Streaming free-viewpoint video (FVV) in real-time still faces significant challenges, particularly in training, rendering, and transmission efficiency. Harnessing superior performance of 3D Gaussian Splatting (3DGS), recent 3DGS-based FVV methods have achieved notable breakthroughs in both training and rendering. However, the storage requirements of these methods can reach up to 10MB per frame, making stream FVV in real-time impossible. To address this problem, we propose a novel FVV representation, dubbed StreamSTGS, designed for real-time streaming. StreamSTGS represents a dynamic scene using canonical 3D Gaussians, temporal features, and a deformation field. For high compression efficiency, we encode canonical Gaussian attributes as 2D images and temporal features as a video. This design not only enables real-time streaming, but also inherently supports adaptive bitrate control based on network condition without any extra training. Moreover, we propose a sliding window scheme to aggregate adjacent temporal features to learn local motions, and then introduce a transformer-guided auxiliary training module to learn global motions. On diverse FVV benchmarks, StreamSTGS demonstrates competitive performance on all metrics compared to state-of-the-art methods. Notably, StreamSTGS increases the PSNR by an average of 1dB while reducing the average frame size to just 170KB.

Long-term Time Series Forecasting is crucial across numerous critical domains, yet its accuracy remains fundamentally constrained by the receptive field bottleneck in existing models. Mainstream Transformer- and Multi-layer Perceptron (MLP)-based methods mainly rely on finite look-back windows, limiting their ability to model long-term dependencies and hurting forecasting performance. Naively extending the look-back window proves ineffective, as it not only introduces prohibitive computational complexity, but also drowns vital long-term dependencies in historical noise. To address these challenges, we propose CometNet, a novel Contextual Motif-guided Long-term Time Series Forecasting framework. CometNet first introduces a Contextual Motif Extraction module that identifies recurrent, dominant contextual motifs from complex historical sequences, providing extensive temporal dependencies far exceeding limited look-back windows; Subsequently, a Motif-guided Forecasting module is proposed, which integrates the extracted dominant motifs into forecasting. By dynamically mapping the look-back window to its relevant motifs, CometNet effectively harnesses their contextual information to strengthen long-term forecasting capability. Extensive experimental results on eight real-world datasets have demonstrated that CometNet significantly outperforms current state-of-the-art (SOTA) methods, particularly on extended forecast horizons.

The robustness of intelligent IoT device networking is vital for maintaining communication connectivity within intelligent manufacturing systems, impacting the reliability of the customized Industrial Internet of Things (CuIoT). Current studies enhance network connectivity and resilience against cyber attacks through combinatorial optimization theory by redeploying topologies. However, these approaches often overlook the transformative potential of network motifs in the optimization process. To address this, we introduce CuIoT-MET, an innovative approach that enhances CuIoT robustness by leveraging motif evolutionary transfer knowledge from historical evolution processes. By analyzing changes in connection relationships and emphasizing network motifs' unique contributions, we design a novel robustness metric to optimize the evolutionary trajectory, resulting in more robust CuIoT connection patterns. Extensive experiments show that CuIoT-MET outperforms state-of-the-art methods in improving network robustness.

Efficient task offloading is vital for latency-sensitive Industrial IoT (IIoT) systems. Existing deep learning-based approaches, however, face long training time, poor adaptability, and heavy reliance on labeled data. We propose SRCO, a Semi supervised Reservoir Computing-based Offloading framework that uses a fixed dynamic reservoir and trains only the readout layer, enabling fast model updates with minimal overhead. A semi-supervised strategy further exploits unlabeled data to reduce labeling cost. Experiments show that SRCO improves of floading accuracy by up to 15.6% and reduces training time by up to 59.6% compared with state-of-the-art methods, demonstrating strong efficiency and adaptivity for real-time IIoT applications.

360$^\circ$video streaming emerges as an innovative video presentation form that offers users an immersive and interactive experience, where the quality of experience (QoE) is a vital indicator to measure user viewing perception. In multicategory 360$^\circ$video streaming, existing QoE-driven approaches typically assume a fixed request distribution to enhance users' average QoE, prioritizing the optimization of edge caching and bitrate selection decisions for the video category with a larger request number. Inevitably, these unfair approaches would lead to average QoE reduction in real-world scenarios, in which the request distribution exhibits significant variations and is challenging to predict accurately. To this end, we propose a fairness-aware 360$^\circ$video streaming strategy in cloud-edge collaboration networks for improving users' average QoE. Specifically, we first formulate the joint edge caching and bitrate selection problem as a multi-agent cooperative input-driven Markov decision process to maximize users' average QoE and guarantee QoE fairness for users. Subsequently, we devise an adaptive learning-based multi-agent deep reinforcement learning (MADRL) approach, which can adaptively adjust the learning rate of each agent according to the dynamic user request distribution, thus helping agents make optimal decisions. Finally, experimental results on real-world datasets show that the proposed algorithm significantly improves users' average QoE while ensuring QoE fairness for users.

Distributed stream processing has recently gained much interest due to the need of extracting meaningful results from continuous data stream. To keep the extracted results fresh, the underlying network flows are often required to transmit packets continuously. Otherwise, these results will become stale, and their staleness is determined by the slowest flow. At this point,coflowscan be semantically comprised. Hence, efficient coflow transmission is critical for streaming applications. However, prior coflow-based solutions have significant limitations. They use a one-shot performance metric—CCT (coflow completion time), which cannot continuously reflect the staleness of the output results for a streaming application. To this end, we propose a new performance metric—coflow age(CA), for coflows generated by distributed streaming applications. The CA tracks thelongest time-since-last-serviceamong all flows in a coflow. In such a context, we consider a data center network with multiple coflows that continuously transmit packets between their source-destination pairs and address the problem of minimizing the average long-term CA while simultaneously satisfying the throughput constraints from the coflows. To solve this problem efficiently, we design a randomized algorithm and a drift-plus-age algorithm, and show that they can make the average long-term CA to achieve nearly two times and arbitrarily close to the optimal value, respectively. Through extensive simulations, we further demonstrate that both of the proposed algorithms can significantly reduce the CA of coflows, without violating the throughput requirement of any coflow, when compared to the state-of-the-art solution in both scenario with the packet arrival probability being known and unknown a prior.

The robust network topology of the Internet of Things (IoT) system facilitates uninterrupted service provisioning when encountering device failures. Traditional topology optimization strategies use link-level algorithms to design robust network topologies for IoT device deployment, ensuring network resilience against failures. These algorithms struggle to provide a robust topology for large-scale networks due to the high complexity and computational cost of optimizing each link individually. To overcome this limitation, we introduceLEGO-Motif, a motif-based IoT topology generation algorithm inspired by preferential attachment (PA) and evolutionary theory. By sequentially integrating network motifs, similar to assembling LEGO bricks, the algorithm efficiently enhances topology robustness while reducing computational overhead. Specifically, we propose a novel metric based on motif density to measure topology robustness; then, guided by this metric, we design a topology generation algorithm that ensures optimal topology with high robustness against cyberattacks throughout its growth, inspired by an evolutionary neural network framework. The LEGO-Motif algorithm introduces novel recombination, PA-based mutation, and pruning operators to enhance optimization performance and reduce running-time costs. Comprehensive case studies and evaluations show that LEGO-Motif outperforms current topology optimization algorithms, achieving more robust network topologies with reduced running time, which offers a promising optimal solution for deploying the IoT topology.

This paper targets interactive object-level editing (e.g., deletion, recoloring, transformation, composition) in dynamic scenes. Recently, some methods aiming for flexible editing static scenes represented by neural radiance field (NeRF) have shown impressive synthesis quality, while similar capabilities in time-variant dynamic scenes remain limited. To solve this problem, we propose 4D-Editor, an interactive semantic-driven editing framework, allowing editing multiple objects in a dynamic NeRF with user strokes on a single frame. Specifically, we extend the original dynamic NeRF by incorporating Hybrid Semantic Feature Distillation to maintain spatial-temporal consistency after editing. In addition, a Recursive Selection Refinement module is presented to significantly boost object segmentation accuracy within a dynamic NeRF to aid the editing process. Moreover, we develop Multi-view Reprojection Inpainting to fill holes caused by incomplete scene capture after editing. Extensive quantitative and qualitative experiments on real application scenarios demonstrate that 4D-Editor achieves photo-realistic editing on dynamic NeRFs. Project page: https://patrickddj.github.ioI/4D-Editor

Recent texture generation methods achieve impressive results due to the powerful generative prior they leverage from large-scale text-to-image diffusion models. However, abstract textual prompts are limited in providing global textural or shape information, which results in the texture generation methods producing blurry or inconsistent patterns. To tackle this, we present FlexiTex, embedding rich information via visual guidance to generate a high-quality texture. The core of FlexiTex is the Visual Guidance Enhancement module, which incorporates more specific information from visual guidance to reduce ambiguity in the text prompt and preserve high-frequency details. To further enhance the visual guidance, we introduce a Direction-Aware Adaptation module that automatically designs direction prompts based on different camera poses, avoiding the Janus problem and maintaining semantically global consistency. Benefiting from the visual guidance, FlexiTex produces quantitatively and qualitatively sound results, demonstrating its potential to advance texture generation for real-world applications.

Human action recognition in industrial manufacturing (HAR-IM) plays a crucial role in automating the identification and classification of worker activities. These tasks are inherently fine-grained, requiring the recognition of subtle movements and intricate tool interactions in complex and cluttered environments. While recent advancements in skeleton- and RGB-based recognition methods have demonstrated success in general scenarios, their performance often declines in industrial settings due to challenges such as visually similar actions and significant background noise, which obscure critical motion cues. To address these issues, we propose FusionARM, a multimodal framework that harnesses the complementary strengths of skeleton and RGB modalities. FusionARM leverages skeleton data to prioritize salient spatio-temporal regions within RGB frames, effectively filtering redundant background information and improving the localization of fine-grained activities. The framework introduces a Spatial-Temporal Relevance Selection (STR) mechanism, which aligns keyframes and joint positions to ensure effective fusion of skeleton dynamics with visual context. Additionally, a Model Fusion strategy adaptively balances the contributions of each modality, generating representations that are both discriminative and noise-resilient. Extensive experiments on HAR-IM benchmark datasets validate that FusionARM outperforms state-of-the-art methods, demonstrating its effectiveness in tackling the unique challenges posed by industrial environments.

In the Industrial Internet of Things (IIoT), Protocol Reverse Engineering (PRE) is a technique that analyzes protocol message samples to facilitate the understanding of unknown protocol specifications, enabling intercommunication between heterogeneous devices using industrial control protocols (ICPs). However, capturing message samples from the IloT environment is time-consuming, and the collected samples may lack sufficient diversity to comprehensively cover the protocol specifications, thereby affecting PRE's effectiveness in understanding protocol specifications. To address this limitation, we propose a message expansion method driven by device interaction to efficiently generate message samples that offer comprehensive feature coverage, ultimately enhancing the protocol understanding. Our approach operates in two stages. During the exploration stage, it modifies the initial input messages captured from the network and, through interactions with the device, determines which fields should be excluded from further exploration, thereby narrowing the exploration space. In the expansion stage, we design a neighborhood particle swarm optimization (NPSO) algorithm to thoroughly explore the remaining search space, generating diverse messages and validating them through interaction with the device to comprehensively cover the protocol's structure and functionality. Experimental results show that our method surpasses existing algorithms in both message generation speed and message quality.

