device can be significantly different from another one, making request redirection strategies used in conventional CDNs which only aim to balance request loads ineffective. In this paper, we explore request redirection strategies that take both workload balance of devices and content requested by users into consideration. Our contributions are as follows. First, we conduct measurement studies, coving 1.8M users watching 0.4M videos, to understand request patterns in crowdsourced CDN. We observe that the loads of nearby devices can be very different and the contents requested at nearby devices can also be significantly different. These observations lead to our design for request balancing at nearby devices. Second, we formulate the request redirection problem by taking both the content access latency and the content replication cost into consideration, and propose a request balancing and content aggregation solution. Finally, we evaluate the performance of our design using trace-driven simulations, and observe our scheme outperforms the traditional strategy in terms of many metrics, e.g., we observe a content access latency reduction by 50% over traditional mechanisms such as the Nearest/Random request routing scheme.

Crowdsourced content sharing has become a fast-growing activity in today's online social networks, where contents of interest are created by diverse source users and conveyed over the network as friends view and reshare. The rapid and boundless propagation in a mobile crowd however often creates severe bottlenecks on the server side and incurs significant energy and monetary costs on the mobile side, particularly with the still expensive 3G/4G cellular connections. This paper presents SoCrowd, a novel framework for large-scale content sharing in a mobile crowd by exploiting contacts, i.e., users happen to move close with such short range low power communications as WiFi and bluetooth being enabled. We formulate the scheduling problem for social content propagation in a mobile crowd with contacts, and present optimal dynamic programming solution, which further motivates a series of practical heuristics. The effectiveness of SoCrowd has been demonstrated by extensive simulations driven by realworld traces and datasets.

Multipath TCP (MPTCP) enables transmission via multiple routes for an end-to-end connection to improve resource usage of regular TCP. Due to the increasing concern in green computing, there has been significant interest in designing energy-efficient multipath transport. For existing MPTCP congestion control algorithms, the research community still lacks a comprehensive understanding of which components in such an algorithm play the fundamental role in energy efficiency, how various algorithms compare against each other from energy-consuming perspective, or whether there exist potentially better solutions for energy saving. In this paper, we take a first step to answer these questions. Based on the MPTCP Linux kernel experiments, we first summarize that the energy consumption is related to three aspects: average throughput, path delay and different network scenarios. In order to bridge congestion control to the three aspects, we analyze the existing algorithms and capture the essential parameters of multipath congestion control model related to MPTCP's energy-efficiency. Then we design a window increase factor to shift traffic to low-delay energy-efficient paths. We further extend this design by using an energy-aware compensative parameter to fit the general hierarchical Internet topology. We evaluate the performance of existing multipath congestion control algorithms and our proposed algorithm in different network scenarios. The results successfully validate the improved energy efficiency of our design.

Despite that IaaS users are busy scaling up/out their cloud instances to meet the ever-increasing demands, the dynamics of their demands, as well as the coarse-grained billing options offered by leading cloud providers, have led to substantial instance underutilization in both temporal and spatial domains. This paper theoretically examines an instance subletting service, where underutilized instances are leased to others within user-specified periods. Serving as a secondary market that complements the existing instance market of IaaS providers,we specifically identify the theoretical challenges in instance subletting services, and design an online auction mechanism tomake allocation and pricing decisions for the instances to besublet. Our mechanism guarantees truthfulness and individualrationality with the best possible competitive ratio. Extensivetrace-driven simulations show that our proposed mechanismachieves significant performance gains in both cost and socialwelfare.

Recently backscatter networks have received booming interest because, they offer a battery-free communication paradigm using propagation radio waves as opposed to active radios in traditional sensor networks while providing comparable sensing functionalities, ranging from light and temperature sensors to recent microphones and cameras. While sensing data on backscatter nodes has been seen on a clear path to increase in both volume and variety, backscatter communication is not well prepared and optimized for transferring such continuous and high-volume data. To bridge this gap, we propose a high-throughput rate adaptation scheme for backscatter networks by exploring the unique characteristics of backscatter links and the design space of the ISO 18000-6C (C1G2) protocol. Our key insight is that while prior work has left the downlink unattended, we observe that the quality of downlink is affected significantly by multipath fading and thus can degrade the uplink and overall throughput considerably. Therefore, we introduce a novel rate mapping algorithm that chooses the best rate for both the downlink and uplink. Also, we design an efficient channel estimation method fully compatible with the C1G2 protocol and a reliable probing trigger, substantially saving probing overhead. Our scheme is prototyped using a COTS RFID reader and tags. The results show that we achieve up to 2.5x throughput gain over state-of-the-art approaches across various mobility, channel, and network-size conditions.

The state-of-the-art cloud computing platforms are facing challenges, such as the high volume of crowdsourced data traffic and highly computational demands, involved in typical deep learning applications. More recently, Edge Computing has been recently proposed as an effective way to reduce the resource consumption. In this paper, we propose an edge learning framework by introducing the concept of edge computing and demonstrate the superiority of our framework on reducing the network traffic and running time.

Recent years have witnessed an explosion of gamecasting applications in the market, in which game players (or gamers in short) broadcast their game scenes in real-time. Such pioneer applications as YouTube Gaming, Twitch, and Mobcrush have attracted a massive number of online broadcasters, and each of them can attract hundreds or thousands of fellow viewers. The growing number however has created significant challenges to the network and end-devices, particularly considering bandwidth- and battery-limited smartphones or tablets are becoming dominating for both gamers and viewers. Yet the unique touch operations of the mobile interface offer opportunities, too. In this paper, our crowdsourced measurement reveals that strong associations exist between the gamers' touch interactions and the viewers' gazing patterns. Motivated by this, we present a novel interaction-aware optimization framework to improve the energy utilization and stream quality for mobile gamecasting (MGC). Our framework incorporates a touch-assisted prediction module to extract association rules for gazing pattern prediction and a tile-based optimization module to utilize energy on mobile devices efficiently. Trace-driven simulations illustrate the effectiveness of our framework in terms of energy consumption and streaming quality. Our user study experiments also demonstrate much improved (3%-13%) quality satisfaction than the state-of-the-art solution with similar network resources.

Nearly 50% of the energy overhead in today's datacenters comes from host-to-host data transfers, which largely depend on the transport layer performance. Multipath TCP (MPTCP) has recently been suggested as a promising transport protocol to improve datacenter network throughput, yet it also increases the host CPU power consumption. It remains unclear whether datacenters can indeed benefit from using MPTCP from the perspective of energy efficiency. By analyzing the performance of MPTCP, we show that (1) despite consuming higher host CPU power than TCP, MPTCP can largely reduce the long flow completion time and thus save the aggregated energy; (2) link-sharing subflows in MPTCP not only has negative impact on both throughput-sensitive long flows and latency-sensitive short flows, but also noticeably increases the host CPU power, especially for short flows. We present MPTCP-D, an energy-efficient variant of multipath TCP for datacenters. MPTCP-D incorporates a novel congestion control algorithm that can provide energy efficiency by minimizing the flow completion time, and an extra subflow elimination mechanism that can preclude link-sharing subflows from increasing the host CPU power. We implement MPTCP-D in the Linux kernel and study its performance by experiments on Amazon EC2. Our results show that, without degrading the performance of the long flow throughput and short flow completion time, MPTCP-D reduces the long flow energy consumption by up to 72% compared to DCTCP for data transfers, and reduces the short flow power consumption by up to 46% compared to MPTCP with linksharing subflows.

In the public cloud market, there has been a constant battle over the billing options of the cloud instances between their providers and their users. The users generally have to pay for the entire billing cycle even on fractional usage. Ideally, the residual life-cycles should be resalable by the users, which demands efficient resource consolidation and multiplexing; otherwise, the revenue and use cases are confined by the transient nature of the instances. This paper presents HARV, a novel cloud service that facilitates the management and trade of cloud instances through a third-party platform to run buyers' tasks. The platform relies on hybrid virtualization, an infrastructure layout integrating both the hypervisor-based virtualization and lightweight containerization. It further incorporates a truthful online auction mechanism for instance trading and resource allocation. Our design achieves efficient resource consolidation with no need for provider-level support, and we have deployed a prototype of HARV on the Amazon EC2 public cloud. Our evaluations on both micro-benchmarks and real-life workloads reveal that applications experience negligible performance overhead when hosted on HARV. Trace-driven simulations further show that HARV can achieve substantial cost savings.

Ever-increasing research effort has been dedicated to multi-group radio frequency identification (RFID) systems where all tags are partitioned into multiple groups, such as group-level queries, and multi-group missing tag detection. However, it is assumed in the existing work that all tags know their individual group IDs, which thus leaves group labeling problem unaddressed. To tackle the under-investigated problem, this paper is devoted to devising an efficient group labeling protocol to inform each tag of its corresponding group ID fast and accurately. To this end, we employ multiple seeds to build a Composite Indicator Vector (CIV) indicating the assigned seed in each slot, which reduces transmissions of useless information and thus improves time efficiency. Specifically, we first theoretically show that the Seed Assignment Problem (SAP) arising in establishing the CIV is NP-hard and then develop a myopic approximation algorithm. Finally, the simulation results confirm the superiority of the proposed protocol over the state-of-the-art solution in terms of time efficiency.

During the past decade, we have witnessed the pervasive penetration of mobile smart devices such as smartphones, tablets, and wearable devices, which significantly enrich Internet applications and improve user experience. In the foreseeable future, mobile smart devices are predicted to take up over 50% of global devices/connections and surpass 4/5 of mobile data traffic by 2021 [1]. Such mobile smart devices as smartphones, phablets, and tablets, undoubtedly reshape the way that users access Internet services, e.g., web browsing. Different from traditional desktop applications, in which users interact via interfaces like large displays, keyboards, and mouses, mobile applications require users to enter the inputs through touch screens and allow them to view the outputs on limited size of displays. This distinct feature introduced by mobile hardware interfaces brings both challenges and opportunities to mobile-based Internet applications. On one hand, mobile service providers should prepare multiple copies of media contents with different resolutions and even multiple versions of application UI layouts to fit various sizes of screens on heterogeneous devices. On the other hand, as media contents are usually organized in certain order in mobile-based Internet applications, it is possible to predict the viewing region (referred as viewport hereafter) given the user inputs and the fixed size of display.

There has been explosive growth in Mobile Personal Livestreaming (MPL) market since 2016. MPL services are booming not only because they introduce the popular live content by spontaneous and personalized broadcasters, but also because they are deliberately designed to be the innovative social networking service (SNS) platforms. The latter is a very important aspect that distinguishes MPL from the traditional livestreaming services. In this paper, we study the social networking of a large scale MPL service “Inke” (with more than 200 million registered users, 15 million daily active users) in China. By analyzing the dataset we crawl and the features of Inke app, we show that the social media stickiness of Inke comes from three aspects: the follower-followee model, the virtual-gift-based incentive mechanism, and the multi-perspective interactivity between broadcasters and viewers. First, Inke introduces the follower-followee model rather than the traditional broadcaster-viewer model, and every user in Inke can be a broadcaster. This makes MPL have some different patterns from both the traditional livestreaming services and SNS platforms. Second, Inke use virtual gift giving and user ranking as its incentive mechanism. Our measurement results show that this mechanism can indeed enhance user stickiness. Furthermore, Inke incorporates a variety of features during broadcasting to strengthen interactivity. The insight we gain in this paper has important implications for both existing and future designs.

Live video contents in crowdsourced live streaming services are transcoded into multiple quality versions to better service viewers with different network and device configurations. Cloud computing becomes a natural choice to handle this transcoding service due to its elasticity and significant computational power. However, given the huge concurrent channel numbers in this crowdsourced live streaming service, even the cloud becomes significantly expensive for providing transcoding services to the whole community. In this poster, after observing that abundant computational resources reside in end viewers, we propose a Cloud-Crowd collaborative system, C2, which incentivizes idle end-viewers to join with the cloud to do video transcoding. Specifically, we propose an auction mechanism to carefully select stable viewers and determine the proper payment for them. Desirable economic properties, like incentive compatibility, can be achieved in our mechanism. Large-scale trace-driven simulations further demonstrate the superiority of our mechanisms in cost reduction and service stability.

In the emerging crowd sourced live cast services, numerous amateur broadcasters live stream their video contents to worldwide viewers and constantly interact with them through chat messages. Live video contents are transcoded into multiple quality versions to better service viewers with different network and device configurations. Cloud computing becomes a natural choice to handle these computational intensive tasks due to its elasticity and the "pay-as-you-go" billing model. However, given the significantly large number of concurrent channel numbers and the diverse viewer geo-distributions in this new crowd sourced live cast service, even the cloud becomes significantly expensive to cover the whole community and inadequate in fulfilling the latency requirement. In this paper, after observing the abundant computational resources residing in end viewers, we propose a Cloud-Crowd collaborative system, C2, which combines end viewers with cloud to perform video transcoding in a cost-efficient way. To quantify the heterogeneity and uncertainty of viewers and pass the asymmetric information barrier, we incorporate statistical descriptions into our bidding language and design truthful auctions to recruit stable viewers with appropriate incentives. We further tailor redundancy strategies for workloads with different Quality of Service requirements to improve the stability of our system. Desirable economic properties, like social efficiency, ex-post incentive compatibility, individual rationality, are proved to be guaranteed in our studied scenarios. Using traces captured from the popular Twitch platform, we show that C2 achieves up to 93% more cost saving than a pure cloud-based solution, and significantly outperforms other baseline approaches in both social welfare and system stability.

Combining advanced sensors and powerful processing capabilities smart-phone based augmented reality (AR) is becoming increasingly prolific. The increase in prominence of these resource hungry AR applications poses significant challenges to energy constrained environments such as mobile-phones.; [email protected] that end we present a platform for offloading AR applications to powerful cloud servers. We implement this system using a thin-client design and explore its performance using the real world application Pokemon Go as a case study. We show that with careful design a thin client is capable of offloading much of the AR processing to a cloud server, with the results being streamed back. Our initial experiments show substantial energy savings, low latency and excellent image quality even at relatively low bit-rates.

Mobile personal livecast (MPL) services are emerging and have received great attention recently. Unlike traditional livecast services with commercial content providers (e.g., live TV), the live contents in MPL are crowdsourced from and consumed among geo-distributed individuals. Although there exist typical social relationships in MPL (i.e., follower-followee), different from conventional social networking services like Twitter and Facebook, which have much of a tolerance for interaction delay, the interactions in MPL must be in real-time. These unique characteristics intrigue us to investigate how the relationships (e.g., social links and geo-locations) between viewers and broadcasters influence the user behaviors, which has yet to be explored in depth. In this paper, we carry out extensive measurements of Inke, one of the most popular MPL providers, with a large-scale dataset containing 11M users. Our key findings are as follows. First, compared with traditional livecast services, the user interests shift much more frequently and the average viewing duration is considerably shorter in MPL. Second, the existence of social relationships significantly strengthens viewer stickiness---followers dedicating longer viewing time (contributing 81% of the total viewing time) and being 2x more patient when suffering poor network connectivity than non-followers. Third, most of the broadcasts in MPL are geographically local-popular (the majority of the views come from the same region of the broadcaster). Based on these critical observations, we provide insights that can enhance the MPL system design from the perspectives of efficient resource allocation and envision a future MPL framework that collaboratively utilizes the cloud and edge computing resources to improve efficiency and scalability for Inke-like services.

Recently, Crowdsourced Interactive Live Streaming (CILS), such as Twitch.tv and Periscope, has emerged as one of the most popular streaming applications over the Internet. In such applications, a large number of geo-distributed users publish live sources to broadcast their game sessions, personal activities, and other events, while fellow viewers not only watch these live streams, but also contribute interactive messages to influence streaming content. Such explosively increasing popularity has posed significant challenges to predict viewing patterns using traditional time-series approaches, which lack the start/end knowledge of live streams and cannot capture the viewing burst very well.

Unmanned aerial vehicles (UAVs) are experiencing a major increase in popularity in both consumer and industrial markets as prices fall and the technology matures. No longer are drones limited to military purposes as manufacturers begin to mass produce civilian models, ushering in a new era of transportation technology. While consumer drones are still in their infancy stage, there is little in the way of rules and regulations regarding privacy issues of these new devices. In this paper, we design an energy efficient off-the-shelf hardware system capable of detecting and selectively disabling video feeds of WiFi based consumer drones if they enter a defended area.

Batteries of modern mobile devices remain severely limited in capacity, which makes energy consumption a key concern for mobile applications, particularly for the computation-intensive video applications. Mobile devices can save energy by offloading computation tasks to the cloud, yet the energy gain must exceed the additional communication cost for cloud migration to be beneficial. The situation is further complicated by real-time video applications that have stringent delay and bandwidth constraints. In this paper, we closely examine the performance and energy efficiency of representative mobile cloud applications under dynamic wireless network channels and state-of-the-art mobile platforms. We identify the unique challenges of and opportunities for offloading real-time video applications and develop a generic model for energy-efficient computation offloading accordingly in this context. We propose a scheduling algorithm that makes adaptive offloading decisions in fine granularity in dynamic wireless network conditions and verify its effectiveness through trace-driven simulations. We further present case studies with advanced mobile platforms and practical applications to demonstrate the superiority of our solution and the substantial gain of our approach over baseline approaches.

With detailed digital map of the transport network and even real-time traffic, today's navigation services provide good quality routes in the major route level. Once entering the last mile near the destination, they unfortunately can be ineffective and, instead, local drivers often have a better understanding of the routes there. With the deep penetration of 3G/4G mobile networks, drivers today are well connected anytime and anywhere; they can readily access information from the Internet and share information to the driver's community. This motivates our design of CrowdNavi, a complementary service to existing navigation systems, seeking to combat the last mile puzzle. CrowdNavi collects the crowdsourced driving information from users to identify their local driving patterns, and recommend the best local routes for users to reach their destinations. In this paper, we present the architectural design of CrowdNavi and identifies the unique challenges therein, particularly on identifying the last segment in a route from the crowdsourced driving information and navigate drivers through the last segment. We offer a complete set of algorithms to identify the last segment from the drivers' trajectories, scoring the landmark, and locating best routes with user preferences. We then present effective navigation algorithm to locate the best route along the landmarks for the last segment. We further realize the potential risks of attacks in crowdsourced systems and develop a multisensor cross-validation method against them. We have implemented the CrowdNavi app on Android mobile OS, and have examined its performance under various circumstances. The experimental results demonstrate its superiority in navigating drivers in the last segment toward the destination.

Many radio frequency identification (RFID) applications, such as virtual shopping cart and tag-assisted gaming, involve sensing and recognizing tag mobility. However, existing RFID localization methods are mostly designed for static or slowly moving targets (less than 0.3m/sec). More importantly, we observe that prior methods suffer from serious performance degradation for detecting real-world moving tags in typical indoor environments with multipath interference. In this article, we present i 2 tag, an intelligent mobility-aware activity identification system for RFID tags in multipath-rich environments (e.g., indoors). i 2 tag employs a supervised learning framework based on our novel fine-grain mobility provile, which can quantify different levels of mobility. Unlike previous methods that mostly rely on phase measurement, i 2 tag takes into account various measurements, including RSSI variance, packet loss rate, and our novel relative phase--based fingerprint. Additionally, we design a multidimensional dynamic time warping--based algorithm to robustly detect mobility and the associated activities. We show that i 2 tag is readily deployable using off-the-shelf RFID devices. A prototype has been implemented using a ThingMagic reader and standard-compatible tags. Experimental results demonstrate its superiority in mobility detection and activity identification in various indoor environments.

