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.

With the increasing adoption of IIoT in industrial production producing massive heterogeneous data, Retrieval-Augmented Generation (RAG) has become a promising approach for industrial knowledge-based Question Answering (QA). However, retrieved top-k documents often contain distracting content that degrades the quality of the generation. Existing research focuses on optimizing retrieval and reranking while overlooking semantic enrichment of useful information and targeted handling of distractions. To address this issue, we propose CP-RAG (Categorize and Process RAG), which categorizes retrieved documents using attention scores and processes them with tailored strategies to mitigate distracting content. Direct-assist documents, which contain concentrated useful information, are enhanced via multi-level semantic optimization to enhance information density. Indirect-assist documents, which carry contextual but distracting elements, are processed through rearrangement with noise mixing to mitigate interference. To maximize LLMs utility, direct-assist documents are placed at both ends of the context window, while indirect-assist documents are positioned centrally. Experimental results show that CP-RAG improves QA accuracy and demonstrates strong practical effectiveness in industrial systems, supporting intelligent decision making over heterogeneous industrial data streams.

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.

Underwater networking is vital for enabling collaboration between divers, vehicles, and sensors in marine exploration, monitoring, and emergency response. Yet achieving reliable communication in such dynamic, bandwidth constrained environments remains challenging. Acoustic and radio frequency technologies suffer from attenuation, latency, and hardware overhead, while optical wireless systems typically require specialized transceivers or strict alignment, limiting practicality in mobile underwater networks. To address these limitations, we present AquaLink, a QR code–driven Optical Camera Communication (OCC) framework that enables robust underwater messaging using commodity smartphones and tablets. At its core, AquaQR employs blue–green 2-bit color encoding, Low-Density Parity-Check (LDPC) error correction, and geometric augmentations tailored for optical stability in turbid waters. An auto-configuration module adapts parameters before transmission, and a lightweight enhancement pipeline ensures real-time robustness under diverse conditions. Field trials in pool, lake, and coastal environments achieve over 90% decoding success at 5 m and up to 2× higher throughput than prior QR-based systems. By eliminating specialized hardware, AquaLink provides a scalable, low cost foundation for underwater visual networking, supporting message exchange, peer interaction, and localized link formation.

Despite convenience and reliability of mmWave-based action recognition, it still raises privacy concerns on identity leakage threat since human behaviors could meanwhile expose massive user information in real-world applications. Existing solutions attempt to send anonymized features extracted from mmWave signals; however, features not only reduce the application flexibility but also increase the privacy disclosure risk due to original data reconstruction. Instead, in this paper we propose a de-identification system, AE-IPP, which customizes learned noises into the raw data to generate adversarial examples for identity privacy and action utility balance. In other words, the noises are sample-specific perturbations that are automatically learned for each sample through our presented network. To achieve the performance balance and ensure robustness to other models, we are faced with two challenges, including the decoupling of action and identity information and the transferability of models. To this end, AE-IPP focuses on respective attention areas by leveraging task-specific gradients and designs a dynamic attention mechanism to update the attention weights according to the final optimization objective. Moreover, we present a multidirectional perturbation strategy to improve the model generalization capabilities, enabling robust de-identification. Extensive experiments on mmWave datasets demonstrate the superiority of our method over state-of-the-art approaches.

MmWave radar-based person re-identification (ReID), namely catching a specified person from the database, demonstrates enormous prospects for practical applications such as public security and intelligent surveillance. Towards adaptive ReID across different scenarios, we attempt to explore abundant spatial-temporal features from point clouds for walking individuals. Most existing approaches fail to describe fine-grained 3D spatial properties associated with gaits and neglect the impacts of walking speed changes on perception. To address the two problems, we extract gait features by integrating the anchor-based orientation descriptor (AOD) and multi-scale gait catch (MGC) modules into a ReID system. Specifically, AOD automatically learns virtual anchors, selects anchor-centered neighbors from eight different subspaces and designs orientation-driven feature aggregation to elaborately describe 3D local space. Then, MGC adopts the sub-sampling strategy on AOD results to estimate multiple temporal resolution pathways reflecting relative speeds, on which sequence-specific and cross-sequence dependencies are respectively characterized by our self-attention (SA) and hierarchical attention (HA) to generate more discriminative gait representations. For evaluation, we collect mmWave ReID datasets at three scenes, and comprehensive experiments illustrate that we can maintain superior ReID performances over 90.0% Top-1 accuracy under different scenario settings. Our code and dataset are available athttps://github.com/dpjqw195/ReID-AOD-MGC

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

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.

Deploying quantum networks involves the placement of quantum repeaters, which generate entangled qubits for quantum computers to perform quantum teleportation. Besides placing repeaters on ground devices, there have been efforts towards the use of satellites or drones as repeaters. This paper considers the scenario of using drones due to their cost-efficiency and support for flexible network formation. While several issues in this scenario, such as enhancing network connectivity, have been studied, this paper addresses a new problem: how to minimize the number of drones placed in the air to cover all quantum computers on the ground and make the entire quantum network connected. Given that this problem is NP-hard, we propose a suboptimal but polynomial-time approach for it. Our approach consists of two stages. Stage 1 aims to minimize the number of drones needed to cover the ground computers, and Stage 2 aims to minimize the number of drones needed for connecting the drones returned by Stage 1. Both stages use algorithms of no more than quadratic time complexity. We conduct experiments to determine the best algorithm for the setting of quantum networks from several high-performing existing algorithms and design optimization techniques for existing algorithms. Our experiments confirm that our approach reduces the number of drones placed effectively.

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.

The decentralized and immutable nature of blockchainprovides a resilient foundation for covert communication in adversarial and untrusted environments, specifically in scenarios requiring unobservable multi-recipient messaging. Most existing schemes, however, are limited to one-to-one transmission and lack mechanisms to handle untrusted receivers, which constrains their scalability and security. To address these challenges, we propose ORCA (Orthogonal Covert Architecture), a group covert communication framework based on strictly orthogonal, integer-valued codewords. ORCA selects codewords from a Hadamard matrix and applies secret column permutations to ensure decoding isolation and resistance against inference attacks. Each receiver recovers only its assigned message through projection, without coordination or leakage. This encoding structure supports scalable embedding, seamless receiver revocation, and clean integration with standard transaction fields. In contrast to prior work, we analyze the impact of imperfect orthogonality and provide theoretical bounds on decoding interference. Extensive experiments on real-world Bitcoin blockchain data and comparative evaluation against representative covert communication schemes confirm ORCA’s robustness, high embedding capacity, and statistical indistinguishability from normal blockchain activity. These results establish ORCA as a scalable and secure solution for multi-recipient covert communication in adversarial environments.

With the increasingly complex urban transportation system and the continuous growth of vehicle ownership, it is impossible to effectively guarantee the priority of police cars, ambulances, and other emergency vehicles, threatening the timeliness of tasks and public safety. Given the limitations of traditional signal control strategies in ensuring the priority of emergency vehicles, this paper proposes a traffic signal control strategy based on the three-layer architecture of fog computing (DNLight), which utilizes deep reinforcement learning methods to enable continuous strategy adjustments. In this architecture, the bottom fog node is responsible for collecting real-time traffic status and vehicle interaction information; the middle layer fog node agent uses the deep reinforcement learning algorithm, introduces dynamic noise for hybrid exploration, combines Dueling network diversion architecture to improve stability, and introduces the penalty coefficient C to ensure fairness between emergency vehicles and social vehicles. In terms of the reward function, this paper modifies the calculation of reward value to be dynamic, allowing it to adjust the weight of emergency vehicles and social vehicles according to traffic conditions, thereby improving flexibility. The top layer cloud service layer is mainly responsible for receiving the data backed up at the bottom layer and conducting training. The simulation experiment demonstrates that the DNLight strategy enhances the traffic efficiency of emergency vehicles by more than 40% under various traffic scenarios and traffic flow intensities, and reduces the negative impact on social vehicle traffic by up to 50%, providing strong support for urban traffic optimization and emergency management.

This article presents a safety-critical control framework for navigation in complex environments with numerous obstacles. An online robust path planning scheme is developed by integrating reinforcement learning (RL) with control barrier functions (CBFs). First, a disturbance observer is designed to estimate the unknown disturbance along with a derived upper bound of the estimation error. Then, a nominal controller is designed using RL, where a critic neural network (NN) structure is established by using state-following (StaF) kernel function. Additionally, by employing a state extrapolation technique, the learning process leverages both real-time and simulated experience data. To ensure safety, obstacle avoidance is formulated as a forward invariance problem of safe sets defined by CBFs. Subsequently, the CBF-based safety-critical constraints are integrated into a quadratic programming (QP) framework to modify the nominal controller. Furthermore, these CBFs are incorporated into a composite CBF using smooth approximation, enabling efficient constraint consolidation. Then, an explicit safe control policy is proposed that guarantees collision-free path planning. Finally, the effectiveness of the proposed scheme is demonstrated through numerical simulations, and comparative results show the advantages over the existing methods in motion trajectory.

A federated learning framework enables multiple clients to jointly train models locally without uploading their private data, effectively protecting the clients’ data privacy. However, existing federated learning auction mechanisms have not considered heterogeneity in client training time, making it difficult for the server to aggregate client models effectively within a constrained time. Moreover, continuously selecting specific clients in each round can lead to overfitting. This article proposes an Uncertainty-aware Auction Mechanism (UAMARD) based on Age of Update (AoU), Reputation, and Data Quantity, which considers training time and provides guidance on the number of data points to participate in training for selected clients. Firstly, we model a reverse auction system that considers the uncertainty of training time to promote client participation. We introduce AoU to quantify the time interval required for the server to receive the latest updates from the client to avoid overfitting. Then, we prove that solving the problem of maximizing social welfare is NP-hard. Subsequently, we introduce a dynamic programming algorithm (VCG RA) to solve the problem of maximizing social welfare. To further reduce time complexity, we propose our UAMARD method, which achieves a near-optimal level of social welfare while ensuring minimal time complexity. Ultimately, simulation experiments confirmed the efficacy of UAMARD and VCG RA. When benchmarked against other mechanisms, UAMARD and VCG RA demonstrated superior performance with quicker convergence and higher accuracy in testing the MNIST and CIFAR-10 datasets.

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.

With the scale of the Internet of Things (IoT) system growing constantly, node failures frequently occur due to device malfunctions or cyberattacks. Existing robust network generation methods utilize heuristic algorithms or neural network approaches to optimize the initial topology. These methods do not explore the core of topology robustness, namely how edges are allocated to each node in the topology. As a result, these methods use massive iterative processes to optimize the initial topology, leading to substantial time overhead when the scale of the topology is large. We examine various robust networks and observe that uniform degree distribution is the core of topology robustness. Consequently, we propose a novel UNIformity driven robusT topologY generation scheme (UNITY) for IoT systems to prevent the node degree from becoming excessively high or low, thereby balancing node degrees. Comprehensive experimental results demonstrate that networks generated with UNITY have an “olive-like” topology consisting of a substantial number of medium-degree nodes and possess strong robustness against both random node failures and targeted attacks. This promising result indicates that the UNITY makes a significant advancement in designing robust IoT systems.

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.

Robust Topology is a key prerequisite to providing consistent connectivity for highly dynamic Internet-of-Things (IoT) applications that are suffering node failures. In this paper, we present a two-step approach to organizing the most robust IoT topology. First, we propose a novel robustness metric denoted as$I$, which is based on network motifs and is specifically designed to sensitively analyze the dynamic changes in topology resulting from node failures. Second, we introduce a Distributed duAl-layer collaborative competition optimization strategy based on Motif density (DAiMo). This strategy significantly expands the search space for optimal solutions and facilitates the identification of the optimal IoT topology. We utilize the motif density concept in the collaborative optimization process to efficiently search for the optimal topology. To support our approach, extensive mathematical proofs are provided to demonstrate the advantages of the metric$I$in effectively perceiving changes in IoT topology and to establish the convergence of the DAiMo algorithm. Finally, we conduct comprehensive performance evaluations of DAiMo and investigate the influence of network motifs on the resilience and reliability of IoT topologies. Experimental results clearly indicate that the proposed method outperforms existing state-of-the-art topology optimization methods in terms of enhancing network robustness.

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.

Cloud service centers (CSCs) can purchase edge computation resources to improve service quality in mobile cloud-edge computing networks. However, edge servers (ESs) are owned by different entities, and dishonest entities may launch computational forgery attacks, i.e., the ES falsely reports its idle computation resources to win more tasks for increased revenue. Most existing approaches ignore the threat of dishonest ESs. To address the challenges, we design aTrust-basedComputationOffloading (TCO) framework. First, we construct the problem for minimizing thedifference between the CSC'scost and theexpectedrevenue (DCER), which is a mixed-integer nonlinear programming problem. Second, we develop a trust-based computation offloading method that quickly finds a good solution by decomposing the problem. Finally, a two-tier trust evaluation method was proposed to obtain accurate trust values. Experimental results indicate that TCO's comprehensive performance surpasses the benchmarks and significantly enhances computation offloading reliability with a lower performance loss. Notably, tasks are preferentially offloaded to honest ESs to ensure their revenue and promote ESs’ honesty under the TCO framework. Additionally, compared with no trust mechanisms, TCO reduces the service timeout count in an interval by 34.37% - 73.80% with a performance loss of only 1.42% - 4.10%.

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.

Mobile crowdsourcing (MCS) has attracted widespread attention by recruiting users with mobile devices to collect crowdsourcing data. Existing research on MCS assumes that the platform has sufficient users. However, platforms in their early stages of development face the cold start problem, which can lead to their inability to grow or even result in bankruptcy. While some studies try to solve it by recruiting users through social networks to participate in crowdsourcing tasks, they only focus on how to recruit more users without addressing the issue of user retention. This can lead to an increasing proportion of users losing interest in the platform and dropping out and thus it fails to solve the cold start problem truly. In light of this, we present a task recommendation-based method to recruit new users via the social network and keep registered users active on the platform. Specifically, we first use an extended independent cascade model to describe the recruitment of users through social networks. Secondly, we use a task acceptance model to describe user decisions. Finally, we utilize a fuzzy control system that incorporates spatiotemporal crowdsourcing information to predict user behaviour and recommend tasks to users most likely to complete them. Extensive experiments on large-scale real datasets were conducted to evaluate the proposed solution. The results indicate that compared to existing methods such as SocialRecruiter, our solution reduces the 30-day average user churn rate by 23.90% while significantly boosting user retention and task completion rates by up to 23.73% and 48.7%, respectively.