Dynamic scene reconstruction is a long-term challenge in 3D vision. Recent methods extend 3D Gaussian Splatting to dynamic scenes via additional deformation fields and apply explicit constraints like motion flow to guide the deformation. However, they learn motion changes from individual timestamps independently, making it challenging to reconstruct complex scenes, particularly when dealing with violent movement, extreme-shaped geometries, or reflective surfaces. To address the above issue, we design a plug-and-play module called TimeFormer to enable existing deformable 3D Gaussians reconstruction methods with the ability to implicitly model motion patterns from a learning perspective. Specifically, TimeFormer includes a Cross-Temporal Transformer Encoder, which adaptively learns the temporal relationships of deformable 3D Gaussians. Furthermore, we propose a two-stream optimization strategy that transfers the motion knowledge learned from TimeFormer to the base stream during the training phase. This allows us to remove TimeFormer during inference, thereby preserving the original rendering speed. Extensive experiments in the multi-view and monocular dynamic scenes validate qualitative and quantitative improvement brought by TimeFormer. Project Page: https://patrickddj.github.io/TimeFormer/

Free-Viewpoint Videos (FVVs) enable immersive viewing of a scene from any position and angle using virtual reality (VR) head-mounted displays (HMDs), thus have great potential in various applications such as telepresence, gaming, and education. Recently, 3D Gaussian Splatting (3DGS) has emerged as a promising method for FVV construction due to its superior reconstruction quality. However, its real-time rendering on untethered HMDs remains challenging due to high computational demands. To address this challenge, we propose EdgeGaussian, a novel edge-client collaborative framework for real-time FVV rendering. Our approach employs decomposed static-dynamic 4D Gaussian splatting (SD-4DGS) to separately reconstruct static and dynamic components of a scene. We further introduce a hybrid mesh-4DGS neural representation, where static components are modeled as textured meshes for local rendering, while dynamic components are offloaded to edge servers as 4DGS. This decomposition significantly reduces the computational burden on the client device while maintaining high rendering quality. Our testbed experiments demonstrate that Edge-Gaussian achieves up to 128 FPS, outperforming state-of-the-art local rendering methods by 4x and edge rendering methods by 5x.

Time-Sensitive Networking (TSN), renowned for its deterministic properties, has become a pivotal technology under-pinning real-time industrial control in Cyber-Physical Systems. Existing research emphasizes enhancing the transmission services of TSN networks for control applications by improving flow schedulability and minimizing end-to-end delay. However, these studies abstract the performance requirements of control applications into rigid, impractical constraints for flow scheduling, disrupting the connection between control optimization and transmission enhancement, and eventually undermining genuine progress in industrial control. Within a co-optimization framework of communication and control, this paper proposes AFS-RT, an Adaptive TSN Flow Scheduling method for Robotic arm Teleoperation, a representative industrial control application. Specifically, through a comprehensive analysis of the teleoperation case, we first integrate slot allocation-based flow scheduling with remote control to formulate a control-driven co-optimization model. To tackle the complexities arising from the implicit mapping between communication and control, we augment the Deep Reinforcement Learning agent responsible for slot allocation with slot-correlation-guided feature extraction, improving feature comprehension by leveraging inherent correlations between slots and thereby boosting the agent’s decision-making capabilities. Extensive testbed and simulation experiments demonstrate that AFS-RT significantly improves teleoperation performance under diverse network conditions compared to SOTA algorithms.

Fingerprint localization methods typically require a substantial amount of manual effort to collect fingerprint data from various scenarios to construct an accurate radio map. While some existing research has attempted to use path planning strategies to save on labor costs, these approaches often suffer from being time-consuming and prone to locally optimal solutions. To address these shortcomings, our paper proposes a novel approach that utilizes imitation learning to construct and update a highly accurate radio map with minimal manual intervention in dynamic environments. Specifically, we employ a multivariate Gaussian process model to fit a rough standby fingerprint database with only a few pilot data points. We then utilize a state space model to calculate the variation range of the pilot data, which forms the CSI error band used to filter the rough radio map. Imitation learning and a confidence coefficient are utilized to predict and calibrate the global CSI data distribution. And we utilize the K-nearest neighbor algorithm to achieve the real-time localization function. Experimental results show that our proposed algorithm outperforms several state-of-the-art approaches in most test cases, exhibiting low computation complexity, lower localization error, and saving 73.3% of the manual workload.

Identifying and detecting anomalies in industrial Internet of Things (IIoT) systems is vital for maintaining industrial safety. In IIoT scenarios, various industrial machines operate with differing periods that overlap temporally, resulting in complex multiperiodic temporal patterns. In addition, varying production tasks and environmental conditions alter sensor dependencies, complicating the modeling of intersensor dependency topologies. Existing methods, which rely on a fixed global topologies, struggle to adapt to these complex multiperiodic temporal patterns and evolving dependency topologies, leading to low anomaly detection accuracy. To tackle these problems, we propose MAGE, a multiperiodic adaptive graph evolution guided anomaly detection framework. MAGE first segments sensor data into distinct temporal periods, then employs a dynamic graph structure learning module to model evolving dependencies. Finally, a global-local association discrepancy module is employed to enhance the anomaly detection capability. Comprehensive experiments on five real-world datasets demonstrate MAGE's superior performance compared to state-of-the-art approaches.

In resource-constrained and dynamic Industrial Internet of Things (IIoT) environments, ensuring robust and adaptable network topologies remains a significant challenge. Existing reinforcement learning-based approaches tackle topology optimization but face scalability issues due to high computational complexity and latency under strict time constraints. To address these challenges, we propose FRED-ABL (FastRobustnessEnhancement forDynamic IIoT topology optimization withAdaptiveBayesianLearning), a novel paradigm that delivers lightweight topology solutions within a constrained time frame. FRED-ABL introduces an innovative topology structure compression method leveraging auxiliary continuous coding, enabling lossless representation of network structures as model inputs. It further defines a new robustness performance metric that integrates considerations of node failures and connection capabilities, serving as a comprehensive evaluation function. By developing an adaptive Bayesian learning model, FRED-ABL efficiently maps the relationship between topology structures and robustness metrics, enabling rapid optimization while significantly reducing computational overhead. Extensive experiments demonstrate that FRED-ABL consistently outperforms state-of-the-art methods, delivering superior robustness and optimization efficiency even in large-scale IIoT deployments.

Pricing strategy is crucial for improving the revenue of mobility on-demand (MoD) systems by achieving supply-demand equilibrium across different city zones. Modern MoD systems commonly utilize order ridesharing and vehicle repositioning to improve the order completion rate while supporting this equilibrium, thereby improving the revenue. However, most existing pricing strategies overlook the effects of ridesharing and repositioning, resulting in supply-demand mismatch and revenue decline. To fill this gap, we propose a multi-agent reinforcement learning (MARL) based pricing strategy via a mutual attention mechanism, named R2Pricing, where the impact of ridesharing and repositioning is considered. First, we formulate the pricing with ridesharing and repositioning as an optimization problem toward maximum overall revenue. Then, we transform it into a MARL model, where the agent makes coupled decisions about order fare with ridesharing and vehicle income with repositioning for each zone. Next, the agents are clustered based on supply-demand observation and reward to train more efficiently. The pricing messages between agents are generated based on mutual information theory, which is then aggregated with an attention mechanism to estimate the impact of price differences among zones. Finally, simulations based on real-world data are conducted to demonstrate the superiority of R2Pricing over the benchmarks.

Autonomous driving systems are fundamentally composed of sequential modular tasks, i.e., perception, prediction, and planning. For connected autonomous vehicles (CAVs), cooperative perception offers a promising solution to surpass their perception limitations, such as occlusion, by sharing sensing data with each other through wireless communication. Existing works typically prioritize sharing data from potential object-containing areas to maximize object detection accuracy under constrained communication resources. However, such detection-oriented approaches ignore a crucial fact that more accurate detection does not equal safer planning. Sharing large amounts of sensing data for detection accuracy can lead to communication resource wastage and performance degradation of subsequent driving tasks. To address this, we introduce Plan2comm, a communication-efficient cooperative perception framework via planning-oriented feature sharing, which shares only sensing data around planned trajectories to enable safer planning rather than mere detection accuracy. Specifically, a planning-oriented communication mechanism is designed to select and transmit the most valuable features from the perspective of the planning task. Moreover, an uncertainty-aware spatial-temporal feature fusion strategy is proposed to enhance high-quality information aggregation. Comprehensive experiments demonstrate that Plan2comm outperforms all other cooperative perception methods on motion prediction performance, and is more communication-efficient.

The robustness of Internet of Things (IoT) topologies measures a network structure's tolerance to random failures, or attacks, which is crucial for stable network communication. Research on optimizing network topology robustness has shifted from empirical rules and heuristics to machine learning, which can extract the features of robust network from topology data, thereby reducing the complexity of traditional topology optimization. However, machine learning approaches typically require a large number of parameters, resulting in high costs associated with parameter tuning and inference. To address these issues, this paper combines parameterized quantum circuits, and proposes a Quantum-Driven efficient Learning Model (QDLM) for enhancing robustness of IoT topology. This model leverages quantum exponential states to significantly reduce the number of training parameters while preserving learning performance. For inputs, QDLM integrates arithmetic encoding and quantum state encoding based on topological adjacency matrix, reducing the number of neurons. In training phase, parameterized quantum rotation gates and controlled quantum gates are used to achieve efficient training. A quantum measurement method is designed to ensure the output topology is a connected graph with the required number of edges. Compared to existing topology learning models, QDLM achieves an order-of-magnitude reduction in training parameters while maintaining topology learning effectiveness.

Vehicle repositioning is widely used in Mobility on-Demand (MoD) systems to address supply-demand imbalances and improve order completion rates. Existing methods typically offer repositioning recommendations focused on enhancing vehicle coordination toward supply-demand re-balance. However, these methods often overlook the possibility that drivers may not follow these recommendations due to their personal preferences, leading to recommendation-decision inconsistency and further disrupting the supply-demand balance. To address this issue, we propose a preference-aware vehicle repositioning recommendation strategy for MoD systems, named FREE, which is based on a Coulomb Force directed approach. The core idea is to strike a balance between vehicle coordination and consistency between recommendations and driver decisions. First, we introduce a Coulomb force-based representation (CFR) to model coordination among vehicles. In this model, the interactions between vehicles and orders are represented as forces that drive the repositioning of vehicles. Next, we develop a driver preference learning model that accurately captures drivers’ preferences using triplet and consistency loss. We then integrate these preferences with the CFR into a multi-agent deep reinforcement learning (MADRL) based repositioning algorithm to generate optimal recommendations. Finally, we validate the effectiveness of FREE through simulations using real-world data, demonstrating its superiority over existing benchmarks.

Accurate object detection with on-board LiDAR sensors is crucial for ensuring driving safety of Connected Automated Vehicles (CAVs), especially at accident black spots with more occlusions. Fortunately, road-side infrastructure equipped with traffic cameras is usually available at these places, offers an extensive field of view and encounters fewer occlusions, and thus can provide sustained assistance to CAVs to improve their object detection performance. However, vehicle-to-infrastructure (V2I) cooperative object detection is quite challenging due to modality heterogeneity, agent heterogeneity, and bandwidth limitations. To address these challenges, in this paper, we propose V2I-Coop, an accurate object detection approach with V2I cross-modality cooperation for CAVs to improve perception performance at accident black spots. In V2I-Coop, first, we extract bird-eye-view (BEV) features from both multi-view 2D images and 3D point clouds, which facilitates the feature fusion of different modalities. Next, the most valuable features from the images are adaptively selected according to available bandwidth and then transmitted to CAVs. Then, a cross-modality feature fusion algorithm is adopted at CAVs to mitigate the modality difference and improve the feature fusion efficiency. Finally, extensive experiments demonstrate that V2I-Coop significantly improves the 3D object detection performance of CAVs at accident black spots.

The ever-increasing quality of experience (QoE) demand for video streaming has prompted the integration of video super-resolution and multi-access edge computing networks (MEC). With super-resolution, the low-resolution frames can be reconstructed into high-resolution ones by edge node and end device collaboratively, which is beneficial in improving QoE. However, the existing works focus on designing video streaming strategies in single-user scenarios, which cannot be applied to multi-user scenarios due to the resource contention among users, as well as the huge solution space of coupled bitrate selection and workload share between edge-end. To fill this gap, we propose a collaborative video streaming strategy with super-resolution in multi-user MEC networks, named Co-Video, to maximize the average QoE by making optimal bitrate selection and workload share. We first formulate the problem as an optimization problem towards maximum average QoE, where the QoE incorporates playback delay, video quality, and smoothness. Then, we transform the optimization problem into a partially observable Markov decision process (POMDP) and exploit the Co-Video strategy based on the multi-agent soft actor-critic (MASAC) algorithm. Specifically, Co-Video utilizes the branching actor network to converge to good policy stably. Finally, trace-driven simulations on real-world bandwidth traces demonstrate that Co-Video outperforms the state-of-the-art baselines.

