With the rise of Web3, Non-Fungible Tokens (NFTs) have become a new class of digital assets, driving demand for large-scale NFT recommendation systems. Each NFT can be associated to a rich set of semantic, stylistic, and thematic labels, forming a highly complex label space. Similar to e-commerce platforms where detailed product labels enable personalized recommendations, such semantic dependencies between labels can potentially enhance NFT recommendation performance. Thus, NFT recommendation can be naturally formulated as an extreme multi-label (XML) classification problem. Many existing probabilistic label tree (PLT)-based approaches address XML problem by recursively partitioning the label space, which greatly alleviates the demands on expensive computer resources. Yet, the highly skewed distribution of labels in datasets in XML makes tail labels more challenging to predict than head labels. In this paper, Our preliminary analysis reveals that inherent label dependencies can be leveraged to improve tail label recommendations for NFTs. We propose ChainTail, a dependency-aware framework that enhances PLT-based NFT label partitioning and prediction re-scoring. It includes: (1) a Dependency-aware partition module that partitions highly dependent NFT labels into subsets. (2) a Dependency-aware ReScore module that re-ranks prediction scores of labels to eliminate the label-priors. Our experimental results show that ChainTail boosts tail label recommendation on widely used item recommendation datasets.

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.

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.

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.

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%.

Featured by low cost and high mobility, unmanned aerial vehicles (UAVs)-ground assisted communication has been considered as a promising solution to provide fast service recovery for rescue and emergency response with IoT devices across disaster regions. As the terrestrial infrastructures can be annihilated or partially damaged after disaster, the UAV assisted communication system has to be self-organized in highly dynamic and partially observable environment. In this article, we will explore an emergency communication system in post disaster areas with edge nodes and UAV assistance. The system is designed to provide cost effective communication with temporary infrastructures for end users in sophisticated environments. To this end, we first formulate the problem and propose optimal solutions according to observable workloads and communication channel connectivity. Moreover, considering the unknown environment scenario, we further present a cooperative learning-based solution, including hybrid design of edge agents and UAV agents, in which the environmental statistics learned by geo-distributed edge agents can also be utilized by UAV agents for UAV-based node selection and UAV hovering control. Through extensive experiments, we demonstrate the superiority of our proposed solutions in terms of service quality and energy conservation in diverse environments.

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.

Recently, using deep learning to achieve WiFi-based human activity recognition (HAR) has drawn significant attention. While capable of achieving accurate identification in a single domain (i.e., training and testing in the same consistent WiFi environment), it would become extremely tough when WiFi environments change significantly. As such, domain adversarial neural networks-based approaches have been proposed to handle such diversities across domains, yet often found to share the same limitation in practice: the imbalance between high-capacity of feature extractors and data insufficiency of source domains. This article proposes i-Sample, an intermediate sample generation-based framework, striving to tackle this issue for WiFi-based HAR. i-Sample is mainly designed as two-stage training, where four data augmentation operations are proposed to train a coarse domain-invariant feature extractor in the first stage. In the second stage, we leverage the gradients of classification error to generate intermediate samples to refine the classifiers together with original samples, making i-Sample also capable to be integrated into most domain adversarial adaptation methods without neural network modification. We have implemented a prototype system to evaluate i-Sample, which shows that i-Sample can effectively augment the performance of nowadays mainstream domain adversarial adaptation models for WiFi-based HAR, especially when source domain data is insufficient.

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, 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.

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.

Incorporating domain adaptation is a promising solution to mitigate the domain shift problem of WiFi-based human activity recognition (HAR). The state-of-the-art solutions, however, do not fully exploit all the data, only focusing either on unlabeled samples or labeled samples in the target WiFi environment. Moreover, they largely fail to carefully consider the discrepancy between the source and target WiFi environments, making the adaptation of models to the target environment with few samples become much less effective. To cope with those issues, we propose a Target-Oriented Semi-Supervised (TOSS) domain adaptation method for WiFi-based HAR that can effectively leverage both labeled and unlabeled target samples. We further design a dynamic pseudo label strategy and an uncertainty-based selection method to learn the knowledge from both source and target environments. We implement TOSS with a typical meta learning model and conduct extensive evaluations. The results show that TOSS greatly outperforms state-of-the-art methods under comprehensive 1 on 1 and multi-source one-shot domain adaptation experiments across multiple real-world scenarios.