Mobile edge computing (MEC) can improve execution efficiency and reduce overhead for offloading computing tasks to edge servers with more resources. In the microservice system, the current research only considers the cross segment communication cost of computing tasks, does not consider the case of the same end, and ignores the discovery and invocation optimization of associated services. In this paper, we proposeCACO, which is a novel content-aware classification offloading framework for MEC based on correlation matrix.CACOfirst designs an adaptive service discovery model, which can make timely response and adjustment to the changes of the external environment. It then investigates an efficient affinity matrix based service discovery algorithm, which expresses the association relationship between services by constructing a service association matrix. In addition,CACOconstructs a relational model by giving different weight coefficients to the delay and energy loss, which improves the delay and energy loss of message processing in a satisfying manner. Simulation results indicate thatCACOreduces the total traffic of redundant messages by 46.2%$\sim$76.5%, respectively compared with state-of-the-art solutions. Testbed benchmarks show that it can also improve the stability by reducing control overhead by 34.5%$\sim$81.6% .

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.

With the scale of Internet of Things (IoT) continually expanding, the topology is growing rapidly and the probability of cascading collapse due to node failures or malicious attacks is increasing. The decrease in the Quality of Service (QoS) of IoT could be mitigated by robust topology. Existing optimization strategies usually use heuristic algorithms to enhance topology robustness. However, when the scale of topology is large, these algorithms involve a significant amount of iterative searching for the optimal solution, which is time-consuming and prone to getting stuck into local optimum. To tackle this situation, this study introduces arithmetic encoding and proposes a novel probability-based robust topology generation model that can quickly generate IoT robust topology. We losslessly compress robust topologies using arithmetic encoding and extract their features. Based on the extracting features, we design a unique probability-based topology generation approach that avoids the time overhead of iterative calculations. Experimental results demonstrate that the proposed solution in this paper can construct robust topologies in less time for different network scales.

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/.

Handover (HO) is a critical component of mobility management in the 5th generation (5G) of communication networks, which ensures seamless connectivity and optimal communication performance for user equipment (UE) in motion across different cells. In previous studies, the deep reinforcement learning (DRL) techniques were employed to solve the HO problem. However, for most of these methods, the growing complexity in action space was not considered as the number of UEs increases, leading to inefficient model convergence and HO failures. To address this issue, this paper proposes a novel PPO-MH (Proximal Policy Optimization with Masking for Handover) model for multi-cell selection handover problem. This model calculates the action mask for each UE before each handover using the UE's measurement report, providing prior information for the decision-making process. By dynamically masking base stations (BSs) that do not meet the handover conditions, the model avoids invalid hand overs and improves sampling efficiency. Experimental results demonstrate that the PPO- MH outperforms the traditional PPO across various scenarios, ensuring Quality of Service (QoS) for UEs and reducing the handover frequency. Additionally, PPO- MH converges significantly faster than PPO, which validates the effectiveness of the action mask strategy. Benefiting from these advantages, our methods have broad potential applications, especially in scenarios requiring efficient resource management and low-latency handovers.

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%.

Thanks to the applications such as Quantum Key Distribution and Distributed Quantum Computing, the deployment of quantum networks is gaining great momentum. A major component in quantum networks is repeaters, which are essential for reducing the error rate of qubit transmission for long-distance links. However, repeaters are expensive devices, so minimizing the number of repeaters placed in a quantum network while satisfying performance requirements becomes an important problem. Existing solutions typically solve this problem optimally by formulating an Integer Linear Program (ILP). However, the number of variables in their ILPs is O ( n 2 ) , where n is the number of nodes in a network. This incurs infeasible running time when the network scale is large. To overcome this drawback, this paper proposes to solve the repeater placement problem by two steps, with each step using a linear program of a much smaller scale with O ( n ) variables. Although this solution is not optimal, it dramatically reduces the time complexity, making it practical for large-scale networks. Moreover, it constructs networks that have higher node connectivity than those by existing solutions, since it deploys slightly more number of repeaters into networks. Our extensive experiments on both synthetic and real-world network topologies verified our claims.

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.

Sharding is widely regarded as a highly promising solution to address the scalability limitations of blockchain. However, the scalability and throughput improved by using sharding are limited by the verification of cross-shard transactions. To reduce cross-shard transaction and improve the throughput of the blockchain, the existing sharding schemes are based on factors such as the edge-end structure of Industrial Internet of Things (IIoT) for sharding. But these schemes are centralized, leading to the problems of low sharding efficiency, poor scalability, and poor security. Moreover, these schemes adopt a nonoverlapping sharding architecture, so the verification cost of cross-shard transactions is significantly higher than that of intrashard transactions. In order to solve the above problems, this article proposes an overlapping self-organizing sharding scheme (deep reinforcement learning (DRL)-OSS) for large-scale IIoT blockchain. Based on local blockchain information, such as nodes’ information and transaction interaction frequency, DRL-OSS uses DRL to achieve self-organizing sharding with the aim to maximize the throughput and security of blockchain. In addition, based on the threat model, this article also designs a block complaint scheme (BCS) to further improve the security of the blockchain, thereby avoiding the reduction in resistance to 1% attack due to the poor anti-predictability of shards and the dilution of computing power. Through experimental verification and analysis, DRL-OSS improves the throughput by 50% when compared to state-of-the-art sharding schemes and has higher system security.

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.

Due to the large volume of IoT data, conventional sensor network based and the cloud base IoT systems cannot handle latency-sensitive and resource-consuming IoT applications. Sensor networks do not have enough computation resources and also suffer from a limited network lifetime. On the other hand, the cloud based IoT system is far away from the users and the physical world, and cannot satisfy the real-time requirement of IoT applications. We adopt the IoT edge network to address these challenges and process IoT applications in modern IoT systems. The IoT edge network is an emerging computing architecture in the IoT. Compared to the sensor nodes in conventional sensor networks, the edge servers have more computation resources. Compared to the remote cloud, the edge servers are closer to the users and the physical world. However, processing IoT applications in the edge network still remains challenging. First, how to process concurrent IoT applications has not been fully investigated. Second, the inner relationship between the network resource and the application latency has not been deeply analyzed. Third, the function conflict problem in edge servers has not been taken seriously. To solve the above challenges, we propose the Energy and Latency Efficient Processing Plan for Concurrent IoT Applications Problem which aims to construct an application processing plan by jointly considering the concurrency, the energy-latency relationship, and the function conflict problems. We prove that such a problem is NP-Hard, and algorithms are proposed accordingly. Furthermore, we also estimate the performance of the proposed algorithms by numerical results.

Numerous sensor nodes deployed in the Internet of Things (IoT) can form a large heterogeneous network. The increased energy consumption of sensor nodes and the unbalanced communication load on multiple sink nodes reduce the energy efficiency of the network. Moreover, frequent network attacks also pose severe challenges to topology robustness. Optimizing the network topology to achieve the balance between energy efficiency and robustness is a complex problem. Multi-objective heuristic algorithms based on genetic evolution are commonly used to solve joint optimization problems. However, due to the lack of global search ability caused by the loss of genetic diversity, genetic operations are prone to premature convergence during multi-objective evolution. Therefore, this paper introduces multi-population cooperation into the multi-objective evolution process and proposes a novel layered-cooperation Topology Evolution Algorithm for Multi-sink IoT (TEAM). In TEAM, information entropy is used to measure the effectiveness of load balancing on multiple sink nodes. The crossover and mutation probabilities of different populations are dynamically adjusted to ensure genetic diversity. A layered-cooperation mechanism is designed to avoid premature convergence. Extensive experiments confirm that TEAM can effectively improve the energy efficiency and robustness of network topology while balancing the communication load on multi-sink nodes.

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.

Trust evaluation is critical for many applications such as cyber security, social communication, and recommender systems. Users and trust relationships among them can be seen as a graph. Graph neural networks (GNNs) show their powerful ability for analyzing graph-structural data. Very recently, existing work attempted to introduce the attributes and asymmetry of edges into GNNs for trust evaluation, while failed to capture some essential properties (e.g., the propagative and composable nature) of trust graphs. In this work, we propose a new GNN-based trust evaluation method named TrustGNN, which integrates smartly the propagative and composable nature of trust graphs into a GNN framework for better trust evaluation. Specifically, TrustGNN designs specific propagative patterns for different propagative processes of trust, and distinguishes the contribution of different propagative processes to create new trust. Thus, TrustGNN can learn comprehensive node embeddings and predict trust relationships based on these embeddings. Experiments on some widely-used real-world datasets indicate that TrustGNN significantly outperforms the state-of-the-art methods. We further perform analytical experiments to demonstrate the effectiveness of the key designs in TrustGNN.

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.

Topology robustness is critical to the connectivity and lifetime of large-scale Internet-of-Things (IoT) applications. To improve robustness while reducing the execution cost, the existing robustness optimization methods utilize neural learning schemes, including neural networks, deep learning, and reinforcement learning. However, insufficient exploration of reinforcement learning agents for topological environments is likely to yield local optima. Moreover, convergence speed is influenced by the sparse reward problem generated while exploring topological environments. To address these problems, this study proposes a self-adaptive robustness optimization method with an evolutionary multi-agent for IoT topology (ROMEM). ROMEM introduces a new multi-agent co-evolution scheme that leverages a non-deterministic strategy to extend the exploration in multi-directions, enabling the reinforcement learning agent to transcend local optima. Furthermore, ROMEM presents a novel distributed training mechanism for multiple agents to accelerate convergence. Experimental results demonstrate that ROMEM can achieve multi-directional collaborative training and outperform other state-of-the-art learning-based robustness optimization methods in terms of convergence efficiency and robustness.

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.

Mobile edge computing (MEC) is becoming increasingly popular due to its remarkable computing capacities in close proximity to end users or devices. With the widespread use of Industrial Internet of Things, more and more cloud service providers move their services to the edge of the network for a better quality of service and become MEC providers. These MEC providers require to rent wide area network (WAN) connections to transfer industrial data, which is a considerable expense. In this article, we propose a framework calledSublessorto reduce the WAN transmission cost for a group of cooperative MEC providers. The key idea ofSublessoris allowing some specific MEC providers to act as Internet transit brokers, transmitting not only their own network traffic but also the traffic of their partners under a reasonable reselling price. This article formulates the problem as a mixed-integer programming and finds the most suitable broker number and corresponding reselling price without damaging the profit of both brokers and partners by a deep-reinforcement-learning-based algorithm. Experimental results show that our algorithm can significantly reduce the traffic transmission cost by up to 35%.

GCN-based clustering schemes cannot interactively fuse feature information of nodes and topological structure information of graphs, leading to insufficient accuracy of clustering results. Moreover, the deep clustering model based on graph structure is vulnerable to the attack of adversarial samples leading to the reduced robustness of the model. To solve the above two problems, this paper proposes a robust clustering model based on attention mechanism and graph convolutional network (GCN), named AG-cluster. This model firstly uses graph attention network and GCN to learn the feature information of nodes and the topological structure information of graphs, respectively. Then the representation results of the above two learning modules are interactively fused by the interlayer transfer operator. Finally, the model is trained end-to-end using a self-supervised training module to optimize the clustering results of the model. In particular, an efficient graph purification defense mechanism (GPDM) is designed to resist adversarial attacks on graph data to improve the robustness of the model. Experimental results show that AG-cluster outperforms the other four benchmark methods, specifically, AG-cluster improves 7.6% in Accuracy and 11.5% in NMI compared to the best benchmark method. Besides, the new model still shows higher robustness and stronger transferability under multiple attacks.

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.

The development of the energy storage industry and the higher electricity prices lead to the continuous increase of users’ demand for battery energy storage in industrial manufacture. To monitor the status of the battery and control the running process of the battery, we need a battery management system (BMS) with good performance and complete functions. Previously proposed BMS generally lacks functionality and is not designed for energy storage systems in industrial manufacture. This paper aims to design and implement a BMS for energy storage. The system can collect various data such as battery voltage, temperature, current, smoke, and so on. The functions of calculating status data, detecting faults, passive battery balance, and display are also supported. Moreover, the system optimizes the functions of battery balance and display. Finally, the experimental results show that the BMS achieves a battery voltage measurement error within 2mV, supports a passive balance current of about 100mA, and makes the computing resource allocation more balanced.

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.

Frequently task offloading in the Multi-access Edge Computing (MEC) system takes up a lot of network resources, which leads to serious network congestion problems. To deal with such problems, applying data pricing strategies in task offloading to schedule traffic data is perceived as a promising solution for Internet service providers (ISPs). However, the traditional application-oriented data pricing strategies do not consider user satisfaction of the task executing process, resulting in lower ISP profit. In this paper, a novel scheme named App-set Usage Patterns-Aware task offloading scheme (AUPA) is proposed to alleviate the tension between traffic data supply and user satisfaction. We mine the sequential patterns in frequent application sets to extract temporal association rules. Then these association rules are used to design a smart data pricing strategy to guide the task offloading decision. Finally, we formulate the ISP’s profit maximization problem as a nonlinear programming (NLP) problem based on partial offloading, and we simulate the scheduling processuse using the Stackelberg game model. The performance of our solution is evaluated in terms of ISP’s profit, consumers’ surplus, capacity utilization, and traffic efficiency. The results show that our scheme significantly improves the ISP’s profit by about 20% while ensuring capacity constraints compared with other baseline schemes.

As a key component of most machines, the status of the rotation shaft is a crucial issue in the factories, which affects both the industrial safety and the product quality. Tracking the rotation angle can efficiently monitor the status of the rotation shaft, but traditional solutions either rely on the specialized sensors, suffering from intrusive transformation, or use the computer vision-based solutions, suffering from poor light conditions. In this paper, we present a non-contacting low-cost angle tracking solution, RF-Protractor, to track the rotation shaft based on the surrounding RFID tags. Particularly, instead of directly attaching the tags to the shaft, which may lead to serious miss reading problems due to metal interference, we deploy the tags beside the shaft and leverage the polarization effect of the reflection signal from the shaft for angle tracking. To improve the polarization effect, we exploit the linear polarization feature by using the linear shaft turntable or placing a light aluminum foil on the shaft turntable, which requires no transformation of the shaft. We firstly build a polarization model to quantify the relationship between the rotation angle and the reflection signal. To extract the accurate reflection signal, we then propose to combine the signals of multiple tags to cancel the reflection effect and then estimate the environment-related parameter to calibrate the model. Finally, we propose to leverage both the power trend and the IQ signal to estimate the rotation direction and the rotation angle. We have implemented a real system and the extensive experiments in the real environment confirm the effectiveness of RF-Protractor, which achieves an average error of about 3.1° in angle tracking.