Joint service caching and workload scheduling plays an important role in ultra-dense mobile edge computing (MEC) networks to satisfy the stringent requirements of latency-critical services by leveraging the aggregated edge resources (e.g., storage and computing resources) located near the users. However, most of the existing methods incorporating popularity-based and/or size-aware caching strategies fail to match the resource demands of user requests with the heterogeneous resources of edge nodes, leading to heavier cloud load and higher latency. It becomes even worse when user requests exhibit dynamic variations over time. To address these issues, we propose an online approach for resource-efficient joint service caching and workload scheduling in ultra-dense MEC networks, called CoShare. The core idea is to fully utilize the heterogeneous resources of the edge layer to further reduce the cloud load and thus the service latency. First, we formulate the joint service caching and workload scheduling problem as a mixed integer nonlinear programming problem with the goal of minimizing the cloud load. Then, an online algorithm is developed to transform this optimization problem into a series of per-slot sub-problems by leveraging Lyapunov optimization. Next, to solve these sub-problems, we design a cacheability-based alternating iterative algorithm utilizing Gibbs sampling, in which the cacheability indicator considers both service resource demands and service popularity. Finally, simulation results show that CoShare can effectively exploit available edge resources to achieve lower cloud loads compared to other strategies.

Unknown industrial control protocols (ICPs) seriously hamper the device intercommunication and security analysis of the Industrial Internet of Things due to the absence of public specification information. Protocol reverse analysis has emerged as a promising technology to infer their specifications, where the primary step is to extract protocol fields by locating their boundaries in the network packet. Previous works leverage various algorithms, such as sequence alignment, keyword mining, and statistic analysis for field extraction. However, they have limitations in excavating the unique features of ICP fields, leading to inaccuracies in boundary localization. To address this problem, we propose an entropy-based field segmentation method for unknown ICPs. After stacking protocol packets vertically, we calculate the information entropy and information gain ratio of data values at each location in the packet. By analyzing the distribution variations of these entropy features in diverse ICP fields, we derive multiple packet segmentation rules to locate the field boundaries. Extensive comparative experiments demonstrate the superiority of our method for ICP field extraction.

Implicit neural representations for video (NeRV) have recently become a novel way for high-quality video representation. However, existing works employ a single network to represent the entire video, which implicitly con-fuse static and dynamic information. This leads to an inability to effectively compress the redundant static information and lack the explicitly modeling of global temporal-coherent dynamic details. To solve above problems, we propose DS-NeRV, which decomposes videos into sparse learnable static codes and dynamic codes without the need for explicit optical flow or residual supervision. By setting different sampling rates for two codes and applying weighted sum and interpolation sampling methods, DS-NeRV efficiently utilizes redundant static information while maintaining high-frequency details. Additionally, we design a cross-channel attention-based (CCA) fusion module to efficiently fuse these two codes for frame decoding. Our approach achieves a high quality reconstruction of 31.2 PSNR with only 0.35M parameters thanks to separate static and dynamic codes representation and outperforms existing NeRV methods in many downstream tasks. Our project website is at https://haoyan14.github.io/DS-NeRV/.

In the realm of industrial Internet of Things(IIoT), the data pattern is subject to change over time, necessitating the implementation of online anomaly detection to adapt to the change of data pattern. Given the multiple stages in the production process in IIoT, data at different times exhibit varying periodic characteristics. Existing training methods primarily use fixed time windows, which struggle to adapt to complex time patterns, leading to decreased accuracy in anomaly detection. Furthermore, the incremental update method which utilizes a single model cannot effectively capture changing data characteristics. This paper introduces an online anomaly detection architecture named Adaptive Time Window enabled Model Pool (ATWMP). The framework utilizes a reinforcement learning model to dynamically select the optimal time window for model update and anomaly detection. Within the model pool framework, anomaly detection is conducted based on model reliability, and model updates are performed according to concept drift in order to ensure accurate adaptation to changing data features. Comprehensive experiments conducted on eight concept-drifted datasets and IIoT datasets demonstrate the superiority of this proposed method compared with other advanced methods.

Cooperative perception is an emerging paradigm that expects to conquer the sensory limitations of individual vehicles by sharing sensor information with each other and significantly improve driving safety. However, achieving highly precise data sharing and low communication overhead remains a challenge for cooperative perception, especially when real-time communication is necessary in autonomous driving. As a result, it is essential to decrease the transmitted sensor data while maintaining the perception performance. For this purpose, we propose a communication-efficient raw-level cooperative perception system for connected autonomous vehicles (CAVs), which is able to significantly compress the raw sensor data each CAV shares with each other by only transmitting the most informative keypoints. Specifically, at the local level, a voxel-based instance-aware keypoints selection strategy is proposed to select the points that belong to regions of interest. To further supervise the local keypoints selection, we present a collaborative global-local learning strategy, enabling each vehicle to consider both the local scenario and the global context when selecting the transmitted data. Comprehensive evaluations indicate the superiority of the proposed system, which achieves more than 300× lower communication volume compared to the raw data, with a performance degradation of less than 1%.

In multiaccess edge computing (MEC) systems, service migration has been extensively applied to ensure service quality by migrating services to follow mobile users. The existing migration methods mainly focus on optimizing service response latency and migration costs by predicting user’s movements. However, some malicious adversaries can learn auxiliary knowledge, i.e., users’ mobility model and service migration trajectory, and launch location inference attacks to infer user locations. This leads to serious personal security threats, like malvertising, fraud and kidnapping. In this article, we propose a location privacy-aware service migration method to against adversaries’ location inference attacks in multiuser MEC systems. First, we adopt an entropy-based location privacy metric to accurately measure user’s location privacy leakage risk. Then, we formulate the service migration progress as a joint optimization problem that minimizes service response latency and location privacy leakage risk. To cope with interuser interference, we developed a multiagent soft actor–critic (MASAC) algorithm to help users collaboratively make service migration decisions. Finally, simulations based on real-world user movement trajectories were conducted to demonstrate the superiority of the proposed method. Evaluation and analysis results showed that our proposed method can effectively protect user location privacy while maintaining a low service response latency.

360° video streaming has gained increasing attraction in the current popular virtual reality, AR, and MR applications, which can provide users with an immersive experience. In tile-based 360° video streaming, edge caching and bitrate selection strategies are jointly designed to improve users’ Quality of Experience (QoE), which incorporates video quality and rebuffer. However, the existing QoE-driven approaches use a unified QoE function to guide the decisions of edge caching and bitrate selection, which neglect the impact of video quality and rebuffer on different categories of 360° videos, thus failing to provide high-average QoE for users. In this article, we propose a multiagent deep-reinforcement-learning-based joint edge caching and bitrate selection strategy for multicategory 360° video streaming to improve users’ average QoE. The key idea is to employ different edge caching and bitrate selection strategies for different video categories to enable fine-grained performance optimization. Based on multicategory 360° video streaming, we first model a joint edge caching and bitrate selection problem as a multiagent cooperative Markov decision process with the goal of maximizing users’ average QoE. Next, an Field-of-View-aware multiagent soft actor–critic (FA-MASAC) algorithm is designed to help agents collaboratively learn optimal edge caching and bitrate selection decisions in a distributed way, in which each video category is treated as an agent. Finally, experimental results on real-world data sets show that our proposed strategy can greatly benefit users’ average QoE compared to existing strategies.

360° video streaming, as an immersive and interactive form of video presentation, has consistently strived for higher video quality and lower latency. A lot of effort has been devoted to improving video quality and reducing latency by utilizing the storage or computing resources of the edge/end layer with edge caching and super-resolution (SR) techniques, respectively. However, it has been conspicuously ignored by existing work that fully leveraging the aggregated caching and computing resources of both edge and end layers can further improve video quality and reduce latency. To this end, in this paper, we propose a MADRL-based Edge-Client collaborative Caching and SR (ECCSR) strategy for high-quality low-latency 360° video streaming. First, we construct a Quality of Experience (QoE) function that involves not only video quality, temporal smoothness, and rebuffering time, but also device energy consumption. Subsequently, we formulate the problem of edge-client collaborative caching and SR as a multi-agent cooperative Markov decision process with the goal of maximizing users’ average QoE. Furthermore, to cope with the decision coupling between agents, an adaptive learning-based multi-agent double actors regularized critics (AL-MADARC) algorithm is developed to help agents make optimal collaborative caching and SR decisions. Through extensive experiments using real-world datasets, we show that ECCSR makes a great improvement in users’ average QoE compared to existing strategies.

Detecting anomalies in multivariate time series (MTS) data collected from industrial Internet of Things (IIoT) systems is essential for a variety of applications, including smart manufacturing. Existing methods typically learn local spatiotemporal representations from nearby time points and neighboring nodes to reconstruct or predict sensor data. However, these local representations are insufficient to model the complex nonlinear topological relationships and dynamic temporal patterns of IIoT systems, which often results in a high-false alarm rate. To address this issue, we propose a new MTS anomaly detection framework called GLAD, which is based on the global–local association discrepancy. The key concept is to detect anomalies based on the difference between the global and local spatiotemporal associations of each data sample, as the association distribution of each data sample provides a more informative description. Specifically, we introduce a Gumbel-Softmax-based graph structure learning strategy to capture the global topological connections from data. Based on the topological graph structure, we utilize a graph attention network (GAT) and transformer to extract both the global and local spatiotemporal associations of each data sample. Finally, we leverage the global–local association discrepancy to effectively detect anomalies from normal data samples. Extensive experiments on five real-world data sets demonstrate the superiority of GLAD over other state-of-the-art methods.

Cooperative perception is an effective way for connected autonomous vehicles (CAVs) to surpass their sensing limitations, by sharing information like intermediate features extracted from images or point clouds with each other. To reduce bandwidth consumption, feature filtering is adopted by existing methods to share only the most valuable information. However, these methods assume that the features on the same channel across all spatial regions or those in the same spatial regions across all the channels are equally important. This assumption results in coarse-grained feature filtering, which greatly decreases the cooperative perception performance. To solve this problem, this paper proposes a fine-grained channel-spatial feature filtering scheme, named SwissCheese, for communication-efficient cooperative perception. The key idea of SwissCheese is to exploit the disparity in semantic information on features between different spatial regions on different channels. Specifically, a fine-grained collaborative attention module is developed to jointly learn fine-grained attention along the channel-spatial dimensions. Moreover, a dual-dimensional feature selection strategy that selects sparse features for transmission based on the current available bandwidth is designed to achieve optimal perception performance. Experiment results show that SwissCheese significantly reduces the transmission data size by 90% with a subtle loss in perception performance.

The Industrial Internet of Things (IIoT) integrates diverse wireless and heterogeneous devices to enable time-sensitive applications. Multi-access edge computing (MEC) offers computing services for nearby tasks to meet their time requirements. However, offloading a large number of tasks to servers with minimal time is a challenging issue. Existing approaches typically allocate tasks into equal-length timeslots for offloading based on optimization or heuristic methods, overlooking the time-varying nature of task arrival density. This neglect significantly increases task execution time. To address this problem, we propose an Adaptive Task Offloading scheme with two-stage hybrid Matching (ATOM). In ATOM, a global buffer with an adjustable threshold is employed to store task information, enabling it to adapt to the time-varying arrival density and execute different offloading stages accordingly. In the online matching stage, if the threshold is not reached, tasks in the buffer are promptly offloaded to the most suitable server. In the offline matching stage, when the threshold is exceeded, all tasks in the buffer are optimally matched with servers and offloaded in batches. Experimental results demonstrate that ATOM outperforms state-of-the-art schemes in terms of average execution time and timeout rate, achieving reductions of 23.3% and 10.4%, respectively.

In the Industrial Internet of Things (IIoT), Multi-access Edge Computing (MEC) emerges as a transformative paradigm for managing computation-intensive tasks, where task offloading plays an important role. However, due to the complex environment of IIoT, existing deep reinforcement learning-based schemes suffer from significant shortcomings in accuracy and convergence speed during model training when addressing the issue of task offloading. In this paper, to solve this problem, we propose an online task offloading scheme based on reinforcement learning, leveraging the double deep Q network (DQN) and dueling DQN with a prioritized experience replay mechanism, called thePrioritized experience-basedDoubleDuelingDQNtask offloading scheme (P-D3QN). P-D3QN enhances action selection accuracy using double DQN and mitigates Q-value overestimation by decomposing state and advantage using dueling DQN. Additionally, we adopt the prioritized experience replay mechanism to enhance the convergence speed of model training by selecting transitions that induce a higher training error between the evaluation network and the target network. Experimental results demonstrate that P-D3QN outperforms several state-of-the-art schemes, achieving a reduction of 21.0% in the average cost of the task and improving the completion rate of the task by 19.5%.