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.

Rapid development and deployment of vehicular ad-hoc networks (VANETs) require an efficient and scalable media access control (MAC) protocol to support high-priority safety applications and infotainment requirements. This paper proposes SATMAC, a self-adaptive time division multiple access (TDMA)-based MAC protocol for VANETs. In order to improve the stability of the time slot scheduling in VANETs, a slot status updating strategy is carefully designed, which utilizes accurate information of the two-hop neighbors and the rough information of the three-hop neighbors to detect the potential packet collisions and avoids potential collisions by adjusting the occupied time slot. Besides, an adaptive frame length (the number of time slots contained in a frame) approach is proposed on the basis of the slot adjustment to support various densities of vehicles, where the frame length between neighbors can be inconsistent. We conduct theoretical analysis and extensive simulations in a realistic VANET environment to evaluate SATMAC. Simulation results show that compared with IEEE 802.11p and LTE-V2X PC5 Mode 4, SATMAC significantly improves PDR of beacons over 60%. Moreover, our SATMAC design is further implemented and validated on our FPGA-based testbed.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Urban Mobility Models (UMMs) are fundamental tools for estimating the population in urban sites and their spatial movements over time. Most existing UMMs were developed primarily in 2D. However, we argue that people’s movements and living patterns involve 3D space, i.e., buildings, which can heavily affect the accuracy of UMMs. In this article, we for the first time conduct a comprehensive study on the impacts of buildings on human movements and the effect on UMMs. We innovatively capture the impacts by developing a Semi-absorbing Urban Mobility model (SUM) and theoretically prove its properties on its difference from that of previous UMMs. We also show that calibrating our original SUM may need a large number of parameters. As such, we develop two SUM extensions with a substantially reduced number of parameters, making calibration practical. Our evaluation also demonstrates that, as a basis for supporting mobile applications in an intracity and hourly scale, the SUM is far superior to previous UMMs. In a case study, we also show that the performance of the resource allocation scheme in a cellular network substantially improves by using SUM, with a reduction in the packet loss probability of 3.19 times.

Wireless visual sensor networks (WVSNs) play a prominent role in a wide range of applications such as security, military, and environmental monitoring. As all the visual streaming data from each sensor are required to be delivered to the base station for further processing, this also differs from the data aggregation approaches often used in traditional wireless sensor networks. Therefore, the deployment of WVSNs must not only guarantee the 3D indoor area coverage and connectivity between visual sensors and the base station, but also ensure that the traffic loads are well balanced among the network in order to prolong the lifetime of the entire network. We note that the tree network topology, which is widely used in WSNs, may yield sub-optimal results, since it eliminates the opportunities that some sensors in the network may connect to multiple neighbors closer to the base station. We propose to optimize the traffic load balance by working on the full network topology, transforming it into a generalized max-flow problem and proposing an efficient solution. With this as a building block, we develop a greedy heuristic and an enhanced DFS algorithm to address traffic-aware WVSN deployment problem. Simulations demonstrate that our solutions can significantly reduce the number of sensors while achieving excellent load balance to cover the 3D indoor monitoring space.

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 recent years, a number of routing protocols have been proposed for urban vehicular ad-hoc networks (VANETs). Yet, a common issue in these protocols is that the building shadow effect at intersections is not carefully considered. As a result, the performance of these routing protocols may degrade as the data transmission at the intersections can be severely affected by the building shadowing. To solve this problem, in this paper, we propose MBR, a map-based relaying algorithm in the MAC layer, to enhance the performance of the routing protocols in urban VANETs. To improve the reliability of data transmission at intersections, MBR novelly utilizes the digital map and the Geohash coding system to relay the packets (frames) in the MAC layer, and selects the optimal relay node based on the relative position of the nodes and the intersection. In this paper, we propose two versions of the algorithm: MBR-S utilizes a single IEEE 802.11p radio; and MBR utilizes a collaboration of the IEEE 802.11p channel and the Sub-1GHz channel, which has border communication range in the intersections than that of MBR-S. Both our real world experiments and extensive simulations demonstrate that MBR-S can significantly improve the performance of the state-of-the-art VANET routing protocols, which can be further enhanced with the collaboration of two channels.

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.

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.

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.

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.

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.

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.