Maximizing user quality of experience (QoE) is the ultimate goal of video players, and adaptive bitrate (ABR) is recognized as one of the most effective solutions. Approaches employing reinforcement learning (RL) have performed well as hybrid ABR algorithms, due to the ability to learn autonomously. However, throughput, which plays a crucial role in low-delay video streaming, is difficult to predict simply in mobile and wireless networks, and the inaccurately predicted throughput can lead to the wrong selection of bitrates. Worse, the general RL approaches are prone to frequent bitrate switching due to bandwidth fluctuation. These obstacles make the RL-based ABR approach unable to truly reflect the user QoE. We propose TP-RL, an application that makes ongoing decisions to maximize user QoE. To realize this, TP-RL adopts three ideas: (i) It takes the RL neural network as the main body of decision-making, which will inherit the advantages of RL and improve on this basis; (ii) Explore Mogrifier LSTM for throughput prediction, and replace the throughput part in the state space of the original RL neural network with a prediction module; (iii) The decided bitrate is further processed to achieve better smoothness when the bandwidth fluctuates. The performance of TP-RL is evaluated in different experimental environments, and experiments show that it can improve QoE by about 14% to 20.7% compared with the best baseline.

Mobile Crowd Sensing (MCS) typically assigns sensing tasks in the same target area to many participants considering data quality and the diversity of sensing devices. However, participant selection is based on the individual in many research. The efficiency of individual recruitment is low. Individuals need higher transportation costs to go to the task location alone, and the data quality perceived by individuals is difficult to guarantee. This paper proposes a team-based multitask data acquisition scheme under time constraints to address these challenges. The scheme optimised the number of participants, traffic cost, and data quality and designed four team-based multitask allocation algorithms under time constraints in the MCS: T-RandomTeam, T-MostTeam, T-RandomMITeam, and T-MostMITeam. The team size is associated with the number of participants required for the first task or the vehicle capacity to perform the task. We conducted extensive experiments based on a real large-scale dataset to evaluate the four algorithms' performances compared to two baseline algorithms (T-Random and T-most). The efficiency of the four algorithms has been significantly improved by team recruitment. The transportation cost can be multiplicatively reduced by carpooling. Data quality can be improved by at least 2% through reputation screening and team members' communication.

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.

In Industrial Internet of Things (IIoT), a large number of wireless nodes communicate through limited channel resources. Using IEEE802.11ac/ah with multiple users multiple-input–multiple-output (MU-MIMO) and channel bonding technology in IIoT, bonding multiple channels for transmission links can improve data transmission quality. How to reasonably bond limited channel resources on demand for data transmissions in IIoT has become a key issue to improve network performance and ensure communication quality of the nodes. In this article, the definition of outage probability is extended from the amount of information to the signal noise ratio (SNR), which changes the outage probability from a statistical quantity to a quantity that can be directly calculated. This article further analyzes various factors affecting the outage probability, and derives the direct calculation formula of the outage probability in single-hop and multihop data transmission, which allows the outage probability to be directly calculated by some simple parameters. Based the outage probability, this article proposes a dynamic channel bonding algorithm based on outage probability (DCB-OP), which can bond multiple channels for the data transmissions with a high outage probability to improve the success rate of data transmissions. The experimental results show that in IEEE 802.11ac/ah networks, the DCB-OP algorithm can improve the utilization of channel resources and increase the throughput by about 40% compared with no channel bonding. Compared with the general channel bonding algorithm, DCB-OP can make the network throughput higher.

Indoor positioning technology based on Wi-Fi fingerprint recognition has been widely studied owing to the pervasiveness of hardware facilities and the ease of implementation of software technology. However, the similarity-based method is not sufficiently accurate, whereas the offline training of the neural network-based method is overly time consuming. An efficient model with high positioning accuracy is therefore not yet available. We propose a stacking ensemble broad learning localization system using channel state information as a fingerprint, which is termed EnsemLoca. A bootstrapping method is used to build the training set, which enables the EnsemLoca system to build the base learner in parallel by using bagging. The broad learning system (BLS), which is a novel neural network model, as a base learner, not only has the advantage of time complexity but also offers a sparse representation in which the features are filtered. A unique base learner is constructed by randomly selecting the samples and features, and they are combined by stack generalization. The experimental results show that the EnsemLoca system achieves higher accuracy than several machine-learning algorithms in both line-of-sight (LOS) and non-LOS environments, and is even stronger than deep neural networks characterized by accuracy. At the same time, it has the same theoretical complexity as BLS, which greatly reduces the offline training time.

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.

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.

In the real world, multivariate time series from the dynamical system are correlated with deterministic relationships. Analyzing them dividedly instead of utilizing the shared-pattern of the dynamical system is time consuming and cumbersome. Multitask learning (MTL) is an effective inductive bias method to utilize latent shared features and discover the structural relationships from related tasks. Base on this concept, we propose a novel MTL model for multivariate chaotic time-series prediction, which could learn both dynamic-shared and dynamic-specific patterns. We implement the dynamic analysis of multiple time series through a special network structure design. The model could disentangle the complex relationships among multivariate chaotic time series and derive the common evolutionary trend of the multivariate chaotic dynamical system by inductive bias. We also develop an efficient Crank-Nicolson-like curvilinear update algorithm based on the alternating direction method of multipliers (ADMM) for the nonconvex nonsmooth Stiefel optimization problem. Simulation results and analysis demonstrate the effectiveness on dynamic-shared pattern discovery and prediction performance.

This article presents an event-sampled integral reinforcement learning algorithm for partially unknown nonlinear systems using a novel dynamic event-triggering strategy. This is a novel attempt to introduce the dynamic triggering into the adaptive learning process. The core of this algorithm is the policy iteration technique, which is implemented by two neural networks. A critic network is periodically tuned using the integral reinforcement signal, and an actor network adopts the event-based communication to update the control policy only at triggering instants. For overcoming the deficiency of static triggering, a dynamic triggering rule is proposed to determine the occurrence of events, in which an internal dynamic variable characterized by a first-order filter is defined. Theoretical results indicate that the impulsive system driven by events is asymptotically stable, the network weight is convergent, and the Zeno behavior is successfully avoided. Finally, three examples are provided to demonstrate that the proposed dynamic triggering algorithm can reduce samples and transmissions even more, with guaranteed learning performance.

Saliency prediction models aim to mimic the human visual system’s attention process, and the research has made significant progress due to recent advancements in deep convolution neural networks. However, the high memory requirements and intensive computational demands make these approaches less suitable for Internet-of-Things (IoT) devices, and there is a need for an improved computational efficiency and reduced memory footprint to facilitate distributed IoT intelligence. This article proposes a pseudoknowledge distillation (PKD) training method for creating a compact real-time saliency prediction model. The proposed method can effectively transfer knowledge from computationally expensive once-for-all (OFA-595) as a single teacher model and a combination of OFA-595 and EfficientNet-B7 as a multiteacher model to an early exit evolutionary algorithm network student model by utilizing knowledge distillation and pseudolabeling. Five saliency benchmark datasets are used to demonstrate PKD’s improved prediction performance and its reduced inference time without modifying the original student model.

Blockchain has been utilized to manage distributed multicloud storage in the industrial Internet of Things. Existing approaches commonly use trusted third-party servers or middlewares to search data allocation strategies and use blockchain to enhance security. However, finding a fair data allocation strategy is hard when the third-party brokers are manipulated. Moreover, the complex computing in generating blocks reduces efficiency and heavy communication cost in consensus leads to critical challenges to scalability. To address that, this article proposes a scalable two-layer blockchain system for distributed multi-cloud storage (STSM). We design a novel consensus mechanism called proof of storage allocation, which integrates data placement problems into leader selection to achieve fair strategy and high QoS of data storage. We also incorporate asynchronous consensus groups into the consensus process to enhance scalability. Extensive experiments verify that STSM gains high scalability and increases efficiency while achieving high QoS in distributed multicloud data allocation.

Considering the high mobility and uneven distribution of vehicles, an efficient routing protocol should avoid that the sent packets are forwarded within road segments with ultra-low density or serious data congestion in vehicular networks. To this end, in this paper, we propose a Traffic aware and Link Quality sensitive Routing Protocol (TLRP) for urban Internet of Vehicles (IoV). First, we design a novel routing metric, i.e., Link Transmission Quality (LTQ), to account for the impact of the number, quality and relative positions of communication links along a routing path on the network performance. Then, to adapt to the dynamic characteristics of IoV, a road weight evaluation scheme is presented to assess each road segment using the real-time traffic and link information quantified by the LTQ. Next, the path with the lowest aggregated weight is selected as the routing candidate. Extensive simulations demonstrate that our proposed protocol achieves significant performance improvements compared to the state-of-the-art protocol MM-GPSR, the typical junction-based scheme E-GyTAR, and the classic connectivity-based routing iCAR, in terms of packet delivery ratio and average transmission delay.

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.

Internet of Things (IoT) includes numerous sensing nodes that constitute a large scale-free network. Optimizing the network topology to increase resistance against malicious attacks is a complex problem, especially on 3-dimension (3D) topological deployment. Heuristic algorithms, particularly genetic algorithms, can effectively cope with such problems. However, conventional genetic algorithms are prone to falling into premature convergence owing to the lack of global search ability caused by the loss of population diversity during evolution. Although this can be alleviated by increasing population size, the additional computational overhead will be incurred. Moreover, after crossover and mutation operations, individual changes in the population are mixed, and loss of optimal individuals may occur, which will slow down the population’s evolution. Therefore, we combine the population state with the evolutionary process and propose an Adaptive Robustness Evolution Algorithm (AREA) with self-competition for scale-free IoT topologies. In AREA, the crossover and mutation operations are dynamically adjusted according to population diversity to ensure global search ability. A self-competitive mechanism is used to ensure convergence. We construct a 3D IoT topology that is optimized by AREA. The simulation results demonstrate that AREA is more effective in improving the robustness of scale-free IoT networks than several existing methods.

The span of Internet of Things (IoT) is expanding owing to numerous applications being linked to massive devices. Subsequently, node failures frequently occur because of malicious attacks, battery exhaustion, or other malfunctions. A reliable and robust network topology can alleviate the cascading collapse caused by local node failures. Existing optimization methods for fixed topologies enhance the robustness of the IoT topology by reconstructing the connections among the devices. However, the application of existing algorithms requires global topology optimizations or local adjustments when new nodes are added, which leads to high computational complexity. To address this problem, based on neuroevolution and network motifs, this study proposes an evolutionary algorithm to generate a robust IoT topology called “Born This Way: a self-organizing evolution scheme with Motif” (BTW-Motif). Using novel mutation and crossover operators, BTW-Motif generates an IoT topology with intrinsic robustness when new nodes are added. We design an adaptive edge density control mechanism to avoid an increase in energy consumption resulting from redundant connections. Specifically, BTW-Motif innovatively introduces network motif as a guide structure which has been proven to have a positive effect on the network robustness. Experiments indicate that BTW-Motif can effectively produce a robust topology. With different network sizes and edge densities, BTW-Motif can generate more robust topologies compared with the existing topology optimization algorithms. And the time consumption for the large-scale topology to achieve similar robustness is reduced by 50%.

In underwater monitoring systems, it is important to guarantee high availability of the data collection service. An effective approach is the use of autonomous underwater vehicles (AUVs) to gather data from the sensor nodes. In mobile underwater wireless sensor networks, node locations change continuously, which increases the difficulty of data collection. In this paper, a dynamic data collection algorithm based on mobile nodes (DDCA) is proposed to collect underwater data. AUVs can move directly to the predicted node location to shorten the time of data collection. The algorithm is divided into two parts: mobility prediction and data collection. The locations of the mobility sensor nodes are predicted, and then the trajectory of the AUV is planned according to the predicted locations of sensor nodes to achieve reliable data collection. Furthermore, a region partitioning strategy is proposed to reduce the time difference of each AUV completing the data collection. The simulation results demonstrate that the DDCA effectively reduces the time of data collection and shortens the time difference of AUVs returning to the sink node.

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.

With the continuous growth of the number of Internet of things(IoT) devices, more and more data are generated by IoT devices. IoT terminal devices need to transfer the data to the edge server for storage, so protecting the security of IoT data has become a huge challenge. As a new technology, blockchain has the characteristics of distributed, tamper-resistant. Smart contract running on the blockchain can automatically perform tasks. Because of these characteristics, blockchain can solve the data security problem of the IoT. In this paper, a blockchain-based data protection framework for the IoT in untrusted storage is proposed. Lightweight streaming authenticated data structures are also used to reduce the storage burden of the blockchain system and improve the efficiency of the framework. Finally, simulation results show that our work can reduce the storage burden of blockchain.

In recent years, crowdsourcing has become a new business model. Quality assessment of crowdsourcing has also become a hot topic of interest for researchers. Most quality control methods are based on centralised platforms and cannot guarantee complete reliability. Therefore, this paper proposes a blockchain-based quality assessment model for crowdsourcing(BC-CQAM). A trusted mechanism is introduced to construct a reputation model, based on that we also propose a blockchain-based worker selection algorithm(BC-WS). A new quality assessment algorithm(BIV-EM) is proposed, resulting in more accurate evaluation results. The validity of the algorithm and the reliability of the worker selection have been demonstrated experimentally.

In Mobile crowdsensing (MCS), the platform needs an adequate user group to accomplish tasks. Its recruiting worker strategy is essential for sensor data quality. Social-network-assisted worker recruitment effectively expands task coverage. However existing studies enclose two impractical assumptions: influence between users is determined by the number of friends and the recruiting reward is fixed. To solve this problem, a novel influence and cost trade-off (ICT) algorithm is proposed to apply the worker recruitment strategy in the real world. ICT uses linear equations to estimate influence and cost iteratively under the impact of the seed set. Using the influence model based on social interaction, the algorithm selects a near-optimal set of seeds by the revenue-cost-ratio. Empirical studies on three realworld datasets verify that ICT achieves higher performance than baseline methods under various settings.