Device-to-device (D2D) communication has been recently proposed to mitigate the burden of base stations by leveraging the underutilized cellular spectrum resources, where high overall network throughput and D2D access rate are critical for its service performance and availability. In this paper, we study the resource allocation problem to push the limit of D2D communication underlaying cellular networks by allowing multiple D2D links to share resource with multiple cellular links. We propose FRESH, afullresourcesharing scheme where each subchannel can be shared by a cellular link and an arbitrary number of D2D links. In particular, FRESH first divides the communication links into so-called full resource sharing sets such that, within each set, all D2D link members are able to reuse the whole allocated resources. Thereafter, it allocates a sum of spectrum resources to each obtained full resource sharing set. As compared with state-of-the-art schemes, FRESH provides fine-grained resource allocation, resulting in throughput improvements of up to one order of magnitude, and D2D access rate improvements of up to 5 times with a moderate node density (e.g., on the order of 1 user per 400 square meters).

Driven by the advances in personal computing devices and the prevalence of high-speed network accesses, crowdsourced livecast platforms have emerged in recent years, through which numerous broadcasters lively stream their video content to fellow viewers. Compared to professional video producers and broadcasters, these new generation broadcasters are highly heterogeneous in terms of the network/system configurations and, therefore, the generated video quality, which calls for massive encoding and transcoding in order to unify the video sources and serve multiple quality versions to viewers with different configurations. On the other hand, with the rapid evolution in the hardware industry, high-performance processors become mainstream in personal computer market. More end devices can easily transcode high-quality videos in realtime. We witness huge computational resource among the massive fellow viewers that could potentially be used for transcoding. In this paper, we propose CrowdTranscoding, a novel framework for crowdsourced livecast systems that offloads the transcoding assignment to the massive viewers. We identify that the key challenges in CrowdTranscoding are to detect qualified stable viewers and to properly assign them to the source channels. We put forward a viewer crowdsourcing transcode scheduler to smartly schedule the workload assignment. Our solution has been evaluated under diverse viewer/channel conditions as well as different parameter settings. The trace-driven simulation confirms the superiority of CrowdTranscoder, while our PlanetLab-based and real world end-viewer experiments show the practical performance of our approach, which also give hint to the further enhancement.

Recent years have witnessed the rapid growth of new live broadcast services, represented by Twitch.tv and YouTube live events, where videos are crowdsourced from amateur users (e.g., game players), rather than from commercial and professional TV broadcaster or content providers. The viewers also actively contribute to the content through embedded open-chat channels. Such community interactions among viewers, or even between broadcasters and viewers, make content generation highly diversified and engaging, particularly for the young generation. In this context, cross-viewer synchronization is highly desirable; otherwise the viewers with shorter broadcast latency may act as spoilers, significantly affecting the user experience of other viewers. In this paper, we show that the end-to-end delay has a dramatically amplified impact on the broadcast latency for individual viewers. We suggest smart rate adaptation to achieve cross-viewer synchronization, and develop distributed algorithms based on dual decomposition. We further extend our solution to the cloud environment, and present the concept of ShadowCast, which moves broadcasters to the cloud to provide high-quality streams beyond broadcasters' network bandwidth constraint. Its practicability and effectiveness is demonstrated by our implementation and test bed experiments.

Recent years have witnessed an explosion of gamecasting applications, in which game players (or gamers in short) broadcast game playthroughs by their personal devices in real time. Such pioneer platforms, such as YouTube Gaming, Twitch, and Mobcrush, have attracted a massive number of online broadcasters, and each of them can have hundreds or thousands of fellow viewers. The growing number, however, has created significant challenges to the network and end-devices, particularly considering that bandwidth- and battery-limited smartphones or tablets are becoming dominating for both gamers and viewers. Yet the unique touch operations of the mobile interface offer opportunities, too. In this paper, our measurements based on the real traces from gamers and viewers reveal that strong associations exist between the gamers' touch interactions and the viewers' gazing patterns. Motivated by this, we present a novel interaction-aware optimization framework to improve the energy utilization and stream quality for mobile gamecasting. Our framework incorporates a touch-assisted prediction module to extract association rules for gazing pattern prediction and a tilebased optimization module to utilize energy on mobile devices efficiently. Trace-driven simulations illustrate the effectiveness of our framework in terms of energy consumption and stream quality. Our user study experiments also demonstrate much improved (3%-13%) quality satisfaction over the state-of-the-art solution with similar network resources.

The past two years have witnessed an explosion of a new generation of livecast services, represented by Twitch.tv , GamingLive , and Dailymotion , to name but a few. With such a livecast service, geo-distributed Internet users can broadcast any event in real-time, for example, game, cooking, drawing, and so on, to viewers of interest. Its crowdsourced nature enables rich interactions among broadcasters and viewers but also introduces great challenges to accommodate their great scales and dynamics. To fulfill the demands from a large number of heterogeneous broadcasters and geo-distributed viewers, expensive server clusters have been deployed to ingest and transcode live streams. Yet our Twitch-based measurement shows that a significant portion of the unpopular and dynamic broadcasters are consuming considerable system resources; in particular, 25% of bandwidth resources and 30% of computational capacity are used by the broadcasters who do not have any viewers at all. In this article, through the real-world measurement and data analysis, we show that the public cloud has great potentials to address these scalability challenges. We accordingly present the design of Cloud-assisted Crowdsourced Livecast (CACL) and propose a comprehensive set of solutions for broadcaster partitioning. Our trace-driven evaluations show that our CACL design can smartly assign ingesting and transcoding tasks to the elastic cloud virtual machines, providing flexible and cost-effective system deployment.

Cardinality estimation is one of the fundamental problems in large-scale radio frequency identification systems. While many efforts have been made to achieve faster approximate counting, the accuracy of estimates itself has not received enough attention. Specifically, most state-of-the-art schemes share a two-phase paradigm implicitly or explicitly, which needs a rough estimate first and then refines it to a final estimate meeting the desired accuracy; we observe that the final estimate can largely deviate from the expectation due to the skewed rough estimate, i.e., the accuracy of final estimates is not rigorously bounded. This negative impact is hidden because former solutions either assume perfect rough estimates or rough estimates that can be produced by uniform random data or perfect hash functions that can turn any data into uniform random data. Unfortunately, both of them are hard to meet in practice. To address the above issues, we propose a novel scheme, namely, “rigorous and practical cardinality (RPC)” estimation. RPC adopts the two-phase paradigm, in which the rough estimate is derived in the first phase using pairwise-independent hashing. In the second phase, we employ t-wise-independent hashing to reinforce the rough estimate to meet arbitrary accuracy requirements. We validate the effectiveness and performance of RPC through theoretical analysis and extensive simulations. The results show that the RPC can meet the desired accuracy all the time with diverse practical settings while previous designs fail with non-uniform data.

One of the most important applications of radio frequency identification (RFID) technology is to detect unknown tags brought by new tagged items, misplacement, or counterfeit tags. While unknown tag identification is able to pinpoint all the unknown tags, probabilistic unknown tag detection is preferred in large-scale RFID systems that need to be frequently checked up, e.g., real-time inventory monitoring. Nevertheless, most of the previous solutions are neither efficient nor reliable. The communication efficiency of former schemes is not well optimized due to the transmission of unhelpful data. Furthermore, they do not consider characteristics of unreliable wireless channels in RFID systems. In this paper, we propose a fast and reliable method for probabilistic unknown tag detection, white paper (WP) protocol. The key novelty of WP is to build a new data structure of composite message that consists of all the informative data from several independent detection synopses; thus it excludes useless data from communication. Furthermore, we employ packet loss differentiation and adaptive channel hopping techniques to combat unreliable backscatter channels. We implement a prototype system using USRP software-defined radio and WISP tags to show the feasibility of this design. We also conduct extensive simulations and comparisons to show that WP outperforms previous methods. Compared with the state-of-the-art protocols, WP achieves more than 2× performance gain in terms of time-efficiency when all the channels are assumed free of errors and the number of tags is 10000, and achieves up to 12× success probability gain when the burstiness is more than 80%.

Internet of Things (IoT) is one of the hottest research fields nowadays and has attracted huge interests and research efforts from both academia and industry.IoT can connect a large number of sensors, actuators, devices, vehicles, buildings, and/or other objects to form a network where data can be collected from the physical world, exchanged and processed in the cyber world, and then fed back into the physical world through actuations.This makes IoT one of the key foundations towards the vision of smart cities, with many promising applications such as environmental monitoring, infrastructure management, manufacturing, energy management, medical and healthcare, building and home automation, and transportation.Recently, the advances in various wireless communication protocols in technologies such as 5G, RFID, Wi-Fi-Direct, Li-Fi, LTE, and 6LoWPAN have greatly boosted the potential capabilities of IoT and made it become more prevalent than ever, which also accelerate the further integration of IoT with emerging technologies in other areas such as sensing, wireless recharging, data exchanging, and processing.Yet, how these technologies especially the corresponding wireless communication protocols can be well aligned with IoT to maximize their benefits on such performance as scalability, service quality, energy efficiency, and cost effectiveness is still open to investigation and thus calls for novel solutions.And the involved privacy and security issues also need to be carefully examined and addressed.This special issue aims to summarize the latest development in wireless communication protocols for Internet of

Novel cloud computing technology makes multiplayer cloud gaming a reality, where players play games that do not run on local devices, but on servers in the cloud. Nevertheless, the necessary communication between player's local device and cloud server increases the response delay of the gaming session. Besides the degrade of responsiveness, the inter-player delay, which is the difference of response delays perceived by players who are interacting with each other, can significantly affect the fairness of the multiplayer game. In this paper, we first introduce the Inter-player Delay Optimization (IDO) problem that aims at minimizing this inter-player delay, while preserving good-enough absolute response delay experienced by players. We further propose an efficient heuristic algorithm to solve the IDO problem. The evaluation result of a comprehensive simulation using a large-scale real-world data trace shows that IDO can reduce up to about 30% of maximum inter-player delay among interacting players comparing with the other existing solution.

Driven by the advances in personal computing devices and the prevalence of broadband network and wireless mobile network accesses, Crowdsourced Live Streaming (CLS) platforms have emerged in recent years, through which numerous broadcasters lively stream their video content, e.g., live events or online game scenes, to fellow viewers. Compared to professional video producers and broadcasters, these new generation broadcasters are highly heterogenous in terms of the network/system configurations and therefore the generated video quality, which calls for massive encoding and transcoding in order to unify the video sources and serve multiple quality versions to viewers with different configurations. On the other hand, with the rapid evolution in the hardware industry, high performance processors (e.g., Intel Core i7-4790K CPU) become mainstream in personal computer market. More end devices can easily transcode high quality videos in realtime. We witness huge computational resource among the massive fellow viewers that could potentially be used for transcoding. In this paper, inspired by fog computing, we propose Crowd-Transcoding, a novel framework for CLS systems that offloads the transcoding assignment to the massive viewers. We identify that the key challenges in CrowdTranscoding are to detect qualified stable viewers and to properly assign them to the source channels. We put forward Viewer Crowdsourcing Transcode Scheduler (VCTS) to smartly schedule the workload assignment. Our solution has been evaluated under diverse viewer/channel conditions as well as different parameter settings. The trace-driven simulation confirms the superiority of CrowdTranscoder, while our PlanetLab-based and real world end-viewer experiments show the practical performance of our approach, which also give hint to the further enhancement.

Virtualization has become a building block for modern IT industry, and many datacenters are now highly virtualized. It is known that virtualization also introduces non-trivial overhead, which can cause severe self-interference inside a VM when CPU intensive tasks and bandwidth intensive tasks are co-located. Energy efficiency of the server can be affected as well. While such overhead is well-studied in application/protocol specific context, a more comprehensive solution is yet to be explored for general cloud services. In this paper, we present MemNet, a novel protocol-independent solution that enables para-virtualized memory sharing between host and guest VMs. This design successfully decouples I/O and computation operations and lifts the offered interface from the physical devices to high-level network services. Our real-world implementation on KVM indicates that, MemNet can achieve 27% and 70% gain in terms of computing and networking performance, respectively, by resolving the self-interference. It also provides 32% improvement in terms of energy efficiency.

Base stations have been massively deployed nowadays to afford the explosive demand to infrastructure-based mobile networking services, including both cellular networks and commercial WiFi access points. To maintain high service availability, backup battery groups are usually installed on base stations and serve as the only power source during power outages, which can be prevalent in rural areas or during severe weather conditions such as hurricanes or snow storms. Therefore, being able to understand and predict the battery group working condition is of immense technical and commercial importance as the first step towards a cost-effective battery maintenance on minimizing service interruptions.

Recent years have witnessed a dramatic increase of user-generated video services. In such user-generated video services, crowdsourced live streaming (e.g., Periscope, Twitch) has significantly challenged today's content delivery infrastructure: today's edge networks (e.g., 4G, Wi-Fi) have limited uplink capacity support, making high-bitrate live streaming over such links fundamentally impossible. In this paper, we propose to let broadcasters (i.e., users who generate the video) upload crowdsourced video streams using aggregated network resources from multiple edge networks. There are several challenges in the proposal: First, how to design a framework that aggregates bandwidth from multiple edge networks? Second, how to make this framework transparent to today's crowdsourced live stream- ing services? Third, how to maximize the streaming quality for the whole system? We design a multi-objective and deployable bandwidth aggregation system BASS to address these challenges: (1) We propose an aggregation framework transparent to today's crowdsourced live streaming services, using an edge proxy box and aggregation cloud paradigm; (2) We dynamically allocate geo- distributed cloud aggregation servers to enable MPTCP (i.e., multi- path TCP), according to location and network characteristics of both broadcasters and the original streaming servers; (3) We maximize the overall performance gain for the whole system, by matching streams with the best aggregation paths.

In today's mobile devices, the battery reservoir remains severely limited in capacity, making power consumption a key concern in the design and implementation of mobile applications. In this paper, we closely examine one widely adopted approach to improve the energy efficiency of mobile applications-adaptively offloading the computation to the remote cloud. In particular, we measure the power consumption of computation offloading for two representative real-world mobile cloud applications under various wireless network conditions and identify the unique features of data transmission for computation offloading. We then formulate the power-aware scheduling problem for computation offloading and present a scheduling algorithm that makes adaptive offloading decisions according to the dynamic network conditions. Simulation results show that our proposed method can achieve better battery performance, which also reveal that computation-intensive and delay-tolerant tasks are more likely to benefit from offloading.

Offering the services of real-time tracking and arrival time prediction is a common welfare for bus riders and transit agencies, especially in urban environments. On the down side, the traditional GPS-based solutions work poorly in urban areas due to urban canyons, while the location systems based on cellular signal also suffer from inherent limitations. In this paper, we present a powerful tool named Signal Voronoi Diagram (SVD) to partition the radio-frequency (RF) signal space of WiFi Access Points (APs), distributed where a bus travels, into Signal Cells, and then into fine-grained Signal Tiles, tackling the problem of noisy received signal strength (RSS) readings and possible AP dynamics. On top of SVD, we present a novel framework so-called WiLocator, to track and predict the arrival time of an urban bus based on the surrounding WiFi information collected by the commodity off-the-shelf (COTS) smartphones of bus riders, the mobility constraint of a bus and the temporal consistency of travel time of buses on the overlapped road segments. We also show the WiLocator's power of generating an accurate and real-time traffic map with the predicted travel time on each road segment. We implement the prototype of WiLocator and conduct the in-situ experiment to demonstrate its accuracy.

Efficient event stream dissemination is a challenging problem in large-scale Online Social Network (OSN) systems due to the costly inter-server communications caused by the per-user view data storage. To solve the problem, previous schemes mainly explore the structure of the social graphs to reduce the inter-server traffics. Based on the observation of high cluster coefficient in OSNs, a state-of-the-art social piggyback scheme proves to be effective in saving redundant messages by exploiting an intrinsic hub structure in an OSN graph. Essentially, finding the best hub structure for piggybacking is equivalent to finding a variation of the densest sub-graph. The existing scheme computes the densest sub-graph by iteratively removing the node with the minimum weighted degree. Such a scheme incurs a worst computation cost of O(n2), making it not scalable to large-scale OSN graphs. Using alternative hub structures instead of the densest sub-graph can speed up the piggybacking assignment. They however greatly sacrifice the communication efficiency of the assignment schedule. Different from the existing designs, in this work, we propose the QuickPoint algorithm, which achieves the removal of a fraction of nodes in each iteration in finding the densest sub-graph. We mathematically prove that QuickPoint converges in O(logan)(a > 1) iterations in finding the densest sub-graph for efficient piggyback. We implement QuickPoint in parallel using Pregel, a vertex-centric distributed graph processing platform. Comprehensive experiments using large-scale data from Twitter and Flickr show that our scheme achieves a 38.8× improvement in efficiency compared to the existing schemes.

In this paper, we take a close look to understand the cloud-based file synchronization and collaboration systems. Using the popular Dropbox as a case study, our measurement reveals its cascaded computation and communication operations that are far more complicated than those in conventional file hosting. We show that this serial design is necessary for the cloud deployment, which effectively avoids the possible task interference inside the computation cloud; yet it also leads to higher service variance across users. Even worse, in a collaborative file editing session, users' updates would be discarded without any warning. The drop rate is unfortunately related to the slowest collaborator, which severely hinders the system scalability and user satisfaction. We further investigate the root causes of this phenomenon as well as other performance bottlenecks and offer hints for practical improvement.

Nowadays technology advances of wireless networking and mobile devices have made anytime anywhere data access become readily available. This also enables crowdsourced content capturing and sharing, especially for such multimedia data as video. One example is Twitter's Vine, which mainly target mobile devices, allowing users to create ultra-short video clips and instantly share with their followers. In this paper, we take an initial study on this new generation of mobile instant video clip sharing service and explore the potentials towards its further enhancement. We closely investigate its unique mobile interface, featured user behaviors with screen scrolling, revealing the key differences between Vine-enabled anytime anywhere data access patterns and that of traditional counterparts. We then examine the scheduling policy to maximize the user watching experience as well as the cost efficiency. We show that the generic scheduling problem involves two subproblems, namely, pre-fetching scheduling and watch-time download scheduling, and develop effective solutions towards both of them. The superiority of our solution is demonstrated by extensive trace-driven simulations. To the best of our knowledge, this is the first work on modeling and optimizing the view experience of the instant video clip sharing service on mobile devices.

One of the most important applications of Radio Frequency Identification (RFID) technology is to detect unknown tags brought by new tagged items moved in, misplacement, or counterfeit tags. While unknown tag identification is able to pinpoint all the unknown tags, probabilistic unknown tag detection is preferred in large-scale RFID systems that need to be frequently checked up, e.g., real-time inventory monitoring. Nonetheless, we find that the efficiency of most previous works is not well optimized due to the transmission of unhelpful data. In this paper, we propose a fast and reliable method for probabilistic unknown tag detection, White Paper (WP) protocol. The key novelty of WP is to build a composite message data structure that consists of all the informative data from several independent detection synopses, i.e., excluding the useless data from communication. Hence, this design allows us to optimize the detection and communication efficiency at the same time. In particular, the compact detection synopsis is designed and tuned to minimize the failure probability for detection and the detection message is compositely constructed to reduce the transmission overhead, achieving the optimal detection and communication efficiency, respectively. We implement a prototype system using USRP software-defined radio and WISP tags to show the feasibility of this design. We also conduct extensive simulations and comparisons to show that WP achieves more than 2x performance gain compared to the state-of-the-art protocols.