Mobility on demand (MoD) systems utilize ridesharing, i.e., multiple orders with high associating utility share a single vehicle, to reduce carbon footprint and alleviate traffic pressure. Existing methods mainly promote ridesharing by flocking multiple orders to the minimum required vehicles. However, supply-demand variations may aggregate undersupply in the long run and affect the order completion rate. Meanwhile, it is difficult to accurately estimate associating utility among ridesharing orders with lane-level features, such as traffic flow. To fill this gap, we propose ERShare, an elastic order dispatching algorithm to maximize the order completion rate in the MoD system. First, the ridesharing order dispatching problem is formulated as an offline optimization problem, and then it is proved that the order completion rate is maximized when the MoD system achieves long-term supply-demand equilibrium. Next, a dummy order/vehicle generation method is proposed to generate dummies as a spinner to achieve supply-demand equilibrium elastically. Also, a lane-level ridesharing rule is designed to accurately estimate the associating utility based on an order association graph. Subsequently, a dummy-based order dispatching algorithm is proposed to find the optimal dispatching decisions. Finally, the simulations on real-world data validate the superiority of ERShare over state-of-the-art solutions regarding order completion rate.

The robustness of scale-free Internet of Things (IoT) topology is seriously affected by malicious attacks. Improving the tolerance to node failures is critical to the stability of IoT systems. Heuristic algorithms, especially genetic algorithms, enhance the stability of network topology through the evolution of population chromosomes. However, the loss of genetic diversity makes the optimization easily fall into local optimum. Although the problem can be alleviated by adjusting population size and genetic probability, the genetic diversity is still not guaranteed in the limited number of iterations. Inspired by the quantum superposition that simultaneously operates on an exponential number of states, we propose a quantum-inspired robust networking model with multiverse co-evolution for the scale-free IoT (Q-Robust). This model designs quantum chromosomes with double-chain structures to represent the connections between all nodes. Then we present the quantum measurement method of quantum chromosomes based on the degree distribution of nodes. Furthermore, this model constructs a primary-secondary quantum multiverse co-evolution mechanism to improve the convergence efficiency of topology evolution. The experimental results show that the topology robustness optimized by Q-Robust is about 60% and 10% higher than the initial topology and the state-of-the-art topology evolution algorithm, respectively.

Multi-access edge computing (MEC)-enabled Internet of Things (IoT) has become a powerful solution to run computation-intensive applications on end devices. These applications are composed of multiple dependent tasks, which can be abstracted as directed acyclic graphs (DAGs). Moreover, applications can share partial intermediate data with each other based on dynamic network conditions to boost its performance, so-called soft cooperation. However, it is quite challenging to make optimal offloading decisions with external dependency between tasks of different DAGs introduced by soft cooperation, as well as the subsequent huge continuous solution space caused. In this paper, we propose a DAG-based dependent tasks offloading method with soft cooperation in MEC-enabled IoT. First, we formulate the problem as a Markov decision process (MDP), aiming to minimize the application latency and energy consumption, and to maximize the cooperation gain simultaneously. Then, we propose a branch soft actor-critic (BSAC) algorithm to make optimal decisions under dynamic network conditions, including the offloaded tasks, the CPU frequency of end devices, and the sharing ratio of intermediate data. Specifically, BSAC uses multiple branch networks to reduce the solution space. Finally, a series of simulations are conducted to establish the superiority of the BASC algorithm over state-of-the-art solutions.

In 5 G small cell networks, edge caching is a key technique to alleviate the backhaul burden by caching user desired contents at network edges such as small base stations (SBSs). However, due to storage space limitation and diverse user preference patterns, a single SBS is unable to cache all the user desired contents and thus leading to low caching efficiency. In this paper, we propose a recommendation-driven multi-cell cooperative caching strategy to improve the caching efficiency. The idea is to aggregate the storage spaces of multiple SBSs into a large shared resource pool, and guide users to access cached contents by content recommendation. First, we formulate the joint cooperative caching and recommendation problem as a multi-agent multi-armed bandit (MAMAB) problem with the aim of minimizing the average download latency. Then, we propose a multi-agent reinforcement learning (MARL)-based algorithm, MARL-JCR, to solve the problem in a fully distributed manner with limited information exchange among the agents. We also develop a modified combinatorial upper confidence bound algorithm to reduce each agent's decision space to reduce computational complexity. The experiment results evaluated on theMovieLensdataset show MARL-JCR decreases the average download latency by up to 60% as compared with the state-of-the-art solutions.

Cloud service providers improve resource utilization by co-locating latency-critical (LC) workloads with best-effort batch (BE) jobs in datacenters. However, they usually treat multi-component LCs as monolithic applications and treat BEs as “second-class citizens” when allocating resources to them. Neglecting the inconsistent interference tolerance abilities of LC components and the inconsistent preemption loss of BE workloads can result in missed co-location opportunities for higher throughput. We present Rhythm , a co-location controller that deploys workloads and reclaims resources rhythmically for maximizing the system throughput while guaranteeing LC service’s tail latency requirement. The key idea is to differentiate the BE throughput launched with each LC component, that is, components with higher interference tolerance can be deployed together with more BE jobs. It also assigns different reclamation priority values to BEs by evaluating their preemption losses into a multi-level reclamation queue. We implement and evaluate Rhythm using workloads in the form of containerized processes and microservices. Experimental results show that it can improve the system throughput by 47.3%, CPU utilization by 38.6%, and memory bandwidth utilization by 45.4% while guaranteeing the tail latency requirement.

Fast-advancing mobile communication technologies have increased the scale of the Internet of Things (IoT) dramatically. However, this poses a tough challenge to the robustness of IoT networks when the network scale is large. In this paper, we present DAC-Motif, a distributed co-evolutionary method for optimizing network robustness based on network motifs. Unlike centralized evolutionary optimization approaches, DAC-Motif uses the technique of Divide-And-Conquer (DAC) to divide the large-scale IoT topology into partitions and then merge the self-evolving partitions into a global robust topology. This approach leverages both distributed computing and asynchronous communication mechanisms to mitigate premature convergence and reduce time complexity for large-scale IoT topologies. In our evaluation, DAC-Motif achieves three to four orders of magnitude shorter running time and over 10% robustness improvement compared to other centralized evolutionary algorithms under a scale of around 5,000 IoT devices.

Mobility on-demand (MoD) systems widely use machine learning to estimate matching utilities of order-vehicle pairs to dispatch orders by bipartite matching. However, existing methods suffer from overestimation problems due to the complex interactions among order-vehicle pairs in the global bipartite graph, leading to low overall revenue and order completion rate. To fill this gap, we propose a multi-agent deep reinforcement learning (MADRL) based order dispatching method with bipartite splitting, named SplitMatch. The key idea is to split the global bipartite graph into multiple sub-bipartite graphs to overcome the overestimation problem. First, we propose a bipartite splitting theorem and prove that the optimal solution of global bipartite matching can be achieved by solving multiple sub-bipartite matching problems when certain conditions are met. Second, we design a spatial-temporal padding prediction algorithm to generate sub-bipartite graphs that satisfy this theorem, where the spatial-temporal feature of orders and vehicles is captured. Next, we propose a MADRL framework to learn the matching utility, where multi-objective, e.g., immediate revenue and quality of service (QoS), are taken into account to deal with varying action space. Finally, a series of simulations are conducted to verify the superiority of SplitMatch in terms of overall revenue and order completion rate.

With the rapid scale expansion of the Internet of Things (IoT), the probability of system failure increases. Frequent system failures degrade the quality of service (QoS) of IoT. Existing optimization strategies utilize reinforcement learning (RL) to enhance the robustness of IoT topology. However, due to the increasing scale of the IoT environment, the unbalanced exploration and exploitation of RL agents make it prone to premature convergence at the local optimum. Large-scale action spaces and state spaces lead to a sparse reward problem, which reduces the convergence efficiency of the algorithm. This paper proposes an evolutionary reinforcement learning scheme for IoT robustness to solve the above problems. We design a multi- agent evolution mechanism to provide multiple experiences for RL, which strengthens exploration capability. We present new evolution operators to promote convergence, which combine dis- tillation crossover and Gaussian mutation. Extensive experiments show that our scheme has a strong exploration capability, and the optimization rate of IoT topology robustness reaches 81.15%, which outperforms other robustness optimization algorithms.

Teacher-student learning has emerged as a promising framework for real-time video inference on mobile devices in multi-access edge computing (MEC) networks, where heavyweight teacher models are deployed on edge servers, and lightweight student models distilled from teacher models are deployed on mobile devices. To deal with data drift and maintain the inference accuracy, the student model has to be updated periodically with the help of the teacher model through a training process. Different training configurations, such as training epochs and frozen layers, lead to different accuracy improvements with different resource requirements. However, in multi-user heterogeneous MEC networks, due to resource heterogeneity and limited computing resources of edge servers, it is quite challenging to update all the student models simultaneously to achieve high inference accuracy. To address this problem, in this paper, we propose an adaptive teacher-student framework in multi-user heterogeneous MEC networks. The key idea is to adaptively make optimal updating decisions (i.e., offloading decision and configuration selection decision) for each user, where the available resources of edge servers and network conditions are taken into account. First, we model the teacher-student collaborative video inference problem as an optimization problem with the aim of maximizing the average inference accuracy. Then, we propose an evolutionary deep reinforcement learning algorithm, CEM-MASAC, to solve this problem. Finally, trace-driven simulations employing real-world bandwidth traces demonstrate the superiority of our algorithm compared to the baseline methods.

The mobility on demand (MoD) system relieves traffic pressure by simultaneously dispatching multiple orders to a vehicle via ridesharing. However, since the supply-demand distribution varies over time, existing dispatching methods for minimum fleet failed to achieve long-term supply-demand equilibrium, and thus greatly reduces the order completion rate. In this paper, ElasticShare, a ride-sharing order dispatch method, is proposed to maximize the order completion rate under dynamic supply-demand distribution. First, we formalize the ridesharing order dispatching problem as an offline optimization problem and then prove it can be solved in an online manner by adding dummy orders and vehicles satisfying long-term supply-demand equilibrium conditions when dispatching. Second, based on the proof, we generate a certain number of dummies according to the supply-demand relation to promote or restrain ridesharing in an elastic manner. The characteristics of the dummies are determined by statistics and a specific selection method to ensure that the solution approximates the long-term supply-demand equilibrium. Next, we decouple the online problem into two sub-problems (order associating and order association dispatching), which are solved by a greedy algorithm and correctional order association dispatching algorithm, respectively. Finally, simulations with real-world data are used to validate the superiority of ElasticShare over state-of-the-art solutions in terms of the order completion rate.

Collaborative mobile virtual reality (VR) has recently emerged as a promising solution to provide an immersive user experience with low motion-to-photo (MTP) latency. The rendering tasks are usually divided into background and foreground ones, which are executed in the edge server and head-mounted display (HMD), respectively. Assuming that the background images are static, they can be reused for temporal redundancy reduction in transmission. However, in high dynamic high-quality scenes, background images are continuously changing, making the temporal reuse strategy ineffective, leading to high MTP latency and hence motion sickness. In this paper, we propose CollabVR, a reprojection-based edge-client collaborative rendering approach for real-time high-quality mobile VR in high dynamic scenes. The key idea is to reduce the spatial redundancy in transmission by exploiting the high similarity between the left view and right view. With CollabVR, only one view is rendered and encoded in the edge server and then be transmitted to, and decoded at the HMD. The another view is reprojected by utilizing depth image-based rendering (DIBR) in the HMD, thereby greatly reducing the MTP latency even in high dynamic scenes. Furthermore, we propose a foveated-based multi-level patch subdivision strategy to achieve real-time reprojection in the resourced-limited HMD. A parallel streaming strategy is also proposed to fill holes that exist in the reprojected image. Experiments we conducted using Commercial Off-The-Shelf (COTS) devices indicate that CollabVR can reduce the average MTP latency by up to 36% compared to the baseline methods.

In this article, we present a work using a smartphone with an off-the-shelf WiFi router for human activity recognition with various scales. The router serves as a hotspot for transmitting WiFi packets. The smartphone is configured with customized firmware and developed software for capturing WiFi channel state information (CSI) data. We extract the features from the CSI data associated with specific human activities, and utilize the features to classify the activities using machine learning models. To evaluate the system performance, we test 20 types of human activities with different scales including seven small motions, four medium motions, and nine big motions. We recruit 60 participants and spend 140 hours for data collection at various experimental settings, and have 36 000 data points collected in total. Furthermore, for comparison, we adopt three distinct machine learning models, including convolutional neural networks (CNNs), decision tree, and long short-term memory. The results demonstrate that our system can predict these human activities with an overall accuracy of 97.25%. Specifically, our system achieves a mean accuracy of 97.57% for recognizing small-scale motions that are particularly useful for gesture recognition. We then consider the adaptability of the machine learning algorithms in classifying the motions, where CNN achieves the best predicting accuracy. As a result, our system enables human activity recognition in a more ubiquitous and mobile fashion that can potentially enhance a wide range of applications such as gesture control, sign language recognition, etc.