With the rapid development of intelligent transportation, various computation-intensive applications have e-merged to improve the safety, efficiency, and comfort on the road. However, due to the mobility and resource dynamics, it is still a challenge for the resource-constrained vehicles to timely process computation-intensive tasks. Fortunately, the computation offloading in the Internet of Vehicles (IoV) greatly eases the contradiction between resource constraints and computing requirements. In this paper, we first present a collaborative computing architecture based on Edge-Cloud (EC) and Mobile-Edge-Platooning-Cloud (MEPC). Then, considering the priority of the Delay-Sensitive Tasks (DSTs), preemptive scheduling is introduced to deal with the hybrid tasks, comprised of DSTs and Delay-Tolerant Tasks (DTTs). Finally, a computation offloading problem based on the collaborative EC-MEPC architecture is established by jointly optimizing the decision-making and resource allocation issue. To solve the above problem, a distributed computation offloading and resource allocation algorithm is designed to achieve the optimal solution. Simulation results show that the proposed collaborative computing architecture and the distributed algorithm can effectively improve the delay and energy consumption performance of this system.

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 this paper, we present a study on person identification using static features extracted from Kinect skeleton data. On the contrary to previous reports that the dynamic features such as gait parameters are more discriminative than static features, we find that by using a combination of a set of easy to obtain static features, we can achieve nearly perfect accuracy in identifying persons with only a few frames. In our study, we experimented with several classifiers, including k-nearest neighbor (KNN), decision tree, Gaussian Naive Bayesian, neural network with multiplayer perception (MLP), and support vector machine (SVM), and several combinations of static skeleton features. In all scenarios, KNN outperforms other classifiers consistently. MLP and SVM require a huge amount of parameter tuning and training time and they do not perform well compared with KNN except for small gallery sizes when all static features available.

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.

Social Internet of Things comes as a new paradigm of Internet of Things to solve the problems of network discovery, navigability, and service composition. It aims to socialize the IoT devices and shape the interconnection between them into social interaction just like human beings. In IoT scenarios, a device can offer multiple services and different devices can offer the same services with different parameters and interest factors. The proliferation of offered services led to difficulties during service filtering and customization, this problem is known as services explosion. The selection of a suitable service that fits the requirements of the applications and devices is a challenging task. Several works have addressed service discovery, composition, and selection in IoT. However, these works did not emphasize on the fact that incorporating the users’ social features can increase the efficiency of the recommended services and help us to offer context-aware services. In this article, we present a service recommendation system that takes advantage of the social relationships between devices’ owners, where the recommendation is based on the different relationships between the service requester and service provider. Experimental results show, in the context of IoT, that incorporating the users’ social relationships in service recommendation increases the accuracy and diversity of the offered services.

Nowadays, more and more sensors, devices and applications are connected in Industrial Internet of Things (IIoT), producing massive real-time flows which need to be scheduled for Quality-of-Service provision. To realize application-aware and adaptive flow scheduling, the problem of traffic classification must be addressed at first. When edge computing paradigm is introduced into IIoT, the traffic classification service can be deployed on edge node in the near-end. Recently, deep-learning-based IIoT traffic classification methods show better performance, but the computational cost of deep learning model is too high to be deployed on edge node. Moreover, increasingly unknown flows generated by new devices and emerging industrial APPs lead to frequent training of traffic classifiers. It is difficult to migrate the complex process of classifier training from cloud server to edge nodes with limited resources. To address these issues, we take the benefits of hash mechanism and consensus mechanism in blockchain to design a lightweight IIoT traffic classification service, which is more applicable for edge computing paradigm. First, inspired by the hash mechanism in blockchain and the learning to hash for big data, we propose a new learning-to-hash method named extension hashing. By this method, we can build the set of binary coding tress (BCT set), then generating hash table for more efficient k-nearest neighbor-based classification without complex classifier training. Then, we design a new voting-based consensus algorithm to synchronize the BCT sets and the hash tables across edge nodes, thereby providing the traffic classification service. Finally, we conduct data-driven simulations to evaluate the proposed service. By comparing traffic classification results on public data set, we can see that the proposed service achieves the highest classification accuracy with the minimal time cost and memory usage.

The complex factory environment of the Industrial Internet of Things (IIoT) greatly increases the energy consumption of sensor nodes and reduces production profits. Especially, in mines, the terrain will change continuously as the mining progresses. Additionally, the heavy traffic load on a single sink node and the unbalanced load on multiple sink nodes also reduce the battery life. Therefore, how to build an energy-efficient topology based on the unique mine terrain characteristics is a critical issue. To address this problem, this article proposes a 3-D topology evolution scheme with self-adaption for mining areas (3D-TES) to reduce energy consumption. We build the multipeak terrain model according to the characteristics of the mining environment. Blocked by the undulating peaks on the mine, the strength of the node signal is quantified by the slope and aspect. The 3D-TES is then applied to determine the optimal number of sink nodes and find the best data transmission path between sensor nodes and multiple sink nodes. The experimental results show that 3D-TES outperforms the directed angulation toward the sink node model (DASM) in terms of reliability, average path length, and data load on sink nodes.

The prompt evolution of Internet of Medical Things (IoMT) promotes pervasive in-home health monitoring networks. However, excessive requirements of patients result in insufficient spectrum resources and communication overload. Mobile Edge Computing (MEC) enabled 5G health monitoring is conceived as a favorable paradigm to tackle such an obstacle. In this paper, we construct a cost-efficient in-home health monitoring system for IoMT by dividing it into two sub-networks, i.e., intra-Wireless Body Area Networks (WBANs) and beyond-WBANs. Highlighting the characteristics of IoMT, the cost of patients depends on medical criticality, Age of Information (AoI) and energy consumption. For intra-WBANs, a cooperative game is formulated to allocate the wireless channel resources. While for beyond-WBANs, considering the individual rationality and potential selfishness, a decentralized non-cooperative game is proposed to minimize the system-wide cost in IoMT. We prove that the proposed algorithm can reach a Nash equilibrium. In addition, the upper bound of the algorithm time complexity and the number of patients benefiting from MEC is theoretically derived. Performance evaluations demonstrate the effectiveness of our proposed algorithm with respect to the system-wide cost and the number of patients benefiting from MEC.

As the application of the industrial Internet of Things (IIoT) becomes more widespread, the IIoT is being combined with social networks. Nodes in the network can be users, machines, and so on. Using the sensing detection technology of the IIoT, industrial machines can realize real-time informatization, which is convenient for users to perform remote management. Nodes can communicate with each other and make ratings. These ratings can be modeled as directed weighted edges between nodes and form directed weighted networks (DWNs). The edge weight represents the “strength” of relationship and the direction of edge points from the edge generator to the edge receiver. Predicting edge weights in DWNs is critical to predicting unknown ratings or recovering lost data. In this article, we propose a directed edge weight prediction model (DEWP) using decision tree ensembles. It extends the local similarity indices to DWNs and extracts a series of similarity indices between nodes as features of each edge. These features are used to construct a blended regression model of random forest, gradient boost decision tree, extreme gradient boosting, and light gradient boosting machine. The proposed algorithm was evaluated experimentally with the Bitcoin OTC and Bitcoin Alpha datasets by removing 10% to 90% of edges in the original network. Compared with other classical algorithms, DEWP has higher prediction accuracy and robustness.

How to make full use of the evolution information of chaotic systems for time-series prediction is a difficult issue in dynamical system modeling. In this article, we propose a maximum information exploitation broad learning system (MIE-BLS) for extreme information utilization of large-scale chaotic time-series modeling. An improved leaky integrator dynamical reservoir is introduced in order to capture the linear information of chaotic systems effectively. It can not only capture the information of the current state but also achieve the compromise with historical states in the dynamical system. Furthermore, the feature is mapped to the enhancement layer by nonlinear random mapping to exploit nonlinear information. The cascading mechanism promotes the information propagation and achieves feature reactivation in dynamical modeling. Discussions about maximum information exploration and the comparisons with ResNet, DenseNet, and HighwayNet are presented in this article. Simulation results on four large-scale data sets illustrate that MIE-BLS could achieve better performance of information exploration in large-scale dynamical system modeling.

The Internet of Things (IoT) has been extensively deployed in smart cities. However, with the expanding scale of networking, the failure of some nodes in the network severely affects the communication capacity of IoT applications. Therefore, researchers pay attention to improving communication capacity caused by network failures for applications that require high quality of services (QoS). Furthermore, the robustness of network topology is an important metric to measure the network communication capacity and the ability to resist the cyber-attacks induced by some failed nodes. While some algorithms have been proposed to enhance the robustness of IoT topologies, they are characterized by large computation overhead, and lacking a lightweight topology optimization model. To address this problem, we first propose a novel robustness optimization using evolution learning (ROEL) with a neural network. ROEL dynamically optimizes the IoT topology and intelligently prospects the robust degree in the process of evolutionary optimization. The experimental results demonstrate that ROEL can represent the evolutionary process of IoT topologies, and the prediction accuracy of network robustness is satisfactory with a small error ratio. Our algorithm has a better tolerance capacity in terms of resistance to random attacks and malicious attacks compared with other algorithms.

The explosive growth of mobile devices promotes the prosperity of novel mobile applications, which can be realized by service offloading with the assistance of edge computing servers. However, due to limited computation and storage capabilities of a single server, long service latency hinders the continuous development of service offloading in mobile networks. By supporting multi-server cooperation, Pervasive Edge Computing (PEC) is promising to enable service migration in highly dynamic mobile networks. With the objective of maximizing the system utility, we formulate the optimization problem by jointly considering the constraints of server storage capability and service execution latency. To enable dynamic service placement, we first utilize Lyapunov optimization method to decompose the long-term optimization problem into a series of instant optimization problems. Then, a sample average approximation-based stochastic algorithm is proposed to approximate the future expected system utility. Afterwards, a distributed Markov approximation algorithm is utilized to determine the service placement configurations. Through theoretical analysis, the time complexity of our proposed algorithm is linear to the number of users, and the backlog queue of PEC servers is stable. Performance evaluations are conducted based on both synthetic and real trace-driven scenarios, with numerical results demonstrating the effectiveness of our proposed algorithm from various aspects.

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.

This paper considers the cyber-attacks that aim to remove nodes or links from network topologies. We particularly focus on one category of such attacks, in which attacks happen by rounds, and in each round, the node with the highest centrality and its adjacent links are removed. Here the centrality can be any centrality measure such as Degree Centrality, Betweenness Centrality, etc. For this attack category, there currently exist two strategies: Initial and Adaptive. In the Initial strategy, node centralities are only calculated initially, while in the Adaptive strategy, node centralities are recalculated after each round of attack. In the literature, it has been shown that the Adaptive strategy is more effective than the Initial strategy for a centrality measure. In this paper, we propose a new strategy called the largest component (LC) strategy which further outperforms the Adaptive strategy in terms of both attack effectiveness and computation complexity. Moreover, we propose the use of current-flow versions of Betweenness Centrality and Closeness Centrality as the centrality measures in the attacks, since they are more granular and supported by the LC strategy. We verify the better performances of the LC strategy by extensive experiments on four kinds of artificial networks and two realworld networks. Our experiments also show that the Currentflow Betweenness Centrality makes attacks the most effective among the five centrality measures studied in this paper.

Internet of Things (IoT) includes numerous sensing nodes that constitute a large scale-free network. Optimizing the network topology for increased resistance against malicious attacks is an NP-hard problem. Heuristic algorithms, particularly genetic algorithms, can effectively cope with such problems. However, conventional genetic algorithms are prone to falling into premature convergence owing to the lack of global search ability caused by the loss of population diversity during evolution. Although this can be alleviated by increasing population size, additional computational overhead will be incurred. Moreover, after crossover and mutation operations, individual changes in the population are mixed, and loss of optimal individuals may occur, which will slow down the evolution of the population. Therefore, we combine the population state with the evolutionary process and propose an Adaptive Robustness Evolution Algorithm (AREA) with self-competition for scale-free IoT topologies. In AREA, the crossover and mutation operations are dynamically adjusted according to population diversity to ensure global search ability. Moreover, a self-competitive mechanism is used to ensure convergence. The simulation results demonstrate that AREA is more effective in improving the robustness of scale-free IoT networks than several existing methods.

In this paper, we present an exploratory work towards the recognition of activities and performing real-time objective assessment in human patient simulation (HPS). Although HPS has been pervasively used in medical and nursing programs in developed countries, there is a huge need in providing consistent and objective assessment on student performance during HPS. Current methods all depend on instructor subjective observation, which not only could lead to inconsistency in evaluation across different students and different instructors, but also are very time and resource intensive. Recognizing complex human activities in the context of HPS is very challenging because it involves the recognition of human actions, gestures, as well as human-object and human-mannequin interactions. Hence, we study the feasibility of developing such a system for a particular simulation where a student is required to first identify the patient and then place a neck brace on the patient's neck. The system we that we have developed identifies the actions and activities in the simulation and provides qualitative assessment on the student performance using computer vision, OpenPose, and TensorFlow. The system also consists of a debriefing mobile app that the student and instructor could use to view an automatically generated report with supporting key frames captured and annotated by our system.

Mobile-edge nodes, as an efficient approach to the performance improvement of wireless sensor networks (WSNs), play an important role in edge computing. However, existing works only focus on connected networks and suffer from high calculational costs. In this article, we propose a rendezvous selection strategy for data collection of disjoint WSNs with mobile-edge nodes. The goal is to achieve full network connectivity and minimize path length. From the perspective of the application scenario, this article is distinctive in two aspects. On the one hand, it is specially designed for partitioned networks which are much more complex than conventional connected scenarios. On the other hand, this article is specially designed for delay-harsh applications rather than usual energy-oriented scenarios. From the viewpoint of the implementation method, a simplified ant colony optimization (ACO) algorithm is performed and displays two characteristics. The first one is the path segmenting mechanism, simplifying the path construction of each part and consequently reducing the computational cost. The second one is the candidate grouping mechanism, reducing the search space and accordingly speeding up the convergence speed. Simulation results demonstrate the feasibility and advantages of this approach.