Crowdsourced Live Streaming (CLS), most notably Twitch.tv, has seen explosive growth in its popularity in the past few years. In such systems, any user can lively broadcast video content of interest to others, e.g., from a game player to many online viewers. To fulfill the demands from both massive and heterogeneous broadcasters and viewers, expensive server clusters have been deployed to provide video ingesting and transcoding services. Despite the existence of highly popular channels, a significant portion of the channels is indeed unpopular. Yet as our measurement shows, these broadcasters are consuming considerable system resources; in particular, 25% (resp. 30%) of bandwidth (resp. computation) resources are used by the broadcasters who do not have any viewers at all. In this paper, we closely examine the challenge of handling unpopular live-broadcasting channels in CLS systems and present a comprehensive solution for service partitioning on hybrid cloud. The trace-driven evaluation shows that our hybrid cloud-assisted design can smartly assign ingesting and transcoding tasks to the elastic cloud virtual machines, providing flexible system deployment cost-effectively.

Cloud computing has recently attracted a substantial amount of attention from both industry and academia. Its growing demand gives normal users an opportunity to sell their local resources to the cloud market, which introduces new challenges for the existing coarse-grained pricing models. In this paper, we examine the potential of applying continuous double auction framework to handle these heterogeneous cloud resources. First, we establish an e-auction platform, on which cloud service providers and users can trade computing and storage resources online. Then we formulate a continuous double auction model for cloud market and further develop a novel belief-based hybrid bidding strategy (BH-strategy) for cloud players to ensure their profit maximization. At last, we conduct three simulation scenarios to compare the performance between BH-strategy and other dominating bidding strategies, and plenty of simulation results show that our BH-strategy outperforms others in all the scenarios on user surpluses by 20% or above. Besides, the BH-strategy can obtain a 16% higher efficiency in 1/3 the amount of time of other strategies.

The Internet of Things (IoT) is connecting people and smart devices on a scale that was once unimaginable. One major challenge for the IoT is to handle vast amount of sensing data generated from the smart devices that are resource-limited and subject to missing data due to link or node failures. By exploring cloud computing with the IoT, we present a cloud-based solution that takes into account the link quality and spatio-temporal correlation of data to minimize energy consumption by selecting sensors for sampling and relaying data. We propose a multiphase adaptive sensing algorithm with belief propagation (BP) protocol (ASBP), which can provide high data quality and reduce energy consumption by turning on only a small number of nodes in the network. We formulate the sensor selection problem and solve it using both constraint programming (CP) and greedy search. We then use our message passing algorithm (BP) for performing inference to reconstruct the missing sensing data. ASBP is evaluated based on the data collected from real sensors. The results show that while maintaining a satisfactory level of data quality and prediction accuracy, ASBP can provide load balancing among sensors successfully and preserves 80% more energy compared with the case where all sensor nodes are actively involved.

In recent years, there has been a growing interest in the ability of embedded devices, sensors, and actuators to communicate, and create a ubiquitous cyber-physical world. The growth of the notion of the Internet of Things (IoT) and the rapid development of technologies such as short range mobile communication and improved energy-efficiency is expected to create a pervasive connection of “things.” This will inevitably result in the generation of enormous amount of data, which have to be stored, processed, and accessed. Cloud computing has long been recognized as a paradigm for big data storage and analytics. The combination of cloud computing and IoT can enable ubiquitous sensing services and powerful processing of sensing data streams beyond the capability of individual things, thus stimulating innovations in both fields. For example, cloud platforms allow the sensing data to be stored and used intelligently for smart monitoring and actuation with the smart devices. Novel data fusion algorithms, machine learning methods, and artificial intelligence techniques can be implemented and run centralized or distributed on the cloud to achieve automated decision making. These will boost the development of new applications, such as smart cities, grids, and transportation systems. New challenges, however, arise when IoT meets cloud—there is an urgent need for novel network architectures that seamlessly integrate them, and protocols that facilitate big data streaming from IoT to the cloud. QoS and QoE, as well as data security, privacy, and reliability, are critical concerns during the integration.

MapReduce-like frameworks have achieved tremendous success for large-scale data processing in data centers. A key feature distinguishing MapReduce from previous parallel models is that it interleaves parallel and sequential computation. Past schemes, and especially their theoretical bounds, on general parallel models are therefore, unlikely to be applied to MapReduce directly. There are many recent studies on MapReduce job and task scheduling. These studies assume that the servers are assigned in advance. In current data centers, multiple MapReduce jobs of different importance levels run together. In this paper, we investigate a schedule problem for MapReduce taking server assignment into consideration as well. We formulate a MapReduce server-job organizer problem (MSJO) and show that it is NP-complete. We develop a 3-approximation algorithm and a fast heuristic design. Moreover, we further propose a novel fine-grained practical algorithm for general MapReduce-like task scheduling problem. Finally, we evaluate our algorithms through both simulations and experiments on Amazon EC2 with an implementation with Hadoop. The results confirm the superiority of our algorithms.

Mobile data traffic has been experiencing a phenomenal rise in the past decade. This ever-increasing data traffic puts significant pressure on the infrastructure of state-of-the-art cellular networks. Recently, device-to-device (D2D) communication that smartly explores local wireless resources has been suggested as a complement of great potential, particularly for the popular proximity-based applications with instant data exchange between nearby users. Significant studies have been conducted on coordinating the D2D and the cellular communication paradigms that share the same licensed spectrum, commonly with an objective of maximizing the aggregated data rate. The new generation of cellular networks, however, have long supported heterogeneous networked applications, which have highly diverse quality-of-service (QoS) specifications. In this paper, we jointly consider resource allocation and power control with heterogeneous QoS requirements from the applications. We closely analyze two representative classes of applications, namely streaming-like and file-sharing-like, and develop optimized solutions to coordinate the cellular and D2D communications with the best resource sharing mode. We further extend our solution to accommodate more general application scenarios and larger system scales. Extensive simulations under realistic configurations demonstrate that our solution enables better resource utilization for heterogeneous applications with less possibility of underprovisioning or overprovisioning.

In this article, we have classified cloud gaming platforms into three types based on how games are integrated with platforms. We have also reviewed the history of cloud gaming services, and noted that it is a key moment for cloud gaming services to increase their penetration rates. Last, built upon our extensive research experience in cloud gaming, we share several of our visions into future cloud gaming technologies, business models, and social impacts, in the format of forecasts. While our forecasts may not be an exhausted list, we firmly believe this article will stimulate more discussions among cloud gaming researchers and practitioners, resulting in a sustainable cloud gaming ecosystem.

Auction-style pricing policies can effectively reflect the underlying trends in demand and supply for the cloud resources, and thereby attracted a research interest recently. In particular, a desirable cloud auction design should be (1) online to timely reflect the fluctuation of supply-demand relations, (2) expressive to support the heterogeneous user demands, and (3) truthful to discourage users from cheating behaviors. Meeting these requirements simultaneously is non-trivial, and most existing auction mechanism designs do not directly apply. To meet these goals, this paper conducts the first work on a framework for truthful online cloud auctions where users with heterogeneous demands could come and leave on the fly. Concretely speaking, we first design a novel bidding language, wherein users' heterogeneous requirement on their desired allocation time, application type, and even how they value among different possible allocations can be flexibly and concisely expressed. Besides, building on top of our bidding language we propose COCA, an incentive-Compatible (truthful) Online Cloud Auction mechanism. To ensure truthfulness with heterogenous and online user demand, the design of COCA is driven by a monotonic payment rule and a utility-maximizing allocation rule. Moreover, our theoretical analysis shows that the worst-case performance of COCA can be well-bounded, and our further discussion shows that COCA performs well when some other important factors in online auction design are taken into consideration. Finally, in simulations the performance of COCA is seen to be comparable to the well-known off-line Vickrey-Clarke-Groves (VCG) mechanism [19].

With the advances in personal computing devices and the prevalence of broadband network and wireless mobile network accesses, end-users are no longer pure content consumers, but contributors, too. In today's crowdsourced streaming systems, numerous broadcasters lively stream their video content, e.g., live events or online game scenes, to fellow viewers. Compared to professional video producers and broadcasters, these new generation broadcasters are geo-distributed globally and highly heterogeneous in terms of the generated video quality and the network/system configurations. The scalability and heterogeneity challenges therefore lie on both broadcasters and the viewers, which call for massive transcoding, and two critical issues: 1) choosing video representation set that maximizes viewer satisfaction and 2) allocating computational resources that minimize operational costs, must be systematically optimized in the global scale. In this paper, we present a generic framework utilizing the powerful and elastic cloud computing services for crowdsourced live streaming with heterogeneous broadcasters and viewers. We jointly consider the viewer satisfaction and the service availability/pricing of geo-distributed cloud resources for transcoding. We develop an optimal scheduler for allocating cloud instances with no regional constraints. We then extend the solution to accommodate regional constraints, and discuss a series of practical enhancements, including popularity forecasting, initialization latency, and viewer feedbacks. Our solutions have been evaluated under diverse networks and cloud system configurations as well as parameter settings. The trace-driven simulation confirms the superiority of our design, while our Planetlab-based experiment offers further practical hints toward real-world migration.

The in-network data storage and retrieval are fundamental functions of sensor networks. Among many proposals, geographical hash table GHT is perhaps most appealing as it is very simple yet powerful with low communication cost, where the key is to correctly define the bounding box. It is envisioned that the skeleton has the power to facilitate computing a precise bounding box. In existing works, the focus has been on skeleton extraction algorithms targeting for 2D sensor networks, which usually deliver a 1-manifold skeleton consisting of 1D curves. It faces a set of non-trivial challenges when 3D sensor networks are considered, in order to properly extract the surface skeleton composed of a set of 2-manifolds and possibly 1D curves. In this paper, we study the problem of surface skeleton extraction in 3D sensor networks. We propose a scalable and distributed connectivity-based algorithm to extract the surface skeleton of 3D sensor networks. First, we propose a novel approach to identifying surface skeleton nodes by computing the extended feature nodes such that it is robust against boundary noise, etc. We then find the maximal independent set of the identified skeleton nodes and triangulate them to form a coarse-grained surface skeleton, followed by a refining process to generate the fine-grained surface skeleton. Furthermore, we design an efficient updating scheme to react to the network dynamics caused by node failure, insertion, etc. We also investigate the impact of boundary incompleteness and present a scheme to extract the surface skeleton under incomplete boundary. Finally, we apply the extracted surface skeleton to facilitate the design of data storage protocol and curve skeleton extraction algorithm. Extensive simulations show the robustness of the proposed algorithm to shape variation, node density, node distribution, communication radio model and boundary incompleteness, and its effectiveness for data storage and retrieval application with respect to load balancing.

Efficient in-network information storage and retrieval is of paramount importance to sensor networks and has attracted a large number of studies while most of them focus on 2D fields. In this paper, we propose novel Reeb graph based information storage and retrieval schemes for 3D sensor networks. The key is to extract the line-like skeleton from the Reeb graph of a network, based on which two distance-sensitive information storage and retrieval schemes are developed: one devoted to shorter retrieval path and the other devoted to more balanced load. Desirably, the proposed algorithms have no reliance on the geographic location or boundary information, and have no constraint on the network shape or communication graph. The extensive simulations also show their efficiency in terms of sensor storage load and retrieval path length.

Live media streaming has become one of the most popular applications over the Internet. We have witnessed the successful deployment of commercial systems with content delivery network (CDN)- or peer-to-peer-based engines. While each being effective in certain aspects, having an all-round scalable, reliable, responsive, and cost-effective solution remains an illusive goal. Moreover, today's live streaming services have become highly globalized, with subscribers from all over the world. Such a globalization makes user behaviors and demands even more diverse and dynamic, further challenging state-of-the-art system designs. The emergence of cloud computing, however, sheds new light into this dilemma. Leveraging the elastic resource provisioning from the cloud, we present Cloud-Assisted Live Media Streaming (CALMS), a generic framework that facilitates a migration to the cloud. CALMS adaptively leases and adjusts cloud server resources in a fine granularity to accommodate temporal and spatial dynamics of demands from live streaming users. We present optimal solutions to deal with cloud servers with diverse capacities and lease prices, as well as the potential latencies in initiating and terminating leases in real-world cloud platforms. Our solution well accommodates location heterogeneity, mitigating the impact from user globalization. It also enables seamless migration for existing streaming systems, e.g., peer-to-peer, and fully explores their potentials. Simulations with data traces from both cloud service providers (Amazon EC2 and SpotCloud) and a live streaming service provider (PPTV) demonstrate that CALMS effectively mitigates the overall system deployment costs and yet provides users with satisfactory streaming latency and rate.

The open shortest path first (OSPF) protocol has been widely applied to intra-domain routing in today's Internet. Since a router running OSPF distributes traffic uniformly over equal-cost multi-path (ECMP), the OSPF-based optimal traffic engineering (TE) problem (i.e., deriving optimal link weights for a given traffic demand) is computationally intractable for large-scale networks. Therefore, many studies resort to multi-protocol label switching (MPLS) based approaches to solve the optimal TE problem. In this paper we present a generalized routing framework to realize the optimal TE, which can be potentially implemented via OSPFor MPLS-based approaches. We start with viewing the conventional optimal TE problem in a fresh way, i.e., optimally allocating the residual capacity to every link. Then we make a generalization of network utility maximization (NUM) to close this problem, where the network operator is associated with a utility function of the residual capacity to be maximized. We demonstrate that under this framework, the optimal routes resulting from the optimal TE are also the shortest paths in terms of a set of non-negative link weights that are explicitly determined by the optimal residual capacity and the objective function. The network entropy maximization theory is employed to enable routers to exponentially, instead of uniformly, split traffic over ECMP. The shortest-path penalizing exponential flow-splitting (SPEF) is designed as a link-state protocol with hop-by-hop forwarding to implement our theoretical findings. An alternative MPLS-based implementation is also discussed here. Numerical simulation results have demonstrated the effectiveness of the proposed framework as well as SPEF.

Recently, datacenter carbon emission has become an emerging concern for the cloud service providers. Previous works are limited on cutting down the power consumption of datacenters to defuse such a concern. In this paper, we show how the spatial and temporal variabilities of the electricity carbon footprint can be fully exploited to further green the cloud running on top of geographically distributed datacenters. Specifically, we first verify that electricity cost minimization conflicts with carbon emission minimization, based on an empirical study of several representative geo-distributed cloud services. We then jointly consider the electricity cost, service level agreement (SLA) requirement, and emission reduction budget. To navigate such a three-way tradeoff, we take advantage of Lyapunov optimization techniques to design and analyze a carbon-aware control framework, which makes online decisions on geographical load balancing, capacity right-sizing, and server speed scaling. Results from rigorous mathematical analysis and real-world trace-driven evaluation demonstrate the effectiveness of our framework in reducing both electricity cost and carbon emission.

MapReduce has emerged as a powerful tool for distributed and scalable processing of voluminous data. For skewed data input, load balancing is necessary among the MapReduce worker nodes to minimize the overall finishing time, which however can incur massive data movement in a data center network. In this paper, we for the first time examine this problem of data center-network-aware load balancing in the shuffle sub phase in MapReduce. Different from earlier studies that generally assume the network inside a data center has negligible delay and infinite capacity, we consider the traffic and bottlenecks in real data center networks by introducing the constraints on available network bandwidth, and demonstrate that the corresponding problem can be decomposed into two sub problems for network flow and load balancing, respectively. We show effective solutions to both of them, which together yield a complete solution towards near optimal data center-network-aware load balancing. A much simpler yet performance-wise comparable greedy algorithm is also developed for fast implementation in practice. The effectiveness of our solution has been demonstrated on synthetic and real public datasets.

The deep penetration of Online Social Networks (OSNs) has made them as major portals for video information sharing. Propagated through chains of friends, the coverage of OSN-shared videos can be much broader with stronger micro- and macro-dynamics. Given that the contents are still hosted by external Video Sharing Sites (VSSes), such distinct access patterns from OSN users have created significant new challenges to VSSes. In this paper, we present SNACS, a cost-effective social network-aware cloud assistance for video sharing. The SNACS module sits between VSSes and an OSN, and is managed by the OSN to improve its users' video access experience using both centralized cloud resources and edge servers. Given the strong dynamics of the access patterns, we are particularly interested in the content management and update strategies in the SNACS' implementation. Motivated by real world data traces, we show that conventional cache replacement can be quite inefficient in this context. We then develop optimal offline algorithms with minimized cache misses and replacements, which also motivate an online solution that makes effective use of the video sharing patterns in the OSN. Our design has been extensively evaluated and its superiority has been validated under diverse network and user configurations.

With the rapid development of mobile networking and end-terminals, anytime and anywhere data access becomes readily available nowadays. Given the crowd sourced content capturing and sharing, the preferred length becomes shorter and shorter, even for such multimedia content as video. A representative is Twitter's Vine service, which, mainly targeting mobile users, enables them to create ultra-short video clips, and instantly post and share them with their followers. In this paper, we present an initial study on this new generation of instant video clip sharing service enabled by mobile platforms and explore the potentials for its further enhancement. Taking Vine as a case study, we closely investigate its unique user behaviors, revealing how such Vine-enabled anytime anywhere data access patterns differentiate mobile instant video clip sharing from its traditional counterparts. We then formulate a generic scheduling problem to maximize the user watching experience as well as the efficiency on the monetary and energy costs. To better solve it, we divide the problem into two sub problems, specifically, the pre-fetching scheduling problem and the watch-time download scheduling problem, and conquer them separately. We further demonstrate the preliminary evaluation result to show the superiority of our solution. To the best of our knowledge, this is the first work on modeling and optimizing the instant video clip sharing on mobile devices.

Streaming services are gaining popularity and have contributed a tremendous fraction of today's cellular network traffic. Both playback fluency and battery endurance are significant performance metrics for mobile streaming services. However, because of the unpredictable network condition and the loose coupling between upper layer streaming protocols and underlying network configurations, jointly optimizing rebuffering time and energy consumption for mobile streaming services remains a significant challenge. In this paper, we propose a novel framework that effectively addresses the above limitations and optimizes video transmission in cellular networks. We design two complementary algorithms, Rebuffering Time Minimization Algorithm (RTMA) and Energy Minimization Algorithm (EMA) in this framework, to achieve smoothed playback and energy-efficiency on demand over multi-user scenarios. Our algorithms integrate cross-layer parameters to schedule video delivery. Specifically, RTMA aims at achieving the minimum rebuffering time with limited energy and EMA tries to obtain the minimum energy consumption while meeting the rebuffering time constraint. Extensive simulation demonstrates that RTMA is able to reduce at least 68% rebuffering time and EMA can achieve more than 27% energy reduction compared with other state-of-the-art solutions.

Empowered by today's rich tools for media generation and distribution, and the convenient Internet access, crowdsourced streaming generalizes the single-source streaming paradigm by including massive contributors for a video channel. It calls a joint optimization along the path from crowdsourcers, through streaming servers, to the end-users to minimize the overall latency. The dynamics of the video sources, together with the globalized request demands and the high computation demand from each sourcer, make crowdsourced live streaming challenging even with powerful support from modern cloud computing. In this paper, we present a generic framework that facilitates a cost-effective cloud service for crowdsourced live streaming. Through adaptively leasing, the cloud servers can be provisioned in a fine granularity to accommodate geo-distributed video crowdsourcers. We present an optimal solution to deal with service migration among cloud instances of diverse lease prices. It also addresses the location impact to the streaming quality. To understand the performance of the proposed strategies in the realworld, we have built a prototype system running over the planetlab and the Amazon/Microsoft Cloud. Our extensive experiments demonstrate that the effectiveness of our solution in terms of deployment cost and streaming quality.

