The neural-enhanced video streaming (NeVS) has been an emerging technique to integrate neural models into video codecs for higher streaming efficiency. The state-of-the-art methods, e.g., DeNC and Gemino, typically compress videos in RGB space and restore video quality via a neural enhancement model hosted on the external media server. However, these methods are not always accessible in resource-constrained edge environments due to their heavy reliance on the media server's computation, which undermines end-to-end performance and restricts NeVS's usage boundary. This limitation raises an interesting question: is it possible to make NeVS lightweight so that all neural codec operations can be handled directly by clients' edge devices? In this paper, we present the answer yes and develop a new plug-and-play module called DeNC++, which significantly improves the compression-restoration-overhead trade-off over existing methods. Our core design philosophy is to wrap all the codec operations within a latent semantic space, in which the original high-dimensional visual signals are efficiently embedded into low-dimensional semantic representations. With this fundamental transformation, DeNC++'s neural encoder introduces the triple semantic-bitwidth-resolution compression to effectively lower the streaming traffic. Meanwhile, we make DeNC++'s neural decoder aware of the perceptual loss caused by its encoder and design tiny generative models to guarantee high restoration quality. We also strictly restrict the runtime computational overhead and accelerate the neural enhancement process, making DeNC++ compatible with commodity edge devices. Real-world evaluations reveal that DeNC++ consistently provides higher restoration quality while achieving 24-55 times higher compression ratio and 5-7 times end-to-end speedup over the latest NeVS solutions.

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

In the realm of cybersecurity, unsupervised anomaly detection models have emerged as pivotal tools for identifying novel threats in dynamic and evolving environments. However, the opaque nature of these black-box models presents a significant barrier to their adoption in high-stakes applications, where model interpretability is essential for trust and deployment. This paper presents a rule-based approach called Ano2Rule that enhances the interpretability of unsupervised anomaly detection. First, we propose the concept ofdistribution decomposition rulesthat decompose the complex distribution of normal data into multiple compositional distributions. To find such rules, we design an unsupervised Interior Clustering Tree that incorporates the model prediction into the splitting criteria. Then, we propose the Compositional Boundary Exploration (CBE) algorithm to obtain theboundary inference rulesthat estimate the decision boundary of the original model on each compositional distribution. By merging these two types of rules into a rule set, we can present the inferential process of the unsupervised black-box model in a human-understandable way, and build a surrogate rule-based model for online deployment at the same time. We validate Ano2Rule through extensive experiments on diverse real-world datasets, including network intrusion detection and IoT security, demonstrating superior fidelity and robustness compared to baseline methods. The results show that Ano2Rule achieves high fidelity with the original model's predictions while providing human-understandable insights.

Differential-privacy federated learning (DP-FL) has emerged as a promising paradigm capable of mitigating the inherent threat of traditional FL architectures that are vulnerable to inferential attacks due to the frequent exchange and updating of model parameters. However, existing DP-FL frameworks often assume that the client's perturbations remain constant throughout the FL process, while ignoring the varying influence of the client's perturbations in distinct communication rounds on the model performance. Besides, existing DP-FL frameworks posit the FL server as a fully rational actor, thereby neglecting the bounded rationality that the FL server may exhibit in the face of risk and uncertainty. In this paper, we propose a novel long-term (i.e., throughout the FL process) privacy-preserving FL framework to address the optimal incentive design, in the presence of the bounded rationality inherent in the FL server and the dynamic influence of perturbations on model performance. Specifically, we first investigate the impact of local perturbations of the client on the model's convergence performance in different communication rounds, elucidating the trade-off between learning performance and privacy loss. Then, to reconcile learning performance with privacy loss, we design a long-term privacy-preserving incentive scheme, where the interactions between clients and the FL server throughout the FL process are modeled as a multi-stage privacy-preserving game. Furthermore, by applying prospect theory (PT) to formulate the risk-aware behavior of the bounded rationality FL server, we employ contract theory to derive the equilibrium of the game, thereby ensuring optimality and fairness. Finally, extensive simulations illustrate that our scheme can motivate clients to provide high-quality models and improve the accuracy of the global model, compared with benchmarks.

Federated learning (FL) on mobile edge devices suffers from heterogeneous computation and communication capabilities, time-varying wireless channels, and strict battery constraints. Existing client selection methods typically optimize per-round performance while ignoring heterogeneous energy consumption and per-device energy budgets, which may cause premature client dropout and degraded model accuracy. This paper proposes FEPCS, a Fine-grained Energy Planning Client Selection scheme for energy-efficient FL in mobile edge environments. At the client level, FEPCS dynamically adjusts local training workloads based on residual energy and estimates transmission energy using historical channel information. At the system level, it introduces a progressive participation rate and an energy budget deviation metric, and combines these with data contribution into a unified selection criterion. Extensive simulations under heterogeneous settings show that FEPCS achieves higher test accuracy, significantly extends client survival time, and effectively reduces client dropouts compared with existing schemes, while operating under the same energy constraints.

The provision of performance-aware video conferencing services today relies on approaches that focus on data compression and client-side bitrate adaptation techniques to optimize transmission. However, these methods fail to quickly respond to fluctuations in network conditions, thereby compromising the quality of service for transmissions. For this reason, this article aims to propose a novel traffic scheduling-based video transmission optimization solution from the perspective of the network service provider. We first investigate the resource requirements of video conferences and present the experiential performance of video conferences under different network conditions and network competition. Based on these results, we design a service-customized routing mechanism called VCApather that minimizes network contention. We then provide implementation solutions for the control plane and the data plane of VCApather . We evaluate VCApather using a fully meshed topology with five nodes and real-world video conference traffic. The results show that VCApather is capable of achieving high link utilization and balance, while also meeting predefined user metrics. Compared to other schemes, VCApather could satisfy 69.8% more QoE requirements and yielded an average bitrate improvement of 1.74 \(\times\) .

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

With the advancement of graph representation learning, self-supervised graph contrastive learning (GCL) has emerged as a key technique in the field. In GCL, positive and negative samples are generated through data augmentation. While recent works have introduced model-based methods to enhance positive graph augmentations, they often overlook the importance of negative samples, relying instead on rule-based methods that can fail to capture meaningful graph patterns. To address this issue, we propose a novel model-based adversarial contrastive graph augmentation (ACGA) method that automatically generates both positive graph samples with minimal sufficient information and hard negative graph samples. Additionally, we provide a theoretical framework to analyze the process of positive and negative graph augmentation in self-supervised GCL. We evaluate our ACGA method through extensive experiments on representative benchmark datasets, and the results demonstrate that ACGA outperforms state-of-the-art baselines.

Recent years have witnessed the rise of Neural-enhanced Video Streaming (NeVS), which integrates neural restoration models into video codecs for higher compression-restoration performance. Despite its benefit, existing work has not well explored the full potential of NeVS paradigm, due to: (1) post-streaming restoration by decoder while lacking the proactive collaboration of encoder, (2) end-to-end optimization based on conventional rate-distortion theory, which has been verified that low distortion is not always a synonym for high perceptual quality, and (3) coupled design for domain-specific tasks that cannot generalize to various video codecs. Observing these limitations, our objective is not to incrementally present an improved restoration model. Instead, we focus on the encoder-decoder synergy, i.e., the codec, which is non-trivial since it inherently strikes the rate-distortion-perception trade-off of NeVS. Aiming at this target, we propose the Diffusion-enhanced Neural Codec (DeNC), a plug-and-play module for current NeVS paradigm, to significantly reduce the required bitrates while preserving high perceptual quality of restored videos. Our key design is twofold. First, DeNC improves the encoder's compression efficiency by simultaneously reducing the resolution and color bit-depth of frame referencing. Second, DeNC empowers the decoder with perception-oriented restoration capability by making its diffusion-based restoration process aware of the encoder's compression conditions. Real-world evaluations show that DeNC improves compression ratios with nearly an order of magnitude and achieves much higher restoration quality (e.g., 93+ VMAF and 23% higher MOS) over the latest baselines.

Real-time conferencing applications (RCA) are crucial for online learning and e-commerce. However, they can be affected by network fluctuations because they are heavily dependent on cloud network connections. However, there is a dearth of systematic studies that aim to pinpoint the specific network links where these fluctuations occur. We introduce a contrastive learning approach for locating anomalies, based on actual traffic from real-time conferencing applications. This method is trained on unlabeled data, which means that it does not require the creation of a large-scale training dataset. The results illustrate the robust localization ability, achieving an accuracy rate of more than 95%, demonstrating its adaptability to commonly used real-time conferencing applications.

Wireless networks are pivotal to modern communication, yet traditional address-centric protocols often fail to leverage the inherent advantages of wireless transmission potentials in dynamic edge environments. While Information-centric Networking (ICN) offers promising data-centric alternatives, the integration with wireless systems in resource-constrained edge settings faces critical challenges including channel instability, unreliable transmission, and fluctuating link quality. This paper introduces RENCA, a framework designed to enable seamless NDN-based wireless communication tailored for edge environments. RENCA addresses these challenges through three key innovations. First, it introduces a distributed collision avoidance strategy that leverages wireless broadcast properties to select high-quality communication pairs in real time, minimizing localized interference in wireless networks. Second, it incorporates a reliable transmission mechanism using Selective Negative Acknowledgement (SNAK) to ensure data integrity while aligning with content-centric principles in dynamic edge topologies. Third, it presents a dynamic Modulation Coding Scheme (MCS) adaptation that improves communication goodput in response to real-time channel fluctuations. We implement the entire RENCA protocol stack on real wireless hardware and drivers, and conduct extensive experiments over real wireless scenarios. The system achieves a 92.96% overall goodput improvement in multi-device edge environments compared to traditional protocols, demonstrating the usefulness of robust, high-performance communication for edge applications.

The rapid evolution of wireless edge computing faces fundamental limitations from connection-oriented protocols, particularly in dynamic scenarios with distributed edge environments. In this paper, we present SWICE, a novel transmission system with modified frame injection techniques that eliminates connection establishment overhead while ensuring reliable data delivery in wireless distributed edge environments. Our system introduces a connection-free measurement strategy that uses minimal packets to assess communication status while keeping device identities private. We formulate an edge node selection problem for efficiency and develop a heuristic algorithm for optimal data delivery. Additionally, we implement a reliability mechanism that combines adaptive retransmission to enhance communication stability. We conduct real-world experiments with commercially available Wi-Fi devices and modified wireless radios. The results show that SWICE achieves up to a 36.88 % increase in goodput compared to conventional transmission methods, demonstrating its effectiveness through real-world experiments.