The extensive applications in the Internet of Things (IoT) have inspired a growing network scale. However, due to the resource-limited IoT devices and the numerous cyber attacks against applications, maintaining the robustness and communication capabilities for the applications is increasingly challenging. In this article, we consider IoT network topologies that provide robust communication for heterogeneous networks and study the networking stability of IoT devices and the intelligent evolution computing in network architectures. We explicate the network robustness problem both for the network architecture and the resistance to cyber attacks. For the network architecture, we optimize the robustness of IoT network topology with a scale-free network model which has good performance in random attacks. In the case with the resistance to cyber attacks, a deep deterministic learning policy (DDLP) algorithm is proposed to improve the stability for large-scale IoT applications. Simulations show that the proposed algorithms greatly advance the robustness of IoT network topology compared to other algorithms, with a less computational cost.

The fifth-generation (5G) mobile communication systems support the millimeter-wave (mmWave) communications, which enable content dissemination for enhanced V2X (eV2X) services. However, the current content dissemination architectures face various problems, such as limited radio coverage of mmWave communications and differentiated Quality-of-Experience (QoE) requirements of users. To address these, we propose a regional-centralized content dissemination (RC-CD) scheme for eV2X services. The RC-CD can deal with the requests from distributed vehicles by the regional-centralized service architecture. By introducing vehicle-to-vehicle and vehicle-to-infrastructure mmWave communications, the content dissemination services are expanded to the areas that the mmWave base stations cannot cover. According to different QoE requirements, the requests of eV2X services are categorized into two groups: 1) elastic requests and 2) inelastic requests. Our algorithm considers the channel characteristics of mmWave and the different QoE requirements of requests to jointly optimize the waiting time of elastic requests and the failure ratio of inelastic requests. It also adopts an efficient heuristic approximate algorithm to solve the NP-completed optimization problem. The performance of our proposed scheme is evaluated by simulations in a realistic city layout. The results show that our scheme can provide an efficient architecture for eV2X content dissemination that supports different types of requests, provides a better QoE for the users, and achieves broader service coverage.

Connectivity restoration plays an important role in maintaining the long-term operation in wireless sensor networks (WSNs), especially, in environment-harsh cases. However, current solutions lack the ability to handle the second damages and the capacity of designing requirement-different connectivity approaches according to different needs. In this article, we propose a durability-based connectivity establishment (DBCE) scheme for disjoint segments of WSNs. This scheme includes three approaches regarding segment evaluation or segment selection: 1) a segment shape evaluation approach; 2) a region different connectivity approach; and 3) a data traffic transfer approach, for their respective objectives. The unique characteristics of this article are twofold. On the one hand, this is the first attempt to investigate segment shapes, which we demonstrate have great impact on the robustness of the network. On the other hand, distinguished from the existing networks with uniform connectivity rule, the network is divided into two parts and different connectivity sequences and connectivity approaches are designed according to disparate features and requirements of the network. The performance of DBCE is validated through extensive simulation experiments.

Smart vehicles can cooperate in teams to perform crowdsensing tasks in smart cities. A critical challenge in this regard is to build a secure model for nondeterministic vehicle teams to achieve maximum social welfare. Although several crowdsensing models have been proposed, none of them has focused on real-time vehicle teamwork. In this article, to the best of our knowledge, we propose the first secure model, called blockchain-based nondeterministic teamwork cooperation (BNTC), for nondeterministic teamwork cooperation in a vehicular crowdsensing system. We model the system as a multiconditional NP-complete problem by explicitly considering the dynamic features of task issuers and workers. To solve the problem, we propose the winning teams selected (WTS) algorithm based on a reverse auction and utilize a knapsack-based method to solve the models. We consider the credit of teams for determining the payment. Thus, we propose a credit-based team payment (CTP) algorithm for BNTC to maximize the welfare of the system. We also propose a general blockchain-based framework to address trust issues and security challenges to make the method suitable for use in practical applications. Based on theoretical analyses and extensive simulations, we demonstrate that the proposed model performs better than the baselines and can achieve the maximum social welfare. Implementation with Ethereum suggests our model can operate within a reasonable cost.

The existence of botnet puts people in an extremely insecure environment, which has seriously affected the development of Internet of Things (IoT). In order to prevent the formation of a botnet, it is necessary to understand the propagation behaviors and influence factors. As IoT devices become more intelligent, they can gradually generate social characteristics by mimicking human behaviors. However, in the process of coping with the botnet problem, little consideration has been given to the potential social characteristics of IoT. In this article, we build a dynamic botnet propagation model (i.e., IoT-BSI model) to study the influences of two social characteristics (i.e., device's spread capability and device's identification ability) on botnet formation. First, in terms of device's spread capability, this article makes great improvements on the K- shell decomposition algorithm and calculates this characteristic more accurately. Second, this article divides the device's identification ability into the rational identification ability and the irrational identification ability based on the sociological theory, and the calculation of the former is mainly realized by utilizing the PageRank idea. Third, this article applies the mean-field equation theory to analyze the dynamic characteristics of botnet propagation theoretically. Finally, the comprehensive results show that our model is not merely more consistent with the actual situation but also performs better than four compared models.

The construction of smart cities is the concentrated embodiment of the application of Internet of Things (IoT), which provides a variety of solutions to various problems faced by urban development and urban management. However, the openness, dynamics, and heterogeneity of city IoT increase the risk of attacks, especially the social attributes contained in such systems accelerate virus propagation. To reduce the risk and harm of virus propagation, it is of great significance to analyze the mode and the characteristics of virus propagation. This article proposes a dynamic virus propagation model [i.e., Ignorant-DKs- HN-Exposed-Pvirus-Spread-Recover (IDEPSR)], which focuses on two social attributes (i.e., intelligent device's propagation capability and identification ability). First, this article presents a new algorithm (i.e., DKs-HN) to measure the first attribute based on the evidence theory and an improved K-shell method. The DKs- HN merges the direct influence of a particular node and the indirect influence of 1-hop neighbors by utilizing the combination rules of the evidence theory. Second, this article proposes a Pvirus method to measure the second attribute based on the social hierarchy theory and the nonnegative matrix factor method. Every intelligent device can identify its trust relationship with other devices by using the Pvirus, and then the probability of virus activation lurking in devices can be predicted. Finally, a real data set is used to simulate a social-aware city IoT environment. Convincing experimental results show that the IDEPSR is more reasonable in design and good in performance. The IDEPSR performs better in controlling the virus propagation than the other five models.

With the rapid development of the Internet-of-Things (IoT) networks, millions of IoT services provided through wireless networks are waiting for people’s exploration. Such a large number of heterogeneous IoT services produce huge amounts of data in almost real time, known asbig data, many of which cannot be measured or quantified. Hence, a recommended system that aims to deal with the unquantifiable big data is urgently needed. To solve the problem, we propose a novel quantile contextual tree-based multiarmed bandits algorithm to support the large-scale recommendation with both quantifiable and unquantifiable data. Furthermore, the high failure rate of communication has a serious influence on the recommendation accuracy of our system with the widely used D2D technology in today’s IoT network. To improve recommendation accuracy under the D2D communication, we take into account the feedback of historical service receivers and the historical successful delivery rate (SDP) of data transmission at the same time for the service recommendation system. We give theoretical analysis to prove a sublinear bound of the regret. Numerical experiments with tremendously large data sets show that we can balance the regret with the system time cost and guarantee a high SDP.

The broad learning system (BLS) is an emerging approach for effective and efficient modeling of complex systems. The inputs are transferred and placed in the feature nodes, and then sent into the enhancement nodes for nonlinear transformation. The structure of a BLS can be extended in a wide sense. Incremental learning algorithms are designed for fast learning in broad expansion. Based on the typical BLSs, a novel recurrent BLS (RBLS) is proposed in this paper. The nodes in the enhancement units of the BLS are recurrently connected, for the purpose of capturing the dynamic characteristics of a time series. A sparse autoencoder is used to extract the features from the input instead of the randomly initialized weights. In this way, the RBLS retains the merit of fast computing and fits for processing sequential data. Motivated by the idea of "fine-tuning" in deep learning, the weights in the RBLS can be updated by conjugate gradient methods if the prediction errors are large. We exhibit the merits of our proposed model on several chaotic time series. Experimental results substantiate the effectiveness of the RBLS. For chaotic benchmark datasets, the RBLS achieves very small errors, and for the real-world dataset, the performance is satisfactory.

Vehicular named data networking (VNDN) has recently emerged as a novel paradigm to facilitate content-centric data sharing for Internet of Vehicles. However, an information holder can spread fake data to clients for malicious purposes, which may affect the driving decision of the recipient, or even worse, cause traffic congestion and accidents. In this article, we build a data-sharing system that consists of a double-layer blockchain. The nodes at the bottom layer request for service by announcing their requirements in the NDN paradigm. For the upper layer, the nodes submit their demands and supplies to the nearest roadside unit for further matching. We model the balance between the demand and supply as a matching game. To encourage nodes to provide positive services, a reputation management mechanism that combines negative and positive transaction records is proposed. Simulation results verify the validity of our system, and the data-sharing mechanism fosters a secure information interaction in the VNDN.

To improve the urban traffic condition and reduce accidents, we propose a platoon-driving model for autonomous vehicles in a free-flow traffic state in this article. This model allows vehicles with successful path matching to be grouped in a platoon and led by the platoon head (PH). In addition, a PH selection scheme is introduced to provide an incentive for vehicles to be PHs and maintain the dynamic update of platoons. Next, a smart contract is employed to enable the payment based on a blockchain between the PH and platoon members (PMs), avoiding the malicious and false payments. The numerical results show that the platoon model is superior to the individual driving model in terms of fuel consumption. The comparison between carpooling and noncarpooling modes within the platoon shows that our model has a better performance in terms of PH revenue and PM's service charge.

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.

The development of the smart ocean requires that various features of the ocean be explored and understood. The Underwater Internet of Things (UIoT), an extension of the Internet of Things (IoT) to the underwater environment, constitutes powerful technology for achieving the smart ocean. This article provides an overview of the UIoT with emphasis on current advances, future system architecture, applications, challenges, and open issues. The UIoT is enabled by the most recent developments in autonomous underwater vehicles, smart sensors, underwater communication technologies, and underwater routing protocols. In the coming years, the UIoT is expected to bridge diverse technologies for sensing the ocean, allowing it to become a smart network of interconnected underwater objects that has self-learning and intelligent computing capabilities. This article first provides a horizontal overview of the UIoT. Then, we present a five-layer system architecture for the future UIoT, which consists of a sensing, communication, networking, fusion, and application layer. Finally, we suggest the current challenges and the future UIoT research trends, in which cloud computing, fog computing, and artificial intelligence are combined.

Due to the increasing medical data for coronary heart disease (CHD) diagnosis, how to assist doctors to make proper clinical diagnosis has attracted considerable attention. However, it faces many challenges, including personalized diagnosis, high dimensional datasets, clinical privacy concerns and insufficient computing resources. To handle these issues, we propose a novel blockchain-enabled contextual online learning model under local differential privacy for CHD diagnosis in mobile edge computing. Various edge nodes in the network can collaborate with each other to achieve information sharing, which guarantees that CHD diagnosis is suitable and reliable. To support the dynamically increasing dataset, we adopt a top-down tree structure to contain medical records which is partitioned adaptively. Furthermore, we consider patients' contexts (e.g., lifestyle, medical history records, and physical features) to provide more accurate diagnosis. Besides, to protect the privacy of patients and medical transactions without any trusted third party, we utilize the local differential privacy with randomised response mechanism and ensure blockchain-enabled information-sharing authentication under multi-party computation. Based on the theoretical analysis, we confirm that we provide real-time and precious CHD diagnosis for patients with sublinear regret, and achieve efficient privacy protection. The experimental results validate that our algorithm {outperforms} other algorithm benchmarks on running time, error rate and diagnosis accuracy.

Driving safety has been a hot topic in recent vehicular research. However, research on active control strategy, by which an accident might be avoided before it really happens, is still lacking, especially those appealing to machine learning methods with real traffic data. In addition, previous works constructed models with only one or a few factors considered, while the impact of multiple factors on a collision probability is overlooked. In this paper, based on machine learning methods with an actual traffic dataset, we propose a multi-level active safety control strategy taking the Multi-source, Multi-parameter, and Multi-purpose (3M) properties of an accident into consideration. First, by analyzing the impact of different conditions on an accident with the AHP (Analytic Hierarchy Process)-Ridge regression and bisecting K-means clustering model, the safety inter-vehicle distance is derived by learning from an actual traffic dataset. Besides, ELM(Extreme Learning Machines) is adopted as a verification scheme for safety distance calculation. Subsequently, we design a three-level active safety control scheme using the LQG (Linear Quadratic Gaussian) optimal-control model based on the obtained safety inter-vehicle distance. Numerical results show that by comparing with some classical braking and car-following models, our strategy can always keep the distance of two followed vehicles at a safety state. To further explore the impact of the time complexity on the rear-end collisions, we also implemented a road-test and verified that our model can timely respond to the risks and keep two cars always in safety.

As an outstanding discriminant analysis technique, Fisher discriminant analysis (FDA) gained extensive attention in supervised dimensionality reduction and fault diagnosis fields. However, it typically ignores the multimodality within the measured data, which may cause infeasibility in practice. In addition, it generally incorporates all process variables without emphasizing the key faulty ones when modeling the complex process, thus leading to degraded fault classification capability and poor model interpretability. To ease the above two drawbacks of conventional FDA, this brief presents an advantageously sparse local FDA (SLFDA) model, it first preserves the within-class multimodality by introducing local weighting factors into scatter matrix. Then, the responsible faulty variables are identified automatically through the elastic net algorithm, and the current optimization problem is subsequently settled through the feasible gradient direction method. Since then, the local data structure characteristics are exploited from both the sample dimension and variable dimension so that the fault diagnosis performance and model interpretability are significantly enhanced. In addition, we naturally extend SLFDA model to nonlinear variant (i.e., sparse kernel local FDA) by the kernel trick, which is substantially more resistant to strong nonlinearity. The simulation studies on Tennessee Eastman (TE) benchmark process and real-world diesel engine working process both validate that the novel diagnosis strategy is more accurate and reliable than the existing state-of-the-art methods.