With rapid development in online shopping, e-commerce websites are facing intensive user requests from an increasing number of customers. Especially in promotion seasons, these websites may encounter flash crowds which pull heavy pressure o private infrastructure and even make he website unavailable. Such severe flash crowds can be addressed by leveraging hybrid cloud solution, which relieves workloads of the private cloud by offloading the excessive user requests to the IaaS public cloud. However, the bursty and fluctuation of flash crowds bring challenges to distributing user requests with targest of delay-minimizing and cost-saving. In his paper, we apply the queueing theory to evaluate the average response time and explore the tradeoff between performance and cost in the hybrid cloud. By taking advantage of Lyapunov optimization techniques, we design an online decision algorithm for request distribution which achieves the average response time arbitrarily close to the theoretically optimum and controls he outsourcing cost based on a given budge. The simulation results demonstrate ha in a hybrid cloud, our solution can reduce he cost of e-commerce services as well as guarantee performance when encountering flash crowds.

This paper presents Pop-Forecast, a systematic method for accurately forecasting the popularity of videos promoted through social networks. Pop-Forecast aims to optimize the forecasting accuracy and the timeliness with which forecasts are issued, by explicitly taking into account the dynamic propagation of videos in social networks. The forecasting is performed online and requires no training phase or a priori knowledge. We analytically bound the performance loss of Pop-Forecast as compared to that obtained by an omniscient oracle and prove that the bound is sublinear in the number of video arrivals, thereby guaranteeing its fast rate of convergence as well as its asymptotic convergence to the optimal performance. We validate the performance of Pop-Forecast through extensive experiments using real-world data traces collected from the videos shared in RenRen, one of the largest online social networks in China. These experiments show that our proposed method outperforms existing approaches for popularity prediction (which do not take into account the propagation in social network) by more than 30% in terms of prediction rewards.

Recent years have witnessed the great popularity of dynamic spectrum access networks. Such an approach is adopted between three players: government, Internet Service Providers (ISPs) and end-users. ISPs need to purchase spectrum from the government before subletting it to end-users, but currently most researches focus on the subletting process and ignore the purchasing process. In this paper, we try to investigate the game between government and ISPs in spectrum access networks. In this framework, the former aims to optimize user experience yet the later want to maximize their own profits. Such a conflict of interests introduces significant challenges to ensure end-user's performance and thus leads to a severe bottleneck to the spectrum access networks. Inspired by cooperative trends among users, we proposed a novel Channel Allocation model based on Teamwork (CAT). This approach considers both ISP's respective bands and end-user's experience and enables a smart profit sharing algorithm to address the problem. The evaluation results indicate that CAT improves the overall social welfare by about 30% than the Vickrey Clarke Groves (VCG) mechanism and obtains higher stability.

Motivated by our systematic study on the diverse aspects of migrating gaming services to a virtualized cloud environment, we designed and implemented a fully virtualized cloud gaming platform, Rhizome, utilizing the latest hardware support for both remote servers and local clients. Our platform takes the first step towards bridging online gaming systems and public clouds. To accomplish ultra-low latency and a low power consumption gaming experience, we further optimized Rhizome's thin-client configuration and its interaction modules. In this proposed demo, we demonstrate that gaming over a virtualized cloud can be made possible with careful optimization and integration of different modules. It also helps us reveal the critical challenges towards full-fledged deployment of gaming services over public virtualized cloud.

With advancements in wireless communication technologies, broadband wireless services will be prevalent in the near future. Meanwhile, the capability of mobile devices is drastically increasing the mobile data usage, which is far in excess of mobile network capacities. Therefore, despite the high availability of these networks, the large scale of users they support, and their improved spectral efficiencies, effective utilization of wireless resources is still required to keep up with the ever increasing user demands for mobile services. This paper targets high-throughput data transmission in advanced cellular wireless networks that have been widely used for broadband access and are constantly enhanced for future applications. We present a power-efficient resource allocation solution to meet the transmission requirements for bandwidth-intensive applications -- video streaming. In particular, our design strategically groups mobile users into multicast groups with different video quality requirement, and utilizes cellular resource to meet their video requirements. We compare our proposed methods with state-of-the-art solutions and prove their effectiveness: our design achieves 5% to 18% improvement in base station power consumption, and 13% to 25% improvement in user device power conservation.

Recent years have witnessed the emergence and growth of Cloud Gaming, where players interact with the remote game instance and receive rendered game scenes in video stream. Meanwhile, broadcasting and viewing games through live streaming platforms, e.g., Twitch.tv, have become increasingly popular. The interaction and performance of the many modules involved in this new generation of gaming and streaming platforms have yet to be closely investigated. In this paper, we present an initial experiment-based performance study, in which we profile the architecture of realworld gaming and streaming platforms, namely the Open Broadcast Software (OBS) module and its connection to the Twitch server. Our investigation shows that the recording operation can greatly increase the CPU utilization and the power consumption can increase over 60% on the game streaming computer. The use of advanced hardware encoding found on modern GPUs can greatly alleviate these performance issues. Yet, through profiling, we show that hardware encoding can introduce remarkable delays to the whole pipeline. We track this to a complicated interplay between the CPUs power saving methods and the implementation of hardware encoders.

Empowered by today's rich tools for media generation and collaborative production, the multimedia service paradigm is shifting from the conventional single source, to multi-source, to many sources, and now toward crowdsource. Such crowdsourced live streaming platforms as Twitch.tv allow general users to broadcast their content to massive viewers, thereby greatly expanding the content and user bases. The resources available for these non-professional broadcasters however are limited and unstable, which potentially impair the streaming quality and viewers' experience. The diverse live interactions among the broadcasters and viewers can further aggravate the problem.

As wireless sensor networks (WSNs) gradually move from specialized fields such as military and industry toward domains with general purposes, more and more sensors locate around our living areas. The reality that various wireless devices coexist in new circumstances encourages us to come up with new ideas to solve the extremely energy-constrained problem in WSNs. In this paper, we propose energy conservation with assistance of resourceful mules (ECARM), a mechanism that opportunistically utilizes resourceful mules (RMs) such as specifically designed powerful sensors or ubiquitously used laptops, tablet PCs, and smart phones to act as assistants and save energy for WSNs. We verify ECARM through extensive simulations written on the OMNET$\boldsymbol{++}$ platform. Single RM simulation shows that 43% sensors in an RM's communication range enjoy power reduction by decreasing their wake-up time to 16% at most. Multiple RM simulations illustrate that 86% sensors in the simulated network benefit from 14 RMs, and wake-up time of 56% sensors decrease to 50% below. We emphasize that ECARM can also be applied in duty-cycled WSNs that adopt schemes such as ContikiMAC and X-MAC. Simulation results demonstrate that the duty-cycling ratio of ContikiMAC is further decreased by at least 20.9% after the ECARM application.

Cloud computing involves a large number of terminals connected through a real-time high-speed network (such as the Internet). The adoption rates for private and hybrid cloud services increased to 40% in 2013, with computing shifting from on-premise infrastructure to the cloud. To keep pace with the ever-accelerating rate of innovation, companies are moving to the cloud. However, visual computing in the cloud brings great challenges, such as how to measure and then improve the quality of experience in cloud computing. This Special Section provides the image/video community a forum to present new academic research and industrial development in running visual computing services in the cloud. This Special Section aims to address fundamental and practical aspects of visual computing in the cloud, such as how to build cloud platforms that can cope with seemingly unlimited supply of content coming from traditional media sources as well as new media uploaded to the Internet (YouTube, Facebook, etc.); how to leverage cloud technology to build high-quality image/video browsing and delivery experiences for a global audience; how to ingest, encode, process, adapt, as well as protect contents and privacy of users; how to provide both on-demand and live-streaming capabilities; how to tag image/video and allow consumers to access the image/video contents with high availability; how to support image/video services in mobile devices; and how to perform real-time image/video analytics in the cloud, to mention a few among a diverse range of challenges.

Existing cloud gaming platforms have mainly focused on private nonvirtualized environments with proprietary hardware. Modern public cloud platforms heavily rely on virtualization for efficient resource sharing, the potentials of which have yet to be explored. Migrating gaming to a public cloud is nontrivial, however, particularly considering the overhead for virtualization and that the graphics processing units (GPUs) for game rendering has long been an obstacle in virtualization. This paper takes a first step toward bridging the online gaming system and the public cloud platforms. We present the design and implementation of a fully virtualized cloud gaming platform with the latest hardware support for both remote servers and local clients. We explore many critical design issues inherent in cloud gaming, including the choice of hardware or software video encoding, and the configuration and the detailed power consumption of thin client. We demonstrate that with the latest hardware and virtualization support, gaming over virtualized cloud can be made possible with careful optimization and integration of the different modules. We also highlight critical challenges toward full-fledged deployment of gaming services over the public virtualized cloud.

Empowered by today's rich tools for media generation and distribution, and the convenient Internet access , streaming crowdsourced multimedia content (crowdsourced streaming, in brief) generalizes the single-source streaming paradigm by including massive contributors for a video/data channel. It calls a joint optimization along the path from crowdsourcers , through streaming servers, to the end-users to minimize the overall latency. The dynamics of the video sources, together with the globalized request demands and the high computation demand from each sourcer, make crowdsourced live streaming challenging even with powerful support from modern cloud computing. In this paper, we present a generic framework that facilitates a cost-effective cloud service for crowdsourced live streaming. Through adaptively leasing, the cloud servers can be provisioned in a fine granularity to accommodate geo-distributed video crowdsourcers. We present an optimal solution to deal with service migration among cloud instances of diverse lease prices. It also addresses the location impact to the streaming quality. To understand the performance of the proposed strategies in the real world, we have built a prototype system running over the planetlab and the Amazon/Microsoft Cloud. Our extensive experiments demonstrate that the effectiveness of our solution in terms of deployment cost and streaming quality.

Multi-hop coverage extension can be utilized as a feasible approach to facilitating uncovered users to get Internet service in public area WLANs. In this paper we introduce a relay-union network (RUN), which refers to a public area WLAN in which users often wander in the same area and have the ability to provide data forwarding services for others. We develop a RUN framework to model the cost of providing forwarding services and the utility obtained by gaining services. The objective of the RUN is to maximize the total Quality of Cooperation (QoC) of users in the RUN. Two optimal bandwidth allocation schemes are proposed for both free and dynamic bandwidth demand models. To make our scheme more pragmatic, we then consider a more practical scenario in which the bandwidth capacity of the relays and the minimum demand of the clients are bounded. We prove that the problems under both the single relay and the multi-relay scenario are NP-hard. Three heuristic algorithms are proposed to deal with bandwidth allocation and relay-client association. We also propose a distributed signaling protocol and divide the centralized MRMC algorithm into three distributed ones to better adapt for real network environment. Finally, extensive simulations demonstrate that our RUN framework can significantly improve the efficiency of cooperation in the long term.

Recent years have witnessed cloud computing as an efficient means for providing resources as a form of utility. Driven by the strong demands, industrial pioneers have offered commercial cloud platforms, mostly datacenter-based, which are known to be powerful and effective. Yet, as the cloud customers are pure consumers, their local resources, though abundant, have been largely ignored. In this paper, We present SpotCloud, a real working system that seamlessly integrates the customers' local resources into the cloud platform, enabling them to sell, buy, and utilize these resources. We also investigate the potentials and challenges towards enabling customer-provided resources for cloud computing. Given that these local resources are highly heterogeneous and dynamic, we closely examine two critical challenges in this new context: (1) How can the customers be motivated to contribute or utilize such resources? and (2) How can high service availability be ensured out of the dynamic resources? We demonstrate a distributed market for potential sellers to flexibly and adaptively determine their resource prices through a repeated seller competition game. We also present an optimal resource provisioning algorithm that ensures service availability with minimized lease and migration costs. The evaluation results indicate it as a flexible and less expensive complement to the pure datacenter-based cloud.

Recent years have witnessed the prevalence of MapReduce-based systems, e.g., the Apache Hadoop, in large-scale distributed data processing. Fetching data from remote servers across multiple network switches is known to be costly. Hence, it is highly desirable to co-locate computation with data. State-of-the-art popularity-based replication achieves data locality through replicating popular files and spreading the replicas over multiple servers. While working well for independent files, they can store highly dependent files in different servers, resulting in excessive remote data accesses exchanges and consequently prolonging the job completion time. In this paper, we develop DALM (Dependency-Aware Locality for MapReduce), a novel replication strategy for general real-world input data that can be highly skewed and dependent. DALM accommodates data-dependency in a data-locality framework that comprehensively weights such key factors as popularity and storage budget. We extensively evaluate DALM through both simulations and real-world implementations, and have compared with state-of-the-art solutions, including the Hadoop system and the popularity-based Scarlett. The results show that DALM can significantly improve data locality for different inputs. For a popular iterative graph processing application on Hadoop, our prototype implementation of DALM reduces the remote data access and job completion time by 34.3% and 9.4%, respectively.

Networking and virtualization are two key building blocks of modern cloud computing. The energy consumption of physical machines has been carefully examined in the past research, including the impact of network traffic. When it comes with virtual machines, the inter-play between energy consumption and network traffic however becomes much more complicated. The traffic are now generated by and exchanged between virtual machines (VMs), which could reside in different physical machines with their respective network interface cards (NICs), or share the same physical machine. When multiple VMs share a physical NIC, their traffic can interfere with each other, causing extra overhead. Yet the VM's allocation can be dynamic and they can even migrated across physical machines, thereby changing the traffic pattern. These factors combined make the network traffic highly diverse and dynamic, so is the corresponding energy consumption. A close examination on the network traffic and energy consumption in virtualized environments is thus of need. In this paper, we present an initial measurement study on the interplay between energy consumption and network traffic in representative virtualization environments. Our study reveals a series of unique energy consumption patterns of the network traffic in this context. We show that state-of-the-art virtualization designs noticeably increase the demand of CPU resources when handling networked transactions, generating excessive interrupt requests with ceaselessly context switching, which in turn increases energy consumption. Even when the physical machine is in an idle state, the VM network transactions will will incur remarkable energy consumption. Furthermore, even with identical number of VMs and amount of traffic on a physical machine, the energy consumptions vary significantly with different VM allocation strategies. Our close examination pinpoints the root cause, and offers new angles to revisit the existing resource usage and energy consumption models, so as to optimize the service provisioning as well as virtual machine placement and migration.

The state-of-the-art cloud computing service has attracted significant interests from the Internet users. However, in the existing cloud platforms, the cloud users are pure consumers; their local resources, though abundant, have been largely ignored. In this paper, we for the first time explore the resource pricing as well as the incentive issues in SpotCloud, a real-world system that enables customer-provided cloud computing service on the Internet. In this system, the resource providers are largely heterogeneous and are not forced to contribute their resources. A working business model is therefore important to offer them enough sharing incentive. Instead of setting a standardized pricing rule for unit resource, we suggest a distributed market that allows the sellers to decide the quality, quantity, and pricing of their own resources. We demonstrate the efficiency of this business model through a repeated seller competition game. The trace-analysis further indicates that the proposed business model can successfully motivate the resource sharing in our Spotcloud system.

The advancement of smart phones enables mobile users to collect data from their surrounding sensors using short-range wireless communication. However, the limited contact time and the wireless capacity constrain the amount of data to be collected by the mobile users. It is crucial for mobile users to collect sensing data that can maximize their data utility. In this paper, we propose a distributed algorithm to provide information-centric ubiquitous data collection for multiple mobile users. The mobile users construct data collection trees adaptively according to their dynamic moving speeds. They prioritize data collection according to the information value carried by the sensing data. The distributed algorithm can support smooth data collection and coordination among multiple mobile users. We evaluate the data utility, energy efficiency and scalability of our solution with extensive simulations. The results showed that our distributed algorithm can improve information value up to 50% and reduce energy consumption to half compared with the existing approach.

Distributed interactive applications (DIAs) such as online gaming have attracted a vast number of users over the Internet. It is however known that the deployment of DIA systems comes with peculiar hardware/software requirements on the users' consoles. Recently, such industrial pioneers as Gaikai, Onlive and Ciinow have offered a new based distributed interactive applications generation of cloud (CDIAs), which shift the necessary computing loads to cloud platforms and largely relieve the pressure on individual user consoles. In this paper, we take a first step towards understanding the CDIA framework and highlight its design challenges. Our measurement reveals the inside structure as well as the operations of real CDIA systems and identifies the critical role of the cloud proxies. While this design makes effective use of cloud resources to mitigate the clients' workloads, it can also significantly increase the interaction latency among clients if not carefully handled. Besides the extra network latency due to the involvement of cloud proxies, we find that the computation-intensive tasks (e.g., Game rendering) and bandwidth-intensive tasks (e.g., Streaming the game screen to the clients) together create a severe bottleneck in CDIA. Our experiment indicates that when the cloud proxies are virtual machines (VMs) in the cloud, the computation-intensive and bandwidth-intensive tasks will seriously interfere with each other if not handled carefully. We accordingly capture this feature in our model and present an interference-aware solution. This approach not only smartly allocates the workloads but also dynamically assigns the capacities across VMs.

The popularity of wireless local area networks has led to a dramatic increase in the density of access points, especially in urban areas. These access points are individually owned, placed, and power-tuned for their local users and are generally oblivious to others. On the other hand, the abundance of access points that mostly share the same upstream provider, offers opportunities for optimization of association to mitigate the negative impact of the overlapped coverage. We use this opportunity to enable collaboration by using a share of each access point's bandwidth to serve non-local users and gain access to their bandwidth in return. We extend the conventional proportional fair association through sharing and collaboration among individual networks, and present centrally optimized solutions. Our performance evaluation, based on data traces collected in 100 residential locations, demonstrate the superiority of our solution, outperforming the throughput of non-collaborative optimal access by up to 140%.

MapReduce has emerged as a powerful tool for distributed and scalable processing of voluminous data. In this paper, we, for the first time, examine the problem of accommodating data skew in MapReduce with online operations. Different from earlier heuristics in the very late reduce stage or after seeing all the data, we address the skew from the beginning of data input, and make no assumption about a priori knowledge of the data distribution nor require synchronized operations. We examine the input in a continuous fashion and adaptively assign tasks with a load-balanced strategy. We show that the optimal strategy is a constrained version of online minimum makespan and, in the MapReduce context where pairs with identical keys must be scheduled to the same machine, there is an online algorithm with a provable 2-competitive ratio. We further suggest a sample-based enhancement, which, probabilistically, achieves a 3/2-competitive ratio with a bounded error.

There have been significant studies on virtual machines (VMs), including their power consumption in performing different types of tasks. The VM's power consumption with network transactions, however, has seldom been examined. This paper presents an empirical study on the power consumption of typical virtualization packages while performing network tasks. We find that both Hardware Virtualization and Paravirtualization add considerable energy overhead, affecting both sending and receiving, and a busy virtualized web-server may consume 40% more energy than its non-virtualized counterparts. Our detailed profiling on packet path reveals that a VM can take 5 times more cycles to deliver a packet than a bare-metal machine, and is also much less efficient on caching. Without fundamental changes to the hypervisor-based VM architecture, we show that the use of adaptive packet buffering potentially reduces the overhead. Its practicality and effectiveness in power saving are validated through driver-level implementation and experiments.

Existing research on cloud network (in)stability has primarily focused on communications between Virtual Machines (VMs) inside a cloud, leaving that of VM communications over higher-latency wide-area networks largely unexplored. Through measurement in real-world cloud platforms, we find that there are prevalent and significant degradation and variation for such VM communications with both TCP and UDP traffic, even over lightly utilized networks. Our in-depth measurement and detailed system analysis reveal that the performance variation and degradation are mainly due to the dual-role of the CPU in both computation and network communication in a VM, and they can be dramatically affected by the CPU's scheduling policy. We provide strong evidence that such issues can be addressed in the hypervisor level and present concrete solutions. Such remedies have been implemented and evaluated in our cloud testbed, showing noticeable improvement for long-haul network communications with VMs.