Federated learning (FL) enables the training of a global machine learning model among multiple local clients in a collaborative fashion without directly sharing the details of their data. Due to this advantage, it has been utilized in a wide range of applications where privacy is a critical concern and has attracted great attention for graph representation learning (GRL). Despite the offered advances, there still exist two major challenges in the FL for GRL across distributed graph data, including heterogeneity and complementarity. In order to tackle these challenges, a novel personalized federated graph augmentation (PFGA) framework is proposed in this work. Unlike existing techniques, it utilizes generative models as bridges to enable information sharing among clients, thereby facilitating the collaborative training of GRL models. Instead of directly using the generative model trained on each client individually, we aggregate them into the globally generative model to gain a global view of the entire graph, which effectively alleviates the heterogeneity and complementarity issues simultaneously. We formulate the training of the generative and GRL models as a min-max adversarial learning problem and theoretically prove the convergence. Furthermore, the effectiveness of the method is demonstrated using experimental results on six real-world datasets.

Nowadays, data centers contribute to a large portion of global carbon emissions due to the growing demand for cloud services. Traditional green data center technologies have utilized all kinds of methods to reduce energy consumption, e.g., optimizing HVAC (Heating, Ventilation, and Air Conditioning), shutting down idle servers or switches and using renewable energy, but they typically target a single data center and have pushed the optimization space to its limits. However, carbon emissions vary wildly across data centers due to differences in local energy sources, and delay-tolerant tasks can be flexibly allocated to data centers with lower emissions. This opens new opportunities for carbon-aware task scheduling across geographical locations. However, carbon-aware scheduling faces key challenges, including the fluctuation of carbon intensity due to weather and power dynamics, and the dynamic nature of task arrivals with diverse QoS (Quality of Service) requirements. To address this, we propose CEDTS-RL (Carbon-Emission Driven Task Scheduling based on Reinforcement Learning), a real-time cross-geographical scheduling framework based on reinforcement learning, which unlocks new potential for reducing carbon emissions by relaxing the location restriction during scheduling. The evaluation is conducted using real-world datasets from Google, Alibaba, and Microsoft, along with solar and meteorological data from NASA. Results demonstrate that CEDTS-RL significantly outperforms both traditional and recent scheduling algorithms.

In dyadic interactions, facial reactions are crucial for conveying an individuals' responses to their conversational partners. Individuals may exhibit varied but appropriate facial reactions (AFRs) when perceiving the same behavioral expression. Although some recent methods can already respond multiple appropriate facial reactions to the given human speaker behaviors, the AFRs generated by these methods often fail to adequately preserve crucial head motions, leading to visual jitter and unnatural transitions between generated AFR segments. In this paper, we propose a novel and generic PFLPosNet framework which addresses the aforementioned problems at both pre-processing and post-processing stages, where a new pose-aware face behavior localization method PFL is introduced to retain the head pose displacement information from the source data. In addition, the framework proposes a real-time head pose adjustment method, PosNet, to ensure continuity and smoothness in the visual output of the model when using data with correct head pose displacement. Experimental results demonstrate that our approach not only generates more coherent and natural facial reaction sequences but also significantly outperforms existing online MAFRG methods in terms of continuity and smoothness. Our code is made available at https://github.com/rainforcetime/PFLPosNet.

Federated Learning (FL) has emerged as a promising training framework that enables a server to effectively train a global model by coordinating multiple devices, i.e., clients, without sharing their raw data. Keeping data locally can ensure data privacy, but also makes the server difficult to assess data quality, leading to the noisy data issue. Specifically, for any given training task, only a portion of each client's data is relevant and beneficial, while the rest may be redundant or noisy. Training with excessive noisy data can degrade performance. Motivated by this, we investigate the limitations of existing studies and develop an incentive mechanism with flexible pricing tailored for noisy data settings. The insight lies in mitigating the impact of noisy data by selecting appropriate clients and incentivizing them to clean their data spontaneously. Further, both rigorous theoretical analysis and extensive simulations compared with state-of-the-art methods have been well-conducted to validate the effectiveness of the proposed mechanism.

Federated learning (FL) in symbiotic Internet of Things (IoT) networks is a promising collaborative paradigm that utilizes IoT devices to co-train machine learning models, promising to accelerate edge intelligence for 6G. Existing studies on heterogeneous FL in IoT networks focus mainly on the differences in link capacity, ignoring the fundamental impact of channel fluctuation on model transmission and communication energy consumption. FL in symbiotic IoT networks still faces the challenges of heterogeneous and dynamic wireless links and inter-round competition of limited resource allocation, significantly impacting energy efficiency and learning performance. To address this issue, we first model wireless channel fading and dynamics for FL over symbiotic IoT networks and develop a joint optimization model for energy efficiency and learning performance. Then, we propose a novel heterogeneous-aware client sampling scheme to achieve energy-efficient training by exploiting prompt channel state tracking to predict energy consumption and update the deviation of the energy budget of each client promptly to select the optimal set of clients for each training phase. Finally, extensive experiments show that our proposed client sampling scheme significantly outperforms the existing methods and improves energy efficiency by up to$1.6\times $.

The rapid growth of the Industrial Internet of Things (IIoT) requires a secure meta-computing environment to support applications like industrial monitoring and remote control. However, this environment faces major security challenges, especially the risk of source address forgery, which can enable DDoS and botnet attacks, disrupting operations and compromising equipment. Current Internet infrastructure forwards packets based only on destination addresses, lacking the capability for source address verification. Although edge-based solutions like firewalls and systems, such as source address validation architecture (SAVA) and source address validation improvement (SAVI), are deployed, they fall short of comprehensive source address validation (SAV), allowing malicious traffic to propagate through core networks. To enhance security, a collaborative approach based on meta-computing principles is needed, allowing routers to verify source addresses cooperatively. Given the impracticality of fully upgrading routers, incremental deployment is essential. We show that optimizing incremental SAV deployment is NP-hard. To address this, we propose collaborative optimized source address filtering (COSAF), a heuristic algorithm that uses a sink-tree structure to effectively filter attack flows and optimize resource allocation. COSAF also takes SAV table capacity into account to improve resource utilization. Extensive simulations demonstrate that COSAF outperforms traditional methods.

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

Heterogeneous devices in edge computing bring challenges as well as opportunities for edge computing to utilize powerful and heterogeneous hardware for a variety of complex tasks. In this paper, we propose a service function chain placement strategy considering the heterogeneity of devices in deviceless edge computing environments. The service function chain system utilizes lightweight virtualization technologies to manage resources, considering the heterogeneity of devices to support various complex tasks, and offer low latency services to user requests. We propose an optimal service function chain placement problem minimizing the service delay and formulate it into a quasi-convex problem. We implement different edge applications that can be served by function chains and conduct extensive experiments over real heterogeneous edge devices. Results from the experiments and simulations show that our proposed service function chain scheme is applicable in edge environments, and perform well over services latency, resource utilization as well as the power consumption of edge devices.

Cloud-of-clouds storage systems are widely used in online applications, where user data are encrypted, encoded, and stored in multiple clouds. When some cloud nodes fail, the storage systems can reconstruct the lost data and store it in the substitute nodes. It is a challenge to reduce the latency of data recovery to ensure data reliability. In this paper, we adopt a Reinforcement Learning-based Data Recovery (RLDR) approach to reduce the regeneration time. By employing the Monte-Carlo method, our approach can construct the tree-topology-based regeneration process, a.k.a. regeneration tree, to effectively reduce the regeneration time. Through rigorous analysis, we apply the information flow graph to optimize the inter-cloud traffic for a given regeneration tree. To verify the merit of RLDR, We conduct extensive experiments on real-world traces. Experiments demonstrate that RLDR can significantly accelerate the regeneration process. Specifically, RLDR can reduce the regeneration time by up to 92% and increase the throughput by up to twelve-fold, compared to the prior art.

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

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

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

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.

Federated Learning (FL) in mobile environments faces significant communication bottlenecks. Gradient compression has proven as an effective solution to this issue, offering substantial benefits in environments with limited bandwidth and metered data. Yet, it encounters severe performance drops in non-IID environments due to a one-size-fits-all compression approach, which does not account for the varying data volumes across workers. Assigning varying compression ratios to workers with distinct data distributions and volumes is therefore a promising solution. This work derives the convergence rate of distributed SGD with non-uniform compression, which reveals the intricate relationship between model convergence and the compression ratios applied to individual workers. Accordingly, we frame the relative compression ratio assignment as an$n$-variable chi-squared nonlinear optimization problem, constrained by a limited communication budget. We propose DAGC-R, which assigns conservative compression to workers handling larger data volumes. Recognizing the computational limitations of mobile devices, we propose the DAGC-A, which is computationally less demanding and enhances the robustness of compression in non-IID scenarios. Our experiments confirm that the DAGC-R and DAGC-A can speed up the training speed by up to 25.43% and 16.65% compared to the uniform compression respectively, when dealing with highly imbalanced data volume distribution and restricted communication.

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

Cloud-of-clouds storage can enhance the data security and reliability of online applications by encrypting, encoding, and distributing user data across multiple clouds. Fast transferring large volumes of data through networks with limited bandwidths remains a practical challenge, especially in the event of disaster backup. To address this, we model a data storage process using an information flow graph and estimate inter-cloud traffic. We propose a new Network Coding-based Cloud-of-Clouds Backup (NC3B) framework, which enables collaborative encoding and data exchange among backup clouds to utilize inter-cloud bandwidth efficiently. We analytically corroborate that NC3B effectively reduces write operation latency. We also demonstrate the NC3B framework by incorporating two cutting-edge Reed-Solomon (RS) based data storage techniques, namely All-Or-Nothing Transform-RS (AONT-RS) and Converge AONT-RS (CAONT-RS), referred to as Network coding-based Backup AONT-RS (NBAONT-RS) and Network coding-based Backup CAONT-RS (NBCAONT-RS), respectively. To validate our approach, we deploy a real-world prototype storage system on Amazon EC2 using a cluster trace set, and underscore the effectiveness of NC3B, showcasing reductions in latency of up to 50% compared to state-of-the-art approaches, alongside throughput improvements of up to 98%. These findings underscore the benefits of NC3B in real-world storage scenarios.

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

Transforming a constrained optimization problem (COP) into a multi/many-objective optimization problem (MOP/MaOP) represents a practical approach for solving COPs. This article introduces an adaptive multi/many-objective transformation technique, termed adaptive many-objective transformation technique (AMaOTCO), designed to effectively address COPs. The transformed many-objective optimization problem (MaOP) defines an objective using a convex combination of the objective function (or constraint violation function) and an auxiliary function. This auxiliary function is constructed through a convex combination of the objective function and a weighted constraint violation function. The adaptive tuning of all combination coefficients is based on population information. This adaptive tuning ensures an intelligent balance between minimizing various constraint violations and managing the tradeoff between objective function minimization and constraint violation reduction. The effectiveness of the proposed AMaOTCO is demonstrated through comparisons with state-of-the-art constrained evolutionary algorithms (CEAs) on a set of real-world COPs.