Crowdsourcing uses collective intelligence to finish complicated tasks and is widely applied in many fields. However, the crowdsourcing dilemmas between the task requester and the task completer restrict the efficiency of system severely, e.g., the cooperation dilemma leads to the failure in the interactions and the quality of service dilemma results in the inability of task completer to provide high-quality service. Current research usually focuses on solving only one aforementioned dilemma and fails to integrate perfectly with the service architectural pattern of crowdsourcing systems. In this article, combined with the crowdsourcing interaction phase, we limit the objects that cause dilemma and propose a$\boldsymbol {t}$wo-stage$\boldsymbol {g}$ame$\boldsymbol {p}$ayoff$\boldsymbol {d}$ecision-making scheme (TGPD) to overcome these shortcomings. To solve the cooperation dilemma between the requester and the crowdsourcing platform, we first propose a dynamic payment method based on the reputation-quality rules for the task requester, and then develop a cos-evaluation algorithm to estimate platform’s cost, last design a co-determine algorithm to determine whether the platform adopts a cooperative strategy. To address the quality of service dilemma between the crowdsourcing platform and the workers, we first present an auction-screening method to estimate the reasonable recruitment range of workers which can be optimized by the result of cos-evaluation algorithm, and then use a reward distribution method to motivate workers to complete tasks with high quality and on time. The experimental results indicate that our new scheme successfully increases the worker’s and platforms’ payoffs at the same time, improves the accuracy of screening workers, enhances the worker’s quality of service, and decreases the platform’s cost.

Underwater image processing is an intelligence research field that has great potential to help developers better explore the underwater environment. Underwater image processing has been used in a wide variety of fields, such as underwater microscopic detection, terrain scanning, mine detection, telecommunication cables, and autonomous underwater vehicles. However, underwater imagery suffers from strong absorption, scattering, color distortion, and noise from the artificial light sources, causing image blur, haziness, and a bluish or greenish tone. Therefore, the enhancement of underwater imagery can be divided into two methods: 1) underwater image dehazing and 2) underwater image color restoration. This paper presents the reason for underwater image degradation, surveys the state-of-the-art intelligence algorithms like deep learning methods in underwater image dehazing and restoration, demonstrates the performance of underwater image dehazing and color restoration with different methods, introduces an underwater image color evaluation metric, and provides an overview of the major underwater image applications. Finally, we summarize the application of underwater image processing.

For the development of the Underwater Internet of Things, reliable transmission of underwater wireless sensor networks to monitor the marine environment is important. However, for ocean monitoring, the reliability of data transmission is difficult to guarantee because of node mobility. In addition, energy consumption must be reduced during data transmission because node energy is limited. To entirely address these problems, this paper proposes a self-organising routing algorithm based on a joint clustering and routing strategy for ocean monitoring (JCR-OM) to increase reliable data transmission in underwater wireless sensor networks. Firstly, the reliable communication distance of the node is calculated in a multilayer current model by using a force analysis of the anchor node. Then, in cluster head selection, the reliable transmission distance and a backoff strategy are introduced to improve the impact of node mobility on data transmission. In intercluster routing selection, a greedy strategy is used to construct a routing strategy with minimum communication cost. The simulation results verify that JCR-OM can improve data transmission and prolong network lifetime.

In Vehicular Ad Hoc Networks (VANETs), the unreliable wireless environment and highly dynamic topology make multi-content distribution inefficient due to the large amount of content requests from vehicles. This gives rise to the need for new content distribution schemes in VANETs. Current researches on content distribution for VANETs generally focus on the single type of content. In this paper, we consider both elastic (with no hard delay requirement) and inelastic (with a hard deadline) contents and propose a joint content distribution scheme in VANETs. We model the content distribution as an optimization problem which jointly minimizes the waiting delay of elastic requests and the failure ratio of inelastic requests. Then, we propose an efficient clonal selection-based approximate algorithm to solve the optimization problem. The performance of our scheme is evaluated by simulation using realistic vehicular traces. Simulation results show that our proposed scheme has better performance than previous solutions.

Underwater wireless sensor networks (UWSNs) have become crucial for many different applications, such as marine pastures, which needs stratified aquaculture according to the habitat and range of activities of marine organisms. This type of application poses a significant challenge to network topology because strengthening the monitoring of the living conditions and collecting underwater data on organisms in different regions is necessary. In this paper, a new concept of "subregional monitoring" is proposed, and subregions with frequent activities of marine organisms are taken as the key monitoring areas, i.e., the areas of interest (AOIs). Then, SFRG, an energy-balanced and robust topology based on scale-free network and rigid graph theory, is proposed to strengthen the monitoring of the AOIs. The topology of a rigid graph is constructed in AOI, and the entire network is scale-free network with power-law degree distribution, when the rigid graph is regarded as a "node". Furthermore, a transmission algorithm based on the SFRG is suggested to prolong the network lifetime. The algorithm guarantees that 90% of the received packets are from the AOIs. The simulation results demonstrate that the SFRG effectively prolongs the network lifetime and improves the network robustness, which provides a strong support in strengthening the monitoring of AOIs and balancing the energy consumption.

The incumbent sharding schemes usually assign the nodes to different committees randomly to meet the demands of security and efficiency at the same time. For example, Elastico protocol obtains a random value by letting the node perform proof of work, and then uses this value for sharding. However, the strategy of random sharding ignores the objective differences between nodes, causing performance gaps between different committees in blockchain. This creates a bottleneck in the transaction throughput of the blockchain. In the paper, we propose a node rating based sharding scheme for blockchain system called NRSS. The key idea of NRSS is to evaluate nodes in the network by both the speeds and results of transactions verification before, and then assign them into different committees by balancing the score to reduce the performance gap between committees and increase the speed of transaction process. We implement NRSS in a local blockchain system, and the experiment results show that NRSS can increase the sharding effect of a blockchain, with an average throughput increase of 32.2% in the simulation environment where the node performance difference is up to 75%, depending on the number of nodes in the committee that are preset in the blockchain.

The research on three-dimensional topology is important for Internet of Thing. Small-world with shorter average path lengths has proven to be an effective model for building evolutional network topologies. In order to build three-dimensional topology in IoT, the ant colony algorithm is used to plan shortcuts in this paper. First, Gaussian integration is used to simulate the ups and downs of terrain in three-dimensional space. Second, a significant number of nodes are randomly deployed on the modeled terrain. Taking into account the information about slope and aspect around the node, the actual sensing range of the node is calculated. Third, a certain percentage of nodes are selected as super sensor nodes. Finally, the ant colony algorithm is used to add shortcuts between super sensor nodes. Extensive experimental results show that an energy-efficient three-dimensional network topology in IoT can be built by the algorithm.

The traditional many-to-one transmission pattern allows all sensor nodes to propagate their packets toward a single sink in wireless sensor networks, resulting in imbalanced energy depletion in the network. In this paper, we propose a data dissemination strategy named transmission with multiple load balancing schemes (TMLBSs) which utilizes a nature-inspired approach, ant colony optimization, to construct transmission paths for nodes in different places. The distinct characteristics of TMLBS are three load balancing schemes, which help to construct transmission paths formed into a path tree. The first one is the load decentralization scheme, which creates multiple path subtrees in the early stage and scatters the whole load to such path subtrees, so as to avoid excessive load concentration. The second one is the load maintenance scheme, which adopts an appropriate pheromone update mechanism to retain previous good paths, yielding excellent next-generation solutions. The last one is the load diversion scheme, which employs the heuristic factor to transfer traffic load to paths with light traffic load to eliminate poor solutions. Finally, extensive simulations are conducted to validate the effectiveness and advantages of the new transmission strategy.

State space reconstruction is the foundation of chaotic system modeling. Selection of reconstructed variables is essential to the analysis and prediction of multivariate chaotic time series. As most existing state space reconstruction theorems deal with univariate time series, we have presented a novel nonuniform state space reconstruction method using information criterion for multivariate chaotic time series. We derived a new criterion based on low dimensional approximation of joint mutual information for time delay selection, which can be solved efficiently through the use of an intelligent optimization algorithm with low computation complexity. The embedding dimension is determined by conditional entropy, after which the reconstructed variables have relatively strong independence and low redundancy. The scheme, which integrates nonuniform embedding and feature selection, results in better reconstructions for multivariate chaotic systems. Moreover, the proposed nonuniform state space reconstruction method shows good performance in forecasting benchmark and actual multivariate chaotic time series.

Chaotic time series widely exists in nature and society (e.g., meteorology, physics, economics, etc.), which usually exhibits seemingly unpredictable features due to its inherent nonstationary and high complexity. Thankfully, multifarious advanced approaches have been developed to tackle the prediction issues, such as statistical methods, artificial neural networks (ANNs), and support vector machines. Among them, the interval type-2 fuzzy neural network (IT2FNN), which is a synergistic integration of fuzzy logic systems and ANNs, has received wide attention in the field of chaotic time series prediction. This paper begins with the structural features and superiorities of IT2FNN. Moreover, chaotic characters identification and phase-space reconstruction matters for prediction are presented. In addition, we also offer a comprehensive review of state-of-the-art applications of IT2FNN, with an emphasis on chaotic time series prediction and summarize their main contributions as well as some hardware implementations for computation speedup. Finally, this paper trends and extensions of this field, along with an outlook of future challenges are revealed. The primary objective of this paper is to serve as a tutorial or referee for interested researchers to have an overall picture on the current developments and identify their potential research direction to further investigation.

Kernel recursive least squares (KRLS) is a kind of kernel methods, which has attracted wide attention in the research of time series online prediction. It has low computational complexity and updates in a recursive form. However, as data size increases, computational complexity of calculating kernel inverse matrix will raise. And it has some difficulties in accommodating time-varying environments. Therefore, we have presented an improved KRLS algorithm for multivariate chaotic time series online prediction. Approximate linear dependency, dynamic adjustment, and coherence criterion are combined with quantization to form our improved KRLS algorithm. In the process of online prediction, it can bring computational efficiency up and adjust weights adaptively in time-varying environments. Moreover, Lorenz chaotic time series, El Nino-Southern Oscillation indexes chaotic time series, yearly sunspots and runoff of the Yellow River chaotic time series online prediction are presented to prove the effectiveness of our proposed algorithm.

Multivariate chaotic time series prediction is a hot research topic, the goal of which is to predict the future of the time series based on past observations. Echo state networks (ESNs) have recently been widely used in time series prediction, but there may be an ill-posed problem for a large number of unknown output weights. To solve this problem, we propose a hybrid regularized ESN, which employs a sparse regression with the L1/2regularization and the L2regularization to compute the output weights. The L1/2penalty shows many attractive properties, such as unbiasedness and sparsity. The L2penalty presents appealing ability on shrinking the amplitude of the output weights. After the output weights are calculated, the input weights, internal weights, and output weights are fine-tuning by a Hessian-free optimization method-conjugate gradient backpropagation algorithm. The fine-tuning helps to bubble up the input information toward the output layer. Besides, the largest Lyapunov exponent is used to calculate the predictable horizon of a chaotic time series. Experimental results on benchmark and real-world datasets show that our proposed method is superior to other ESN-based models, as sparser, smaller-absolute-value, and more informative output weights are obtained. All of the predictions within the predictable horizon of the proposed model are accurate.

In Industrial Internet of Things (IIoT), sensor nodes are vulnerable to withstand node failures due to energy exhaustion or external attacks, which leads to the low connectivity of networks. In this situation, how to improve network reliability has became a crucial problem. Adding a small amount of shortcuts to build a small world model in IIoT not only can reduce the delay, but also increases the reliability of networks. In this paper, we propose a Topology Optimization Scheme with Global Small World (TOSG) based on ant colony for IIoT. First, according to the number of appearing on the all shortest paths obtained by the ant colony optimization algorithm, we give the definition of importance for each node. The node with the highest importance in the communication range of a node is defined as an important node. We can find the important nodes in the network topology so that some shortcuts can be created between them to build a global small world model. The experiment results show that the TOSG model has a smaller average shortest path length and higher reliability compared with Greedy Model with Small World properties and Directed Angulation toward the Sink Node Model.

An industrial mobile network is crucial for industrial production in the Internet of Things. It guarantees the normal function of machines and the normalization of industrial production. However, this characteristic can be utilized by spammers to attack others and influence industrial production. Users who only share spams, such as links to viruses and advertisements, are called spammers. With the growth of mobile network membership, spammers have organized into groups for the purpose of benefit maximization, which has caused confusion and heavy losses to industrial production. It is difficult to distinguish spammers from normal users owing to the characteristics of multidimensional data. To address this problem, this paper proposes a spammer identification scheme based on Gaussian mixture model (SIGMM) that utilizes machine learning for industrial mobile networks. It provides intelligent identification of spammers without relying on flexible and unreliable relationships. SIGMM combines the presentation of data, where each user node is classified into one class in the construction process of the model. We validate the SIGMM by comparing it with the reality mining algorithm and hybrid fuzzy c-means (FCM) clustering algorithm using a mobile network dataset from a cloud server. Simulation results show that SIGMM outperforms these previous schemes in terms of recall, precision, and time complexity.

In order to meet low latency, service sensitive and location awareness requirements of digital manufacturing, fog computing is introduced to be an intermediate layer between industrial Internet of Things and cloud. The distributed, dynamic characteristics and the collaboration requirement make it face many new security and privacy issues that cannot be solved by the traditional public key or symmetric cryptosystem. For addressing them, a registered but anonymous attribute credential is designed to manage the network entities. Based on it, an attribute credential based public key cryptography (AC-PKC) is constructed to provide flexible key management by taking the advantage of the certificate-less public key cryptography and the combination property of the elliptic curve cryptography. Encryption, authentication, and access control with privacy preserving can be realized on the basic operations of AC-PKC, which can meet various security requirements of fog computing based digital manufacturing. The performance analyses and comparison with the existing public key schemes and attribute based encryption solutions show that the proposed scheme can work flexibly at a relatively low cost.