MapReduce has achieved tremendous success for large-scale data processing in data centers. A key feature distinguishing MapReduce from previous parallel models is that it interleaves parallel and sequential computation. Past schemes, and especially their theoretical bounds, on general parallel models are therefore, unlikely to be applied to MapReduce directly. There are many recent studies on MapReduce job and task scheduling. These studies assume that the servers are assigned in advance. In current data centers, multiple MapReduce jobs of different importance levels run together. In this paper, we investigate a schedule problem for MapReduce taking server assignment into consideration as well. We formulate a MapReduce server-job organizer problem (MSJO) and show that it is NP-complete. We develop a 3-approximation algorithm and a fast heuristic. We evaluate our algorithms through both simulations and experiments on Amazon EC2 with an implementation in Hadoop. The results confirm the advantage of our algorithms.

In-network data storage and retrieval are fundamental functions of sensor networks. Among many proposals, geographical hash table (GHT) is perhaps most appealing as it is very simple yet powerful with low communication cost, where the key is to correctly define the bounding box. It is envisioned that the skeleton has the power to facilitate computing a precise bounding box. In existing works, the focus has been on skeleton extraction algorithms targeting for 2D sensor networks, which usually delivers a 1-manifold skeleton consisting of 1D curves. It faces a set of non-trivial challenges when 3D sensor networks are considered, in order to properly extract the surface skeleton composed of a set of 2-manifolds and possibly 1D curves.

YouTube is arguably the most popular online videos sharing site nowadays. To further augment its service with better revenue, it has started working with content owners (known as YouTube partners) whose copyrighted videos and channels have pulled massive audience. By uploading high-quality premium videos, the partners have essentially changed the user-generated content feature of YouTube and further increased YouTube's popularity. Understanding the latest YouTube access pattern is thus crucial to both YouTube and its partners, as well as to other providers of relevant services. In this paper, we for the first time analyze a large-scale YouTube dataset from a partner's view. We make effective use of Insight, a new analytics service of YouTube that offers inside statistics for partners about their content accesses and audience behaviours. From the raw Insight data that are confined to simple scalars and charts, we reveal the inherent relationship among the various metrics that affect the popularity of the videos. Our findings facilitate YouTube partners to adapt their content deployment and user engagement strategies, having great potentials for them to collaborate with YouTube to generate more views and subsequently increasing their revenues.

With the rapidly development of mobile networking and end-terminals, anytime and anywhere data access become readily available nowadays. Given the crowdsourced content capturing and sharing, the preferred length becomes shorter and shorter, even for such multimedia content as video. A representative is Twitter's Vine service, which, available exclusively to mobile users, enables them to create ultra-short video clips, and instantly post and share them with their followers. In this paper, we present an initial study on this new generation of instant video clip sharing service over mobile platforms, taking Vine as a case. We closely investigate the architecture of Vine, and reveal how its service is empowered with a combination of advanced mobile and cloud computing platforms. Through a dataset of over 50, 000 video clips and over 1, 000, 000 user profiles, which is available online for academic use, we examine the unique viewing behaviors of Vine uses, particularly batch viewing and passive viewing. We further analyze the video lifetime and propagation patterns in this new service, as well as the distinct social relations therein. Our study lead to critical observations that would help with improving the energy-efficiency and scalability of Vine-like services.

Recent years have witnessed video-on-demand (VoD) as an efficient means for providing reliable streaming service for Internet users. It is known that peer-assisted VoD systems, such as NetFlix and PPlive, generally incur a lower deployment cost in terms of server bandwidth consumption. However, some fundamental issues still need to be further clarified, particularly for VoD service providers. In particular, how far can we push peer-assisted VoD forward, and at the scale of VoD systems, the maximum reduction of server bandwidth consumption that can be achieved with peer-assisted approaches. In this paper, we provide extensive model analysis to understand the minimum server bandwidth consumption for peer-assisted VoD systems. We first propose a basic model that can optimally schedule user demands at given snapshots. Our model analysis reveals the optimal performance bound and shows that the existing peer-assisted protocols are still far from being optimal. How to push the server bandwidth consumption to the limit remains a big challenge in VoD system design. To approach the optimal bandwidth consumption in real deployment, we further extend our model to a realistic case to capture the peer dynamic across continuous time-slots. The simulation result indicates that the optimal load scheduling problem is still achievable through a dynamic programming algorithm. Its design principle further motivates a fast priority-based algorithm that achieves near-optimal performance. These proposed algorithms can significantly reduce the bandwidth consumption of dedicated VoD servers.

Modern online social networking has drastically changed the information distribution landscape. Recently, video has become one of the most important types of objects spreading among social networking service users. The sheer and ever-increasing data volume, the broader coverage, and the longer access durations of video objects, however, present significantly more challenges than other types of objects. This article takes an initial step toward understanding the unique characteristics of video sharing propagation in social networks. Based on realworld data traces from a large-scale online social network, we examine the user behavior from diverse aspects and identify different types of users involved in video propagation. We closely investigate the temporal distribution during propagation as well as the typical propagation structures, revealing more details beyond stationary coverage. We further extend the conventional epidemic models to accommodate diverse types of users and their probabilistic viewing and sharing behaviors. The model, effectively capturing the essentials of the propagation process, serves as a valuable basis for such applications as workload synthesis, traffic prediction, and resource provision of video servers.

In this paper, we present a simultaneous planning of Base Stations (BSs) and Relay Stations (RSs) with link flow for a broadband wireless network. Infrastructure costs (BS cost, RS cost and their operational costs) of a wireless network is a key factor for network service providers while planning a network. The objective of this problem is to determine a set of BSs and RSs that can serve all users and fulfill their demands at the lowest cost. This problem settings is equally important for planning networks from scratch or enhancements in existing networks. This combinatorial optimization problem is NP-hard in nature. Evolutionary Algorithms (EAs) are intelligent tools that can provide high quality solution to this type of problems. Usually, efficiency of EAs depends on the problem. The aim is to find effective EAs with minimum resources such as low computational complexity, processing time and number of fitness functions evaluations. We formulate this problem as a non-linear discrete optimization and introduce four recent EAs that are motivated by natural intelligent behaviors. The objective function of this planning problem is computationally costly, and there exist a tradeoff between resources and quality of solution. These algorithms include Biogeography-based Optimization (BBO) that is inspired by the natural migration phenomenon of species between different islands, Artificial Bee Colony (ABC) based on the intelligent behavior of honey bee swarms, Quantum-inspired Evolutionary Algorithm (QEA) from the idea of quantum computing, and Immune Quantum Evolutionary Algorithm (IQEA) motivated by both the immune theory and quantum computing. Simulation results demonstrate insights of EAs' and present tradeoff between resources and quality of solutions.

Popularity prediction, with both technological and economic importance, has been extensively studied for conventional video sharing sites (VSSes), where the videos are mainly found via searching, browsing, or related links. Recent statistics however suggest that online social network (OSN) users regularly share video contents from VSSes, which has contributed to a significant portion of the accesses; yet the popularity prediction in this new context remains largely unexplored. In this paper, we present an initial study on the popularity prediction of videos propagated in OSNs along friendship links.

Cloud computing has recently garnered significant interests from both industries and academia. The industrial pioneers such as Enomaly therefore offered commercial platforms which enable customer-provided resources for cloud computing. Such systems provide very flexible service especially to the customers who seek to run short-term and customized tasks at minimum costs. In this paper, we investigate the service availability challenges on the customer-provided clouds. We find that their cloud resources are highly heterogeneous and dynamic, the service availability remains a critical problem in such systems. This introduces a severe bottleneck to provide reliable cloud service to support long-term tasks. To mitigate such a problem, we present an optimal resource provisioning algorithm that ensures service availability with minimized lease costs. The trace-based simulation further demonstrates its reliability as a promising complement to the datacenter-based cloud services.

The social networking services (SNS) have drastically changed the information distribution landscape and people's daily life. With the development in broadband accesses, video has become one of the most important types of objects spreading among social networking service users, yet presents more significant challenges than other types of objects, not only to the SNS management, but also to the network traffic engineering. In this paper, we take an important step towards understanding the characteristics of video sharing propagation in SNS, based on the real viewing event traces from a popular SNS in China. We further extend the epidemic models to accommodate the diversity of the propagation, and our model effectively captures the propagation process of video sharing in SNS.

The deep penetration of Online Social Networks (OSNs) have made them major portals for video content sharing. It is known that a significant portion of the accesses to video sharing sites are now coming from OSN users. Yet the unique features of video sharing over OSNs and their impact remain largely unknown. In this paper, we present a measurement study towards understanding the video requests from OSNs. We closely collaborated with a large-scale Facebook-like OSN to analyze its user access logs spanning over four months. Our measurement reveals a number of distinctive features on the popularity distribution of videos shared over the OSN. In particular, we observe that the OSN amplifies the skewness of video popularity so largely that about 2% most popular videos account for 90% of total views; the video requests distribution also exhibits perfect powerlaw feature; video popularity evolution shows more dynamics. All these noticeably differ from that of conventional videos, such as YouTube videos. To further understand the characteristics, we model the video viewing and sharing behaviors in OSNs, leading to the development of a practical emulator. It reveals the gap between the sharing rate and the viewing rate, and generates user requests that well capture the video popularity distribution and dynamics as observed in our empirical data.

The paradigm of cloud computing has spontaneously prompted a wide interest in market-based resource allocation mechanisms by which a cloud provider aims at efficiently allocating cloud resources among potential users. Among these mechanisms, auction-style pricing policies, as they can effectively reflect the underlying trends in demand and supply for the computing resources, have attracted a research interest recently. This paper conducts the first work on a framework for truthful online cloud auctions where users with heterogeneous demands could come and leave on the fly. Our framework desirably supports a variety of design requirements, including (1) dynamic design for timely reflecting fluctuation of supply-demand relations, (2) joint design for supporting the heterogeneous user demands, and (3) truthful design for discouraging bidders from cheating behaviors. Concretely speaking, we first design a novel bidding language, wherein users' heterogeneous demands are generalized to regulated and consistent forms. Besides, building on top of our bidding language we propose COCA, an incentive-Compatible (truthful) Online Cloud Auction mechanism based on two proposed guidelines. Our theoretical analysis shows that the worst-case performance of COCA can be well-bounded. Further, in simulations the performance of COCA is seen to be comparable to the well-known off-line Vickrey-Clarke-Groves (VCG) mechanism [11].

Today's peer-to-peer (P2P) Video-on-Demand (VoD) systems are known to be highly scalable in a steady state. For the dynamic scenario, much effort has been spent on accommodating sharply increasing requests (known as flash crowd) with effective solutions being developed. The high popularity upon a flash crowd however does not necessarily last long, and indeed often drops very fast after the peak. Compared to growth, a decay is seemingly less challenging or even beneficial given the less user demands. While this is true in a conventional client/server system, we find that it is not the case for peer-to-peer. A quick decay can easily de-stabilize an established overlay, and the resultant smaller overlay is generally less effective for content sharing. The replication of data segments, which is critical during flash crowd, will not promptly respond to a fast and globalized population decay, either. Many of the replicas can become redundant and, even worse, their spaces cannot be utilized for an extended period. In this paper, we seek to understand the impact of such decays and the key influential factors. To this end, we develop a mathematical model to trace the evolution of peer upload and replication during population churns, specifically during decays. Our model captures peer behaviors with common data replication and scheduling strategies in state-of-the-art peer-to-peer VoD systems. It quantitatively reveals the root causes toward escalating server load during a population decay. The model also facilitates the design of a flexible server provision to serve highly time-varying demands.

Recent advances in cloud-based big data analysis offers a convenient mean for providing an elastic and cost-efficient exploration of voluminous data sets. Following such a trend, industry leaders as Amazon, Google and IBM deploy various of big data systems on their cloud platforms, aiming to occupy the huge market around the globe. While these cloud systems greatly facilitate the implementation of big data analysis, their real-world applicability remains largely unclear. In this paper, we take the first steps towards a better understanding of the big data system on the cloud platforms. Using the typical MapReduce framework as a case study, we find that its pipeline-based design intergrades the computational-intensive operations (such as mapping/reducing) together with the I/O-intensive operations (such as shuffling). Such computational-intensive and I/O-intensive operations will seriously affect the performance of each other and largely reduces the system efficiency especially on the low-end virtual machines (VMs). To make the matter worse, our measurement also indicates that more than 90 % of the task-lifetime is in the shadow of such interference. This unavoidably reduces the applicability of cloud-based big data processing and makes the overall performance hard to predict. To address this problem, we re-model the resource provisioning problem in the cloud-based big data systems and present an interference-aware solution that smartly allocates the MapReduce jobs to different VMs. Our evaluation result shows that our new model can accurately predict the job completion time across different configurations and significantly improve the user experience for this new generation of data processing service.

In this paper, we present simultaneous planning technique for Base Stations (BSs) and Relay Stations (RSs) for a broadband wireless network while taking data flow also known as link flow into consideration. Infrastructure cost (BS cost, RS cost and their operational costs) of a wireless network proves to be a key factor for network service providers while planning a network. The objective of this study is to help determine the set of BSs and RSs that can serve all the subscribed users and fulfill their demands at the lowest cost to the utility firm. This problem setup can be used for laying new networks as well as enhancing the already existing ones. The combinatorial optimization problem at hand is NP-hard in nature. We formulate this problem as a non-linear discrete optimization problem and compare two recent Evolutionary Algorithms (EAs) in providing approximate solution to this problem. The Quantum Inspired Evolutionary Algorithm (QEA) is a probabilistic algorithm based on quantum computing with the concept of qubits and superposition of states. The Immune theory based Immune Quantum Evolutionary Algorithm (IQEA) adopts immune operator to raise the fitness and prevent deterioration during the evolutionary process. Simulation results show better performance of IQEA as compared to QEA.

Virtual MIMO (Multiple Input Multiple Output) groups multiple single-antenna mobile devices to form an antenna array, offering higher degrees of freedom and improved spatial diversity gain as a real MIMO does, yet with much lower costs. In this paper, we focus on the user grouping problem in uplink transmission from multiple single-antenna users to one multiple-antenna base station. State-of-the-art solutions mostly target two single-antenna users, solving a pairing problem. Having more than two uplink users in a grouping has yet to be addressed. Intuitively, a higher number of users in a VMIMO group offers better spectrum efficiency, and thus more throughput gains could be expected; the group dynamics however becomes higher too, making fairness harder to be achieved with reasonable computation overhead. To address these challenges, we present a novel solution that decomposes the VMIMO user grouping into two steps. We lighten the computations in user grouping by using instantaneous signal to noise ratio (SNR) as selection criteria, and combining it with proportional fairness for larger groups of users. Lightweight computation in using instantaneous SNR in our solution allows faster grouping and feasible scheduling for a large number of users, as well as fast decision on the efficiency of the number of users in each group. We have evaluated our solution under different network configurations, and the results demonstrate that it achieves much higher data throughput as compared to existing solutions and also well preserves fairness.

Peer-to-peer file sharing systems, most notably BitTorrent (BT), have achieved tremendous success among Internet users. Recent studies suggest that long-term relationships among BT peers could be explored for peer cooperation, so as to achieve better sharing efficiency. However, whether such long-term relationships exist remain unknown. From an 80-day trace of 100,000 real world swarms, we find that less than 5% peers can meet each other again throughout the whole period, which largely invalidates the fundamental assumption of these peer cooperation protocols. Yet the recent emergence of online social network applications sheds new light on this problem. In particular, a number of BT swarms are now triggered by Twitter, reflecting a new trend for initializing sharing among communities. In this paper, we for the first time examine the challenges and potentials of accelerating peer-to-peer file sharing with Twitter social networks. We show that the peers in such swarms have stronger temporal locality, thus offering great opportunity for improving their degree of sharing. Based on the Hadamard Transform of peers' online behaviors, we develop a social index to quickly locate peers of common patterns. We further demonstrate a practical cooperation protocol that identifies and utilizes the social relations with the index. Our PlanetLab experiments indicate that the incorporation of social relations remarkably accelerates the downloading time. The improvement remains noticeable even in a hybrid system with a small set of socially active peers only.

Established in 2005, YouTube has become the most successful Internet website providing a new generation of short video sharing service. Today, YouTube alone consumes as much bandwidth as did the entire Internet in year 2000 . Understanding the features of YouTube and similar video sharing sites is thus crucial to their sustainable development and to network traffic engineering. In this paper, using traces crawled in a 1.5-year span (from February 2007 to September 2008), we present an in-depth and systematic measurement study on the characteristics of YouTube videos. We find that YouTube videos have noticeably different statistics compared to traditional streaming videos, ranging from length, access pattern, to their active life span. The series of datasets also allow us to identify the growth trend of this fast evolving Internet site, which has seldom been explored before. We also look closely at the social networking aspect of YouTube, as this is a key driving force toward its success. In particular, we find that the links to related videos generated by uploaders' choices form a small-world network. This suggests that the videos have strong correlations with each other, and creates opportunities for developing novel caching and peer-to-peer distribution schemes to efficiently deliver videos to end users.

Peer-to-peer file sharing systems, most notably BitTorrent (BT), have achieved tremendous success among Internet users. Recent studies suggest that the long-term relationships among BT peers can be explored to enhance the downloading performance; for example, for re-sharing previously downloaded contents or for effectively collaborating among the peers. However, whether such relationships do exist in real world remains unclear. In this paper, we take a first step towards the real-world applicability of peers' long-term relationship through a measurement based study. We find that 95% peers cannot even meet each other again in the BT networks; therefore, most peers can hardly be organized for further cooperation. This result contradicts to the conventional understanding based on the observed daily arrival pattern in peer-to-peer networks. To better understand this, we revisit the arrival of BT peers as well as their long-range dependence. We find that the peers' arrival patterns are highly diverse; only a limited number of stable peers have clear self-similar and periodic daily arrivals patterns. The arrivals of most peers are, however, quite random with little evidence of long-range dependence. To better utilize these stable peers, we start to explore peers' long-term relationships in specific swarms instead of conventional BT networks. Fortunately, we find that the peers in Twitter-initialized torrents have stronger temporal locality, thus offering great opportunity for improving their degree of sharing. Our PlanetLab experiments further indicate that the incorporation of social relations remarkably accelerates the download completion time. The improvement remains noticeable even in a hybrid system with a small set of social friends only.

For over two decades, video streaming over the Internet has received a substantial amount of attention from both academia and industry. Starting from the design of transport protocols for streaming video, research interests have later shifted to the peer-to-peer paradigm of designing streaming protocols at the application layer. More recent research has focused on building more practical and scalable systems, using Dynamic Adaptive Streaming over HTTP. In this article, we provide a retrospective view of the research results over the past two decades, with a focus on peer-to-peer streaming protocols and the effects of cloud computing and social media.

Online social networks have reshaped how multimedia contents are generated, distributed, and consumed on today's Internet. Given the massive number of user-generated contents shared in online social networks, users are moving to directly access these contents in their preferred social network services. It is intriguing to study the service provision of social contents for global users with satisfactory quality of experience. In this article, we conduct large-scale measurement of a real-world online social network system to study the social content propagation. We have observed important propagation patterns, including social locality, geographical locality, and temporal locality. Motivated by the measurement insights, we propose a propagation-based social-aware delivery framework using a hybrid edge-cloud and peer-assisted architecture. We also design replication strategies for the architecture based on three propagation predictors designed by jointly considering user, content, and context information. In particular, we design a propagation region predictor and a global audience predictor to guide how the edge-cloud servers backup the contents, and a local audience predictor to guide how peers cache the contents for their friends. Our trace-driven experiments further demonstrate the effectiveness and superiority of our design.