5G communication performance is highly correlated with the locations of cellular base stations (BSs). Many previous works have studied the placement of BSs, however, millimeter-wave-based transmission and MEC (Mobile Edge Computing) technology brought by 5G makes the BS placement problem more complex in the 5G scenario. 5G communication enhances transmission rates, but it limits transmission distance. Besides, user experience are sensitive to MEC locations, which is also highly related to BS placement. In this paper, we design a 5G BS placement model and propose an EMBP (Efficient MEC BS placement) algorithm to compute the 5G BS locations and MEC locations. Using the historical location and computational task information of users, EMBP can choose BS and MEC deployment locations which will optimize QoE (Quality of Experience) performance in the future. The simulation results show that EMBP greatly outperforms traditional algorithms in terms of QoE and coverage. We also conducted a case study with real-world data collected, and the results validated our conclusions.

Nowadays, load balancing is more and more important for huge data centers. Most data centers usually adopt a software-based load balancer (LB), which consumes too many server resources and does not scale with the increasing traffic volume. However, there is an increased demand for layer-4 load balancers, which need to check more bits in packet headers. Although hardware-based load balancers can meet the requirements, they are much more expensive. During the past years, data centers frequently update their devices, including kinds of switches. Thus, there exists a huge number of obsolete switches, some of which are in good condition and equipped with high-performance storage like SRAM (Static Random Access Memory). In this paper, we proposed BCLB (Bit-based Collaborative Load Balancer), a new cooperative LB, which builds more powerful load balancing based on existing switches. Different with previous cooperative mechanisms that distribute rules to different LBs, BCLB lets switches cooperate based on bits. Many switches along the data path check different bits, and cooperatively check all bits in 5-tuple (which would be 296 bits in IPv6) for layer-4 packet header. In this way, rule updating will not influence the load balancing in BCLB. To optimize the performance of BCLB, we formulate the problem as an optimization problem, then we propose a dynamic programming algorithm to solve it. Finally, we conduct comprehensive simulations using both real-world traffic datasets and a P4-based prototype, the results show that BCLB performs much better than previous rule-based cooperative schemes.

Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates with a parameter server via Internet. To accelerate the communication speed, it has been explored to deploy a programmable switch (PS) in lieu of the parameter server to coordinate clients. The challenge to deploy the PS in FL lies in its scarce memory space, prohibiting running memory consuming aggregation algorithms on the PS. To overcome this challenge, we propose Federated Learning in-network Aggregation with Compression (FediAC) algorithm, consisting of two phases: client voting and model aggregating. In the former phase, clients report their significant model update indices to the PS to estimate global significant model updates. In the latter phase, clients upload global significant model updates to the PS for aggregation. FediAC consumes much less memory space and communication traffic than existing works because the first phase can guarantee consensus compression across clients. The PS easily aligns model update indices to swiftly complete aggregation in the second phase. Finally, we conduct extensive experiments by using public datasets to demonstrate that FediAC remarkably surpasses the state-of-the-art baselines in terms of model accuracy and communication traffic.

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

Incentive mechanisms in Spatial Crowdsourcing (SC) have been widely studied as they provide an effective way to motivate mobile workers to perform spatial tasks. Yet, most existing mechanisms only involve single tasks, neglecting the presence of complementarity and substitutability among tasks. This limits their effectiveness in practice cases. Motivated by this, we consider task bundles for incentive mechanism design and closely analyze the mutual exclusion effect that arises with task bundles. We then develop a combinatorial incentive mechanism, including three key policies: In the offline case, we propose a combinatorial assignment policy to address the conflict between mutual exclusion and assignment efficiency. We next study the conflict between mutual exclusion and truthfulness, and build a combinatorial pricing policy to pay winners that yields both incentive compatibility and individual rationality. In the online case with unknown workers’ utilities, we present an online combinatorial assignment policy that balances the exploration-exploitation trade-off under the mutual exclusion constraints. Through theoretical analysis and numerical simulations using real-world mobile networking datasets, we demonstrate the effectiveness of the proposed mechanism.

Streaming 360° videos demands excessive bandwidth. Tile-based streaming and super-resolution are two widely studied approaches to alleviate bandwidth shortage and enhance user experience in such real-time video streaming systems. The former prioritizes the transmission of a fraction of the 360° video according to the user viewport, while the latter enhances the streamed video in higher resolutions through computations. However, these two approaches bring substantial complexity and computation overhead and thus suffer from resource bottlenecks due to the constrained mobile hardware. This paper proposes TBSR, a practical mobile 360° video streaming system that incorporates in-time super-resolution with tile-based streaming on commodity mobile devices. We present the designs of three key mechanisms, including a rate adaptation method with macro tile grouping to reduce decoding computations, a decoding and SR scheduler for different types of tasks to achieve the best cost efficiency, and the workload adjustment method to control the amount of tasks given the available capabilities. We further implement the TBSR prototype. Our performance evaluation shows that TBSR outperforms the existing methods, improving QoE quality by up to 32% and bandwidth savings by 26%.

The rapid advancement of generative artificial intelligence (GAI) has led to the creation of transformative applications such as ChatGPT, which significantly boosts text processing efficiency and diversifies audio, image, and video content. Beyond digital content creation, GAI’s capability to analyze complex data distributions holds immense potential for next-generation networks and communications, especially given the swift rise of video conferencing applications (VCAs). This paper presents a dynamic, real-time method for detecting anomalous network links in video conferencing applications. The proposed tool, MonkeyGPT, generates tracing representations of network activity and trains a large language model from scratch to serve as a detection system based on network traffic data. Unlike traditional methods, MonkeyGPT provides an unrestricted search space and does not rely on predefined rules or patterns, enabling it to detect a wider range of anomalies. We demonstrate the effectiveness of MonkeyGPT as an anomaly detection tool in real-world VCAs. The results indicate that the model possesses strong detection capabilities, achieving an accuracy rate of over 97%. It is applicable to various platforms, including Zoom, Microsoft Teams, Tencent Meeting, and Feishu, showcasing its robust adaptability.

HTTP/3 is designed to enhance performance and security by utilizing QUIC as its underlying transport protocol. Integrating HTTP/3 and its features can expand the application scope of ICN. Through an analysis of critical elements of HTTP/3, we implement it on NDN forwarding daemons and run applications in browsers to assess its capabilities and potential benefits in ICN environments. Our system demonstration illustrates compatibility with mainstream browsers while supporting ICN-based transmissions.

Information-centric Networking in edge computing environments exhibits the potential to significantly enhance the efficiency, reliability, and security of data transmission, making it a promising technology for future network deployments. However, the differences from traditional networks require applications to actively redevelop and redeploy onto edge devices, incurring additional costs for the proliferation of ICN applications. In this paper, we propose a system to adapt QUIC protocol over ICN networks in multi-access edge networks. The system aims to expand the application repertoire for ICN by providing a smooth transition of applications using QUIC to run on ICN networks. We design an ICN-QUIC conversion layer to manage the transmission of data from QUIC-based applications. We implement and evaluate the designed system. The experiment results show that, compared to existing networks, our system can enhance the transmission efficiency, i.e., up to 20 times better goodput in multicast situations, and achieves comparable results in unicast scenarios. We test the scalability of our system in real edge and wireless environments. We also deploy the real applications over the proposed system to demonstrate its compatibility in ICN and MEC environments.

Ocean network communication is more and more important nowadays. However, it faces significant challenges due to its heterogeneity, low reliability, and narrow bandwidth. Compared with traditional networks, the problem worsens under severe weather conditions because communication channels, e.g., microwave links, may degrade badly due to weather changes. While many ocean applications put higher QoS (Quality of Service) requirements on communications, it is difficult to meet them due to fluctuating weather changes. Thus, it is more challenging to model and operate the ocean network as more factors may influence the performance.Currently, artificial intelligence opens up new possibilities to meet the challenges because it can adapt to the dynamic changes of the network. In this paper, we formulate the problem by establishing a model between network performance and weather conditions. We try to optimize network utility given the traffic matrix and important weather factors, such as rain, atmospheric absorption, and clouds. To solve the problem, we propose a two-stage and multi-agent optimization algorithm named MAEON (Multi-Agent Efficient Ocean Network). We conduct a comprehensive simulation using generated network topology traffic and real-world weather datasets. We also carry out a case study with datasets from a real-world scenario. The results show that MAEON can improve performance by 21.5% compared with traditional algorithms.

We propose a network service as a solution for video conference applications by constructing network layer routing strategies. Our approach takes into account the characteristics of conferencing flows, addresses various self-customized metrics, and proactively ensures a positive user experience by preventing contention. The performance of VCApather is evaluated using a fully-meshed topology with five nodes and real-world video conference traffic. The results show that VCApather is capable of achieving high link utilization and balance, while also meeting predefined user metrics. Compared to other schemes, VCApather was found to satisfy 69.8% more QoE requirement and to yield an average bitrate improvement of 1.74×.

Federated learning (FL) has been extensively exploited in the training of machine learning models to preserve data privacy. In particular, wireless FL enables multiple clients to collaboratively train models by sharing model updates via wireless communication without exposing raw data. The state-of-the-art wireless FL advocates efficient aggregation of model updates from multiple clients by over-the-air computing. However, a significant deficiency of over-the-air aggregation lies in the infeasibility of TopK model compression given that top model updates cannot be aggregated directly before they are aligned according to their indices. In view of the fact that TopK can greatly accelerate FL, we design a novel wireless FL with voting based TopK algorithm, namely WFL-VTopK, so that top model updates can be aggregated by over-the-air computing directly. Specifically, there are two phases in WFL-VTopK. In Phase 1, clients vote their top model updates, based on which global top model updates can be efficiently identified. In Phase 2, clients formally upload global top model updates so that they can be directly aggregated by over-the-air computing. Furthermore, the convergence of WFL-VTopK is theoretically guaranteed under non-convex loss. Based on the convergence of WFL-VTopK, we optimize model utility subjecting to training time and energy constraints. To validate the superiority of WFL-VTopK, we extensively conduct experiments with real datasets under wireless communication. The experimental results demonstrate that WFL-VTopK can effectively aggregate models by only communicating 1%-2% top models updates, and hence significantly outperforms the state-of-the-art baselines. By significantly reducing the wireless communication traffic, our work paves the road to train large models in wireless FL.