Mobile edge computing (MEC) is a key enabler for ultra-low latency in heterogeneous dense cellular networks in the 5G era and beyond, by deploying services at the network edge. Due to high user mobility, the services are usually migrated to follow the users by predicting the user trajectory to achieve a balance between energy consumption and service latency. However, service migration for multi-user heterogeneous dense cellular networks is challenging because (1) the user trajectory prediction, which is crucial for service migration, becomes intractable with a large number of users, and (2) making service migration decisions for each user independently is subjected to interference among the users. Therefore, in this study, we formulated the service migration of all the users in MEC-enabled heterogeneous dense cellular networks as an optimization problem, with the objective of minimizing the average energy consumption while satisfying the service latency requirements, taking into account the interference among different users. Next, we developed an efficient energy-efficient online algorithm based on the Lyapunov and particle swarm optimizations, called EGO, to resolve the original problem without predicting the trajectories of the users. Finally, a series of simulations based on real-world mobility traces of vehicles in Bologna were conducted to establish the superiority of the EGO algorithm over state-of-the-art solutions.

With the rapid growth in the demand for location-based services in indoor environments, wireless fingerprint localization has attracted increasing attention because of its high precision and easy implementation. However, an effective method does not exist owing to the problems of data loss, noise interference in the fingerprint database, and being time-consuming during the offline training phase. Therefore, this paper presents a novel indoor wireless fingerprint localization algorithm, termed BLS-Location, based on a broad learning system (BLS) that utilizes channel state information (CSI) to overcome the aforementioned problems. It includes an offline training phase and an online localization phase. In the offline training phase, the Kalman filter and the expectation-maximization (EM) algorithm are utilized for completing and denoising the data. Moreover, principal component analysis (PCA) is used to reconstruct the CSI data to reduce complexity and train the weights by BLS. In the online localization phase, we employ a novel probabilistic method based on the regression results of BLS to obtain the estimated location. The experimental results show that BLS-Location can significantly reduce the training time with a high accuracy, compared to several machine learning algorithms and four existing methods in two representative indoor environments.

With the growing demand of Location-Based Service, the fingerprint localization based on Channel State Information (CSI) has become a vital positioning technology because it has easy implementation, low device cost and adequate accuracy which benefits from fine-grained information provided by CSI. However, the main drawback is that the approach has to construct the fingerprint map manually during the off-line stage, which is tedious and time-consuming. In this paper, we propose a novel data collection strategy for path planning based on reinforcement learning, namely Asynchronous Advantage Actor-Critic (A3C). Given the limited exploration step length, it needs to maximize the informative CSI data for reducing manual cost. We collect a small amount of real data in advance to predict the rewards of all sampling points by multivariate Gaussian process and mutual information. Then the optimization problem is transformed into a sequential decision process, which can exploit the informative path by A3C. We complete the proposed algorithm in two real-world dynamic environments and extensive experiments verify its performance. Compared to coverage path planning and several existing algorithms, our system not only can achieve similar indoor localization accuracy, but also reduce the CSI collection task.

Decentralized federated learning (DFL) is a promising technology to implement distributed machine learning in Internet of Vehicles (IoV), which enables vehicles to share and aggregate models with their neighbors in a vehicle-to-vehicle (V2V) network. However, due to the high mobility of vehicles, model sharing via V2V links may fail as the topology of the V2V network is time-varying, which greatly reduces the efficiency of model aggregating and the speed of model training. To address this problem, in this paper, we propose a graph convolution network (GCN)-based topology design method, named G-DFL, to improve the training efficiency of DFL in IoV by properly selecting a subgraph of the underlay V2V network, which is referred to as overlay network, in each round of model sharing. First, by encoding the state of vehicles, we utilize a GCN to extract the features of V2V network topology to predict the effective V2V links for model sharing. In addition, to further reduce the delay of model training, we use Christofides' Algorithm to find the Hamiltonian circuit with the least delay as the overlay network. Simulation results validate that the proposed method significantly improves the model training performance in DFL compared with the other baseline methods.

Internet of Things (IoT) is growing with various applications linked in, and node failures are becoming more common as a result of malicious strikes and other issues. The cascading collapse induced by local node failures can be mitigated by robust network topology. Existing approaches for fixed topology enhance the robustness of IoT topology by reconstructing device connections. However, using existing techniques necessitates global topology optimization when new nodes are added, which takes time. To tackle this situation, this study introduces an evolutionary algorithm based on neuroevolution that generates robust IoT topology. It provides IoT topology with inherent robustness when adding extra nodes by utilizing unique mutation and crossover operators. What’s more, we establish an adaptive edge density management method to reduce the rise in energy consumption caused by redundant connections when nodes join. Experimental results indicate that the proposed scheme can effectively build robust topology than multiple existing topology optimization methods in less time for diverse network sizes.

In mobile video streaming, the ever-increasing user expectations for Quality of Experience (QoE) have prompted the integration of video super-resolution and adaptive bitrate techniques on either the mobile device or the edge server. By reconstructing high-resolution frames from low-resolution frames that have been downloaded, both high video quality and a short rebuffer time can be enjoyed. However, the exiting methods merely leverage the computing resources of the edge server or mobile device, leaving significant room for further QoE improvement. In this paper, we present an adaptive Video Streaming system with Edge-Client collaborative Super-resolution, named VSECS, to enhance users' QoE by simultaneously utilizing the computing resources of both the edge server and mobile device to reconstruct high-resolution frames collaboratively. First, we deploy a large-scale super-resolution model on the edge server and a lightweight model on the mobile device. Then, we exploit the Asynchronous Advantage Actor-Critic (A3C) algorithm to make decisions regarding the download resolution, the reconstructed target resolution, and the workload share of the mobile device, considering the network bandwidth, computing resources, and reconstruction complexity of video tiles. Furthermore, we utilize the branching actor network to enable the agent to converge to good policy stably. Trace-driven simulations on real-world bandwidth traces demonstrate that our approach can improve QoE by up to 10% compared to the state-of-the-art video streaming solutions.

A two-untrusted-relay transmission scheme is proposed with lossy-forward (LF) relaying being utilized. Two untrusted relays are located between one source and one common destination and there is no direct link between the source and the destination, which referred to as diamond relaying network. Since LF relaying allows intra-link errors and always forwards the decoded information sequences from the relays to the destination, it realizes the reliable and secure transmission of decoder-and-forward based relay networks with untrusted relays. Reliable-and-secure probability is derived to evaluate the performance of the untrusted relay networks, which represents the probability that the destination can recover the original message sent from the source whereas the relays cannot. We find that as long as the contributions of the transmit power of source and relays are balanced, a certain degree of reliable-and-secure probability can be held, even without the support from a friendly jamming signal.

We consider correlated sources coding over a up-link non-orthogonal multiple access shadowed κ-µ fading channel. The sufficient condition for lossless coding is determined by the intersection of the Slepian-Wolf region and multiple access channel region, assuming source-channel separation holds. The exact expression for the outage probability upper bound is derived by dividing the sufficient conditions into three cases. The accuracy of the analytical results is verified by the Monte-Carlo simulations. The analytical results indicate that more than 2ndorder diversity gain can be achieved with a larger ratio of line-of-sight dominant component in single cluster or multiple clusters with non-line-of-sight component. It is also found that the shadowed κ-µ fading well represents one-sided Gaussian, Rayleigh, Rician, and Nakagami-m fading in calculating the outage probability. Furthermore, the ϵ-outage achievable rate is analyzed, which is found to be larger with higher source correlation and/or average signal-to-noise ratio.

Abstract The performance of correlated sources transmission over multiple access shadowed ‐ fading channels is investigated, in which one of the correlated sources needs to be recovered at the destination, whereas the other serves as a helper. The sufficient condition for lossless coding is determined by the intersection of the modified Slepian–Wolf region and the multiple access channel region. The outage probability upper bounds are derived based on the sufficient condition, with the Gaussian codebook capacity and the constellation constrained capacity, respectively. The difference between the outage probabilities derived with the two kinds of capacities is found to be very minor, when the spectrum efficiency or source rate is low; however, with high spectrum efficiency or high source rate, such difference becomes significant. A closed‐form outage approximation is also obtained at the high signal‐to‐noise ratio region. The accuracy of the analytical results is verified by the Monte‐Carlo simulations. It is found that shadowing significantly affects the outage performance, however, it has no effect on the diversity gain. Furthermore, the power allocation between the source and the helper is studied to minimize the outage probability and it is found that generally more power should be allocated to the helper in the case with higher source‐helper correlation.

Blockchain, which stores data in an appending form, cannot achieve the purpose of expanding the storage capacity by increasing the number of nodes. As the system runs, nodes will face problems of insufficient storage space. In the existing peer-to-peer (P2P) blockchain network model, all network nodes participate in data storage, and the generated blocks need to be verified among the network-wide nodes. This approach suffers from low system transaction throughput and data storage redundancy. In order to solve the above existing problems, this article proposes a block data storage model based on the double-layer blockchain network (DLBN), which improves the internal data composition structure of the blockchain. The DLBN contains two types of blockchain nodes, which form the storage and consensus layers of the system, respectively. The consensus layer is responsible for tasks, such as transaction sequencing, validation, and block packing, thus increasing the system transaction throughput. The nodes in the storage layer are divided into multiple storage units (SUs), and all nodes in the SU jointly maintain a copy of the complete blockchain, thereby reducing the storage pressure on the nodes. Based on the DLBN model, we design a reputation-based consensus mechanism, block storage allocation algorithm, and transaction query optimization algorithm, respectively. Through experimental verification and analysis, the storage model based on the DLBN can effectively improve the system transaction throughput and reduce the node storage capacity while ensuring system security.

We analyze the outage performance of a lossy forwarding (LF) relaying system with the simultaneous wireless information and power transfer (SWIPT) capability. In the system of LF with SWIPT (LF-SWIPT), a source broadcasts its message to both a relay and a destination. A relay node with SWIPT functionality harvests energy and decodes information from the source signal. The energy is split into two parts for information processing and message forwarding, respectively. For information processing, the relay attempts to decode the incoming source signal and forms an estimate. Unlike the existing decode-and-forward SWIPT system (DF-SWIPT), the estimate is always forwarded using the harvested energy. The destination performs joint decoding to recover the message with the signals received from both the source node and the relay node. We derive the outage probability for the LF-SWIPT system based on the theorem ofsource coding with side information. The simulation results demonstrate that the proposed system achieves significant gains (around 1–2 dB) compared to the DF-SWIPT system. We further evaluate the impact of the distance and the power splitting (PS) strategy on the system performance using simulations. Finally, we build an optimization algorithm on the PS ratio by maximizing the admissible region from the theoretical perspective.

Cooperative perception is an effective way for connected autonomous vehicles to extend sensing range, improve detection precision, and thus enhance perception ability by combining their own sensing information with that of other vehicles. The existing cooperation perception schemes share only raw-, feature-, or object-level data, thus lacking the flexibility to adapt to highly dynamic vehicular network conditions, which leads to either bandwidth saturation or bandwidth underutilization, degrading the detection precision in the long run. In this article, we propose ML-Cooper, a multilevel cooperative perception framework, to fully utilize the bandwidth and hence improve detection precision. The key idea of ML-Cooper is to divide each frame of sensing data of the sender vehicle into three parts, and the corresponding raw data, feature data, and object data are transmitted to and fused at the receiver vehicle. We also develop a soft actor–critic (SAC) deep reinforcement learning algorithm to adaptively adjust the proportion of the three parts according to the channel state information of the Vehicle-to-Vehicle (V2V) link. The experimental results on KITTI and our collected data sets on two real vehicles show that ML-Cooper can achieve the highest average detection precision compared to existing single-level cooperative perception schemes.