Providing field coverage is a key task in many sensor network applications. With unevenly distributed static sensors, quality coverage with acceptable network lifetime is often difficult to achieve. Fortunately, recent advances on embedded and robotic systems make mobile sensors possible, and we suggest that a small set of mobile sensors can be leveraged toward a cost-effective solution for field coverage. There are, however, a series of fundamental questions to be answered in such a hybrid network of static and mobile sensors: (1) Given the expected coverage quality and system lifetime, how many mobile sensors should be deployed? (2) What are the necessary coverage contributions from each type of sensors? (3) What working and moving patterns should the sensors adopt to achieve the desired coverage contributions? In this article, we offer an analytical study on these problems, and the results lead to a practical system design. Specifically, we present an optimal algorithm for calculating the contributions from different types of sensors, which fully exploits the potentials of the mobile sensors and maximizes the network lifetime. We then present a random walk model for the mobile sensors. The model is distributed with very low control overhead. Its parameters can be fine-tuned to match the moving capability of different mobile sensors and the demands from a broad spectrum of applications. A node collaboration scheme is then introduced to further enhance the system performance. We demonstrate through analysis and simulation that, in our mobile assisted design, a small set of mobile sensors can effectively address the uneven distribution of the static sensors and significantly improve the coverage quality.

ABSTRACT Network coding (NC) has showed to be beneficial to improve transmission performance in wireless mesh networks. Random linear coding is usually applied as the default coding schema. However, random linear coding causes significant decoding delay and jitter at receiver. Further, current NC does not support weight assignment to original packets, which is however indispensable for popular applications such as quality of service control and multipath media streaming in wireless mesh networks. Partial network coding (PNC) can largely reduce decoding delay and receiving fluctuation while keeping the benefit of NC. However, PNC does not support weight‐based data replacement and weight assignment to original packets. In this work, we propose weighted partial network coding (WPNC), which is a generalized coding schema of PNC. WPNC inherits all merits of PNC and part of NC. With WPNC, both decoding delay and receiving fluctuation will be reduced as observed in PNC. Also, WPNC is quite suitable for those applications that require weight assignment to original packets. After providing the whole framework of WPNC and thorough theoretical analysis to its performance, we have demonstrated how WPNC can be integrated with quality of service control and multipath routing supported media streaming in wireless mesh networks. Performance of WPNC is inter‐validated by both theoretical analysis and numeric evaluations. Copyright © 2011 John; Wiley & Sons, Ltd.

Massive content delivery in metropolitan networks has recently gained much attention with the successful deployment of commercial systems and an increasing user popularity. With an enormous volume of content available in the network, as well as the growing size of content owing to the popularity of high-definition video, the exploration of capacity in the caching network becomes a critical issue in providing guaranteed service. Yet, collaboration strategies among cache servers in emerging scenarios, such as IPTV services, are still not well understood so far. In this paper, we propose an efficient collaborative caching mechanism based on the topology derived from a real-world IPTV system, with a particular focus on exploring the capacity of the existing system infrastructure. We observe that collaboration among servers is largely affected by the topology characteristics and heterogeneous capacities of the network. Meanwhile, dynamic request routing within the caching network is strongly coupled with content placement decisions when designing the mechanism. Our proposed mechanism is implemented in a distributed manner, and is amenable to practical deployment. Our simulation results demonstrate the effectiveness of our proposed mechanism, as compared to conventional cache cooperation with static routing schemes.

Many mobile sensing applications consider users reporting and accessing sensing data through the Internet. However, WiFi and 3G connectivities are not always available in remote areas. Existing data dissemination schemes for opportunistic networks are not sufficient for sensing applications as sensing context has not been explored. In this work, we present a novel context-aware sensing data dissemination framework for mobile users in a remote sensing field. It maximizes information utility by considering such sensing context as sensing type, locality, time-to-live, mobility and user interests. Different from existing works, the mobile users not only collect sensing data, but also upload data to sensors for information sharing. We develop a context-aware deployment algorithm and a hybrid data exchange mechanism for generic sensors and mobile users. We evaluate our solution by both analysis and simulations, and show that it can provide high information utility for mobile users at low communication overhead.

Live media streaming has become one of the most popular applications over the Internet. We have witnessed the successful deployment of commercial systems with CDN- or peer-to-peer based engines. While each being effective in certain aspects, having an all-round scalable, reliable, responsive and cost-effective solution remains an illusive goal. Moreover, today's live streaming services have become highly globalized, with subscribers from all over the world. Such a globalization makes user behaviors and demands even more diverse and dynamic, further challenging state-of-the-art system designs. The emergence of cloud computing however sheds new lights into this dilemma. Leveraging the elastic resource provisioning from cloud, we present CALMS (Cloud-Assisted Live Media Streaming), a generic framework that facilitates a migration to the cloud. CALMS adaptively leases and adjusts cloud server resources in a fine granularity to accommodate temporal and spatial dynamics of demands from live streaming users. We present optimal solutions to deal with cloud servers with diverse capacities and lease prices, as well as the potential latencies in initiating and terminating leases in real world cloud platforms. Our solution well accommodates location heterogeneity, mitigating the impact from user globalization. It also enables seamless migration for existing streaming systems, e.g., peer-to-peer, and fully explores their potentials. Simulations with data traces from both cloud service provider (Amazon EC2) and live media streaming service provider (PPTV) demonstrate that CALMS effectively mitigates the overall system deployment costs and yet provides users with satisfactory streaming latency and rate.

Wireless sensor networks have been widely suggested to be used in Cyber-Physical Systems for Structural Health Monitoring. However, for nowadays high-rise structures (e.g., the Guangzhou New TV Tower, peaking at 600m above ground), the extensive vertical dimension creates enormous challenges toward sensor data collection, beyond those addressed in state-of-the-art mote-like systems. One example is the data transmission from the sensor nodes to the base station. Given the long span of the civil structures, neither a strategy of long-range one-hop data transmission nor short-range hop-by-hop communication is cost-efficient. In this paper, we propose EleSense, a novel high-rise structure monitoring framework that uses elevators to assist data collection. In EleSense, an elevator is attached with the base station and collects data when it moves to serve passengers; as such, the communication distance can be effectively reduced. To maximize the benefit, we formulate the problem as a cross-layer optimization problem and propose a centralized algorithm to solve it optimally. We further propose a distributed implementation to accommodate the hardware capability of sensor nodes and address other practical issues. Through extensive simulations, we show that EleSense has achieved a significant throughput gain over the case without elevators and a straightforward 802.11 MAC scheme without the cross-layer optimization. Moreover, EleSense can greatly reduce the communication costs while maintaining good fairness and reliability. We also conduct a case study with real experiments and data sets on the Guangzhou New TV Tower, which further validates the effectiveness of our EleSense.

Peer-to-peer file sharing systems, most notably Bit-Torrent (BT), have achieved tremendous success among Internet users. Recent studies suggest that long-term relationships among BT peers could be explored for peer cooperation, so as to achieve better sharing efficiency. However, whether such long-term relationships exist remain unknown. From an 80-day trace of 100, 000 real world swarms, we find that less than 5% peers can meet each other again throughout the whole period, which largely invalidates the fundamental assumption of these peer cooperation protocols. Yet the recent emergence of online social network applications sheds new light on this problem. In particular, a number of BT swarms are now triggered by Twitter, reflecting a new trend for initializing sharing among communities. In this paper, we for the first time examine the challenges and potentials of accelerating peer-to-peer file sharing with Twitter social networks. We show that the peers in such swarms have stronger temporal locality, thus offering great opportunity for improving their degree of sharing. We further demonstrate a practical cooperation protocol that utilizes the social relations. Our PlanetLab experiments indicate that the incorporation of social relations remarkably accelerates the downloading time.

Recent years have witnessed cloud computing as an efficient means for providing resources as a form of utility. Driven by the strong demands, such industrial leaders as Amazon, Google, and Microsoft have all offered practical cloud platforms, mostly datacenter-based. These platforms are known to be powerful and cost-effective. Yet, as the cloud customers are pure consumers, their local resources, though abundant, have been largely ignored. In this paper, we for the first time investigate a novel customer-provided cloud platform, SpotCloud, through extensive measurements. Complementing data centers, SpotCloud enables customers to contribute/sell their private resources to collectively offer cloud services. We find that, although the capacity as well as the availability of this platform is not yet comparable to enterprise datacenters, SpotCloud can provide very flexible services to customers in terms of both performance and pricing. It is friendly to the customers who often seek to run short-term and customized tasks at minimum costs. However, different from the standardized enterprise instances, SpotCloud instances are highly diverse, which greatly increase the difficulty of instance selection. To solve this problem, we propose an instance recommendation mechanism for cloud service providers to recommend short-listed instances to the customers. Our model analysis and the real-world experiments show that it can help the customers to find the best trade off between benefit and cost.

Recent statistics suggest that online social network (OSN) users regularly share video contents from video sharing sites (VSSes), and a significant amount of views of VSSes are indeed from OSN users nowadays. By crawling and comparing the statistics of same videos shared in both RenRen (the largest Facebook-like OSN in China) and Youku (the largest Youtube-like VSS in China), we find that the huge and distinguished video requests from OSNs have substantially changed the workload of VSSes. In particular, OSNs amplify the skewness of video popularity so largely that about 0.31% most popular videos account for 80% of total views. Another interesting phenomenon is that many popular videos in VSSes may not receive many requests in OSNs. To further understand these findings, we track the propagation process of videos shared in RenRen since their introduction to this OSN, and analyze the effect of potential parameters to such process, including the number of initiators (users who bring the video to the OSN directly from a VSS), branching factor (the number of users who watch the friend's shared video), and share rate (the probability that the viewers of a video will further share this video). Beyond our expectation, none of these factors determine a video's popularity in an OSN. Instead, it shows great randomness for the number of a video's potential requests when it is shared to an OSN. By modifying the basic Galton-Watson stochastic branching process, we develop a simple yet effective model to simulate the video propagation process in an OSN. Simulation results show that it can well capture the randomness of a video's popularity and the skewed video popularity distribution.

Virtualization, which allows multiple Virtual Machines (VMs) to reside on a single physical machine, has become an indispensable technology for today's IT infrastructure. It is known that the overhead for virtualization affects system performance; yet it remains largely unknown whether VMs are more vulnerable to networked Denial of Service (DoS) attacks than conventional physical machines. A clear understanding here is obviously critical to such networked virtualization system as cloud computing platforms. In this paper, we present an initial study on the performance of modern virtualization solutions under DoS attacks. We experiment with the full spectrum of modern virtualization techniques, from paravirtualization, hardware virtualization, to container virtualization, with a comprehensive set of benchmarks. Our results reveal severe vulnerability of modern virtualization: even with relatively light attacks, the file system and memory access performance of VMs degrades at a much higher rate than their non-virtualized counterparts, and this is particularly true for hypervisor-based solutions. We further examine the root causes, with the goal of enhancing the robustness and security of these virtualization systems. Inspired by the findings, we implement a practical modification to the VirtIO drivers in the Linux KVM package, which effectively mitigates the overhead of a DoS attack by up to 40%.

Powered by cloud computing, Dropbox not only provides reliable file storage but also enables effective file synchronization and user collaboration. This new generation of service, beyond conventional client/server or peer-to-peer file hosting with storage only, has attracted a vast number of Internet users. It is however known that the synchronization delay of Dropbox-like systems is increasing with their expansion, often beyond the accepted level for practical collaboration. In this paper, we present an initial measurement to understand the design and performance bottleneck of the proprietary Dropbox system. Our measurement identifies the cloud servers/instances utilized by Dropbox, revealing its hybrid design with both Amazon's S3 (for storage) and Amazon's EC2 (for computation). The mix of bandwidth-intensive tasks (such as content delivery) and computation-intensive tasks (such as compare hash values for the contents) in Dropbox enables seamless collaboration and file synchronization among multiple users; yet their interference, revealed in our experiments, creates a severe bottleneck that prolongs the synchronization delay with virtual machines in the cloud, which has not seen in conventional physical machines. We thus re-model the resource provisioning problem in the Dropbox-like systems and present an interference-aware solution that smartly allocates the Dropbox tasks to different cloud instances. Evaluation results show that our solution remarkably reduces the synchronization delay for this new generation of file hosting service.

Online social network has reshaped the way how video contents are generated, distributed and consumed on today's Internet. Given the massive number of videos generated and shared in online social networks, it has been popular for users to directly access video contents in their preferred social network services. It is intriguing to study the service provision of social video contents for global users with satisfactory quality-of-experience. In this paper, we conduct large-scale measurement of a real-world online social network system to study the propagation of the social video contents. We have summarized important characteristics from the video propagation patterns, including social locality, geographical locality and temporal locality. Motivated by the measurement insights, we propose a propagation-based social-aware replication framework using a hybrid edge-cloud and peer-assisted architecture, namely PSAR, to serve the social video contents. Our replication strategies in PSAR are based on the design of three propagation-based replication indices, including a geographic influence index and a content propagation index to guide how the edge-cloud servers backup the videos, and a social influence index to guide how peers cache the videos for their friends. By incorporating these replication indices into our system design, PSAR has significantly improved the replication performance and the video service quality. Our trace-driven experiments further demonstrate the effectiveness and superiority of PSAR, which improves the local download ratio in the edge-cloud replication by 30%, and the local cache hit ratio in the peer-assisted replication by 40%, against traditional approaches.

Youtube-like video sharing sites (VSSes) have gained increasing popularity in recent years. Meanwhile, Facebook-like online social networks (OSNs), have seen their tremendous success in connecting people of common interests. These two new generation of networked services are now bridged in that many users of OSNs share video contents originating from VSSes with their friends, and it has been shown that a significant portion of views of VSSes are attributed to this sharing scheme of social networks. To understand how the video sharing behavior, which is largely based on social relationship, impacts users' viewing pattern, we have conducted a long-term measurement with RenRen and YouKu, the largest online social network and the largest video sharing site in China, respectively. We show that social friends are more likely to have common interests and their sharing behaviors provide guidance to enhance recommended video lists. In this paper, we take a first step toward learning OSN video sharing patterns for VSS video recommendation. An auto-encoder model is developed to learn the social similarity of different videos in terms of their sharing in OSN. We therefore propose a similarity-based strategy to enhance recommended video lists for VSSes. Evaluation results demonstrate that this strategy can remarkably improve the precision in VSSes, as compared to state-of-the-art strategies without social information.

Interactive multi-view video enables users to enjoy the video from different viewpoints. Yet multi-view dramatically increases the video data volume and their computation, making realtime transmission and interactions a challenging task. It therefore calls for efficient view synthesis strategies that flexibly generate visual views. In this paper, we present a collaborative view synthesis strategy for online interactive multi-view video streaming based on Depth-Image Based Rendering (DIBR) view synthesis technology, which generates a visual view with the texture and depth information on both sides. Different from the traditional DIBR algorithm for single view synthesis, we explore the collaboration relationship between different viewpoints synthesis for a range of visual views generation, and propose Shift DIBR (S-DIBR). In S-DIBR, only the projected pixels, rather than all the pixels of the reference view, are utilized for next visual view generation. Therefore, the computation complexity of projection transform, which is the most computation intensive process in the traditional DIBR algorithm, is reduced to fulfill the requirement of online interactive streaming. Experiment results validate the efficiency of our collaborative view synthesis strategy, as well as the bandwidth scalability of the streaming system.

Online social networks (OSNs) have become popular destinations for connecting friends and sharing information. Recent statistics suggest that OSN users regularly share contents from video sites, and a significant amount of requests of the video sites are indeed from them nowadays. These behaviors have substantially changed the workload of online video services. To better understand this paradigm shift, we conduct a long-term and extensive measurement of video sharing in RenRen, the largest Facebook-like OSN in China. In this paper, we focus on the video popularity distribution and evolution. In particular, we find that the video popularity distribution exhibits perfect power-law feature (while videos in YouTube exhibit a power-law waist with a long truncated tail). Moreover, we observe that the requests for the new published videos generally experience two or three days latency to reach the peak value, and then change dynamically with a series of unpredictable bursts (while in YouTube, videos reach the global peak immediately after introduction to the system, and then the accesses generally decrease overtime, except possibly on some special days). These differences can raise new challenges to content providers. For example, the video popularity is now hard to predict based on their historical requests. We further develop a simple yet effective model to simulate user requests process across videos in OSNs. Trace-based simulation shows that it can well capture the observed features.

Video streaming has become one of the most popular networked applications and, with the increased bandwidth and computation power of mobile devices, anywhere and anytime streaming has become a reality. Unfortunately, it remains a challenging task to compress high-quality video in real-time in such devices given the excessive computation and energy demands of compression. On the other hand, transmitting the raw video is simply unaffordable from both energy and bandwidth perspective.

Wireless sensor networks (WSNs) have been widely deployed for environmental monitoring and urban sensing applications. With the advancement of mobile phones, mobile users have increasing demands for sensing data relevant to their locations and activities. It is crucial to support reliable ubiquitous sensing for mobile users to retrieve sensing data of their interests anytime and anywhere. In this paper, we propose a novel location-based publish/subscribe framework for mobile users to subscribe for sensing data by simply specifying the event types and target locations of their interests. The framework is developed on a location-based Distributed Hash Table (DHT) overlay formed by a network of brokers. Event data can be subscribed by mobile users and then multicast to the subscribers through efficient location-based routing. To support client mobility, we further propose a reliable protocol to handle client registration, location look-up and client relocation. It can provide reliable data delivery without causing any data loss even when the users are disconnected or moved to a new place. Extensive simulation are conducted to evaluate the message delay, communication overheads, and the support of client mobility.

BitTorrent is todays most influential peer-to-peer content distribution system. Currently BitTorrent has two very different operating models: (i) public trackers, and (ii) private trackers (a.k.a. PTs, Darknets). A PT can only be accessed by its registered users, and can provide ultrahigh downloading speed because of its effective share-ratio enforcement (SRE) incentive mechanism which stimulates the users to upload contents as much as possible. Although PTs are becoming more and more popular, they receive little attention from the research literature, possibly because they are operated underground. To understand the popularity of Darknets, the authors have traced 17 PT sites, 2 public tracker sites and 1 BitTorrent search engine for over a year. The authors investigate these PT sites from several aspects and try to understand why they are so successful in terms of attracting loyal users and providing high downloading speed. The authors then analyse the SRE mechanism and ratio free system which are commonly used by PTs. Our results unveil the reason of popularity and effectiveness of PTs. These understandings are essential to the sustainable development of future BitTorrent content distribution systems.

Recent advances in wireless communications and mobile networking have dramatically increased the popularity of multimedia services for mobile users, with wireless video streaming at their fingertips. To facilitate efficient acquisition of video content, proxy caching has been widely used by wireless service providers (WSPs), which typically deploy cache servers at mobile switching centers (MSCs). However, capacity provisioning of cache servers is challenging, given the dynamic user demands and the limited cache server resources. With increased densities of wireless service deployment, it is increasingly common that mobile users are covered by more than one WSP within an area. This brings opportunities of a collaborative caching paradigm among the cache servers deployed at different MSCs. In this paper, we explore the benefits of collaborative caching in wireless streaming services, addressing both challenges of incentives and truthfulness of selfish WSPs. We propose a collaborative mechanism that maximizes the social welfare in the context of Vickrey-Clarke-Groves (VCG) auctions, in which cache servers cooperate in the trading of their resources in a self-enforcing manner. Experimental results demonstrate that superior performance can be achieved with respect to the quality of video streaming.