Edge Computing is promising for latency-sensitive applications. However, current edge resource scheduling is inefficient. Deep Learning as a Service (DLaaS) provides deep learning methods to optimize the resource scheduling problem, but faces great challenges of security and reliability. On one hand, DLaaS training agents and raw data are exposed to various adversarial attacks. On the other hand, dishonest DLaaS trainers can generate poisoned models to attack the DLaaS system. In this article, we proposeSAPE, aSecure and decentralized DLAaSPlatform inEdge computing. SAPE allows users to submit their tasks, which will be scheduled to the appropriate edge clusters to minimize the task execution time. We formulate the resource scheduling problem and develop the federated deep reinforcement learning (DRL) method to optimize the problem and resist the adversarial attacks of DLaaS. We utilize blockchain and propose a consortium-based verification scheme to improve the reliability of the federated training process, protecting the DLaaS models from being poisoned and compromised. We conduct experiments to evaluate the latency and security performance of SAPE and the federated DRL scheduling policy. The results show that SAPE outperforms the traditional schemes when defending against adversarial attacks towards the DLaaS platform in edge computing.

Federated Learning (FL) enables geo-distributed clients to collaboratively train a learning model without exposing their private data. By only exposing local model parameters, FL well preserves data privacy of clients. Yet, it remains possible to recover raw samples from over frequently exposed parameters resulting in privacy leakage. Differentially private federated learning (DPFL) has recently been suggested to protect these parameters by introducing information noises. In this way, even if attackers get these parameters, they cannot exactly infer true parameters from these noisy information. Directly incorporating Differentially Private (DP) into FL however can severely affect model utility. In this paper, we present an optimized sparse response mechanism (OSRM) that seamlessly incorporates DP into FL to diminish privacy budget consumption and improve model accuracy. Through OSRM, each FL client only exposes a selected set of large gradients, so as not to waste privacy budgets in protecting valueless gradients. We theoretically derive the convergence rate of DPFL with OSRM under non-convex loss. Then, OSRM is optimized by minimizing the loss of the convergence rate. Based on analysis, we present an effective algorithm for optimizing OSRM. Extensive experiments are conducted with public datasets, including MNIST, Fashion-MNIST and CIFAR-10. The results suggest that OSRM can achieve the average improvement of accuracy by 18.42% as compared to state-of-the-art baselines with a fixed privacy budget.

Surrogate-assisted evolutionary algorithms (SAEAs) have proven to be effective in solving computationally expensive optimization problems (EOPs). However, the performance of SAEAs heavily relies on the surrogate model and infill criterion used. To improve the generalization of SAEAs and enable them to solve a wide range of EOPs, this paper proposes an SAEA called AutoSAEA, which features model and infill criterion auto-configuration. Specifically, AutoSAEA formulates model and infill criterion selection as a two-level multi-armed bandit problem (TL-MAB). The first and second levels cooperate in selecting the surrogate model and infill criterion, respectively. A two-level reward (TL-R) measures the value of the surrogate model and infill criterion, while a two-level upper confidence bound (TL-UCB) selects the model and infill criterion in an online manner. Numerous experiments validate the superiority of AutoSAEA over some state-of-the-art SAEAs on complex benchmark problems and a real-world oil reservoir production optimization problem.

Serverless Computing and Function-as-a-Service (FaaS) offer convenient and transparent services to developers and users. The deployment and resource allocation of services are managed by the cloud service providers. Meanwhile, the development of smart mobile devices and network technology enables the collection and transmission of a huge amount of data, which shifts tasks to the network edge for mobile users. In this paper, we propose a deviceless edge computing system targeting the mobility of end users using the data migration of virtual functions. We focus on the adjustment of migration among virtual functions to provide uninterrupted services to mobile users. We introduce the deviceless edge computing model and propose a seamless data migration scheme of virtual functions with limited involvement of function developers. We formulate the migration decision problem into integer linear programming and use receding horizon control (RHC) for online solutions. We implement the migration system to support delay-sensitive scenarios over real edge devices and develop a streaming game as the virtual function to test the performance. Extensive experiments in real scenarios exhibit the system has the ability to support high-mobility and delay-sensitive application scenarios. Extensive simulation results show the applicability of the proposed system over large-scale networks.

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, federated learning (FL) has received intensive research because of its ability in preserving data privacy for scattered clients to collaboratively train machine learning models. Decentralized federated learning (DFL) is upgraded from FL which allows clients to aggregate model parameters with their neighbours directly. DFL is particularly feasible for dynamic systems, in which the neighbour set of each client is dynamic. However, due to the restrictions of client trajectories and communication distances, it is hard for individual clients to sufficiently exchange models with others, resulting in poor model accuracy. To address this challenge, we propose the DFL-DMS (DFL with Diversified Model Sources) algorithm to diversify sources for model aggregation, and improve model utility. Specifically, models exchanged between DFL-DMS clients are jointly determined by their staleness scores and the bandwidth constraint. An asynchronous learning mode is adopted so that DFL-DMS clients can temporarily store and relay fresh models collected from different client sources to accelerate the propagation of rare models. A state vector is maintained to track the contribution weight of each source to its model aggregation, and an entropy based metric (EBM) is optimized by clients in a distributed manner. Finally, the superiority of DFL-DMS is evaluated by extensive experiments (with MNIST and CIFAR-10 datasets) which demonstrate that DFL-DMS can accelerate the convergence of DFL and improve the model accuracy significantly compared with the state-of-the-art baselines.

Dimensionality reduction through feature selection becomes inevitable to overcome the problem of the Curse of dimensionality. In this article, we propose a feature (gene) selection method for high dimensional gene expression (GE) data through a Multi-objective optimization-based Multi-view Co-Clustering algorithm (named MMCo-Clus). A thorough comparative analysis with existing feature selection algorithms using external/internal evaluation metrics supports our proposed method’s potency.

With the development of cross-datacenter services, accurate and low-cost network performance measurement enables better traffic scheduling. However, the existing network measurement suffers from a lack of telemetry granularities and excessive costs. Besides, the implementation of INT in the WAN remains undefined. In this work, We propose WAN-INT, a two-phase path orchestration algorithm designed to address the challenges posed by limited communication resources and varying link states in WAN scenarios. By generating a telemetry policy based on this algorithm, it can effectively adapt to the requirements of various applications and network states, achieving a balance between telemetry quality and cost limitations. We conduct experiments in a commercial WAN environment. Results show that WAN-INT outperforms existing schemes and reaches a good compromise between the telemetry quality and cost constraints. Compared with the state-of-the-art telemetry system, WAN-INT effectively reduces by at least 43% of telemetry cost while ensuring telemetry quality.

The current Internet makes forwarding decisions based on only destination addresses, leading to a prevalence of IP source address spoofing. To mitigate the risks posed by IP spoofing, Source Address Validation in Intra-domain and Interdomain Networks (SAVNET) has been recently proposed and become a hot topic in both academia and industry. While all network equipment can not be upgraded to support SAVNET during one night, incremental deployment is needed for network operators. However, SAVNET can not defend against all spoofing attacks under partial deployment. Thus, we need to carefully choose nodes to be deployed, to improve incentive benefits during incremental deployment. In this paper, we formulate the incremental problem and prove that the problem is NP-Complete. To efficiently solve the problem, we propose a heuristic deployment scheme named SOID (SAV protocol optimized incremental deployment). The intuitive idea of SOID is using the sink-tree to get the detectable flow sets of each router. Then, it selects the routers which have the maximum total weight during each iteration. To evaluate the performance of the proposed algorithm, we conduct comprehensive simulations with generated and real topologies. The simulation results show that SOID performs much better compared with traditional schemes, such as random deployment and minimum vertex cover algorithms, with a manageable overhead.

Gradient compression algorithms are widely used to alleviate the communication bottleneck in distributed ML. However, existing gradient compression algorithms suffer from accuracy degradation in Non-IID scenarios, because a uniform compression scheme is used to compress gradients at workers with different data distributions and volumes, since workers with larger volumes of data are forced to adapt to the same aggressive compression ratios as others. Assigning different compression ratios to workers with different data distributions and volumes is thus a promising solution. In this study, we first derive a function from capturing the correlation between the number of training iterations for a model to converge to the same accuracy, and the compression ratios at different workers; This function particularly shows that workers with larger data volumes should be assigned with higher compression ratios1to guarantee better accuracy. Then, we formulate the assignment of compression ratios to the workers as an n-variables chi-square nonlinear optimization problem under fixed and limited total communication constrain. We propose an adaptive gradient compression strategy called DAGC, which assigns each worker a different compression ratio according to their data volumes. Our experiments confirm that DAGC can achieve better performance facing highly imbalanced data volume distribution and restricted communication.

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

In recent years, vaccine safety incidents have occurred frequently. To protect vaccine safety, researchers have proposed to use blockchain to secure the vaccine circulation process. Technically, blockchain has some limitations in solving vaccine and other supply chain problems, such as large on-chain storage consumption and low throughput. To better alleviate these restrictions, we propose an improved, blockchain-based, storage-efficient vaccine safety protection scheme in this work. Specifically, we first model the vaccine circulation process. We then design a system to protect vaccine circulation using blockchain, cloud, and cryptographic mechanisms. The proposed system leverages the cloud to implement the vaccine circulation model. Correspondingly, it uses the blockchain to store circulating data certificates and signatures. We evaluated the proposed conceptual model using a consortium blockchain. The experimental results show that the proposed system is efficient.

Empowered by 5G, it has been extensively explored by existing works on the deployment of differentially private federated learning (DPFL) in the Industrial Internet of Things (IIoT). Through federated learning, decentralized IIoT devices can collaboratively train a machine learning model by merely exchanging model gradients with a parameter server (PS) for multiple global iterations. Differentially private (DP) mechanisms will be incorporated by IIoT devices (also called clients) to prevent the leakage of privacy due to the exposure of gradients because original gradients will be distorted DP noises. Yet, learning with distorted gradients can seriously deteriorate model accuracy, making DPFL unusable in reality. To address this problem, we propose a novel DPFL with sparse responses (DPFL-SR) algorithm, which applies the sparse vector technique to reduce the privacy budget consumption in each global iteration. Specifically, DPFL-SR evaluates the value of each gradient, and only distorts and uploads significant gradients to the PS because significant gradients are more essential for model training. Since insignificant gradients are not disclosed, the reserved privacy budget can be used to return significant gradients for more iterations so that DPFL-SR can achieve higher model accuracy without lowering the privacy protection level. Extensive experiments are conducted with the MNIST and Fashion-MNIST datasets to demonstrate the practicability and superiority of DPFL-SR in IIoT systems.