Recently, vehicular ad hoc networks (VANETs) have been attracting significant attention for their potential for guaranteeing road safety and improving traffic comfort. Due to high mobility and frequent link disconnections, it becomes quite challenging to establish a reliable route for delivering packets in VANETs. To deal with these challenges, an artificial spider geographic routing in urban VAENTs (ASGR) is proposed in this paper. First, from the point of bionic view, we construct the spider web based on the network topology to initially select the feasible paths to the destination using artificial spiders. Next, the connection-quality model and transmission-latency model are established to generate the routing selection metric to choose the best route from all the feasible paths. At last, a selective forwarding scheme is presented to effectively forward the packets in the selected route, by taking into account the nodal movement and signal propagation characteristics. Finally, we implement our protocol on NS2 with different complexity maps and simulation parameters. Numerical results demonstrate that, compared with the existing schemes, when the packets generate speed, the number of vehicles and number of connections are varying, our proposed ASGR still performs best in terms of packet delivery ratio and average transmission delay with an up to 15% and 94% improvement, respectively.

High-dimensional and large-scale time series processing has aroused considerable research interests during decades. It is difficult for traditional methods to reveal the evolution state in dynamical systems and discover the relationship among variables automatically. In this paper, we propose a unified framework for nonuniform embedding, dynamical system revealing, and time series prediction, termed as Structured Manifold Broad Learning System (SM-BLS). The structured manifold learning is introduced for nonuniform embedding and unsupervised manifold learning simultaneously. Graph embedding and feature selection are both considered to depict the intrinsic structure connections between chaotic time series and its low-dimensional manifold. Compared with traditional methods, the proposed framework could discover potential deterministic evolution information of dynamical systems and make the modeling more interpretable. It provides us a homogeneous way to recover the chaotic attractor from multivariate and heterogeneous time series. Simulation analysis and results show that SM-BLS has advantages in dynamic discovery and feature extraction of large-scale chaotic time series prediction.

As one of the key services for non-safety applications in Vehicular Ad-hoc Networks (VANETs), the Popular Content Distribution (PCD) has become a hot issue in recent years. In popular content distribution, the On-Board Units (OBUs) passing the Area of Interest (AoI) receive popular content broadcast by the RoadSide Units (RSUs). However, due to the high speed of OBUs, limited bandwidth, and unstable wireless connections, only a portion of the popular content can be received by OBUs. To address this issue, in this paper, a cooperative V2V-aided transmission scheme based on a coalitional game (CVCG) is proposed. The scheme allows the OBUs to cooperate with their neighbors to provide the missing popular content. In addition, a coalition graph game algorithm is designed for optimizing the cooperative behaviors among OBUs. The performance of our CVCG scheme is evaluated by different metrics compared to other three content distribution schemes. The numerical results show that the proposed CVCG scheme could outperform the three schemes in terms of the number of iterations for 99 percent finished PCD, the average content completion percentage, and the number of completed OBUs.

Improving universality and robustness of the control method is one of the most challenging problems in the field of complex networks (CNs) synchronization. In this paper, a special unilateral coupling finite-time synchronization (UCFTS) method for uncertain CNs is proposed for this challenging problem. Multiple influencing factors are considered, so that the proposed method can be applied to a variety of situations. First, two kinds of drive-response CNs with different sizes are introduced, each of which contains two types of nonidentical nodes and time-varying coupling delay. In addition, the node parameters and topological structure are unknown in drive network. Then, an effective UCFTS control technique is proposed to realize the synchronization of drive-response CNs and identify the unknown parameters and topological structure. Second, the UCFTS of uncertain CNs with four types of nonidentical nodes is further studied. Moreover, both the networks are of unknown parameters, time-varying coupling delay and uncertain topological structure. Through designing corresponding adaptive updating laws, the unknown parameters are estimated successfully and the weight of uncertain topology can be automatically adapted to the appropriate value with the proposed UCFTS. Finally, two experimental examples show the correctness of the proposed scheme. Furthermore, the method is compared with the other three synchronization methods, which shows that our method has a better control performance.

Spatio-temporal series prediction has attracted increasing attention in the field of meteorology in recent years. The spatial and temporal joint effect makes predictions challenging. Most of the existing spatio-temporal prediction models are computationally complicated. To develop an accurate but easy-to-implement spatio-temporal prediction model, this paper designs a novel spatio-temporal prediction model based on echo state networks. For real-world observed meteorological data with randomness and large changes, we use a cubic spline method to bridge the gaps between the neighboring points, which results in a pleasingly smooth series. The interpolated series is later input into the spatio-temporal echo state networks, in which the spatial coefficients are computed by the elastic-net algorithm. This approach offers automatic selection and continuous shrinkage of the spatial variables. The proposed model provides an intuitive but effective approach to address the interaction of spatial and temporal effects. To demonstrate the practicality of the proposed model, we apply it to predict two real-world datasets: monthly precipitation series and daily air quality index series. Experimental results demonstrate that the proposed model achieves a normalized root-mean-square error of approximately 0.250 on both datasets. Similar results are achieved on the long short-term memory model, but the computation time of our proposed model is considerably shorter. It can be inferred that our proposed neural network model has advantages on predicting meteorological series over other models.

Due to the random delay, local maximum and data congestion in vehicular networks, the design of a routing is really a challenging task especially in the urban environment. In this paper, a distributed routing protocol DGGR is proposed, which comprehensively takes into account sparse and dense environments to make routing decisions. As the guidance of routing selection, a road weight evaluation (RWE) algorithm is presented to assess road segments, the novelty of which lies that each road segment is assigned a weight based on two built delay models via exploiting the real-time link property when connected or historic traffic information when disconnected. With the RWE algorithm, the determined routing path can greatly alleviate the risk of local maximum and data congestion. Specially, in view of the large size of a modern city, the road map is divided into a series of Grid Zones (GZs). Based on the position of the destination, the packets can be forwarded among different GZs instead of the whole city map to reduce the computation complexity, where the best path with the lowest delay within each GZ is determined. The backbone link consisting of a series of selected backbone nodes at intersections and within road segments, is built for data forwarding along the determined path, which can further avoid the MAC contentions. Extensive simulations reveal that compared with some classic routing protocols, DGGR performs best in terms of average transmission delay and packet delivery ratio by varying the packet generating speed and density.

Wireless sensor networks (WSNs) have been the popular targets for cyberattacks these days. One type of network topology for WSNs, the scale-free topology, can effectively withstand random attacks in which the nodes in the topology are randomly selected as targets. However, it is fragile to malicious attacks in which the nodes with high node degrees are selected as targets. Thus, how to improve the robustness of the scale-free topology against malicious attacks becomes a critical issue. To tackle this problem, this paper proposes a Robustness Optimization scheme with multi-population Co-evolution for scale-free wireless sensor networKS (ROCKS) to improve the robustness of the scale-free topology. We build initial scale-free topologies according to the characteristics of WSNs in the real-world environment. Then, we apply our ROCKS with novel crossover operator and mutation operator to optimize the robustness of the scale-free topologies constructed for WSNs. For a scale-free WSNs topology, our proposed algorithm keeps the initial degree of each node unchanged such that the optimized topology remains scale-free. Based on a well-known metric for the robustness against malicious attacks, our experiment results show that ROCKS roughly doubles the robustness of initial scale-free WSNs, and outperforms two existing algorithms by about 16% when the network size is large.

In multivariate chaotic time series prediction, correlation analysis is important for reducing input dimensions and improving prediction performance. Grey relational analysis (GRA) has proved to be an effective method for data correlation analysis, especially for inexact data and incomplete data. In GRA, points are usually regarded as objects, and the distance between points or the concave and convex degree are mostly used to measure the correlations. However, with discrete variables, correlation analysis results always tend to have some deviations when using prior GRA methods. Furthermore, GRA methods cannot directly use vector datasets. Therefore, in this paper, an improved GRA method is proposed based on vector projections. The input and output variables are expressed as vectors by linking two adjacent points. The vectors, instants of the points, are regarded as the objects, and the projection length of input variables to output variables is used to measure the correlations. The smaller the difference between the projection length and the input variables, the higher the correlation. Then, a hybrid variable selection and prediction model is proposed based on the improved GRA method for multivariate chaotic time series predictions, in order to overcome the negative effects of irrelevant and redundant variables caused by phase-space reconstruction. The experimental results based on the gas furnace dataset and San Francisco river runoff dataset demonstrate that the improved GRA method is effective for data correlation analysis, and the prediction accuracy is better than prior GRA-based methods.

In recent years, many fields have begun to use Internet of Things(IoT) to monitor the environment, especially in mountain terrain. Node failures bring a significant challenge in mountain terrain monitoring. Thus, how to improve the robustness of networks withstand node failures becomes a critical issue. To address this shortcoming, this article proposes a strategy to improve the robustness of IoT topology based on Genetic Algorithm (GA). First, Gauss Integration is used to build a 3D terrain to simulate the mountain terrain. Then, an initial scale-free topology according to the characteristics of IoT in 3D terrain is built. Furthermore, a novel crossover operator and a novel mutation operator are proposed to optimize the robustness of IoT topology in 3D terrain. Our proposed model keeps the initial degree of each node unchanged such that the edges overhead will not increase. The extensive experiment results show that our algorithm can significantly improve the robustness of topology in 3D terrain. Especially, the robustness of topology still keeps a high level in the case of partial node failures.

Internet of Vehicles (IoV) has drawn more and more attention. However, due to high vehicle movement and frequent topology changing, it is quite challenging to design an efficient routing protocol in the complex urban IoV. In this paper, we propose a Connectivity aware Transmission quality guaranteed Geographic Routing in urban IoV (CTGR). First, we give the connectivity model in case of disconnected link and the transmission quality (TQ) model when the network is connected, respectively. Then, using the two models, with the assistance of road weight evaluation scheme (RWE), each road segment can be assigned with a suitable weight. Based on the weight information, the road segment can be dynamically selected one by one to comprise the optimized routing path, avoiding the local maximum and data congestion. An improved next hop selection strategy is further proposed to forward the packet along the selected road segment, guaranteeing fast and reliable packet transmission. Simulation results show that our proposed protocol achieves higher packet delivery ratio and lower transmission delay compared with the existing protocols.

Vehicular Ad Hoc Networks (VANETs) have been attracting more and more interest. Designing one efficient routing protocol is one of the most important issues for urban VANETs. However, fast node movement, dynamic topology changes and complicated channel environments make it quite challenging. In this paper, a Delay-aware and Backbone-based Geographic Routing (DBGR) protocol for urban VANETs is proposed. This protocol comprehensively exploits the real-time traffic information in case of link connection and the historical traffic information when the link is disconnected to make a route selection for packet forwarding. Based on the current traffic condition, using the road weight evaluation scheme (RWE), each road segment can be assigned with an appropriate weight associated with the corresponding transmission delay, by which the weight matrix of the network topology can be built. Using the matrix, the optimized route with the minimum delay can be selected. Simulation results show that the proposed protocol outperforms existing protocols in terms of packet delivery ratio and end-to-end delay.

In this paper, we introduce a novel mechanism to facilitate automatic user authentication for privacy-aware vision based human activity tracking. The mechanism relies on the Bluetooth beacon technology, which localizes a user who is present in the field of view of a camera. Unlike our previous mechanism designed for the same purpose, which requires a user to push a button on the smartwatch he or she is wearing and make a predefined gesture to register with the activity tracking system, this new mechanism does not require the user to alter his or her work routine when entering the view of the tracking system. Hence, the proposed mechanism significantly increases the usability of the tracking system. While the importance of automatic user authentication might not be obvious to researchers, it is essential for the acceptance of the activity tracking technology in its intended venues where workers will be monitored on their jobs as demonstrated by our previous field study and by our interviews with business owners. We report the experimental result based on two types of Bluetooth beacon devices, one from Estimote, and the other from Gimbal. Through the experiments, we identify several challenges in using these commercial-off-the shelf beacon devices for automatic user authentication, including the lack of synchronized beacon signal transmission by different beacon devices, nonuniform beacon transmission with occasional large gaps, and delay in beacon signal detection or reporting, and propose solutions to these issues.

In this paper, we report our work on applying an artificial neural network (ANN) to interpret brain wave signals into words. Signals were acquired by a four-channel human electroencephalography (EEG) head set. EEG data were recorded through a video-guided user interface with time stamps. The objective of the experiment is to set up a two-word-based EEG library and to predict a random trail of participants' mental activities, in terms of words. We show that our algorithms can achieve a 90% prediction accuracy of the Bernoulli experiments.

In recent years, the rapid improvement of sensor and wireless communication technologies powerfully impels the development of advanced cooperative driving systems, generating the demands to form the Internet of Vehicles (IoV). With the assistance of cooperative communication among vehicles, the road safety can be greatly enhanced in the IoV. In this paper, we propose a cooperative driving scheme for vehicles at intersections in the IoV. First, the driver’s intention is modeled by the BP neural network trained with driving dataset. Then, the identified intention is used as the control matrix of the Kalman filter model, by which the vehicle trajectory can be predicted. Finally, by collecting the information of vehicles’ trajectories at the intersections, we develop a collision probability evaluation model to reflect the conflict level among vehicles at intersections. Through obtained collision probability, the driver or the autonomous control unit can determine the next step to avoid the possible collisions. Numerical results show that our proposed scheme has high accuracy in terms of driver’s intention identification, trajectory prediction and collision probability evaluation.

Image segmentation based on superpixel is used in urban and land cover change detection for fast locating region of interest. However, the segmentation algorithms often degrade due to speckle noise in synthetic aperture radar images. In this paper, a feature learning method using a stacked contractive autoencoder (sCAE) is presented to extract the temporal change feature from superpixel with noise suppression. First, an affiliated temporal change image, which obtains temporal difference in the pixel level, are built by three different metrics. Second, the simple linear iterative clustering algorithm is used to generate superpixels, which tightly adhere to the change image boundaries for the purpose of acquiring homogeneous change samples. Third, a sCAE network is trained with the superpixel samples as input to learn the change features in semantic. Then, the encoded features by this sCAE model are binary classified to create the change result map. Finally, the proposed method is compared with methods based on principal components analysis and Markov random fields. Experiment results show that our deep learning model can separate nonlinear noise efficiently from change features and obtain better performance in change detection for synthetic aperture radar images than conventional change detection algorithms.

With the applications of Internet of Things (IoT) for smart cities, the real-time performance for a large number of network packets is facing serious challenge. Thus, how to improve the emergency response has become a critical issue. However, traditional packet scheduling algorithms cannot meet the requirements of the large-scale IoT system for smart cities. To address this shortcoming, this paper proposes EARS, an efficient data-emergency-aware packet scheduling scheme for smart cities. EARS describes the packet emergency information with the packet priority and deadline. Each source node informs the destination node of the packet emergency information before sending the packets. The destination node determines the packet scheduling sequence and processing sequence according to emergency information. Moreover, this paper compares EARS with a first-come, first-served, multilevel queue algorithm and a dynamic multilevel priority packet scheduling algorithm. Simulation results show that EARS outperforms these previous scheduling algorithms in terms of packet loss rate, average packet waiting time, and average packet end-to-end delay.