Wireless Video Sensor Networks (WVSNs) have been widely proposed for monitoring spatial environments. Dissimilar from traditional Wireless Sensor Networks (WSNs) where nodes such as thermal and light sensors are often considered in 2D and with omni-directional sensing ranges, the sensing range of a video sensor in WVSNs is deemed as directional and usually cognized as a rectangular pyramid shape in 3D environments. Moreover, within indoor spaces, obstacles such as ceiling lights and furniture can easily block the line-of-sight of a video sensor. These new challenges render the traditional deployment solutions for WSNs or 2D modeling environments as impractical to solve the WVSN deployment problem for 3D indoor monitoring. In this paper, we strive to tackle the WVSN deployment problem for 3D indoor monitoring with the consideration of ensuring coverage, connectivity and obstacle awareness. We first model the general problem in a continuous 3D space to minimize the total number of required video sensors. We then convert it into a discrete version problem by incorporating 3D grids, which can achieve arbitrary approximation precision by adjusting the grid granularity. We develop a series of mechanisms to handle the obstacles in the 3D environment and propose an efficient greedy heuristic algorithm yielding high quality solutions. Based on this, we also propose an enhanced Depth First Search (DFS) algorithm that can not only further improve the solution quality, but also return optimal solutions if given enough time. Our extensive simulations demonstrate the superiority of both our greedy heuristic and enhanced DFS solutions.

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.

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.

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

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.

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.

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.

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.

Presented herein is a new protocol, RaptorQP2P, for reliable peer-to-peer sharing of large files-currently on the order of hundreds of MB but expected to grow to the terabyte range-over networks. The new protocol features two levels of RaptorQ encoding. At the top layer, the entire file is RaptorQ encoded to yield a collection of source blocks and repair blocks. At the lower layer, each source/repair block is RaptorQ encoded independently to yield a collection of source symbols and repair symbols for the block. The symbols are independently transferred among the peers and when a sufficient number of distinct symbols for a block have been received, whether source or repair, the block can be reconstructed. Similarly, the file can be reconstructed using a sufficient number of arbitrary distinct blocks. With real world traces measured on the BitTorrent system, we have conducted extensive simulations to evaluate and compare the performance of RaptorQP2P and BitTorrent. The results indicate that the new protocol handles network dynamics and peer churn smoothly, has excellent scalability with respect to both file size and user population, and performs well. As an example, for a 512 MB file distributed to 1000 peers, download completion time using the new protocol was found to be approximately 45.5% of the completion time required using BitTorrent.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

A typical application of wireless sensor networks is navigation for emergency evacuation whose goal is to guide people escaping from hazardous areas safely and quickly. In practical scenarios, the evacuating time depends not only on the length of the path but also on the congestion degree. However, as far as we know, the state of art fails to quantify congestion degree accurately in evacuation process. In this paper, we propose a novel congestion-aware navigation algorithm which has several key advantages: First, we use the concept of moving speed to evaluate the congestion degree to accurately estimate the evacuating time. Second, we avoid the frequent in-situ interactions between users and the navigation system by using the sensors at intersections for displaying the escape directions. Third, our algorithm can reduce the direction oscillations due to the network communication delay and adapt to the variation of hazardous regions. We evaluate our algorithm by simulations under various realistic settings. Simulation results show that our algorithm has the shorter evacuating time and fewer oscillations than state-of-the-art work.

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.

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.

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.

Existing time synchronization algorithms and protocols mostly focus on improving the synchronization accuracy. However, they usually require frequent resynchronization to keep designed precision in actual applications, which leads to high energy consumption and heavy traffic load. This paper presents a Prediction based Long-cycle Time Synchronization algorithm (PLTS), which puts emphasis on reducing the resynchronization frequency while guaranteeing a given accuracy. PLTS is a combination of periodic synchronization and prediction synchronization. It makes use of an existing time synchronization protocol to accomplish the periodic synchronization, while during the intervals of periodic synchronization, each node applies a prediction model to calibrate its own logic time according to the crystal oscillator's frequency characteristics. By this means, all nodes can keep synchronization till next periodic synchronization starts. Experiment results show that PLTS can reduce resynchronization frequency remarkably and possesses good merits in saving energy and reducing traffic load.