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

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

Serverless Computing and Function-as-a-Service (FaaS) offer convenient and transparent services to developers and users. The deployment and resource allocation of services are managed by the cloud service providers. Meanwhile, the development of smart mobile devices and network technology enables the collection and transmission of a huge amount of data, which creates the mobile edge computing shifting tasks to the network edge for mobile users. In this paper, we propose a deviceless edge computing system targeting the mobility of end users. We focus on the migration of virtual functions to provide uninterrupted services to mobile users. We introduce the deviceless edge computing model and propose a seamless migration scheme of virtual functions with limited involvement of function developers. We formulate the migration decision problem into integer linear programming and use receding horizon control (RHC) for online solutions. We implement the migration system and algorithm to support delay-sensitive scenarios over real edge devices and develop a streaming game as the virtual function to test the performance. Extensive experiments in real scenarios exhibit the system has the ability to support high-mobility and delay-sensitive application scenarios. Extensive simulation results also show its applicability over large-scale networks.

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

Recently, federated learning (FL) has received intensive research because of its ability in preserving data privacy for scattered clients to collaboratively train machine learning models. Commonly, a parameter server (PS) is deployed for aggregating model parameters contributed by different clients. Decentralized federated learning (DFL) is upgraded from FL which allows clients to aggregate model parameters with their neighbours directly. DFL is particularly feasible for vehicular networks as vehicles communicate with each other in a vehicle-to-vehicle (V2V) manner. However, due to the restrictions of vehicle routes and communication distances, it is hard for individual vehicles to sufficiently exchange models with others. Data sources contributing to models on individual vehicles may not diversified enough resulting in poor model accuracy. To address this problem, we propose the DFL-DDS (DFL with diversified Data Sources) algorithm to diversify data sources in DFL. Specifically, each vehicle maintains a state vector to record the contribution weight of each data source to its model. The Kullback–Leibler (KL) divergence is adopted to measure the diversity of a state vector. To boost the convergence of DFL, a vehicle tunes the aggregation weight of each data source by minimizing the KL divergence of its state vector, and its effectiveness in diversifying data sources can be theoretically proved. Finally, the superiority of DFL-DDS is evaluated by extensive experiments (with MNIST and CIFAR-10 datasets) which demonstrate that DFL-DDS can accelerate the convergence of DFL and improve the model accuracy significantly compared with state-of-the-art baselines.

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

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

Convolutional neural network (CNN) and Transformer have achieved great success in multimedia applications. However, little effort has been made to effectively and efficiently harmonize these two architectures to satisfy image deraining. This paper aims to unify these two architectures to take advantage of their learning merits for image deraining. In particular, the local connectivity and translation equivariance of CNN and the global aggregation ability of self-attention (SA) in Transformer are fully exploited for specific local context and global structure representations. Based on the observation that rain distribution reveals the degradation location and degree, we introduce degradation prior to help background recovery and accordingly present the association refinement deraining scheme. A novel multi-input attention module (MAM) is proposed to associate rain perturbation removal and background recovery. Moreover, we equip our model with effective depth-wise separable convolutions to learn the specific feature representations and trade off computational complexity. Extensive experiments show that our proposed method (dubbed as ELF) outperforms the state-of-the-art approach (MPRNet) by 0.25 dB on average, but only accounts for 11.7% and 42.1% of its computational cost and parameters.

Nowadays, although the rapidly growing demand for short video sharing has brought enormous commercial value, considerable bandwidth usage becomes a problem for service providers. To save costs of service providers, the short video applications face a critical conflict between maximizing the user quality of experience (QoE) and minimizing the bandwidth usage. Most of existing bitrate adaptation methods are designed for the livecast and video-on-demand instead of short video applications. In this paper, we propose a QoE-aware adaptive download control mechanism to ensure the user QoE and save the bandwidth, which can download the appropriate video according to user retention probabilities and network conditions, and pause the download when the buffered data is enough. The extensive simulation results demonstrate the superiority of our proposed mechanism over the other baseline methods.

Video cameras have been deployed widely today. Although existing systems aim to optimize live video analytics from a variety of perspectives, they are agnostic to the workload dynamics in real-world. We propose EC-MASS, an edge computing-based video scheduling system achieving both cost and performance optimization with multiple cameras and edge data centers . The intuition behind EC-MASS is to adaptively map cameras to different edge data centers according to dynamically updated configurations of cameras. We prove that generating the optimal mapping scheduling scheme is NP-Complete, and develop the scheduling algorithm by leveraging the insights of the economy consideration of camera allocation. Using the algorithm, EC-MASS is able to balance the workload among edge data centers while reducing the cost of video analytics system . We evaluate EC-MASS with datasets of video configurations from real-world cameras which randomly generate configurations for cameras, with a testbed that consists of 60 cameras and 4 edge data centers . Our results show that EC-MASS consistently outperforms the status quo in terms of cost and performance stability.

Edge computing architectures can help us quickly process the data collected by Internet of Things (IoT) and caching files to edge nodes can speed up the response speed of IoT devices requesting files. Blockchain architectures can help us ensure the security of data transmitted by IoT. Therefore, we have proposed a system that combines IoT devices, edge nodes, remote cloud, and blockchain. In the system, we designed a new algorithm in which blockchain-assisted compressed algorithm of federated learning is applied for content caching, called CREAT to predict cached files. In the CREAT algorithm, each edge node uses local data to train a model and then uses the model to learn the features of users and files, so as to predict popular files to improve the cache hit rate. In order to ensure the security of edge nodes’ data, we use federated learning (FL) to enable multiple edge nodes to cooperate in training without sharing data. In addition, for the purpose of reducing communication load in FL, we will compress gradients uploaded by edge nodes to reduce the time required for communication. What is more, in order to ensure the security of the data transmitted in the CREAT algorithm, we have incorporated blockchain technology in the algorithm. We design four smart contracts for decentralized entities to record and verify the transactions to ensure the security of data. We used MovieLens data sets for experiments and we can see that CREAT greatly improves the cache hit rate and reduces the time required to upload data.

The smart construction site is developing rapidly with the intelligentization of industrial management. Intelligent devices are being widely deployed in construction industry to support artificial intelligence applications. Video surveillance is a core function of smart construction, which demands both high accuracy and low latency. The challenge is that the computation and networking resources in a construction site are often limited, and the inefficient scheduling policies create congestions in the network and bring additional delay that is unbearable to realtime surveillance. Adaptive video configuration and edge computing have been proposed to improve accuracy and reduce latency with limited resources. However, optimizing the video configuration and task scheduling in edge computing involves several factors that often interfere with each other, which significantly decreases the performance of video surveillance. In this article, we present an edge-based solution of video surveillance in the smart construction site assisted by a graph neural network. It leverages the distributed computing model to realize flexible allocation of resources. A graph-assisted hierarchical reinforcement learning algorithm is developed to illustrate the feature of the mobile-edge network and optimize the scheduling policy by the Deep-$Q$Network. We implement and test the proposed solution in the commercial residential buildings of a fortune global 500 real estate company and observe that the proposed algorithm is efficient to maintain a reliable accuracy and keep lower delay. We further conduct a case study to demonstrate the superiority of the proposed solution by comparing it with traditional mechanisms.

Recently, blockchain-based federated learning (BFL) has attracted intensive research attention due to that the training process is auditable and the architecture is serverless avoiding the single point failure of the parameter server in vanilla federated learning (VFL). Nevertheless, BFL tremendously escalates the communication traffic volume because all local model updates (i.e., changes of model parameters) obtained by BFL clients will be transmitted to all miners for verification and to all clients for aggregation. In contrast, the parameter server and clients in VFL only retain aggregated model updates. Consequently, the huge communication traffic in BFL will inevitably impair the training efficiency and hinder the deployment of BFL in reality. To improve the practicality of BFL, we are among the first to propose a fast blockchain-based communication-efficient federated learning framework by compressing communications in BFL, called BCFL. Meanwhile, we derive the convergence rate of BCFL with non-convex loss. To maximize the final model accuracy, we further formulate the problem to minimize the training loss of the convergence rate subject to a limited training time with respect to the compression rate and the block generation rate, which is a bi-convex optimization problem and can be efficiently solved. To the end, to demonstrate the efficiency of BCFL, we carry out extensive experiments with standard CIFAR-10 and FEMNIST datasets. Our experimental results not only verify the correctness of our analysis, but also manifest that BCFL can remarkably reduce the communication traffic by 95–98% or shorten the training time by 90–95% compared with BFL.

An exhaustive literature survey shows that finding protein/gene similarity is an important step towards solving widespread bioinformatics problems, such as predicting protein-protein interactions, analyzing Protein-Protein Interaction Networks (PPINs), gene prioritization, and disease gene/protein detection. In this article, we have proposed an improved 3-in-1 fused protein similarity measure called FuSim-II. It is built upon combining the weighted average of biological knowledge extracted from three potential genomic/ proteomic resources such as Gene Ontology (GO), PPIN, and protein sequence. Furthermore, we have shown the application of the proposed measure in detecting potential hub-proteins from a given PPIN. Aiming that, we have proposed a multi-objective clustering-based protein hub detection framework with FuSim-II working as the underlying proximity measure. The PPINs of H. Sapiens and M. Musculus organisms are chosen for experimental purposes. Unlike most of the existing hub-detection methods, the proposed technique does not require to follow any protein degree cut-off or threshold to define hubs. A thorough assessment of efficiency between proposed and existing eight protein similarity measures along with eight single/multi-objective clustering methods has been carried out. Internal cluster validity indices like Silhouette and Davies Bouldin (DB) are deployed to accomplish analytical study. Also, a comparative performance analysis between proposed and five existing hub-proteins detection algorithms is conducted through the enrichment of essentiality study. The reported results show the improved performance of FuSim-II over existing protein similarity measures in terms of identifying functionally related proteins as well as relevant hub-proteins. Supplementary material is available at http://csse.szu.edu.cn/staff/cuilz/eng/index.html.

The Internet of Medical Things is developing rapidly in recent years. However, the timeliness and security of healthcare applications challenge its adoption. In this article, we propose a blockchain-assisted edge computing platform that timely and securely processes time-sensitive healthcare applications. We propose a blockchain-assisted framework that leverages distributed edge servers to achieve fast data processing. We design smart contracts to verify the identity and data credibility of network entities. We formulate the problem as a directed acyclic graph organized scheduling model and develop online orchestrating algorithms to meet the timeliness requirement. We implement the blockchain prototype and evaluate the performance of the proposed algorithm under extensive configurations. Results show that fast blockchain-assisted edge computing platform for healthcare achieves a significant timeliness guarantee, and at the same time outperforms conventional schemes from the latency perspective.