In the era of cloud computing, network services are deployed on geographically distributed cloud platforms, which results in a large amount of inter-datacenter traffic. Multi-tier pricing schemes are widely adopted by cloud service providers (CSPs) to charge cloud users for inter-datacenter transmission services. To avoid a severe penalty associated with missing a deadline, cloud users are prone to selecting a sufficiently high service level. However, they are usually unaware of the total traffic volume before accessing the network; hence, a high transmission cost is introduced. In this paper, we propose an online cost-efficient transmission scheme for cloud users with information-agnostic traffic. The basic idea is to split a long-term transmission request into a series of short-term ones. In this scheme, we take into account the CSP’s countermeasures, and model the interactions between the cloud users and the CSP as a Stackelberg game. We show that the optimal number of short-term requests and the associated transmission service levels can be determined with an online algorithm based on Lyapunov optimization. The experimental results reveal that the CSP and the cloud users can achieve a win-win outcome, whereby the transmission cost of cloud users can be reduced by 59 percent.

In the era of global-scale services, analytical queries are performed on datasets that span multiple data centers (DCs). Such geo-distributed queries generate a large amount of inter-DC data transfers at run time. Due to the expensive inter-DC bandwidth, various methods have been proposed to reduce the traffic cost in geo-distributed data analytics. However, current methods do not attempt to address the throughput issue in geo-distributed analytics. In this article, we target at characterizing and optimizing a cost-throughput tradeoff problem in geo-distributed data analytics. Our objectives are two-fold: (1) we minimize the inter-DC traffic cost when serving geo-distributed analytics with uncertain query demand, and (2) we maximize the system throughput, in terms of the number of query requests that can be successfully served with guaranteed queuing delay. Specifically, we formulate a stochastic optimization problem that seamlessly combines these two objectives. To solve this problem, we take advantage of Lyapunov optimization techniques to design and analyze a two-timescale online control framework. Without prior knowledge of future query requests, this framework makes online decisions on input data placement and admission control of query requests. Rigorous theoretical analyses show that our framework can achieve a near-optimal solution and maintain system stability and robustness as well. Extensive trace-driven simulation results further demonstrate that our framework is capable of reducing inter-DC traffic cost, improving system throughput, and guaranteeing a maximum delay for each query request.

The cloud resource provision policy of a content provider in the presence of competitors on globally distributed cloud platforms plays a significant role in maximizing its profit. However, developing an optimal resource provision policy is quite challenging, due to the difficulty to capture the competition relationship between two competitive CPs and to obtain the budget of the competitors which is usually kept private. To solve this problem, in this article, we propose a learning-driven cloud resource provision policy for a CP with competitors. We formulate the competition between the CPs as alottery Colonel Blottogame in which the payoff of each region is positively related to the resource advantage achieved by the CP, formulate the budget allocation problem as a Markov decision process, and obtain the sub-optimal resource provision policy by reinforcement learning and deep reinforcement learning-based algorithms. We also prove the convergence of the sub-optimal solution. Finally, we validate our proposed method using real-world CPs statistics. The results show that the budget information is critical for a CP to make policy decisions, and it is better for CPs with smaller budget to focus their budget resources in regions with higher values.

Mobile social networks include a large number of social members who forward messages cooperatively. However, spammers post links to viruses and advertisements, or follow a large number of users, which produces many misleading messages in mobile social networks. In this paper, we propose an adaptive social spammer detection (ASSD) model. We build a spammer classifier by using a small number of labeled patterns and some unlabeled patterns. The prediction accuracy is high compared with some conventional supervised learning methods. Moreover, the time and energy required to label the identity of social members are reduced by applying ASSD. Because social spammers frequently change their behavior to deceive the spammer detection model, an incremental learning method is designed to update the spammer detection model adaptively, without retraining. We evaluate ASSD by comparing it with other supervised and semi-supervised machine learning methods using the Social Honeypot Dataset. Experimental results show that the proposed model outperforms the baseline methods in terms of recall and precision. Additionally, ASSD maintains a high detection accuracy by adaptively updating the model with newly generated social media data.

Protocol parsing is to discern and analyze packets' transmission fields, which plays an essential role in industrial security monitoring. The existing schemes parsing industrial protocols universally have problems, such as the limited parsing protocols, poor scalability, and high preliminary information requirements. This paper proposes a text similarity-based protocol parsing scheme (TPP) to identify and parse protocols for Industrial Internet of Things. TPP works in two stages, template generation and protocol parsing. In the template generation stage, TPP extracts protocol templates from protocol data packets by the cluster center extraction algorithm. The protocol templates will update continuously with the increase of the parsing packets' protocol types and quantities. In the protocol parsing phase, the protocol data packet will match the template according to the similarity measurement rules to identify and parse the fields of protocols. The similarity measurement method comprehensively measures the similarity between messages in terms of character position, sequence, and continuity to improve protocol parsing accuracy. We have implemented TPP in a smart industrial gateway and parsed more than 30 industrial protocols, including POWERLINK, DNP3, S7comm, Modbus-TCP, etc. We evaluate the performance of TPP by comparing it with the popular protocol analysis tool Netzob. The experimental results show that the accuracy of TPP is more than 20% higher than Netzob on average in industrial protocol identification and parsing.

Over the last decade, traffic engineering (TE) has always been a research hotspot in data center networks. For routing flows efficiently and practically, existing TE schemes explore experience-driven heuristics or machine learning (ML) techniques to predict/identify network flows’ size information. However, these TE schemes have significant limitations: they either identify the flow size information too late or are unaware of the ML models’ prediction errors. In this paper, we present DarkTE, a novel TE solution that can learn to predict flow size information timely for achieving better routing performance while being robust to the prediction errors. At its heart, DarkTE employs an ensemble learning technique (i.e., random forest) to classify flows into mice and elephant flows with high accuracy. It then leverages a confidence-based rate allocation and path selection scheme to mitigate the occasional classification errors. Large-scale simulations demonstrate that DarkTE classifies flows within hundreds of microseconds, and the classification accuracy is at least 86.4% over three different realistic workloads. Further, DarkTE completes flows 2.94 times faster on average and makes more links to experience over 90% bandwidth utilization than the Hedera solution.

Millimeter-wave (mmWave) communication is a promising technology for future vehicular networks, where plenty of self-driving vehicles transmit a great amount of sensing data to the edge-cloud platform for real-time processing to ensure driving safety. While beam selection has been widely investigated in single mmWave base station (mmBS) scenario to maximize the throughput between the vehicle and the mmBS, it is still quite challenging to perform optimal beam selection in mmWave vehicular networks with multiple mmBSs. On the one hand, performing beam selection at a central controller with global information of the networks is infeasible due to the exponentially increased complexity. On the other hand, a distributed solution may suffer from the interference between overlapping beams among mmBSs which leads to severe throughput degradation. To fill this gap, in this paper, we propose a Multi-Agent Reinforcement Learning based cooperative Beam Selection (MARL-BS) algorithm for mmWave vehicular networks. Specifically, we model the beam selection problem as a multi-agent multi-armed bandit problem and then adopt Q-learning to learn how to coordinate the beam selection decisions. In the proposed approach, each mmBS acts as an agent and learns the Q-values of its own actions in conjunction with those of the other mmBSs. We further propose a modified combinatorial upper confidence bound (CUCB) algorithm to take advantage of exploring and exploiting all the candidate beams to avoid falling into local optimum. Finally, our simulations validate the proposed MARL-BS algorithm and confirm its higher performance compared with the other benchmark algorithms.

In the tile-based 360-degree video streaming, it is essential to predict future viewport and to allocate higher bitrates to tiles inside the predicted viewport to optimize the Quality of Experience (QoE) of the users. However, the majority of existing work focuses on short-term viewport prediction, which is prone to rebuffering in dynamic network conditions. On the other hand, the recently developed on-policy Deep Reinforcement Learning (DRL)-based bitrate allocation approaches suffer from poor sample efficiency. To address these issues, in this paper we present a tile-based adaptive 360-degree video streaming system, named LS360, which consists of long-term viewport prediction and adaptive bitrate allocation. First, we propose a Long Short-Term Memory (LSTM)-based viewport prediction model to make use of the heatmap feature from all users’ previous movement information and the target user’s fixation movement feature to improve prediction accuracy. Next, we employ the off-policy Soft Actor-Critic (SAC) algorithm to make optimal tile bitrate allocation decisions by taking the predicted long-term viewport, playback buffer, and bandwidth-related information into account. Experiments on real-world datasets demonstrate that LS360 outperforms state-of-the-art streaming algorithms in terms of long-term viewport prediction accuracy and QoE under different bandwidth conditions.

In mobile social networks (MSNs), mobile users communicate with each other via mobile devices, such as smartphones and tablets, transmitting data through intermittent connections. Mobile users have high mobility, which creates higher requirements for efficient data forwarding in MSNs. Therefore, forwarding data efficiently and quickly becomes a key problem. To tackle this problem, this article proposes a routing method based on a dynamic transient social community (DTSC) to optimize the routing and forwarding performance in MSNs. In this process, combined with the duration of intensive contact between nodes and the social relations of mobile users, the similarity of each pair of contact nodes is calculated, and community detection is carried out. Then, by analyzing the emergence mode of the DTSC, the measurement value and corresponding routing algorithm of the community’s ability to deliver messages are designed. Our algorithm fully considers the duration of the node’s direct encounter and the social connection of the indirect contact to ensure that the node can deliver successfully in a short time. The experimental results show that the DTSC has an excellent performance in data forwarding.

Data-parallel applications generate a mix of coflows with and without deadlines. Deadline coflows are mission-critical and must be completed within deadlines, while the non-deadline coflows desire to be completed as soon as possible. Scheduling such mix-coflows is an important problem in modern datacenters. However, existing solutions only focus on one of the two types of coflows: they either solely concentrate on meeting the deadlines of deadline-aware coflows or reducing the coflow completion times (CCTs) of non-deadline coflows. In this article, we study the problem of optimizing deadline and non-deadline coflows simultaneously. To this end, we present a new optimization framework,mixCoflow, to schedule deadline coflows to minimize and balance their bandwidth footprint, such that non-deadline coflows can be scheduled as early as possible. Specifically, we develop the mathematical model and formulate the scheduling problem for deadline coflows as a lexicographical min-max integer linear programming (ILP) problem. Through rigorous theoretical analysis, this ILP problem has been proved to be equivalent to a linear programming (LP) problem that can be solved with standard LP solvers. By solving this LP,mixCoflowis able to balance the bandwidth footprint of deadline coflows while guaranteeing their deadlines. As a result, non-deadline coflows can be scheduled as soon as possible whenever they arrive. To demonstrate the effectiveness of our work, we have conducted extensive simulations based on a widely used Facebook data trace. The simulation results verify thatmixCoflowcan achieve significant improvement on the average CCT of non-deadline coflows, at no expense of increasing the deadline miss rates of deadline coflows, when compared to the state-of-art solutions.

Cloud service providers improve resource utilization by co-locating latency-critical (LC) workloads with best-effort batch (BE) jobs in datacenters. However, they usually treat an LC workload as a whole when allocating resources to BE jobs and neglect the different features of components of an LC workload. This kind of coarse-grained co-location method leaves a significant room for improvement in resource utilization.

Geographically distributed cloud is a promising technique to achieve high performance for service providers. For inter-datacenter transfers, deadline guarantee and fairness are the two most important requirements. On the one hand, to ensure more transfers finish before their deadlines, preemptive scheduling policies are widely used, leading to the transfer starvation problem and is hence unfair. On the other hand, to ensure fairness, inter-datacenter bandwidth is fairly shared among transfers with per-flow bandwidth allocation, which leads to deadline missing problem. A mechanism that achieves these two seemingly conflicting objectives simultaneously is still missing. In this paper, we propose TINA to schedule network transfers fairly while providing deadline guarantees. TINA allows each transfer to compete freely with each other for bandwidth. More specifically, each transfer is assigned a probability to indicate whether to transmit or not. We formulate the competition among the transfers as an El Farol game while keeping the traffic load under a threshold to avoid congestion. We then prove that the Nash Equilibrium is the optimal strategy and propose a light-weight algorithm to derive it. Finally, both simulations and testbed experiments results show that TINA achieves superior performance than state-of-art methods in terms of fairness and deadline guarantee rate.