The recent advent of high speed trains introduces new mobility patterns in wireless environments. The LTE-A (Long Term Evolution of 3GPP - Advanced) networks have largely tackled the Doppler effect problem in the physical layer and are able to keep wireless service with 100Mpbs throughput within a cell in speeds up to 350 km/h. Yet the much more frequent handovers across cells greatly increases the possibility of service interruptions, and the problem is prominent for multimedia communications that demand both high-throughput and continuous connections. In this paper, we present a novel LTE-based solution to support high throughput and continuous multimedia services for high speed train passengers. Our solution is based on a Cell Array that smartly organizes the cells along a railway, together with a femto cell service that aggregates traffic demands within individual train cabins. Given that the movement direction and speed of a high-speed train are generally known, our Cell Array effectively predicts the upcoming LTE cells in service, and enables a seamless handover that will not interrupt multimedia streams. To accommodate the extreme channel variations, we further propose a scheduling and resource allocation mechanism to maximize the service rate based on periodical signal quality changes. Our simulation under diverse network and railway/train configurations demonstrates that the proposed solution achieves much lower handover latency and higher data throughput, as compared to existing solutions. It also well resists to network and traffic dynamics, thus enabling uninterrupted quality multimedia services for passengers in high speed trains.

Broadcast is one of the most fundamental services in wireless sensor networks (WSNs). It facilitates sensor nodes to propagate messages across the whole network, serving a wide range of higher level operations and thus being critical to the overall network design. A distinct feature of WSNs is that many nodes alternate between active and dormant states, so as to conserve energy and extend the network lifetime. Unfortunately, the impact of such cycles has been largely ignored in existing broadcast implementations that adopt the common assumption of all nodes being active all over the time. In this paper, we revisit the broadcast problem with active/dormant cycles. We show strong evidence that conventional broadcast approaches will suffer from severe performance degradation, and, under low duty cycles, they could easily fail to cover the whole network in an acceptable time frame. To this end, we remodel the broadcast problem in this new context, seeking a balance between efficiency and latency with coverage guarantees. We demonstrate that this problem can be translated into a graph equivalence, and develop a centralized optimal solution. It provides a valuable benchmark for assessing diverse duty-cycle-aware broadcast strategies. We then extend it to an efficient and scalable distributed implementation, which relies on local information and operations only, with built-in loss compensation mechanisms. The performance of our solution is evaluated under diverse network configurations. The results suggest that our distributed solution is close to the lower bounds of both time and forwarding costs, and it well resists to the wireless loss with good scalability on the network size and density. In addition, it enables flexible control toward the quality of broadcast coverage.

The recently emerged user-generated contents (UGC) services, social networking services (SNS), as well as the pervasive wireless mobile network services have formed social media which has drastically changed the content distribution landscape. Today such UGC applications as YouTube allow any user to be a content provider, generating enormous amount of video contents that are quickly and extensively propagated on the Internet through such SNSes as Facebook and Twitter. Unfortunately, the existing UGC sites are facing critical server bottlenecks and the surges created by the social networking users would make the situation even worse. To better understand the challenges and opportunities therein, we investigate users' social behavior and personal preference of online video sharing from both real-trace measurement study on a popular social networking website and a user questionnaire survey. Our data analysis reveals an interesting coexistence of live streaming and storage sharing, and that the users are generally more interested in watching their friend's videos. It further suggests that even though the traffic is significant, most users are willing to share their resources to assist others, implying user collaboration is a rational choice in this context. In this paper, we present Coordinated Live Streaming and Storage Sharing (COOLS), a system for efficient peer-to-peer posting of user-generated videos. Through a novel ID code design that embeds nodes' locations in an overlay, COOLS leverages stable storage users and yet inherently prioritizes living streaming flows. We also present the improvement of the basic overlay design. The evaluation results show that, as compared to other state-of-the-art solutions, COOLS successfully takes advantage of the coexistence of live streaming and storage sharing, providing better scalability, robustness, and streaming quality.

The last five years have witnessed an explosion of networked video sharing, represented by YouTube, as a new killer Internet application. Their sustainable development however is severely hindered by the intrinsic limit of their client/server architecture. A shift to the peer-to-peer paradigm has been widely suggested with success already shown in live video streaming and movie-on-demand. Unfortunately, our latest measurement demonstrates that short video clips exhibit drastically different statistics, which would simply render these existing solutions suboptimal, if not entirely inapplicable. Our long-term measurement over five million YouTube videos, on the other hand, reveals interesting social networks with strong correlation among the videos, thus opening new opportunities to explore. In this article, we present NetTube, a novel peer-to-peer assisted delivering framework that explores the user interest correlation for short video sharing. We address a series of key design issues to realize the system, including a bi-layer overlay, an efficient indexing scheme, a delay-aware scheduling mechanism, and a prefetching strategy leveraging interest correlation. We evaluate NetTube through both simulations and prototype experiments, which show that it greatly reduces the server workload, improves the playback quality and scales well.

The fast-growing traffic of Peer-to-Peer (P2P) applications, most notably BitTorrent (BT), is putting unprecedented pressure to Internet Service Providers (ISPs). P2P locality has, therefore, been widely suggested to mitigate the costly inter-ISP traffic. In this paper, we for the first time examine the existence and distribution of the locality through a large-scale hybrid PlanetLab-Internet measurement. We find that even in the most popular Autonomous Systems (ASes), very few individual torrents are able to form large enough local clusters of peers, making state-of-the-art locality mechanisms for individual torrents quite inefficient. Inspired by peers' multiple torrent behavior, we develop a novel framework that traces and recovers the available contents at peers across multiple torrents, and thus effectively amplifies the possibilities of local sharing. We address the key design issues in this framework, in particular, the detection of peer migration across the torrents. We develop a smart detection mechanism with shared trackers, which achieves 45 percent success rate without any tracker-level communication overhead. We further demonstrate strong evidence that the migrations are not random, but follow certain patterns with correlations. This leads to torrent clustering, a practical enhancement that can increase the detection rate to 75 percent, thus greatly facilitating locality across multiple torrents. The simulation results indicate that our framework can successfully reduce the cross-ISP traffic and minimize the possible degradation of peers' downloading experiences.

Since user demand for a Video-on-demand (VoD) service varies with time in one-day period, provisioning self-owned servers for the peak load it must sustain a few hours per day leads to bandwidth underutilization at other times. Content clouds, e.g. Amazon Cloud Front and Azure CDN, let VoD providers pay by bytes for bandwidth resources, potentially leading to cost savings even if the unit rate to rent a machine from a cloud provider is higher than the rate to own one. In this paper, based on long-term traces from two large-scale VoD systems and temporal development model of content clouds, we tackle challenges, design and potential benefits in migrating VoD services into the hybrid cloud-assisted deployment, where the user requests are partly served by the self-owned servers and partly served by the cloud. Our measurements show that the popularity of the most popular videos decays so quickly, for example, by 11% after one hour that it poses large challenges on updating videos in the cloud. However, the trace-driven evaluations show that our proposed migration strategies (active, reactive and smart strategies), although simply based on the current information, can make the hybrid cloud-assisted VoD deployment save up to 30% bandwidth expense compared with the Clients/Server mode. They can also handle unpredicted the flash crowd traffic with little cost. It also shows that the cloud price and server bandwidth chosen play the most important roles in saving cost, while the cloud storage size and cloud content update strategy play the key roles in the user experience improvement.

Video streaming is now at the fingertips of mobile users with recent advances in wireless communications and mobile networking. Caching has been widely deployed by wireless service providers (WSPs) to facilitate video content dissemination. Yet, capacity provisioning of cache servers is challenging given dynamic user demands and limited wireless bandwidth resources available.With increased densities of wireless service deployment, it is common that mobile users are now covered by more than one WSP within a geographical region. This brings both challenges and opportunities towards a collaborative caching paradigm among cache servers that are deployed by different WSPs. This paper explores the benefits of collaborative caching for wireless video streaming services, addressing challenges related to both incentives and truthfulness of selfish WSPs. We propose a collaborative mechanism that aims to maximize the social welfare in the context of Vickrey-Clarke- Groves (VCG) auctions, which encourages cache servers to spontaneously cooperate for trading their resources in a self-enforcing manner. Results from simulations demonstrate significant performance improvements with respect to video streaming quality.

Traffic Engineering (TE) leverages information of network traffic to generate a routing scheme optimizing the traffic distribution so as to advance network performance. However, optimizing the link weights for OSPF to the offered traffic is an known NP-hard problem. In this paper, we model the optimal TE as the utility maximization of multi-commodity flows and theoretically prove that any given set of optimal routes corresponding to a particular objective function can be converted to shortest paths with respect to a set of positive link weights, which can be explicitly formulated using the optimal distribution of traffic and objective function. This can be directly configured on OSPF-based protocols. On these bases, we employ the Network Entropy Maximization (NEM) framework and develop a new OSPF-based routing protocol, SPEF, to realize a flexible way to split traffic over shortest paths in a distributed fashion. Actually, comparing to OSPF, SPEF only needs one more weight for each link and provably achieves optimal TE. Numerical experiments have been done to compare SPEF with the current version of OSPF, showing the effectiveness of SPEF in terms of link utilization and network load distribution.

Recently, popularity of multimedia content sharing among the Internet users and development of wireless mobile devices have promoted a trend of deploying Peer-to-Peer (P2P) networks over mobile ad hoc networks (MANETs) for mobile content distribution. However, due to nodes' frequent movement, limited radio transmission range, sparse distribution and power limitations, MANETs may become Delay Tolerant Network (DTN). Therefore, this wireless mobile P2P networks over DTN have to be studied. Sharing multimedia files among users is an important application in wireless mobile P2P networks. Even though various multimedia content sharing mechanism in P2P networks over MANET have been proposed in the literature. However, DTN experiences frequent and long-duration partitions, therefore, the original method does not apply to DTN scenario. In this work, we propose a Distributed Multisource Parallel Coadjutant Transmission (DMPCT) mechanism of multimedia based on P2P lookup protocol. The proposed transmission scheme enables multimedia files to be sent to the receiver fast and reliably in wireless mobile P2P networks over DTN. Simulation results demonstrate that the proposed scheme significantly improves the performance of the file delivery rate and file delivery delay compared with the existing scheme.

Recently wireless sensor networks have been widely suggested for Structural Health Monitoring. In such applications, diverse sensor nodes are deployed in a building structure, collecting ambient data such as temperature and strain from various locations and reporting them to a central base station for processing and diagnosing. For today's high-rise structures (e.g., the Guangzhou New TV Tower, a project that we have participated in, peaks at 600m above ground), the extensive vertical dimension creates enormous challenges toward sensor data collection, beyond those addressed in state-of-the-art motelike systems. For example, with a straightforward base station placement, a huge amount of data will accumulate as being relayed to the base station. As such, the sensor nodes close to the base station would quickly run out of energy for relaying the traffic. The accumulated traffic would also saturate the wireless medium, introducing significant interferences and collisions. The extensive height of these building structures, however, make elevators an indispensable component. This motivates us to develop EleSense, a novel high-rise structure monitoring framework that explores using elevators. In EleSense, an elevator is attached with the base station and collects data when it moves across different floors to serve passengers, which can effectively reduce the traffic accumulation and the collection delay. To maximally exploit the benefit, we take a unique angle with the cross-layer design. We present an abstraction of the high-rise structure monitoring problem that exploits elevators, and model it as a joint optimization across link scheduling, packet routing and end-to-end delivery. We propose a centralized algorithm to solve it optimally. We further propose a distributed implementation to accommodate the hardware capability of a sensor node and address other practical issues. We evaluate EleSense through ns-2 simulations and with real configurations from the Guangzhou New TV Tower. The results show that EleSense has a throughput gain of 30.4% to 200.6% over the case without elevators. We also observe a gain of 40.5% to 127.5% over a straightforward 802.11 MAC scheme without the cross-layer optimization. Moreover, EleSense can significantly reduce the communication costs while maintaining excellent fairness with reliable data delivering.

BitTorrent, the most popular file delivery system over the Internet, has attracted attention from network operators and researchers for its wide deployment. Recent studies suggest that long-term relationships among BT peers could be explored for peer cooperation, as to achieve better sharing efficiency. However, whether such long-term relationships exist remain unknown. In this paper, we for the first time examine the feasibility of social network based content delivery through the study of Twitter initialized/shared torrents. We show that the peers in such swarms have stronger temporal locality, thus offering great opportunity for improving their degree of sharing. Based on the Hadamard Transform of peers' online behaviors, we develop a social index to quickly locate peers of common patterns. Preliminary PlanetLab experiments indicate that the incorporation of social relations remarkably accelerates the downloading time. The improvement remains noticeable even in a hybrid system with a small set of socially active peers only.

We examine power-efficient high-quality scalable video streaming in LTE networks through its eMBMS service. We consider scalable video streaming and download services offered by eMBMS service over LTE networks. We propose an effective and practical solution to jointly optimize user experience and power consumption in both UE and eNodeB. To perform power efficient multimedia transmission in LTE networks, we face three key trade-offs: (1) maximizing energy saving vs. minimizing delay, (2) maximizing sleep time vs. minimizing lost packets, (3) maximizing quality of video vs. minimizing unnecessary video transmissions. We provide a balanced solution that addresses the trade-off by including user preference. Our simulation results indicate 5% to 18% improvement in base station power consumption and 13% to 25% improvement in UE power conservation chances. The provided solution also decreases the transmitted data in the network while preserving the user perceived quality of the video.

Location information improves the routing effectiveness and facilitates the development of diverse novel applications in mobile networking. While they can lead to better user experiences, given privacy concerns and hardware constraints, a mobile user often exposes a limited number of locations only. We are thus interested in the Trajectory Exposure Problem in this context, i.e., to what degree that the user's trajectory (i.e., its route) is exposed? Furthermore, can the user adaptively control the exposure of its trajectory and yet offer useful information for location-based services? In this paper, we explore Gaussian Process Regression, an effective tool to re-construct the trajectory of the mobile user with selected exposed locations. We examine how the re-constructed trajectory differs from the real trajectory, i.e., evaluating the exposure rate. We present an effective heuristic that adaptively controls the trajectory exposure rate by carefully choosing the exposed locations. We further demonstrate a practical routing protocol, MoRPTE, which, controlled by a single parameter, utilizes location information flexibly and adaptively in the spectrum from zero knowledge to full knowledge to fit the applications' demands.

Social networked applications have been more and more popular, and have brought great challenges to the network engineering, particularly the huge demands of bandwidth and storage for social media. The recently emerged content clouds shed light on this dilemma. Towards the migration to clouds, partitioning the social contents has drawn significant interests from the literature. Yet the existing works focus on preserving the social relationship only, while an important factor, user access pattern, is largely overlooked.

The problem of streaming packetized media has been intensively studied for a long time. In this paper, we revisit this problem in the high-speed railway context, where passengers encode and upload videos through increasingly powerful smartphones. The challenge is highlighted by the fast changing channel conditions in high-speed trains and the limited battery of cell phones. Inspired by the unique spatial-temporal characteristics of wireless signals along high-speed railways, we propose a novel energy-efficient and rate-distortion optimized approach for video streaming. Our solution effectively predicts the signal strength through its spatial-temporal periodicity in this new application scenario. It then smartly adjusts the GOF budget, schedules the video transmission to achieve graceful rate-distortion performance and yet conserves the energy consumption. Performance evaluation based on simulated railway scenarios and H.264 video traces demonstrates the effectiveness of our solution and its superiority as compared to existing solutions.

3D (or stereo) video has been a visually appealing and costly affordable technology. More sophisticated multi-view videos have also been demonstrated. Yet their remarkably increased data volume poses greater challenges to the conventional client/server streaming systems, which has already suffered from supporting 2D videos. The stringent multi-stream synchronization further complicate the system design. In this paper, we present an initial attempt toward efficient streaming of stereo/multi-view videos over a peer-to-peer network. We show that the inherent multi-stream nature of stereo video makes segment scheduling more difficult, which is particularly acute with the existence of multiple senders in a peer-to-peer overlay. We formulate the stereo segment scheduling problem as a Binary Quadratic Programming problem and optimally solve it using an MIQP solver. However, given the high peer dynamics and the stringent playback deadline in real-time streaming, the optimal solution is too costly to be obtained. Thus, we develop two efficient algorithms to allow peers frequently compute the scheduling. We show that one of the proposed algorithms can achieve an analytical guarantee in the worst case performance, in particular, the approximation factor is at most 3 comparing with the optimal solution. We implement the proposed algorithms and the optimal in a peer-to-peer simulating system, and show that the proposed algorithms can achieve near-optimal performance efficiently. We further implement two other scheduling algorithms that are used in popular peer-to-peer streaming systems for comparison, and extend our design to support multi-view video with view diversity and dynamics. Under different end-system and network configurations with both stereo and multi-view streaming, the simulation results demonstrate that our algorithms outperform others in terms of streaming quality, stream synchronization/smoothness and scalability.

Peer-to-peer file sharing systems, most notably Bit-Torrent (BT), have achieved tremendous success among Internet users. Recent studies suggest that the long-term relationships among BT peers can be explored to enhance the downloading performance; for example, the cooperation of peers to re-share old contents. However, whether such relationships can be built still remain unknown. In this paper, we take a first step towards the real-world applicability of the content re-sharing through a measurement based study. We find that 95% peers cannot even meet each other again in the BT networks; therefore, most peers can hardly be organized for further cooperation. This result is contradict to the conventional understanding based on the observed daily arrival pattern in peer-to-peer networks. To better understand this, we revisit the arrival of BT peers as well as their long-range dependence. We find that the peers' arrival patterns are highly diverse; only a limited number of peers have very clear self-similar and periodic daily arrival features (which we call them "stable peers"). The arrivals of other peers are, however, quite random with the clear absence of long-range dependence.

To alleviate the server bandwidth cost of online video sharing services, p2p delivering has been suggested as an effective tool with success already seen in accelerating individual sites. The numerous video sharing sites existed however call for a universal solution that provides transparent p2p acceleration beyond ad hoc solutions. More importantly, only a universal platform can fully explore the aggregated video and client resources across sites, particular for identical videos replicated in diverse sites. To this end, we develop PPVA, a working platform for universal and transparent P2SP (peer to server and peer) accelerating. As of May 2011, it has attracted over 190 million distinct clients, with 78 million daily transactions. We will demonstrate the novel features, implementation, and also effectiveness of PPVA.

With the popularity and advancements of smart phones, mobile users can interact with the sensing facilities and exchange information with other wireless devices in the environment by short range communications. Opportunistic exchange has recently been suggested in similar contexts; yet we show strong evidence that, in our application, opportunistic exchange would lead to insufficient data availability and extremely high communication overheads due to inadequate or excessive human contacts in the environment. In this paper, we present OppSense, a novel design to provide efficient opportunistic information exchange for mobile phone users in sensing field with data repositories that tackles the fundamental availability and overhead issues. Our design differs from conventional opportunistic information exchange in that it can provide mobile phone users guaranteed opportunities for information exchange regardless the number of users and contacts in different environments. Through both analysis and simulations, we show that the deployment of data repositories plays a key role in the overall system optimization. We demonstrate that the placement of data repositories is equivalent to a connected K-coverage problem, and an elegant heuristic solution considering the mobility of users exists. We evaluate our proposed framework and algorithm with real mobile traces. Extensive simulations demonstrate that data repositories can effectively enhance the data availability up to 41% in low contact environment and significantly reduce the communication overheads to only 28% compared to opportunistic information exchange in high contact environment.