There are heavy studies recently on applying wireless sensor networks for structural health monitoring. These works usually focus on the computer science aspect, and the considerations include energy consumption, network connectivity, etc. It is commonly believed that for the current resource limited wireless sensors, system design could be more efficient if the application requirements are incorporated. Nevertheless, we often find that, rather than integration, assumptions have to be made due to lack of knowledge of civil engineering; for example, to evaluate routing algorithms, the sensor placement is assumed to be random or on grids/trees. These may not be practically meaningful to the respective application demands, and make the great efforts by the computer science community on developing efficient methods from the sensor network aspect less useful. In this paper, we study the very first problem of the SHM systems: the sensor placement and focus on the civil requirements. We first study the current general framework of structure health monitoring. We redevelop the framework that includes a new sensor placement module. This module implements the most widely accepted sensor placement scheme from civil engineering but focusing on its usefulness for computer science. It provides such interfaces that can rank the placement quality of the candidate locations in a step by step manner. We then optimize system performance by considering network connectivity and data routing issues; with the objective on energy efficiency. We evaluate our scheme using the data from the structural health monitoring system on the Ting Kau Bridge, Hong Kong. We show that a uniform and a state-of-the-art placement are not very meaningful in placement quality. Our scheme achieves almost the same sensor placement quality with that of the civil engineering with five-fold improvement in system lifetime. We conduct an experiment on the in-built Guangzhou New TV Tower, China; and the results validate the effectiveness of our scheme.

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.

Recently, application-layer overlay networks have been suggested as a promising solution for live video streaming over the Internet. To organize a multicast overlay, a natural structure is a tree, which, however, is known vulnerable to end-hosts dynamics. Data-driven approaches address this problem by employing a mesh structure, which enables data exchanges among multiple neighbors, and thus, greatly improves the overlay resilience. It unfortunately suffers from an efficiency-delay trade-off, because data have to be pulled from mesh neighbors by using extra notifications periodically. In this paper, we closely examine the contributions of overlay nodes, and argue that performance of a mesh overlay closely depends on a small set of stable backbone nodes. This is validated through a real trace study on PPLive, the largest commercial application-layer live streaming system to date. Motivated by this observation, we then suggest a novel collaborative tree-mesh design that leverages both mesh and tree structures. The key idea is to identify a set of stable nodes to construct a tree-based backbone, called treebone, with most of the data being pushed over this backbone. These stable nodes, together with others, are further organized through an auxiliary mesh overlay, which facilitates the treebone to accommodate node dynamics and fully exploit the available bandwidth between overlay nodes. This hybrid design, referred to as mTreebone, brings a series of unique and critical design challenges. In particular, the identification of stable nodes and seamless data delivery using both push and pull methods. In this paper, we present optimized solutions to these problems, which reconcile the two overlays under a coherent framework with controlled overhead. We evaluate mTreebone through both simulations and PlanetLab experiments. The results demonstrate the superior efficiency and robustness of this hybrid solution in both static and dynamic scenarios.

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 timeframe. 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 network size and wireless loss increases. In addition, it enables flexible control toward the quality of broadcast coverage.

Wireless sensor networks have been widely used for ambient data collection in diverse environments. While in many such networks the sensor 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 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 it achieves up to 6 to 14 times improvement on the network lifetime over the existing traffic-oblivious strategies.

PUSH and PULL are two common data dissemination algorithms for data-centric sensor networks. The two algorithms work well with only a few sources or a few sinks, respectively; however, when there are many sources and many sinks, both of them become inefficient. In this paper, we propose a novel location-oblivious hybrid PUSH-PULL data diffusion (LOHD) algorithm, which suits a wide range of networks and source/sink settings. Different from existing hybrid approaches, LOHD does not rely on any location information; it adaptively selects an ultra-node through a well-controlled flooding and the ultra-node maintains the gradients from sources to sinks. It then incorporates enhanced PUSH and PULL to distribute messages along the gradients instead of flooding. We model and analyze the algorithms and perform extensive simulations. The results show that LOHD remarkably outperforms both PUSH and PULL, particularly when the number of sources and sinks increases. We also show that the overhead well resists to such increase, suggesting LOHD is highly scalable.