Energy-efficient and robust-time synchronization is crucial for industrial Internet of things (IIoT). Some energy-efficient time synchronization schemes that achieve high accuracy have been proposed recently. However, some unsynchronized nodes namely isolated nodes exist in the schemes. To deal with the problem, this paper presents R-Sync, a robust time synchronization scheme for IIoT. We use a pulling timer to pull isolated nodes into synchronized networks whose initial value is set according to level of spanning tree. Then, another timer is set up to select backbone node and its initial value is related to the distance to parent node. Moreover, we do experiments based on simulation tool NS-2 and testbed based on wireless hardware nodes. The experimental results show that our approach makes all the nodes get synchronized and gets the better performance in terms of accuracy and energy consumption, compared with three existing time synchronization algorithms TPSN, GPA, STETS.

The backpressure scheduling scheme has been applied in Internet of Things, which can control the network congestion effectively and increase the network throughput. However, in large-scale Emergency Internet of Things (EIoT), emergency packets may exist because of the urgent events or situations. The traditional backpressure scheduling scheme will explore all the possible routes between the source and destination nodes that cause a superfluous long path for packets. Therefore, the end-to-end delay increases and the real-time performance of emergency packets cannot be guaranteed. To address this shortcoming, this paper proposes EABS, an event-aware backpressure scheduling scheme for EIoT. A backpressure queue model with emergency packets is first devised based on the analysis of the arrival process of different packets. Meanwhile, EABS combines the shortest path with backpressure scheme in the process of next-hop node selecting. The emergency packets are forwarded in the shortest path and avoid the network congestion according to the queue backlog difference. The extensive experiment results verify that EABS can reduce the average end-to-end delay and increase the average forwarding percentage. For the emergency packets, the real-time performance is guaranteed. Moreover, we compare EABS with two existing backpressure scheduling schemes, showing that EABS outperforms both of them.

In this paper, we report our experiences in designing, deploying, and making continuous improvements of a Privacy-Aware Compliance Tracking System (PACTS) at a skilled nursing facility. The purpose of PACTS is to help state tested nursing assistants (STNAs) get into the habit of using proper body mechanics when performing bedside cares. The system has been deployed in six resident rooms and seven STNAs have been participating our study for over ten weeks. This study makes the following contributions: (1) A registration mechanism that enables an STNA to register with any of the rooms that have PACTS installed, which is essential to protect the privacy of patients and non-participating persons; (2) A wrong activity detection mechanism that is robust against occlusions due to furniture and against temporary inability of floor determination; (3) A lease-based mechanism to improve the usability of PACTS, which allows an STNA to be continuously monitored without having to register with PACTS repeatedly when she/he goes in and out of the view of the Kinect sensor for the duration of the lease.

Poor form of body mechanics has been attributed to as a major risk factor for lower back injuries, which costs billions of dollars a year in the US alone. In this paper, we report a case study on using a Kinect-based system during an annual competency training at a local nursing home to promote safe resident handling. Each participant of the study was asked to perform three specific bedside care tasks while being monitored by our system, and he/she was provided with realtime feedback in the form of a vibration via smart watch that he/she worn on detection of a wrong activity by our system. At the end of the session, the participant was asked to complete a short survey regarding the performance of our system and his/her opinion about the system usability. There are two major findings in this case study: (1) the majority of the nursing assistants engaged in poor body mechanics frequently when performing the designated tasks, which indicated that traditional training is not rigorous and may fail to accomplish its purpose, and (2) most participants expressed positive attitude towards using our system for competency training as well as during their jobs to reduce the risk of injuries.

The total US box office revenue exceeds ten billion dollars a year. Inevitably, new product forecasting and diagnosis have high financially stakes. The motion picture industry has a huge incentive to perform early prediction of movie success or failure. In this paper, we present a study that evaluate viewer responses to short movie trailers using a cap instrumented with Electroencephalogram (EEG) sensors. The method can be used to evaluate prerelease movies regarding how engaging they are to viewers. The primary advantage of our approach is that responses of a viewer can be recorded without any manual inputs from the viewer. The data collected, if analyzed properly, can reveal more accurate information regarding the viewer's emotion states while watching the movie than post-viewing surveys. This approach also enables the delivery of personalized entertainment through the brain interface.

Face identification and resolution technology is crucial to ensure the identity consistency of humans in physical space and cyber space. In the current Internet of Things (IoT) and big data situation, the increase of applications based on face identification and resolution raises the demands of computation, communication, and storage capabilities. Therefore, we have proposed the fog computing-based face identification and resolution framework to improve processing capacity and save the bandwidth. However, there are some security and privacy issues brought by the properties of fog computing-based framework. In this paper, we propose a security and privacy preservation scheme to solve the above issues. We give an outline of the fog computing-based face identification and resolution framework, and summarize the security and privacy issues. Then the authentication and session key agreement scheme, data encryption scheme, and data integrity checking scheme are proposed to solve the issues of confidentiality, integrity, and availability in the processes of face identification and face resolution. Finally, we implement a prototype system to evaluate the influence of security scheme on system performance. Meanwhile, we also evaluate and analyze the security properties of proposed scheme from the viewpoint of logical formal proof and the confidentiality, integrity, and availability (CIA) properties of information security. The results indicate that the proposed scheme can effectively meet the requirements for security and privacy preservation.

For large-scale Internet of Things (IoT), which located in the hostile environment where exists malicious nodes (MNs), the security of time synchronization is a critical and challenging issue. The malicious sensor nodes could decrease the accuracy of the whole network by broadcasting fake timestamp messages. In this paper, we propose a secure time synchronization model for large-scale IoT. In this model, a node utilizes its father node and grandfather node to detect the MN. By employing the model, a spanning tree topology which synchronizes to the reference nodes can be constructed hop by hop. Then a secure time synchronization protocol is developed to against fake timestamps, which adopts the secure model. We use NS2 as the simulation tool to evaluate our protocol, and compare the impact of fake timestamps in various circumstances with the pervious protocols TPSN and STETS. The experiment results show that our protocol is effective to prevent attacks from MNs.

The identification and resolution technology are the prerequisite for realizing identity consistency of physical-cyber space mapping in the Internet of Things (IoT). Face, as a distinctive noncoded and unstructured identifier, has especial advantages in identification applications. With the increase of face identification based applications, the requirements for computation, communication, and storage capability are becoming higher and higher. To solve this problem, we propose a fog computing based face identification and resolution scheme. Face identifier is first generated by the identification system model to identify an individual. Then, a fog computing based resolution framework is proposed to efficiently resolve the individual's identity. Some computing overhead is offloaded from a cloud to network edge devices in order to improve processing efficiency and reduce network transmission. Finally, a prototype system based on local binary patterns (LBP) identifier is implemented to evaluate the scheme. Experimental results show that this scheme can effectively save bandwidth and improve efficiency of face identification and resolution.

With the widespread applications of Internet of Things (IoT), the emergency response performance for large-scale network packets is facing serious challenge, especially for renewable distributed energy resources monitoring in a smart grid. Therefore, how to improve the real-time performance of the emergency data packets has been a critical issue. Traditional packet scheduling schemes and topology optimization strategies are not suitable for a large-scale IoT-based smart grid. To address this problem, this paper proposes a new packet scheduling scheme named LOES, which first combines the priority-based packet scheduling scheme with local optimization. We exchange local geographic information to reduce the hop counts and distance between distributed source nodes and sink nodes. Each destination node determines the packet scheduling sequence according to the received emergency information. Finally, we compare LOES with first come first serve, multilevel scheme, and dynamic multilevel priority packet scheduling scheme using packet loss rate, packet waiting time, and average packet end-to-end delay as metrics. The simulation results show that LOES outperforms these previous scheduling schemes.

Detecting elliptical objects from an image is a central task in robot navigation and industrial diagnosis, where the detection time is always a critical issue. Existing methods are hardly applicable to these real-time scenarios of limited hardware resource due to the huge number of fragment candidates (edges or arcs) for fitting ellipse equations. In this paper, we present a fast algorithm detecting ellipses with high accuracy. The algorithm leverages a newly developed projective invariant to significantly prune the undesired candidates and to pick out elliptical ones. The invariant is able to reflect the intrinsic geometry of a planar curve, giving the value of -1 on any three collinear points and +1 for any six points on an ellipse. Thus, we apply the pruning and picking by simply comparing these binary values. Moreover, the calculation of the invariant only involves the determinant of a 3×3 matrix. Extensive experiments on three challenging data sets with 648 images demonstrate that our detector runs 20%-50% faster than the state-of-the-art algorithms with the comparable or higher precision.

Due to the recent proliferation of cyber-attacks, improving the robustness of wireless sensor networks (WSNs), so that they can withstand node failures has become a critical issue. Scale-free WSNs are important, because they tolerate random attacks very well; however, they can be vulnerable to malicious attacks, which particularly target certain important nodes. To address this shortcoming, this paper first presents a new modeling strategy to generate scale-free network topologies, which considers the constraints in WSNs, such as the communication range and the threshold on the maximum node degree. Then, ROSE, a novel robustness enhancing algorithm for scale-free WSNs, is proposed. Given a scale-free topology, ROSE exploits the position and degree information of nodes to rearrange the edges to resemble an onion-like structure, which has been proven to be robust against malicious attacks. Meanwhile, ROSE keeps the degree of each node in the topology unchanged such that the resulting topology remains scale-free. The extensive experimental results verify that our new modeling strategy indeed generates scale-free network topologies for WSNs, and ROSE can significantly improve the robustness of the network topologies generated by our modeling strategy. Moreover, we compare ROSE with two existing robustness enhancing algorithms, showing that ROSE outperforms both.

Multiple Unmanned Aerial Vehicles(UAVs) are crucial for Cyber-Physical Systems(CPS). No collisions should be ensured between each other when executing a complex task. This paper proposes a state-prediction-based trajectory control strategy for UAVs. We first introduce the concept of “space-time protection volume” under the constraint of the number of needed vehicles and the scope of the task space. The prediction model of hierarchical flight and collision probability is established. Based on the safety threshold, the flight strategy is given under the different condition through calculating the collision probability. Besides, in the communication condition of formation flight of UAVs, we further put forward the compensation method based on Kalman state estimation according to the communication noise caused by the bias of the information state. The simulation results show that our proposed scheme enhances the anti-noise ability of UAVs and effectively reduces the collision probability.

Abstract Because of the stochastic key pre‐distribution and complicated network topology, designing an energy‐efficient node capture attack algorithm is of great challenge. Although many algorithms have been proposed for node capture attack, previous methods lack of concerning minimizing resource expenditure in modeling attacking behavior. In this paper, we propose a novel way of modeling the node capture attack. First, we transform the problem into a set covering problem with a shortest Hamiltonian cycle problem, which has been shown to be NP‐hard. Consequently, we also develop a heuristic called minimum resource expenditure node capture attack (MREA) to maximize destructiveness while minimizing resource expenditure. Moreover, extensive simulations are conducted to show the performance of MREA. Simulation results show that MREA outperforms other algorithms in reducing the attack rounds and saving resource expenditure. Copyright © 2016 John Wiley & Sons, Ltd.

In data management protocols for Ad-hoc Social Networks (ASNETs), involvement of selfish users can pose a serious threat to network performance and fairness. Therefore, it is essential to detect and mitigate their effects on other well behaving users. We contribute to this line of research by combining the benefits of users' social behavior (social tie) with a biologically inspired approach in ASNETs. We designed a bio-inspired scheme (BoDMaS) to detect and mitigate selfish users in replication operations. Its goals include providing greater accessibility and effective detection of selfish users. The proposed scheme not only guarantees accessibility and effective detection rate, but also ensures the reliability of replica allocation operations.

Reliable social connectivity and transmission of data for popular nodes are vital in multihop Ad-hoc Social Networks (ASNETs). However, congestion may occur and hence popular nodes might not achieve required bandwidth when multiple senders share data with a single receiver. The traditional Transport Control Protocol (TCP) might not be able to perform efficiently in ASNETs. Therefore, we propose a Reliable TCP for Popular Data in Socially-aware Ad-hoc Networks called RTPS. RTPS employs a popularity level approach for every single node which partitions the bandwidth among users. The reliability of popular data is ensured since bandwidth will firstly assigned to the popular sender. Preliminary results show that by delaying the acknowledgment and improving the performance of TCP, RTPS reduces collision loss in multihop ASNETs.

To address selfishness in socially-aware networking, we propose a Community-based Incentive Scheme (CIS). CIS utilizes community to stimulate cooperation among selfish nodes and allows all nodes to behave selfishly to imitate the realistic condition. The simulation results show that CIS effectively stimulates selfish nodes to cooperate, achieves higher delivery ratio while not increasing latency dramatically.

The coverage problem of networks is one of the key issues researched in wireless sensor networks (WSNs). To find the optimal coverage solution of sensor networks is a pressing concern now. The terrain of the detection area is often more complex in the applications of three-dimensional sensor networks. In this paper, a new network coverage and optimization control strategy based on genetic algorithm is proposed to solve the deterministic coverage problem of sensor nodes. The fitness function of the associated genetic algorithm is determined on a two-dimensional plane and iterations are utilized to find optimal solutions. The simulations and results indicate that the coverage strategy is an efficient coverage strategy for the three-dimensional terrain.

Based on the research of multi-core load balancing's task scheduling and allocation, we proposed the static task graphs stratification algorithm, the static task group scheduling algorithm, and the minimum dynamic link algorithm, aiming at the characteristics of multi-core processors. When these algorithms allocate tasks, they are expected to complete multi-core load balancing. Firstly, the task allocation is divided into two stages: It needs to break dependencies among tasks and relatively independent tasks will be in the same group at the first stage. It conducts static allocation for the principle of load balancing and it allocates initial tasks which have almost the same time for the system hardware threads in the second stage. It allocates tasks which come from system's running for each hard ware thread with processor's speed as a standard in the third stage. From the verification of simulation experiment, the algorithms can achieve better load balancing and minimum completion time.