The latest increase in mobile data usage and emergence of new applications such as Multimedia Online Gaming (MMOG), mobile TV and streaming contents have motivated advances in wireless broadband systems. Recently, the Long-Term Evolution (LTE) technology, which is based on the Universal Mobile Telecommunications System (UMTS) specifications, joins WiMAX as a competitor to achieve increasing demands of the broadband wireless access. Careful deployment of such a network is required to fulfill the high data rate demands with minimal cost of infrastructure and comprehensive coverage of the subscribers. In this paper, a multi-objective network planning problem is defined as utilizing the minimum number of infrastructure sites (i.e. Base Stations or eNode B in UMTS systems) while maximum number of users in service. We proposed a Quantum Inspired Evolutionary Algorithm (QIEA) in order to achieve optimized solution for this problem. The QIEA can be viewed as a probabilistic evolutionary algorithm and thus it is plausible to expect a reasonably good performance in solving combinatorial optimization problems. In this algorithm, each individual is represented by a string of Q-bits, where a Q-bit is the probabilistic representation inspired by the qubit concept in the quantum computing. Computational experiments show that our algorithm is fairly efficient to different scenarios of the network planning problem and performs better than the Genetic Algorithm (GA).

Application-layer multicast (ALM) has recently emerged as a promising solution for diverse group-oriented applications. Unlike dedicated routers in IP multicast, the autonomous end-hosts are generally unreliable and even selfish. A strategic host might cheat about its private information to affect protocol execution and, in turn, to improve its individual benefit. Specifically, in a link-weighted ALM protocol where the hosts measure the distances from their neighbors and accordingly construct the ALM topology, a selfish end-host can easily intercept the measurement message and exaggerate the distances to other nodes, so as to reduce the probability of being a relay. Such distance cheating, rarely happening in IP multicast, can significantly impact the efficiency and stability of the ALM topology. To defend against this kind of cheating, we present a Vickrey–Clarke–Groves (VCG)-based cheat-proof mechanism in this paper. We demonstrate a practical mapping from the utility, payment, and welfare of a VCG mechanism to the link-weighted ALM context. Based on this, we further discuss practical issues for implementing the cheat-proof mechanism—specifically, a trustworthy distributed algorithm for payment computation. Performance analyses show that the overheads of the computation, storage, and communication of our implementation are controlled at low levels, and extensive simulations further testify the implementation's effectiveness. Although there are other similar studies in this area, the contribution of our cheat-proof mechanism and its implementation primarily lies in two aspects. On one hand, we first explicitly solve the distance cheating problem in link-weighted ALM since its proposal by mapping the VCG mechanism to link-weighted ALM context. On the other hand, our distributed implementation can not only effectively defend against distance cheating, but can also avoid the potential cheating behaviors when selfish ALM nodes fulfill the cheat-proof mechanism itself.

Wireless sensor networks have been widely used for ambient data collection in diverse environments. While in many such networks the nodes are randomly deployed in massive quantity, there is a broad range of applications advocating manual deployment. A typical example is structure health monitoring, where the sensors have to be placed at critical locations to fulfill civil engineering requirements. The raw data collected by the sensors can then be forwarded to a remote base station (the sink) through a series of relay nodes. In the wireless communication context, the operation time of a battery-limited relay node depends on its traffic volume and communication range. Hence, although not bounded by the civil-engineering-like requirements, the locations of the relay nodes have to be carefully planned to achieve the maximum network lifetime. The deployment has to not only ensure connectivity between the data sources and the sink, but also accommodate the heterogeneous traffic flows from different sources and the dominating many-to-one traffic pattern. Inspired by the uniqueness of such application scenarios, in this paper, we present an in-depth study on the traffic-aware relay node deployment problem. We develop optimal solutions for the simple case of one source node, both with single and multiple traffic flows. We show however that the general form of the deployment problem is difficult, and the existing only connectivity-guaranteed solutions cannot be directly applied here. We then transform our problem into a generalized version of the Euclidean Steiner Minimum Tree problem (ESMT). Nevertheless, we face further challenges as its solution is in continuous space and may yield fractional numbers of relay nodes, where simple rounding of the solution can lead to poor performance. We thus develop algorithms for discrete relay node assignment, together with local adjustments that yield high-quality practical solutions. Our solution has been evaluated through both numerical analysis and ns-2 simulations and compared with state-of-the-art approaches. The results show that for all test cases where the continuous space optimal solution can be computed within acceptable time frames, the network lifetime achieved by our solution is very close to the upper bound of the optimal solution (the difference is less than 13.5 percent). Moreover, it achieves up to 6-14 times improvement over the existing traffic-oblivious strategies.

Traffic management is the adaptation of source rates and routing to efficiently utilize network resources. Recently, the complicated interactions between different Internet traffic management modules have been elegantly modeled by distributed primal-dual utility maximization, which sheds new light for developing effective management protocols. For single-path routing with given routes, the dual is a strictly concave network optimization problem. Unfortunately, the general form of multipath utility optimization is not strictly concave, making its solution quite unstable. Decomposition-based techniques like TRaffic-management Using Multipath Protocol (TRUMP) alleviates the instability, but their convergence is not guaranteed, nor is their optimality. They are also inflexible in differentiating the control at different links. In this paper, we address the above issues through a novel logarithm-barrier-based approach. Our approach jointly considers user utility and routing/congestion control. It translates the multipath utility maximization into a sequence of unconstrained optimization problems, with infinite logarithm barriers being deployed at the constraint boundary. We demonstrate that setting up barriers is much simpler than choosing traditional cost functions and, more importantly, it makes optimal solution achievable. We further demonstrate a distributed implementation, together with the design of a practical Logarithm Barrier-based-Multipath Protocol (LBMP). We evaluate the performance of LBMP through both numerical analysis and packet-level simulations. The results show that LBMP achieves high throughput and fast convergence over diverse representative network topologies. Such performance is comparable to TRUMP, and is often better. Moreover, LBMP is flexible in differentiating the control at different links, and its optimality and convergence are theoretically guaranteed.

BitTorrent is today's most influential peer-to-peer system. Currently BitTorrent has two very different operating models: (1) public trackers, and (2) private trackers (also known as Darknets). A private tracker can only be accessed by its registered users, and it can provide ultra high downloading speed due to its effective Share Ratio Enforcement (SRE) incentive mechanism which stimulates the users to upload contents as much as possible. Although private trackers are becoming more and more popular, they receive little attention from the research literature, possibly because they are operated underground. To understand the popularity of Darknets, we have traced 17 private tracker sites, 2 public tracker sites and 1 BitTorrent search engine for 6 months. We investigate these private tracker sites from several aspects and try to understand why they are so successful in terms of attracting loyal users and providing high downloading speed. We then analyze the SRE mechanism, credit/point system and ratio free system used by private trackers. Our results unveil the reason of popularity and effectiveness of private trackers. Furthermore, we point out the “poor downloading motivation” phenomenon caused by the imbalance between supply and demand in private trackers. These understandings are essential to the sustainable development of future BitTorrent content distribution systems.

BitTorrent is currently the most prevalent peer-to-peer file sharing system. Many researchers study and modify BitTorrent protocol in order to improve its performance. A fundamental problem is the evaluation of those newly proposed protocols. The current methods of studying peer-to-peer systems, such as analytical modeling, discrete-event simulations and deployment on real networks, often are limited in scalability, reproducibility, and accuracy. Moreover, many of them are difficult to achieve complete and accurate evaluation results under a wide range of conditions. Emulation is an effective tool to tackle these problems and it is suitable to study and evaluate the behaviors of BitTorrent-like file sharing systems. Thus, we propose a lightweight emulator, Virtual BT, which is scalable, flexible, accurate and easy to deploy. It adopts a distributed network architecture whose function modules are loose-coupled and easy to be modified in order to study BitTorrent protocol design. More than 200 virtual nodes can be executed on a contemporary personal computer by using process-level virtualization; and every virtual node exchanges data without causing any disk I/O overhead. Through experiments, Virtual BT demonstrates its effectiveness and gives accurate predictions that closely match the results observed from real network measurements.

It is well-known that the seeking operation is pervasive in interactive VoD playbacks.Efficient chunk discovery upon seeking thus becomes a critical issue in P2P VoD design. Existing studies have largely focused on uniform chunk access frequencies, which does not reflect real statistics. Also, over 80% seeking requests are of short distances, whose potentials and impacts have yet to be explored. To address the above practical challenges, we develop D-Splay, a novel structure for indexing data chunks in a P2P VoD system. D-Splay is an efficient frequency-aware indexing structure that adaptively adjusts itself to realize quick and low-cost chunk discovering. In this paper, we present the detailed design of D-Splay as well as a practical P2P VoD architecture with D-Splay. We further develop an adaptive pre-fetching policy that explores the knowledge available from the D-Splay overlay. Through extensive simulations, we demonstrate that it greatly improves the responsiveness and success rate of seeking operation, particularly for short-distance seeking.

Wireless Sensor Network (WSN) applications are typically event-driven. While the source codes of these applications may look simple, they are executed with a complicated concurrency model, which frequently introduces software bugs, in particular, transient bugs. Such buggy logics may only be triggered by some occasionally interleaved events that bear implicit dependency, but can lead to fatal system failures. Unfortunately, these deeply-hidden bugs or even their symptoms can hardly be identified by state-of-the-art debugging tools, and manual identification from massive running traces can be prohibitively expensive. In this paper, we present Sentomist (Sensor application anatomist), a novel tool for identifying potential transient bugs in WSN applications. The Sentomist design is based on a key observation that transient bugs make the behaviors of a WSN system deviate from the normal, and thus outliers (i.e., abnormal behaviors) are good indicators of potential bugs. Sentomist introduces the notion of event-handling interval to systematically anatomize the long-term execution history of an event-driven WSN system into groups of intervals. It then applies a customized outlier detection algorithm to quickly identify and rank abnormal intervals. This dramatically reduces the human efforts of inspection (otherwise, we have to manually check tremendous data samples, typically with brute force inspection) and thus greatly speeds up debugging. We have implemented Sentomist based on the concurrency model of TinyOS. We apply Sentomist to test a series of representative real-life WSN applications that contain transient bugs. These bugs, though caused by complicated interactions that can hardly be predicted during the programming stage, are successfully confined by Sentomist.

A fast priority index (PID) assignment algorithm is developed for the MGS (Medium Grain Scalability) packets in the scalable extension of the H.264/AVC. The contributions of the paper are threefold. First, we formulate the index assignment problem as the quantization of the rate-distortion (R-D) slopes of MGS packets, and use the Lloyd-Max algorithm to find the optimal solution. The slope quantization index of a packet is used as its PID. The complexity of our method is much lower than existing method. Secondly, the quantization-based PIDs facilitate the comparisons of packets from different video streams. Video multiplexing results show that the overall PSNR can be improved up to 1 dB. Finally, we propose some real-time and adaptive implementations of the proposed method, which have the same performance as the offline method.

Existing routing protocols for Wireless Mesh Networks (WMNs) are generally optimized with statistical link measures, while not addressing on the intrinsic uncertainty of wireless links. We show evidence that, with the transient link uncertainties at PHY and MAC layers, a pseudo-deterministic routing protocol that relies on average or historic statistics can hardly explore the full potentials of a multi-hop wireless mesh. We study optimal WMN routing using probing-based online anypath forwarding, with explicit consideration of transient link uncertainties. We show the underlying connection between WMN routing and the classic Canadian Traveller Problem (CTP) [1]. Inspired by a stochastic recoverable version of CTP (SRCTP), we develop a practical SRCTP-based online routing algorithm under link uncertainties. We study how dynamic next hop selection can be done with low cost, and derive a systematic selection order for minimizing transmission delay. We conduct simulation studies to verify the effectiveness of the SRCTP algorithms under diverse network configurations. In particular, compared to deterministic routing, reduction of end-to-end delay (51.15∼73.02%) and improvement on packet delivery ratio (99.76%) are observed.

Path diversity exploits multiple routes simultaneously, achieving higher aggregated bandwidth and potentially decreasing delay and packet loss. Unfortunately, for TCP, naive load splitting often results in inaccurate estimation of round trip time (RTT) and packet reordering. As a result, it can suffer from significant instability or even throughput reduction. This is particular severe in Wireless Mesh Networks (WMNs), as validated by our analysis and simulation. To make multi-path TCP viable over WMNs, we propose a novel cross-layer design with a smart traffic split scheme, namely, Path Diversified Retransmission (PDR). PDR differentiates the original data packets and the retransmitted packets, and works with a novel QoS-aware multi-path routing protocol, QAOMDV, to distribute them separately. PDR does not suffer from the RTT underestimation and extra packet reordering, which ensures stable throughput improvement over single path routing. Through extensive simulations, we further demonstrate that, as compared to state-of-the-art multi-path protocols, our PDR with QAOMDV noticeably enhances the TCP throughput and reduces bandwidth fluctuation, with no obvious impact to fairness.

Wireless sensor networks have been widely deployed to perform sensing constantly at specific locations, but their energy consumption and deployment cost are of great concern. With the popularity and advanced technologies of mobile phones, participatory urban sensing is a rising and promising field which utilizes mobile phones as mobile sensors to collect data, though it is hard to guarantee the sensing quality and availability under the dynamic behaviors and mobility of human beings. Based on the above observations, we suggest that wireless sensors and mobile phones can complement each other to perform collaborative sensing efficiently with satisfactory quality and availability. In this paper, a novel collaborative sensing paradigm which integrates and supports wireless sensors and mobile phones with different communication standards is designed. We propose a seamless integrated framework which minimizes the number of wireless sensors deployed, while providing high sensing quality and availability to satisfy the application requirements. The dynamic sensing behaviors and mobility of mobile phone participants make it extremely challenging to estimate their sensing quality and availability, so as to deploy the wireless sensors at the optimal locations to guarantee the sensing performance at a minimum cost. We introduce two mathematical models, a sensing quality evaluation model and a mobility prediction model, to predict the sensing quality and mobility of the mobile phone participants. We further propose a cost-effective sensor deployment algorithm to guarantee the required coverage probability and sensing quality for the system. Extensive simulations with real mobile traces demonstrate that the proposed paradigm can integrate wireless sensors and mobile phones seamlessly for satisfactory sensing quality and availability with minimized number of sensors.

Understanding the peer distribution over the global Internet is the key issue toward building new generation of ISP-friendly peer-to-peer systems. However, there are unfortunately significant scalability and representability challenges in measuring and understanding real-world peer distribution. In this paper, we demonstrate a novel hybrid measurement methodology that uses the PlanetLab as a distributed probing platform to interact with BitTorrent trackers and peers in the global Internet.

In this paper, we develop a novel framework that attempts to reduce network traffic for error-bounded data collection in wireless sensor networks. In many sensor applications, it is acceptable that the monitoring results evaluated based on collected data might deviate from the exact results; as long as the error is bounded by a certain threshold. One well-known technique for error-bounded data collection is data filtering, which explores temporal data correlation to suppress data updates. A concrete scheme was proposed in. The data collection is divided in rounds. A filter is installed on each node and the total filter size is constrained by the user-specified error budget. Intuitively, if the data change from the last update report is smaller than the filter size, the current update is suppressed, i.e., not to report to the base station. To adapt to system dynamics, the sizes of all filters are periodically shrunk and the left-over budget is re-allocated to the node with the highest load. Many follow up studies can be found.

Recent years have witnessed an explosion of online video sharing as a new killer Internet application. Yet, given limited network and server resources, user experience with existing video sharing sites are far from being satisfactory. To alleviate the bottleneck, peer-to-peer delivering has been suggested as an effective tool with success already seen in accelerating individual sites. The numerous video sharing sites existed however call for a universal solution that provides transparent peer-to-peer acceleration beyond ad hoc solutions. More importantly, only a universal platform can fully explore the aggregated video and client resources across sites, particular for identical videos replicated in diverse sites. To this end, we develop PPVA, a working platform for universal and transparent peer-to-peer accelerating. PPVA was first released in May 2008 and has since been constantly updated. As of January 2010, it has attracted over 50 million distinct clients, with 48 million daily transactions. In this paper, we highlight the unique challenges in implementing such a platform, and discuss the PPVA solutions. We have also constantly monitored the service of PPVA since its deployment. The mass amount of traces collected enables us to thoroughly investigate its effectiveness and potential drawbacks, and provide valuable guidelines to its future development.

User generated video sharing (e.g., YouTube) and social-networked micro-blogging (e.g., Twitter) are among the most popular Internet applications in the Web 2.0 era. It is known that these two applications are now tightly coupled, with many new videos being tweeted among Twitter users. Unfortunately, video sharing sites are facing critical server bottlenecks and the surges created by Twitter followers would make the situation even worse. To better understand the challenges and opportunities therein, we have conducted an online user survey on their personal preference and social interest of Internet video sharing. Our data analysis reveals an interesting coexistence of live streaming and storage sharing, and that the users are generally more interested in watching their friend's videos. It further suggests that the users are willing to share their resources to assist others with close relations, implying node collaboration is a rationale choice in this context.

We have recently witnessed an explosion of user-generated content (UGC) sharing, particularly video clips, as the new killer Internet application. Given the sheer amount of resource demands, the peer-to-peer (or peer-assisted) model has been suggested for this new service scenario. There are however a series of unique challenges from the UGC videos to be addressed, in particular, their significantly shorter lengths. As such, any delay, even being minor as compared to those for conventional movie-like videos, will be perceptually amplified.

BitTorrent (BT) is an extremely successful peer-to-peer (P2P) application providing efficient file sharing over the Internet. The ever-increasing traffic among the peers has also put unprecedented pressure to Internet Service Providers (ISPs). P2P locality has therefore been widely suggested, which explores finding local resources to optimize the cross-ISP/AS traffic. However, the ISPs would fail to reduce the cross-AS traffic if they could not control the neighbor selection of their P2P subscribers. In this paper, we examine the applicability of P2P locality through real-world measurement. We find that the widely deployed load balance trackers will greatly reduce the efficiency of traffic locality. Due to peers' random tracker selection, there is no grantee that the peers will always choose the modified trackers as we expected. To make the matter worse, some Internet trackers involve serious copyright violation and may hardly cooperate with the ISPs. Fortunately, our investigation of the AS-Tracker relationship indicates that if we carefully select the trackers during the locality deployment, most peers can still be controlled by the ISPs with relatively high probability. A machine learning based model is then proposed to quantify the similarity of trackers' peer distribution. Our trace-based simulation shows that, the similarity value can provide useful hints to enhance P2P locality. In particular, the peers are more likely to be optimized with higher probability. Moreover, the learning of tracker similarity does not require the global knowledge of Internet trackers, which can hardly be obtained by the individual ISPs.

We propose a cross-layer optimization framework for multi-session broadcast/multicast (BC/MC) over multimedia network. The proposed framework seeks to perform simultaneous adaptations via intelligent co-operations between satellite gateway, terrestrial gap-fillers and respective BC/MC receivers to cope with highly vibrating satellite link, which is severely constrained by propagation delay, transponder power and channel bandwidth. By jointly optimizing multiple performance criteria across protocol stacks, the scheme mitigates the adverse impacts induced by queuing dynamics, channel variations and user diversities, thereby encompassing channel-dependant and network-friendly features. Meanwhile, it well accommodates return link diversity and the imperfect feedbacks, whilst ensuring fairness, scalability and robustness. We evaluate its performance over diverse network and media configurations in comparison with the state-of-the-art approaches. Numerical results show that simultaneous performance gains can be obtained on multiple essential performance metrics.