Distributed streaming applications require the underlying network flows to transmit packets continuously to keep their output results fresh. These results will become stale if no updates come, and their staleness is determined by the slowest flow. At this point, coflows can be semantically comprised. Hence, efficient coflow transmission is critical for streaming applications. However, prior coflow-based solutions have significant limitations. They use a one-shot performance metric-CCT (coflow completion time), which cannot continuously reflect the staleness of the output results for a streaming application.To this end, we propose a new performance metric-coflow age (CA), for coflows generated by distributed streaming applications. The CA tracks the longest time-since-last-service among all flows in a coflow. In such a context, we consider a data center network with multiple coflows that continuously transmit packets between their source-destination pairs and address the problem of minimizing the average long-term CA while simultaneously satisfying the throughput constraints from the coflows. To solve this problem efficiently, we design a randomized algorithm and a drift-plus-age algorithm, and show that they can make the average long-term CA to achieve nearly two times and arbitrarily close to the optimal value, respectively. Through extensive simulations, we further demonstrate that both of the proposed algorithms can significantly reduce the CA of coflows, without violating the throughput requirement of any coflow, when compared to the state-of-the-art solution.

The WiFi backscatter communications offer ultralow power and ubiquitous connections for IoT systems. Caused by the intermittent-nature of the WiFi traffics, state-of-the-art WiFi backscatter communications are not reliable for backscatter link or simple for the tag to do the adaptive transmission. In order to build reliable WiFi backscatter communications, we present GuardRider, a WiFi backscatter system that enables backscatter communications to improve the quality of service (QoS). The key contribution of GuardRider is an optimization algorithm of designing RS codes to follow the statistical knowledge of WiFi traffics and adjust backscatter transmission. With GuardRider, the reliable baskscatter link is guaranteed and a backscatter tag is able to adaptively transmit information without heavily listening to the excitation channel, by taking QoS into account. We built a hardware prototype of GuardRider using a customized tag with FPGA implementation. Both the simulations and field experiments verify that GuardRider could achieve notably gains in bit error rate and frame error rate, which are a hundredfold reduction in simulations and around 99% in filed experiments. Our system is able to achieve around 700 kbps throughput.

To cope with user mobility and resource constraints of the edge servers, various service migration policies have been proposed in mobile edge computing (MEC) to achieve a trade-off between user-perceived delay and the service migration cost by moving the service to the user as close as possible. However, there is a risk of user location privacy leakage if a malicious eavesdropper tracks the service migration trajectory. In this paper, we investigate service migration in MEC by taking the risk of location privacy leakage into account. More specifically, we define the total cost of the system as the combination of the migration cost, user-perceived delay and the risk of location privacy leakage. We formulate the service migration problem as a Markov decision process, and propose an efficient algorithm to find the optimal solution that minimize the long-term total cost. Finally, the simulations based on real-world taxi traces in San Francisco show that the proposed method can make service migration decisions effectively protect the location privacy of users, as well as achieves a lower total cost than other baseline methods.

Data collection with mobile elements can greatly improve the load balance degree and accordingly prolong the longevity for wireless sensor networks (WSNs). In this pattern, a mobile sink generally traverses the sensing field periodically and collect data from multiple Anchor Points (APs) which constitute a traveling tour. However, due to long-distance traveling, this easily causes large latency of data delivery. In this paper, we propose a path planning strategy of mobile data collection, called the Dual Approximation of Anchor Points (DAAP), which aims to achieve full connectivity for partitioned WSNs and construct a shorter path. DAAP is novel in two aspects. On the one hand, it is especially designed for disconnected WSNs where sensor nodes are scattered in multiple isolated segments. On the other hand, it has the least calculational complexity compared with other existing works. DAAP is formulated as a location approximation problem and then solved by a greedy location selection mechanism, which follows two corresponding principles. On the one hand, the APs of periphery segments must be as near the network center as possible. On the other hand, the APs of other isolated segments must be as close to the current path as possible. Finally, experimental results confirm that DAAP outperforms existing works in delay-tough applications.

The Internet of Things integrates a large number of distributed nodes to collect or transmit data. When the network scale increases, individuals use multiple sink nodes to construct the network. This increases the complexity of the network and leads to significant challenges in terms of the existing methods with respect to the aspect of data forwarding and collection. In order to address the issue, this paper proposes a Shortcut Addition strategy based on the Particle Swarm algorithm (SAPS) for multisink network. It constructs a network topology with multiple sinks based on a small-world network. In the SAPS, we create a fitness function by combining the average path length and load of the sink node, to evaluate the quality of a particle. Subsequently, crossover and mutation are used to update the particles to determine the optimal solution. The simulation results indicate that the SAPS is superior both to the greedy model with small world and the load-balanced multigateway aware long link addition strategy in terms of the average path length, load balance, and number of added shortcuts.

Over the last decade, we have witnessed growing data volumes generated and stored across geographically distributed datacenters. Processing such geo-distributed datasets may suffer from significant slowdown as the underlying network flows have to go through the inter-datacenter networks with relatively low and highly heterogeneous available link bandwidth. Thus, optimizing the transmissions of inter-datacenter flows, especially coflows that capture application-level semantics, is important for improving the communication performance of such geo-distributed applications. However, prior solutions on coflow scheduling have significant limitations: they schedule coflows with already-fixed endpoints of flows, making them insufficient to optimize the coflow completion time (CCT). In this article, we focus on the problem of jointly considering endpoint placement and coflow scheduling to minimize the average CCT of coflows across geo-distributed datacenters. To solve this problem without any prior knowledge of coflow arrivals, we present a coflow-aware optimization framework called SmartCoflow. In SmartCoflow, we first apply an approximate algorithm to obtain the endpoint placement and scheduling decisions for a single coflow. Based on the single-coflow solution, we then develop an efficient online algorithm to handle the dynamically arrived coflows. Through rigorous theoretical analysis, we prove that SmartCoflow has a non-trivial competitive ratio. We also extend SmartCoflow to incorporate various design choices or requirements of applications and operators, such as enforcing an inter-datacenter bandwidth usage budget and considering coflow deadline. Through experimental results from testbed implementation and trace-driven simulations, we demonstrate that SmartCoflow can reduce the average CCT, lower bandwidth usage, and improve coflow deadline meet rate, when compared to the state-of-the-art scheduling-only method.

Edge computing is attracting more and more attention in recent years to fulfill the requirements of latency-critical and computation-intensive applications. By using the coding redundancy, coded edge computing has emerged to optimize the total computation latency. Compared with the servers in cloud computing, edge devices located at the edge of network may not be reliable and trustworthy. In coded edge computing, even one incorrect intermediate result will lead to the incorrect final result. Therefore, considering the low computation capabilities of edge devices and low latency requirements of user, we study the result verification problem for coded edge computing. Specifically, we propose an efficient Orthogonal Mark (OM) verification scheme by the properties of linear space. We also conduct solid theoretical analysis to show the successful verification probabilities under two kinds of attack models, respectively. Finally, we conduct extensive simulations to show the effectiveness of the proposed OM verification scheme when comparing with basic coded edge computing scheme and Decoding Comparison (DC) scheme.

As more and more applications are deployed in data centers, they rely on high performance data center networks (DCNs) to meet users' increasing quality of the experience (QoE) requirements. Hence, minimizing the average flow completion time (FCT) has been one of the most important goals for DCNs. However, existing traffic scheduling methods assume either prior knowledge of flows (i.e., sizes and deadlines) or symmetric topologies (i.e., Fat-Tree or Bcube). In practice, it is difficult to obtain the information of flows. Moreover, even with symmetric topology design, the DCNs will become asymmetric due to the inevitable link failure and congestion. In this case, it is a great challenge to minimize the average FCT in DCNs. In this paper, we propose a flowlet based information-agnostic traffic scheduling mechanism. The key idea of our method is leveraging multiple priority queues in switches to demote the priority of flows dynamically on the flowlet level. More specifically, the priority of a flow will be demoted according to the number of flowlets it has sent, which follows the shortest job first discipline. We formulate the average FCT minimization problem as a nonlinear Sum-of-Ratios problem and design two heuristic methods to derive the sub-optimal demotion thresholds. Experiment results show that our method can reduce the average FCT by up to 15.35% with a realistic workload, as compared to the state-of-the-art traffic scheduling methods.

In this paper, we investigate the physical layer security performance of untrusted decode-and-forward (DF) relay networks allowing intra-link errors with cooperative jamming. Different from traditional DF relaying protocol, the decoded message at the relay will always be forwarded to the destination node in decode-and-forward relaying allowing intra-link errors (DF-IE), which can be leveraged to enhance the security of relay networks with untrusted relays. To further improve the security performance of DF-IE, we allow the destination node to send jamming signals to impair the signal reception at the relay node, which is referred to as DF-IE with cooperative jamming (DF-IE-CJ). Reliable-and-secure probability (RSP) is used to evaluate the performance of the untrusted relay networks, which represents the probability that the destination node can successfully decode the original message sent from the source node while the relay node can not decode the original message. First, the RSP of DF-IE-CJ is derived from a fading scenario where all the channels between the nodes suffer from block Rayleigh fading. Next, we propose a power allocation scheme to maximize the RSP with different signal-to-noise ratios (SNRs). To verify the effectiveness of DF-IE-CJ, we compare the RSP performance of DF-IE-CJ with that of a widely used schemes, cooperative jamming (CJ). A series of numerical simulations verified the accuracy of the theoretical analysis, and the superiority of DF-IE-CJ over CJ about 5% in RSP.

In mobile edge computing (MEC) systems with energy harvesting, the mobile devices are empowered with the energy that harvested from renewable energy sources. On the other hand, mobile devices can offload their computation-intensive tasks to the MEC server to further save energy and reduce the task execution latency. However, the energy harvested is unstable and the mobile devices have to make sure that the energy should not be run out. Moreover, the wireless channel condition between the mobile device and the MEC server is dynamically changing, leading to unstable communication delay. Considering the energy constraints and unstable communication delay, the benefit of computation offloading is limited. In this paper, we investigate D2D-assisted computation offloading for mobile edge computing systems with energy harvesting. In our method, the mobile device is allowed to offload its tasks to the MEC server with the help of its neighbor node. More Specifically, the neighbor node acts as a relay to help the mobile device to communicate with the MEC server. Our goal is to minimize the average task execution time by selecting an optimal execution strategy for each task, i.e., whether to execute the task locally, or offload it to the MEC server directly, or offload it to the MEC server with the help of the most suitable neighbor node, or just to drop it. We propose a low-complexity online algorithm, which stem from Lyapunov Optimization-based Dynamic Computation Offloading (LODCO) algorithm, to solve this problem. Extensive simulations verified the effectiveness of the proposed algorithm, where the average task execution time is reduced around 50% as compared to that of the original LODCO algorithm.

Currently, parallel data transmissions in large-scale datacenter networks are becoming increasingly crucial to application performance. Despite fine-grained control by SDN-enabled networks, some transmission errors, such as misconfigurations, will inevitably occur, resulting in high-level forwarding policies that cannot be conformed to at the data plane. Therefore, flow trajectory detection is very important for allowing datacenter network operators to troubleshoot problems and ensure that all traffic flows are running on the correct paths. However, existing solutions detect flow trajectories by recording the entire path of each packet. These methods are prone to imposing significant overheads in terms of both the number of switch entries and the amount of packet header space required. To considerably reduce this overhead, we present FlowTracer, an efficient flow trajectory detection solution, which can sample a path one link at a time instead of recording the entire path. FlowTracer consists of a method of probabilistic packet tagging and a method of trajectory reconstruction. In this paper, we first introduce the method of probabilistic packet tagging, which is performed in OpenFlow-enabled switches with very few switch entries and limited packet header space by means of double VLAN tags. Then, we explore the topological structure of datacenter networks and propose our method of trajectory reconstruction, which is performed at end hosts and achieves rapid convergence. Finally, we evaluate FlowTracer on a 48-ary fat-tree topology. The results show that FlowTracer can detect trajectories quickly while placing far smaller demands on both switch entries and packet header space than state-of-the-art techniques.