In the era of Big Data, cluster analysis of high-dimensional data sets often suffers from theCurse of dimensionality. To overcome this problem, the dimensionality reduction throughfeature selectionbecomes inevitable. Co-clustering or two-way clustering is considered to be a more sophisticated tool than conventional one-way clustering. Moreover, the advent of multi-view learning shows that the subjects of a data set can be interpreted in many ways. Interestingly, a minimal number of existing feature selection algorithms take advantage of the co-clustering method and are designed to consider multi-view data. Motivated by this, in the current article, we propose a feature (gene) selection method for high dimensional gene expression (GE) data through amulti-objective optimization basedmulti-viewCo-Clustering algorithm (namedMMCo-Clus). A popular evolutionary technique – Non-dominated Sorting Genetic Algorithm-II (NSGA-II) has been utilized as the proposed method's underlying optimization strategy. First, we construct two views of a chosen data set, utilizing knowledge from two different biological data sources. Next, we develop the MMCo-Clusalgorithm considering the constructed views to identify a set of “good” co-clustering solutions. Finally, based on a concept ofconsensus operationon the co-clustering outcome, a small number of most relevant and non-redundant features are extracted from the original feature-space. The reduced dimension formed by new feature-space causes to decrease the computational burden and noise level of original data. For experimental analysis, we have chosen three benchmark GE data sets. Our feature selection method's effectiveness is evaluated through sample-classification accuracy, accompanied by the cluster profile plot/Eisen plot/t-SNE plot, and biological/statistical significance test. A thorough comparative analysis with existing feature selection algorithms using external and internal evaluation metrics supports our proposed method's potency.

introduction Share on Introduction to the Special Section on Resiliency for AI-enabled Smart Critical Infrastructures for 5G and Beyond Authors: Laizhong Cui Shenzhen University, China Shenzhen University, ChinaView Profile , Yulei Wu University of Exeter, UK University of Exeter, UKView Profile , Ryan Ko University of Queensland, Australia University of Queensland, AustraliaView Profile , Alex Ladur CTEK - Combined Technologies Ltd, New Zealand CTEK - Combined Technologies Ltd, New ZealandView Profile , Jianping Wu Tsinghua University, China Tsinghua University, ChinaView Profile Authors Info & Claims ACM Transactions on Sensor NetworksVolume 18Issue 319 September 2022Article No.: 40epp 1–3https://doi.org/10.1145/3538515Published:19 September 2022Publication History 0citation78DownloadsMetricsTotal Citations0Total Downloads78Last 12 Months78Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

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.

Emerging multimedia applications like VR, AR, etc., exhibit unique transmission features, such as block-based transmission, dynamic prioritization for different contents, and deadline-aware delivery, which should be carefully managed but fail to be considered in the design of existing transmission control algorithms. In this work, we propose a delay-sensitive congestion control algorithm with a hybrid of coarse-grained and fine-grained control to improve the QoE scores. The coarse-grained control scheme maintains a low queuing delay and avoids missing the deadline in the steady state. The fine-grained control scheme rapidly reacts to the network dynamics based on our bandwidth estimation model. For the block scheduling, we heuristically model the realistic priority of each block by examining the trade-off among the remaining time, the remaining size, and the priority score of each block. Extensive experiments are conducted to evaluate the performance of our solution, which show that our solution significantly outperforms other baseline algorithms.

Today’s multimedia applications usually organize the contents into data blocks with different deadlines and priorities. Meeting/missing the deadline for different data blocks may contribute/hurt the user experience to different degrees. With the goal of optimizing real-time multimedia communications, the transmission control scheme needs to make two challenging decisions: the proper sending rate and the best data block to send under dynamic network conditions. In this paper, we propose a delay-sensitive and priority-aware transmission control scheme with two modules, namely, rate control and block selection. The rate control module constantly monitors the network condition and adjusts the sending rate accordingly. The block selection module classifies the blocks based on whether they are estimated to be delivered before deadline and then ranks them according to their effective priority scores. The extensive simulation results demonstrate the superiority of our proposed scheme over the other representative baseline approaches.

Edge computing is promising to solve the latency issue in the Internet of Vehicles (IoV). However, due to decentralization, traditional edge computing suffers in management, deployment, and security. Containerization relaxes resource deployment and migration problems, but current container scheduling policies are inefficient to process complicated tasks based on directed acyclic graph or DAG structures. In this article, we design a containerized edge computing platform CUTE, which provides low-latency computation services for the Internet of Vehicles. The centralized controller is empowered with resource management and orchestration, and containers are scheduled to appropriate edge servers to optimize the computation delay. CUTE is also integrated with blockchain to improve network security. We formulate the vehicle task offloading and container scheduling problems and develop a heuristic container scheduling algorithm for DAG-based computation tasks submitted by vehicles remotely. We implement and deploy CUTE into the China Mobile Network, and conduct comprehensive experiments and a case study. The experiment results show that CUTE can provide low-latency computation services for vehicular applications and that the heuristic algorithm outperforms traditional container scheduling policies.

Edge computing, especially multiaccess edge computing, is seen as a promising technology to improve the Quality of user Experience (QoE) of many artificial intelligence (AI) applications in the evolution toward Internet-of-Things (IoT) infrastructure. However, the management and deployment of massive edge data centers bring new challenges for the current network. In this article, we propose a new edge-based IoT platform for AI (EBI-PAI), based on software-defined network (SDN) and serverless technology. EBI-PAI provides a unified service calling interface and schedules the resources automatically to satisfy the QoE requirements of users. To optimize performances during incremental deployment, we formulate the deployment problem, prove its complexity, and design heuristic algorithms to solve it. We implement EBI-PAI based on an opensource serverless project and deploy it in real networks. To evaluate EBI-PAI, we conduct comprehensive simulations based on the generated and real-world network topology, and real-world base station data set. The simulation results show that EBI-PAI can greatly improve QoE with the same budget and save the budget to achieve similar QoE. We finally carry out a case study with real user demands, and it further validates the simulation results.

Federated Learning (FL) is an emerging decentralized learning framework through which multiple clients can collaboratively train a learning model. However, a major obstacle that impedes the wide deployment of FL lies in massive communication traffic. To train high dimensional machine learning models (such as CNN models), heavy communication traffic can be incurred by exchanging model updates via the Internet between clients and the parameter server (PS), implying that the network resource can be easily exhausted. Compressing model updates is an effective way to reduce the traffic amount. However, a flexible unbiased compression algorithm applicable for both uplink and downlink compression in FL is still absent from existing works. In this work, we devise the Model Update Compression by Soft Clustering (MUCSC) algorithm to compress model updates transmitted between clients and the PS. In MUCSC, it is only necessary to transmit cluster centroids and the cluster ID of each model update. Moreover, we prove that: 1) The compressed model updates are unbiased estimation of their original values so that the convergence rate by transmitting compressed model updates is unchanged; 2) MUCSC can guarantee that the influence of the compression error on the model accuracy is minimized. Then, we further propose the boosted MUCSC (B-MUCSC) algorithm, a biased compression algorithm that can achieve an extremely high compression rate by grouping insignificant model updates into a super cluster. B-MUCSC is suitable for scenarios with very scarce network resource. Ultimately, we conduct extensive experiments with the CIFAR-10 and FEMNIST datasets to demonstrate that our algorithms can not only substantially reduce the volume of communication traffic in FL, but also improve the training efficiency in practical networks.

Nowadays, surveillance cameras have been pervasively equipped with vehicles in public transport systems. For the sake of public security, it is crucial to upload recorded surveillance videos to remote servers timely for backup and necessary video analytics. However, continuously uploading video content generated by tens of thousands of vehicles can be extremely bandwidth consuming. In this work, we investigate the video uploading problem for moving buses by proposing to deploy dedicated access points (AP) at bus stops to facilitate video uploading. We define the harmonic objective for our problem, which includes minimizing the video uploading delay and minimizing the AP deployment cost. This problem is with two fundamental challenges. Firstly, it is difficult to balance the bandwidth capacity allocated to many buses because a bus obtains bandwidth resource from a series of APs deployed at stops along its route. Secondly, due to the randomness of bus movement and the complexity of bus routes, it is hard to predict the workload of an AP. Hence, it is challenging to estimate the delay of uploading video content through an AP. To cope with these challenges, we propose a water filling placement (WFP) algorithm, aiming to balance the aggregated bandwidth allocated to each bus. A queuing model is established to analyze the uploading delay of video content. We further resort to machine learning models to factor the influence of bus routes into our queuing model. Finally, a convex problem is formulated to optimize the harmonic objective, which can be optimally solved with the gradient descent (GD) based algorithm. We validate the correctness of our theoretical analysis and demonstrate the effectiveness of our method by carrying out extensive experiments using bus traces collected in Shenzhen city of China. In comparison with benchmark algorithms, our solution can always achieve the best performance.

Currently, video content accounts for the majority of network traffic. With increased live streaming, rigorous requirements have been introduced for better Quality of Experience (QoE). It is challenging to meet satisfactory QoE in live streaming, where the aim is to achieve a balance between 1) enhancing the video quality and stability and 2) reducing the rebuffering time and end-to-end delay, under different scenarios with various network conditions and user preferences, where the fluctuation in the network throughput degrades the QoE severely. In this paper, we propose an approach to improve the QoE for live video streaming based on Deep Reinforcement Learning (DRL). The new approach jointly adjusts the streaming parameters, including the video bitrate and target buffer size. With the basic DRL framework, TCLiVi can automatically generate the inference model based on the playback information, to achieve the joint optimization of the video quality, stability, rebuffering time and latency parameters. We evaluate our framework on real-world data in different live streaming broadcast scenarios, such as a talent show and a sports competition under different network conditions. We compare TCLiVi with other algorithms, such as the Double DQN, MPC and Buffer-based algorithms. The simulation results show that TCLiVi significantly improves the video quality and decreases the rebuffering time, consequently increasing the QoE score by 40.84% in average. We also show that TCLiVi is self-adaptive in different scenarios.

In recent years, vaccine incidents occurred around the world, which endangers people’s lives. In the technical respect, these incidents are partially due to the fact that existing vaccine management systems are distributively managed by different entities in the vaccine supply chain. This architecture makes it relatively easy to modify or even delete the vaccine circulation data maliciously, which makes tracing problematic vaccine hard and identifying the responsibility for a vaccine accident hard. To solve these issues, this article presents a blockchain-based solution to protect the whole process of vaccine circulation. We first propose a model to supervise the vaccine circulation process by incorporating existing regulatory practices. Then, we propose a blockchain-based tracing system to implement this model. The proposed system takes the blockchain as a global, unique, and verifiable database to store all the circulation data. Through data insertions and queries on the global and unique database, the proposed system achieves the protection of vaccine circulation. We also implement a proof-of-concept prototype of the proposed system. Experimental results confirm that the proposed system is beneficial.