Broadcast service is widely used during the life time of a wireless sensor network (WSN), such as networking setup, data collection/storage and query answering. In the past few years, many works have been done to improve its efficiency by reducing redundant broadcast messages. However, most of these works assume that all sensor nodes are active throughout a broadcast process and thus are difficult to be deployed in low duty-cycled WSNs, where sensor nodes switch between work and sleep to save energy and extend the network's life time. This problem is further aggravated by the difficulties to achieve global synchronization and rigid work-sleep schedules as the number of sensor nodes increases. To solve this problem, this paper remodels the broadcast problem to consider low duty-cycle and shows the lower bounds for time and message costs. We then propose an adaptive algorithm for dynamic message forwarding scheduling in this context, which enables a reliable and efficient broadcast service with low delay. Also, we demonstrate by extensive simulations that the proposed algorithm is not only robust against wireless communication loss but also performs close to optimal in terms of both time and message costs.

In wireless sensor networks, filters, which suppress data update reports within predefined error bounds, effectively reduce the traffic volume for continuous data collection. All prior filter designs, however, are stationary in the sense that each filter is attached to a specific sensor node and remains stationary over its lifetime. In this paper, we propose mobile filter, a novel design that explores migration of filters to maximize overall traffic reduction. A mobile filter moves upstream along the data collection path, with its residual size being updated according to the collected data. Intuitively, this migration extracts and relays unused filters, leading to more proactive suppressing of update reports. We start by presenting an optimal filter migration algorithm for a chain topology. The algorithm is then extended to general multi-chain and tree topologies. Extensive simulations demonstrate that, for both synthetic and real data traces, the mobile filtering scheme significantly reduces data traffic and extends network lifetime against a state-of-the-art stationary filtering scheme.

In wireless sensor networks, filters, which suppress data update reports within predefined error bounds, effectively reduce the traffic volume for continuous data collection. All prior filter designs, however, are stationary in the sense that each filter is attached to a specific sensor node and remains stationary over its lifetime. In this paper, we propose mobile filter, a novel design that explores migration of filters to maximize overall traffic reduction. A mobile filter moves upstream along the data collection path, with its residual size being updated according to the collected data. Intuitively, this migration extracts and relays unused filters, leading to more proactive suppressing of update reports. While extra communications are needed to move filters, we show through probabilistic analysis that the overhead is outrun by the gain from suppressing more data updates.

This paper presents a systematic in-depth study on the existence, importance, and application of stable nodes in peer- to-peer live video streaming. Using traces from a real large-scale system as well as analytical models, we show that, while the number of stable nodes is small throughout a whole session, their longer lifespans make them constitute a significant portion in a per-snapshot view of a peer-to-peer overlay. As a result, they have substantially affected the performance of the overall system. Inspired by this, we propose a tiered overlay design, with stable nodes being organized into a tier-1 backbone for serving tier-2 nodes. It offers a highly cost-effective and deployable alternative to proxy-assisted designs. We develop a comprehensive set of algorithms for stable node identification and organization. Specifically, we present a novel structure,LabeledTree, for the tier-1 overlay, which, leveraging stable peers, simultaneously achieves low overhead and high transmission reliability. Our tiered framework flexibly accommodates diverse existing overlay structures in the second tier. Our extensive simulation results demonstrated that the customized optimization using selected stable nodes boosts the streaming quality and also effectively reduces the control overhead. This is further validated through prototype experiments over the PlanetLab network.

Application-layer overlay networks have recently emerged as a promising solution for live media multicast on the Internet. A tree is probably the most natural structure for a multicast overlay, but is vulnerable in the presence of dynamic end-hosts. Data-driven approaches form a mesh out of overlay nodes to exchange data, which greatly enhances the resilience. It however suffers from an efficiency-latency tradeoff, given that the data have to be pulled from mesh neighbors with periodical notifications. In this paper, we suggest a novel hybrid tree/mesh design that leverages both overlays. The key idea is to identify a set of stable nodes to construct a tree-based backbone, called treebone, with most of the data being pushed over this backbone. These stable nodes, together with others, are further organized through an auxiliary mesh overlay, which facilitates the treebone to accommodate node dynamics and fully exploit the available bandwidth between overlay nodes. This hybrid design, referred to as mTreebone, is braced by our real trace studies, which show strong evidence that the performance of an overlay closely depends on a small set of backbone nodes. It however poses a series of unique and critical design challenges, in particular, the identification of stable nodes and seamless data delivery using both push and pull methods. In this paper, we present optimized solutions to these problems, which reconcile the two overlays under a coherent framework with controlled overhead. We evaluate mTreebone through both simulations and PlanetLab experiments. The results demonstrate the superior efficiency and robustness of this hybrid solution.