Coflow scheduling is crucial to improve the communication performance of data-parallel jobs, especially when these jobs running in the inter-datacenter networks with limited and heterogeneous link bandwidth. However, prior solutions on coflow scheduling assume the endpoints of flows in a coflow to be fixed, making them insufficient to optimize the coflow completion time (CCT). In this paper, we focus on the problem of jointly considering endpoint placement and coflow scheduling to minimize the average CCT of coflows across geo-distributed datacenters. We first develop the mathematical model and formulate a mixed integer linear programming (MILP) problem to characterize the intertwined relationship between endpoint placement and coflow scheduling, and reveal their impact on the average CCT. Then, we present SmartCoflow, a coflow-aware optimization framework, to solve the MILP problem without any prior knowledge of coflow arrivals. In SmartCoflow, we first apply an approximate algorithm to obtain the endpoint placement and scheduling decisions for a single coflow. Based on the single-coflow solution, we then develop an efficient online algorithm to handle the dynamically arrived coflows. To validate the efficiency and practical feasibility of SmartCoflow, we implement it as a real-world coflow scheduler based on the Varys open-source framework. Through experimental results from both a small-scale testbed implementation and large-scale simulations, we demonstrate that SmartCoflow can achieve significant improvement on the average CCT, when compared to the state-of-the-art scheduling-only method.

Traditional schemes achieving load balancing in asymmetric topology, which need to maintain global or local congestion information, turn out to be complicated to implement. One recent research has verified that flowlet switching is more simple and efficient to achieve adaptive load balancing in asymmetric topology. Nevertheless, one tricky problem lies in determining the flowlet timeout value, δ. Setting it too small would risk reordering issue while setting it too large would reduce flowlet opportunities. In this paper, by formulating the timeout setting problem with a stationary distribution of Markov chain, we give a theoretical reference for setting an appropriate timeout value in flowlet switching based load balancing scheme. Then, we implement a flowlet switching based load balancing scheme, called EasyLB, by extending OpenFlow protocol. Experiment results show that, by setting timeout value following the preceding theoretical reference, EasyLB is adaptive to asymmetric topology and achieves fast convergence of load balancing after link failures.

Data-parallel applications generate a mix of coflows with and without deadlines. Deadline coflows are mission-critical and must be completed within deadlines, while non-deadline coflows desire to be completed as soon as possible. Scheduling such mix-coflows is an important problem in modern datacenters. However, existing solutions only focus on one of the two types of coflows: they either solely focus on meeting the deadlines of deadline-aware coflows or reducing the coflow completion times (CCTs) of non-deadline coflows. In this paper, we study the problem of optimizing deadline and non-deadline coflows simultaneously. To this end, we present a new optimization framework, mixCoflow, to schedule deadline coflows with the objective of minimizing and balancing their bandwidth footprint, such that non-deadline coflows can be scheduled as early as possible. Specifically, we develop the mathematical model and formulate the scheduling problem for deadline coflows as a lexicographical min-max integer linear programming (ILP) problem. Through rigorous theoretical analysis, this ILP problem has been proved to be equivalent to a linear programming (LP) problem that can be solved with standard LP solvers. By solving this LP, mixCoflow is able to balance the bandwidth footprint of deadline coflows while guaranteeing their deadlines. As a result, non-deadline coflows can be scheduled as soon as possible whenever they arrive. To demonstrate the effectiveness of our work, we have conducted extensive simulations based on a widely used Facebook data trace. The simulation results verify that mixCoflow can achieve significant improvement on the average CCT of non-deadline coflows, at no expense of increasing the deadline miss rates of deadline coflows, when compared to the state-of-art solutions.

Nowadays, service function chaining (SFC) becomes more and more widespread and profound to implement flexible and economical virtual network infrastructures for the Internet of Things (IoT). With the benefits of SFC, the IoT service providers can steer massive traffic through a sequence of heterogeneous virtual network function instances based on their business logic. SFC is viewed as an attractive solution for building virtualized IoT-dedicated network. However, the SFC orchestration in IoT is still a challenge problem. Existing work usually focuses on the performance guarantee while ignoring the issue of resource idleness. To meet the sharp increase in IoT traffic amounts and the diversification of IoT traffic requirements, it is necessary to implement the performance and resource aware SFC orchestration system. Motivated by this, we propose a novel linear programming model and an effective approximation optimization algorithm for SFC orchestration, in order to achieve performance guarantee while avoiding resource idleness. Based on the proposed model and algorithm, a new prototype system named performance and resource aware orchestration system of SFC for IoT (PRSFC-IoT) is built upon OpenStack for online SFC orchestration. A large number of simulation experiments show that the PRSFC-IoT outperforms existing solutions for SFC orchestration in IoT.

The emerging deployment of geographically distributed data centers (DCs) incurs a significant amount of data transfers over the Internet. Such transfers are typically charged by Internet Service Providers (ISPs) with the widely adopted q-th percentile charging model. In such charging model, the time slots with top 100-q percent of data transmission do not affect the total transmission cost, and can be viewed as free. This brings the opportunity to optimize the scheduling of inter-DC transfers to minimize the entire transmission cost. However, very little work has been done to exploit those free time slots for scheduling inter-DC transfers. The crux is that existing work either lacks a mechanism to accumulate traffic to free time slots, or inevitably relies on prior knowledge of traffic arrival patterns. In this paper, we attempt to exploit those free time slots by leveraging diverse time-sensitivities among inter-DC transfers, so as to reduce or even minimize the transmission cost. Specifically, we advocate that a simple principle should be followed: more traffic peaks should be scheduled in free time slots, while less traffic differentiation should be maintained among the remaining time slots. To this end, we take advantage of the Lyapunov optimization techniques to design a pricing-aware control framework. This framework efficiently makes online decisions for inter-DC transfers without requiring a prior knowledge of traffic arrivals. To verify our proposed framework, we conduct small-scale testbed implementation. The results show that our framework can realistically reduce the transmission cost by up to 19.38%.

In the era of global-scale services, analytical queries are performed on datasets that span multiple data centers (DCs). Due to the scarce and expensive inter-DC bandwidth, various methods have been proposed to reduce either the traffic cost or the completion time for those analytics queries. However, current methods make no attempt to maximize the number of successfully served query requests. Moreover, most of them rely on unrealistic assumptions - such as analytical queries are repeated or known in advance. In this paper, we target at characterizing and optimizing the cost-performance tradeoff for geo-distributed data analytics. Our objectives are two-fold: (1) we minimize the inter-DC traffic cost when serving geo-distributed analytics with uncertain query demand, and (2) we maximize the system throughput, in terms of the number of query requests that can be successfully served with guaranteed queuing delay. To achieve these objectives, we take advantage of Lyapunov optimization techniques to design a two-timescale online control framework. Without prior knowledge of future query requests, this framework makes online decisions on input data placement and admission control of query requests. Extensive trace-driven simulation results demonstrate that our framework is capable of reducing inter-DC traffic cost, improving system throughput and guaranteeing a maximum delay for each query request.

Parallel and distributed computing has been under many years of development, and paved the way that what information and communication technology looks like nowadays. With the advance of new techniques, such as 5G, cloud computing, and big data, the theory, design, analysis, evaluation, and application of parallel and distributed computing have encountered great challenges to meet the increasing requirements on high performance, energy efficiency, as well as reliability and security. To achieve these goals, interdisciplinary knowledge and some specialized technical skills are required. This special issue is a collection of many examples of how researchers, scholars, vendors, and practitioners are collaborating to address these challenges.

The binary chief executive officer (CEO) problem with an arbitrary number of agents is considered in this paper. A scheme which separates the reconstruction of observations and the final decision of a common source is assumed. Hence, we first derive the outer bound for the rate- distortion region by providing the converse proof of a binary multiterminal source coding problem which is the key to solve the binary CEO problem. The distortion of the binary CEO problem is then determined by the Poisson binomial process based on the using majority voting logic for the final decision. The rate-distortion behavior of the binary CEO problem is then analyzed based on the outer bound by solving a convex optimization problem. It is found that the distortion decreases until it converges to a certain level, as the sum rate and/or the number of agents increases.

A two-node binary chief executive officer (CEO) problem is investigated. Noise-corrupted versions of a binary sequence are forwarded by two nodes to a single destination node over orthogonal additive white Gaussian noise (AWGN) channels. We first reduce the binary CEO problem to a binary multiterminal source coding problem, of which an outer bound for the rate-distortion region is derived. The distortion function is then established by evaluating the relationship between the binary CEO and multiterminal source coding problems. A lower bound approximation on the Hamming distortion (HD) is obtained by minimizing a distortion function subject to constraints obtained based on the source-channel separation theorem. Encoding/decoding algorithms using concatenated convolutional codes and a joint decoding scheme are used to verify the lower bound on the HD. It is found that the theoretical lower bounds on the HD and the computer simulation-based bit error rate performance curves have the same tendencies. The differences in the threshold signal-to-noise ratio between the theoretical lower bounds and those obtained by simulations are around 1.5 dB in AWGN channel. The theoretical lower bound on the HD in block Rayleigh fading channel is also evaluated by performing Monte Carlo simulation.

An outage probability that is independent of signaling schemes is theoretically derived in this paper for an orthogonal multiple-access relay channel (MARC) system, where the estimates of the information sequences sent from source nodes, regardless of whether or not they are correctly decoded at the relay, are exclusive-OR (XOR)-network-coded and forwarded by the relay to the destination. The MARC system described above is referred to as estimates-exploiting MARC (e-MARC) in this paper for convenience. Following the probability derivation of e-MARC, comparisons are then made with the outage probability of the orthogonal MARC with the Select Decode-and-Forward relaying strategy (MARC-SDF). It is found through simulations that when one of the source nodes is far away from both the relay and the destination, the e-MARC system is superior to MARC-SDF in terms of outage performance. We further numerically calculate the outage probabilities for two special cases, and compare them with the probability of e-MARC. Furthermore, the impact of the source correlation on the outage probability of the e-MARC system is also investigated.

In this paper, we theoretically analyze the outage probability of decode-and-forward (DF) relaying system allowing intra-link errors (DF-IE), where the relay always forwards the decoder output to the destination regardless of whether errors are detected after decoding in the information part or not. The results apply to practical fading scenarios where all the links between the nodes suffer from independent block Rayleigh fading. The key idea of DF-IE system is that the data sequence forwarded by the relay is highly correlated with the original information sequence sent from the source, and hence with a proper joint decoding technique at the destination, the correlation knowledge can well be exploited to improve the system performance. We analyze this problem in the information theoretical framework of correlated source coding. Using the theorems for lossy source-channel separation and for source coding with side information, the exact outage probability is derived. It is then shown that the exact expression can be reduced to a simple, yet accurate approximation by replacing the theorem for source coding with side information by the Slepian-Wolf theorem. Compared with conventional DF relaying where relay keeps silent if errors are detected after decoding, DF-IE can achieve even lower outage probability. Moreover, by allowing intra-link errors, the optimal position of the relay is found to be exactly the midpoint between the source and destination. Results of the simulations are provided to verify the accuracy of the analytical results.

In this paper, we consider the problem of transmitting two correlated binary sources over orthogonal multiple access relay channel (MARC), where two sources are communicating with a common destination with the assistance of a single relay. We assume decode-and-forward relaying strategy, and bit-wise exclusive or (XOR) network coding is performed at the relay node. First, a joint source-channel-network (JSCN) decoding technique is proposed to fully exploit the correlation between the sources, as well as the benefit of network coding. Then the achievable compression rate region of this system is derived based on the theorem for source coding with side information. It is found that the region is a 3-dimensional space surrounded by a polyhedron. Furthermore, the performance limit in Additive White Gaussian Noise (AWGN) channels and the outage probability in block Rayleigh fading channels are derived based on the achievable compression rate region. It is shown that the outage probability can be expressed by a set of triple integrals over the achievable compression rate region. The impact of source correlation on the performance of the system is investigated through asymptotic tendency analysis. The effectiveness of the proposed JSCN decoding technique and the accuracy of the theoretical analysis have been verified through a series of computer simulations, assuming practical channel codes. It is also shown that, as long as the source-relay links are perfect, the 2nd order diversity is always achieved with our proposed technique regardless of the strength of the source correlation.

In this paper, we propose a new joint source-channel decoding technique for transmitting binary Markov sources over AWGN channels. Our approach is based on serially concatenated coding and code doping for inner code. By combining the Markov source and the outer code trellis diagrams, a super trellis is constructed to exploit the time-domain correlation of the source. A modified version of BCJR algorithm is derived based on this super trellis, that can achieve considerable gain in terms of mutual information. The standard BCJR algorithm is used for decoding of inner code where code doping is adopted for better matching of extrinsic information transfer (EXIT) characteristics. EXIT chart analysis is performed to investigate convergence property of the proposed technique and to optimize the code parameters. Simulation results for bit error rate (BER) evaluation and EXIT chart analysis indicate that the proposed technique can achieve significant gains over the system in which source redundancy are not exploited, and thereby the BER performance of the proposed system is very close to the Shannon limit.