Social in-feed advertising delivers ads that seamlessly fit inside a user’s feed, and allows users to engage in social actions (likes or comments) with the ads. Many businesses pay higher attention to “engagement marketing” that maximizes social actions, as social actions can effectively promote brand awareness. This paper studies social action prediction for in-feed advertising. Most existing works overlook the social influence as a user’s action may be affected by her friends’ actions. This paper introduces an end-to-end approach that leverages social influence for action prediction, and focuses on addressing the high sparsity challenge for in-feed ads. We propose to learn influence structure that models who tends to be influenced. We extract a subgraph with the near neighbors a user interacts with, and learn topological features of the subgraph by developing structure-aware graph encoding methods. We also introduce graph attention networks to learn influence dynamics that models how a user is influenced by neighbors’ actions. We conduct extensive experiments on real datasets from the commercial advertising platform of WeChat and a public dataset. The experimental results demonstrate that social influence learned by our approach can significantly boost performance of social action prediction.

Recently, Federated Learning (FL) has drawn tremendous attentions due to its ability to protect client's privacy. In FL, clients collaboratively train machine learning models by merely sharing intermediate computations, i.e., gradients of model parameters. However, training a complicated model involves multiple rounds of interactions between clients and the server via the Internet. Consequently, communication is a primary bottleneck of FL attributed to the poor network conditions and the large amount of interchanged computations. To overcome the communication bottleneck, we propose the ClusterGrad algorithm to compress gradients which can considerably reduce the volume of communicated computations. Our design is based on the fact that there is only a small fraction of gradients whose values are far away from the origin in each round of interaction in FL. We first identify these essential gradients that are far away from 0 using the K-means algorithm. These gradient values are approximated by a novel clustering based quantization algorithm. Then, the rest gradients lying close to 0 are approximated with a single value. We can prove that ClusterGrad outperforms the latest FL gradient compression algorithms: Probability Quantization (PQ) and Deep Gradient Compression (DGC). We conduct extensive experiments with the CIFAR-10 datasets which further demonstrate that ClusterGrad can achieve compression ratio (used interchangeably with compression rate) 123 on average in comparison with PQ and DGC with compression ratios 16 and 60 respectively.

BACKGROUND: In the field of computational biology, analyzing complex data helps to extract relevant biological information. Sample classification of gene expression data is one such popular bio-data analysis technique. However, the presence of a large number of irrelevant/redundant genes in expression data makes a sample classification algorithm working inefficiently. Feature selection is one such high-dimensionality reduction technique that helps to maximize the effectiveness of any sample classification algorithm. Recent advances in biotechnology have improved the biological data to include multi-modal or multiple views. Different 'omics' resources capture various equally important biological properties of entities. However, most of the existing feature selection methodologies are biased towards considering only one out of multiple biological resources. Consequently, some crucial aspects of available biological knowledge may get ignored, which could further improve feature selection efficiency. RESULTS: In this present work, we have proposed a Consensus Multi-View Multi-objective Clustering-based feature selection algorithm called CMVMC. Three controlled genomic and proteomic resources like gene expression, Gene Ontology (GO), and protein-protein interaction network (PPIN) are utilized to build two independent views. The concept of multi-objective consensus clustering has been applied within our proposed gene selection method to satisfy both incorporated views. Gene expression data sets of Multiple tissues and Yeast from two different organisms (Homo Sapiens and Saccharomyces cerevisiae, respectively) are chosen for experimental purposes. As the end-product of CMVMC, a reduced set of relevant and non-redundant genes are found for each chosen data set. These genes finally participate in an effective sample classification. CONCLUSIONS: The experimental study on chosen data sets shows that our proposed feature-selection method improves the sample classification accuracy and reduces the gene-space up to a significant level. In the case of Multiple Tissues data set, CMVMC reduces the number of genes (features) from 5565 to 41, with 92.73% of sample classification accuracy. For Yeast data set, the number of genes got reduced to 10 from 2884, with 95.84% sample classification accuracy. Two internal cluster validity indices - Silhouette and Davies-Bouldin (DB) and one external validity index Classification Accuracy (CA) are chosen for comparative study. Reported results are further validated through well-known biological significance test and visualization tool.

BACKGROUND: In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population. RESULTS: In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select. Three important resources of biological data (gene ontology, protein interaction data, protein sequence) along with gene expression values are collectively utilized to design two different views. UMVMO-select aims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets. CONCLUSION: A thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting.

With the development of Internet of Things (IoT), edge computing becomes more and more prevalent currently. However, edge computing needs to deploy a large number of edge servers to reduce the communication latency, which will bring additional costs to the system. Although there exist some idle computing resources at the edge, the owners distrust each other and lack the incentives to contribute to the system. In this article, we propose a new edge computing platform decentralized and trusted platform for edge computing (DeTEC), which provides a unified interface to users, resolves the user's requests to the most appropriate edge server through domain name server, and returns the computational results to the IoT user. To build a trustworthy system, DeTEC integrates the blockchain technology with edge computing, such that the contributions of each participant could be accounted and rewarded. We formulate the task allocation problem, taking both node capacity and reward fairness into consideration, and solve it through a heuristic algorithm. Finally, to guarantee the trustworthiness of computational results, we utilize a police patrol model and try to optimize the system overall reward. We implement DeTEC based on an open source project and conduct comprehensive experiments to test its performance. The results show that our DeTEC system works well in the IoT scenario.

Industrial Internet of Things (IIoT) has been widely used in many fields. Meanwhile, blockchain is considered promising to address the issues of the IIoT. However, the current blockchains have a limited throughput. In this article, we devise an efficient and secure blockchain protocol compacted directed acyclic graph (CoDAG) based on a compacted directed acyclic graph, where blocks are organized in levels and width. New-generated blocks in the CoDAG will be placed appropriately and point to those in the previous level, making it a well-connected channel. Transactions in the network will be confirmed in a deterministic period, and the CoDAG keeps a simple data structure at the same time. We also illustrate the attack strategies by adversary, and it is proved that our protocols are resistant to these attacks. Furthermore, we design a CoDAG-based IIoT architecture to improve the efficiency of the IIoT system. Experimental results show that the CoDAG achieves 164× Bitcoin's throughput and 77× Ethererum's throughput.

Security is one of the critical issues in traditional networks. Software-Defined Networking (SDN) improves the security aspect by separating the control plane and the data plane of networks. To improve the performance of SDN, researchers have designed many advanced controller prototypes and considered the controller placement problem. However, link failures are critical security issues in networks and greatly impact SDN's security. The controller placement problem for link failures is still challenging today. In this paper, we study the SDN controller placement problem for single-link and multi-link failures, respectively. For single-link failures, we develop a heuristic algorithm to address the controller placement problem. For multi-link failures, we introduce the Monte Carlo Simulation to reduce the computational overhead. We conduct experiments with real network topologies, and the simulation results show that the heuristic algorithm can save significantly more time than the optimal algorithm, while achieving good performance.

With increasing demands for more flexible services, the routing policies in enterprise networks become much richer. This has placed a heavy burden to the current router forwarding plane in support of the increasing number of policies, primarily due to the limited capacity in TCAM, which further hinders the development of new network services and applications. The scalable forwarding table structures for enterprise networks have therefore attracted numerous attentions from both academia and industry. To tackle this challenge, in this paper we present the design and implementation of a new forwarding table structure. It separates the functions of TCAM and SRAM, and maximally utilizes the large and flexible SRAM. A set of schemes are progressively designed, to compress storage of forwarding rules, and maintain correctness and achieve line-card speeds of packet forwarding. We further design an incremental update algorithm that allows less access to memory. The proposed scheme is validated and evaluated through a realistic implementation on a commercial router using real datasets. Our proposal can be easily implemented in the existing devices. The evaluation results show that the performance of forwarding tables under the proposed scheme is promising.

The comfortableness of fashion items (e.g., footwear) when people actually wear them has become an increasingly important factor in today's fashion experience. However, existing solutions usually only provide general metrics, e.g., a size of a pair of shoes, for people to roughly infer the fitness possibility, failing to tell the details about how much it fits or why it does not fit a person. In this paper, we propose a fine-grained fitting experience prediction framework based on 3D shapes of both fashion items and people's bodies. First, we propose a 3D-slicing sampling method, by extracting a series of parallel slices from an object, to represent the spatial details of the object with a much smaller amount of features. Second, we propose a spatial self-attention based fitness prediction model including a sub-region attention method and a sequence attention method, which can capture users' comfortable preferences for fine-grained regions divided from slices. Our design can capture users' try-on preferences and landmark positions that may or may not fit (e.g., too tight or too loose). Then, we design a multi-position experience module to predict users' fitting experiences, which can help to explore the spatial differences among slices better. Finally, we use subjective experiments over 500 people trying 32 pairs of fashion shoes with detailed places' comfortableness reported in questionnaires to verify our design, which has accuracies of $77.7%$ and $80.9%$ in reporting the comfortableness of tightness and length respectively, and an overall fitness accuracy of $83.6%$.

Recent advances in traffic engineering offer a series of techniques to address the network problems due to the explosive growth of Internet traffic. In traffic engineering, dynamic path planning is essential for prevalent applications, e.g., load balancing, traffic monitoring and firewall. Application-specific methods can indeed improve the network performance but can hardly be extended to general scenarios. Meanwhile, massive data generated in the current Internet has not been fully exploited, which may convey much valuable knowledge and information to facilitate traffic engineering. In this paper, we propose a learning-based network path planning method under forwarding constraints for finer-grained and effective traffic engineering. We form the path planning problem as the problem of inferring a sequence of nodes in a network path and adapt a sequence-to-sequence model to learn implicit forwarding paths based on empirical network traffic data. To boost the model performance, attention mechanism and beam search are adapted to capture the essential sequential features of the nodes in a path and guarantee the path connectivity. To validate the effectiveness of the derived model, we implement it in Mininet emulator environment and leverage the traffic data generated by both a real-world GEANT network topology and a grid network topology to train and evaluate the model. Experiment results exhibit a high testing accuracy and imply the superiority of our proposal.

With wide adoption of Internet of Things (IoT) across the world, the IoT devices are facing more and more intensive computation task nowadays. However, the IoT devices are usually limited by their computing capability and battery lifetime. Mobile edge computing provides new opportunities for developments of IoT, since edge computing servers which are close to devices can provide more powerful computing resources. The IoT devices can offload the intensive computing tasks to edge computing servers, while saving their own computing resources and reducing energy consumption. However, the benefits come at the cost of higher latency, mainly due to additional transmission time, and it may be unacceptable for many IoT applications. In this paper, we try to find a tradeoff between the energy consumption and latency, in order to satisfy user demands of various IoT applications. We formalize the problem into a constrained multiobjective optimization problem and find the optimal solutions by a modified fast and elitist nondominated sorting genetic algorithm (NSGA-II). To improve the performance of the algorithm, we propose a novel problem-specific encoding scheme and genetic operators in the proposed modified NSGA-II. We also conduct extensive simulation experiments to evaluate the proposed algorithm and its sensitivity under certain major parameters. The experimental results show that the proposed algorithm can find a large number of optimal solutions to adjust the corresponding offloading decision according to the real-world situation.

Learning vector space representation of words, or word embedding, has attracted much recent research attention. With the objective of better capturing the semantic and syntactic information inherent in words, we propose two new embedding models based on the singular value decomposition of lexical co-occurrences of words. Different from previous work, our proposed models allow for injecting linear constraints when performing the decomposition, with which the desired semantic and syntactic information will be maintained in word vectors. Conceptually the models are flexible and convenient to encode prior knowledge about words. Computationally they can be easily solved by direct matrix factorization. Surprisingly simple yet effective, the proposed models have reported significantly improved performance in empirical word analogy and sentence classification evaluations, and demonstrated high potentials in practical applications.

Although the combination of cloud and Peer-to- Peer (P2P) can leverage the performance of video on demand (VoD) system, there has been no mature solution for the cloud leasing strategy for the cloud and peer assisted VoD system. It is hard to get a balance between the operating cost of content provider and the user experience. In this paper, we first model the operating cost and the delay cost. And then, we propose an optimal cloud leasing algorithm based on a well-known multi- objective optimization algorithm NSGA-II to select the most suitable cloud storage server for the requester. Compared with other multi-objective optimization algorithm, i.e. SPEA and linear programming, the experimental results show that our proposed algorithm based on NSGA-II can effectively reduce the operating cost of content provider, without compromising the user's viewing experience.

With the advances in wireless communication and the growing popularity of mobile devices, it has become rather normal to watch videos using mobile devices such as mobile phones and tablets. In order to improve user engagement in mobile video sessions, it is important for content providers and CDN operators to understand the correlations between video quality and Quality of Experience (QoE) of mobile users. In this paper, we measure how video quality metrics affect user engagement, and identify several counter-intuitive relationships, based on the large-scale trace data from a famous IPTV provider. We discover the internal causes of these relationships later, which give us new insights about how we improve user engagement. Based on our new insights, we further design a user engagement prediction framework to let content providers predict how long viewers will remain in video sessions with specific video quality metrics. Finally, we verify the effectiveness of the prediction framework by the real-world data.

Summary Recently, social network has been one of the biggest information exchange platforms of the Internet. Moreover, the users in social network used to watch videos through social network application. To provide a proper recommended video list, the video recommendation algorithm for social network is becoming a hot research issue. On one hand, more and more researchers introduce the concept of trust into video recommendation algorithms. However, most of them only select the trust friends based on the similarity and neglect the characteristics of social network. On the other hand, most previous video recommendation algorithms are only based on the number that a video is viewed to evaluate a video's quality. They do not make good use of the social relationship in social network and the video's reputation. This paper mainly focuses on the challenge that the effectiveness and performance of current video recommendation algorithm in social network cannot satisfy the users. In this paper, we propose a novel video recommendation algorithm based on the combination of video content and social network. Our proposed algorithm consists of the trust friends computing model and video's quality evaluation model. The trust friends computing method takes into account similarity between users, interaction between users, and the active degree of a user. In our video's quality evaluation model, we combine the acceptance ratio of a video with a video's reputation. The video can be given an appropriate rating score through this model. We design corresponding trust friends computing algorithm and video recommendation algorithm respectively for two proposed models. Our integral video recommendation algorithm consists of these two algorithms. The experimental results indicate that the performance and effectiveness of our algorithm are better than those of two classical video recommendation algorithms (i.e., user‐based collaborative filtering algorithm and TBR‐d algorithm), in terms of precision, recall and F1‐measure. Copyright © 2016 John Wiley & Sons, Ltd.

With the rapid development of online social network, people tend to express opinions and obtain information in social network. Due to the overwhelming amount of data in social network, users resort to recommendation system to find appropriate services or items. However, most current recommendation systems rely on the user to calculate the item evaluation results, which has many limitations in the item evaluation. To solve these limitations on item evaluation in social network, we put forward a new trust-based item evaluation model (called CTDR), which integrates trust, domain inclination and item reputation in this paper. First, we use the similarity between users, interaction, as well as the reputation of the target user to introduce a new trust computation method. Second, we introduce the concept of domain inclination and combine the domain activity of user and domain popularity to calculate the domain inclination. Third, we make use of the favorable rate of an item to compute the item reputation. Through extensive experiments, we evaluate CTDR with the traditional collaborative filtering algorithm. The experimental results validate the effectiveness and performance of our trust-based item evaluation model.

Video streaming has been the most popular applications in social networks. However, the current social networks still use the traditional client/server (C/S) architecture, which seriously limits the scale and scalability of video streaming system in social networks. To meet the requirement of more users in social networks, more money are invested to purchase server bandwidth and storage space. Recently, P2P streaming has been a promising way to deliver video streaming and reduce the pressure of the video server. Unfortunately, the traditional mechanisms of P2P streaming systems are suboptimal, even not suitable for social networks. In this paper, we propose a P2P-assisted video streaming system in social networks, called SocialStreaming. The main contribution of this paper is: i) we put forward a network coding based storage strategy, which can improve the utilization of each peer's limited storage space. Moreover, with the same storage space, it can increase the diversity of stored data, which can improve the efficiency of data sharing; ii) we propose a social network based streaming predelivery algorithm in distributed way, derived from the Metropolis-Hastings algorithm. This algorithm could effectively deliver the video streaming to some peers, which will watch this video later with high probability; iii) we present a social network based neighbor selection algorithm, which combines the network performance factor and social factor as a tradeoff to select proper neighbors to exchange data. The trace driven based simulation results also demonstrate the effectiveness and efficiency of SocialStreaming, which can bring good transmission performance and significantly reduce the load of the server.

Current P2P applications are based on random connected overlays, which lead to generating a significant amount of inter-ISP traffic. Asking for more QoS requirements, such as a short delay and a stable streaming rate, few studies are dedicated to optimizing P2P live streaming applications, despite that recent work have proposed some solutions for P2P file distribution applications. In this paper, current traffic localization strategy is analyzed at first, and its two inherent flaws are revealed. Then we propose a novel strategy for ISP-friendly live streaming: based on a hybrid overlay, which is a two-tier structure, all ISPs are organized into an ISP-tree, and then local peers in each ISP form a mesh overlay. In each tier, a data scheduling is designed for inter-ISP traffic reduction and performance guarantee respectively. Compared with a famous live streaming strategy, R2, simulation results demonstrate that our strategy generates much less inter-ISP traffic and achieves a higher system performance.

In this paper, we address the problem of content replication in segmented peer-to-peer on-demand systems, with the objective of minimizing the content server's workload. We consider the system performance under heterogeneous environment. In this P2P-VoD system, multimedia content is divided into segments and peers can seek and cache any segments. Because different segments may be of different popularity, badly designed segment replication may lead to great server's workload. We deduce the “optimal replication ratio” in segmented P2P-VoD system such that peers will receive upload bandwidth from each other and at the same time, minimize the server's workload. We formulate the segment replication as an optimization problem and propose a model to solve it. We show that the proportional replication strategy is not optimal for segmented P2P-VoD systems and the segmented system can lead to less server's workload than non-segmented. We simulate our model, evaluate the performance of segmented P2P-VoD systems and show that our algorithm can greatly reduce the server's workload.

Content-Centric Network is a new and promising architecture with in-network caching. In such a system with universal caching, the cache would play as a high-cut low-pass filter and have a big influence on the requests distribution, and further on cache efficiency. This paper looks into the change of request distribution though caches and presents a model of the “filter effect” in LRU (Least Recently Used) cache. Through the simulation in two common topologies, the experiment results have verified the validity of the new model. Since our model can quantify the network load of any certain cache deployment scheme, we optimize the way of deploying cache when the total cache size is limited. Simulation results show our optimal way can effectively minimize the total network load.

6LoWPAN protocol has been introduced to connect Wireless Sensor Network with the Internet. LoWPAN_IPHC is an up-to-date IPv6 packet header compression method. But there has been little consideration on the maintenance of IPHC CONTEXT TABLE. This paper analyzes this situation and presents a Dynamic 6LowPAN Context Table Maintaining algorithm to solve this problem. In our algorithm, we present the packets dealing process and introduce the age mechanism and the Context Table update mechanism. Experimental results verify the performance of our algorithm.

In most large-scale Peer-to-Peer (P2P) live streaming systems, mesh structures are constructed to provide robustness in dynamic P2P environment. The pull scheduling algorithm is widely used in this mesh structure, but it restricts the performance of entire system. Recently, network coding is introduced in mesh P2P streaming system to improve the performance, which makes the push scheduling strategy feasible. Although some push scheduling algorithms based on network coding have achieved some success, there is still a lack of the theoretical model and optimal solution. In this paper, we propose a novel optimal push scheduling algorithm based on network coding. The main contributions of this paper are: i) We put forward a new theoretical model, with a new evaluation function, which simultaneously considers the scarcity and timeliness of the segment; ii) We formulate the push scheduling strategy as an optimization problem and propose a greedy algorithm to solve it; iii) We systematically realize our proposed optimal push scheduling algorithm. Compared with the most famous push scheduling algorithm based on network coding R2, the simulation results demonstrate that decode delay, decode ratio and redundant fraction of the P2P streaming system with our algorithm can be significantly improved, without losing throughput and increasing overhead.

Peer-to-peer (P2P) streaming is attracting much attention. It requires neighbor selection to construct an overlay and delivers streaming data on it. Therefore, neighbor selection determines the performance of P2P streaming system. There is obvious heterogeneity of bandwidth and latency in current Internet. However, there is still a lack of neighbor selection approach suited for heterogeneous P2P streaming. Most current neighbor selection methods do not consider the heterogeneity or do not consider bandwidth and latency simultaneously. In this paper, we propose a new QoS driven neighbor selection method derived from the Metropolis-Hastings algorithm. The main contribution of this paper is: i) we put forward a new metric for the first time, called bandwidth latency ratio (BLR), which is a tradeoff between bandwidth and latency. We testify the effectiveness of BLR on evaluating QoS sensitive P2P streaming system. Our neighbor selection could optimize the distribution of peers' BLR; ii) in heterogeneous environment, our approach could effectively improve the QoS of peers' neighbors, which will enhance the transmission performance and user experience of P2P streaming system. We provide theoretical analysis of our method's improvement in terms of BLR, compared with SCAMP. Simulation results also demonstrate that the throughput, packet delay and playback quality of the P2P streaming system with our neighbor selection can be significantly improved.