Self-supervised anomaly detection has emerged as a research hotspot in intelligent manufacturing and quality inspection, holding significant practical value in industrial applications. However, anomaly detection in real-world printed circuit board (PCB) production environments remains challenging. Existing methods often exhibit limited generalization when facing diverse anomaly types and environmental disturbances. In addition, high model complexity and insufficient capability for multi-scale fine-grained defect recognition constrain their practical deployment. To address these issues, this paper proposes a novel self-supervised anomaly detection framework (FSDNet). First, this paper proposes an Anomalous Sample Synthesizer Based on Diffusion Model (AnoDiff), which generates diverse and controllable anomalous samples to improve model generalization. Second, this paper designs an Anomaly Feature Perception Module (AFPM) that selects discriminative channels from pretrained features, thereby reducing model complexity while enhancing detection performance. Third, this paper proposes a Multi-scale Residual Reconstruction Network (MRRN) is developed to aggregate multi-scale features, improving sensitivity to fine-grained anomalies. Finally, this paper proposes two novel attention-based modules: a Top-k Sparse & Space Attention Module (TSSM) and a Gated Feature Enhancement Module (GFEM), both of which strengthen the discriminability and robustness of anomaly features. Experimental results demonstrate that the proposed method significantly outperforms state-of-the-art approaches on MVTec-AD, ViSA, and a self-constructed PCB dataset in terms of detection accuracy and robustness, validating its effectiveness and practical utility. The dataset and code are available at https://github.com/QinLi-STUDY/FSDNet/tree/master .

The energy expenditure of underwater sensor nodes significantly exceeds the expenditure of terrestrial sensor nodes due to the challenging conditions of underwater acoustic communication channels. Maintaining and recharging sensor node equipment in underwater wireless sensor networks is particularly difficult due to the complexity of the underwater environment, thereby rendering energy consumption a pivotal concern. Clustering algorithms are regarded as a promising solution to address this challenge. By employing a K-means++-based clustering algorithm for more stable clustering of sensor nodes and taking into account pertinent factors involving node location, residual energy, and node degree in selecting the cluster’s head node, this study targets minimizing network energy consumption over the cluster establishment stage. During the data transfer stage, we prioritize network security and transmission efficiency by implementing a routing strategy that combines single-hop and double-hop propagation paths based on trust security models to mitigate risks from network attacks and black hole nodes. We also introduce a lightweight learning layer for per-link delivery and CH ranking, plus an unsupervised anomaly gate to down-weight suspicious nodes; both fuse with trust and fairness while leaving the physical and energy models unchanged. Simultaneously, AUVs are utilized to anchor sink nodes and directly transmit collected information from CHs. The results of the simulation in MATLAB indicate that the suggested routing protocol KSERP successfully balances and lessens the total energy consumption of the network while offering protection from malicious node attacks.

To overcome the problem of low system resource utilization caused by the lack of exploration of environmental changes in Industrial Internet of Things (IIoT) service orchestration, while ensuring Quality of Service (QoS), we propose an Adaptive Orchestration Algorithm of Service Function Chain (AOASFC) Based on Deep Reinforcement Learning (DRL). Our approach in paper integrates joint deployment and routing information to manage system resources, thereby optimizing orchestration strategies. Furthermore, to enhance the capability of exploring environmental changes, we design a curiosity-driven module that evaluates the environmental changes before and after the DRL agent’s decision-making process, generating intrinsic rewards to guide a more comprehensive exploration process. Our approach effectively mitigates the high bias issue caused by updating value function, because we integrate Proximal Policy Optimization (PPO) with Generalized Advantage Estimation (GAE) and perform weighted averaging on multi-step estimates, optimizing temporal difference learning. In performance comparisons, we have compared DeepCoordblue(a centralized DRL orchestrator) and BSP(a greedy heuristic baseline) algorithm, AOASFC demonstrates superior performance in different traffic arrival patterns of SFC deployment scenarios: it not only improves system throughput by 15.35% and 11.64% respectively, but also keeps end-to-end latency below 50ms while significantly enhancing resource utilization.

Underwater acoustic backscatter communication technology brings a new perspective on addressing the energy dilemmas of Internet of Underwater Things (IoUT). However, time asynchronism in underwater acoustic backscatter devices (UABDs) can significantly degrade the performance of the UABD-based IoUT system. Existing time synchronization algorithms lose practicability leading to high energy consumption in scenarios where charging delays vary. To address these challenges, we propose UAB-Sync, a time synchronization algorithm specifically designed for the system. UAB-Sync introduces a novel three-stage architecture that uses dual constraints of time and energy to dynamically optimize the duration of energy signal, achieving adaptive approximation of optimal results. Besides, a closed-form solution for clock parameter estimation that incorporates Doppler factor estimation and accounts for multi-source measurement errors is developed, ensuring effective synchronous correction. Simulation results demonstrate that UAB-Sync significantly outperforms existing synchronization schemes in terms of both accuracy and energy efficiency for the UABD-based IoUT system.

In Internet of Things (IoT)-enabled marine sensor networks, underwater object detection faces challenges due to resource constraints, such as small object identification and scale variations. These challenges result in sample imbalance and ambiguities in label assignments. While frameworks like the YOLO series are efficient, their underwater performance is often inadequate due to these issues. This paper introduces a training enhancement strategy tailored for underwater object detection (UWDET) in IoT settings, aimed at reducing inference resource consumption while maintaining high detection accuracy. Importantly, our approach preserves existing network architectures and does not extend inference time. The methodology comprises three main elements: Gaussian Overlap Loss (GOL), which interprets bounding boxes through two-dimensional Gaussian distributions, thereby enhancing localization and addressing scale imbalance for small objects in resource-limited environments. Dynamic Task Joint Assignment (DTJA) modifies positive sample assignments based on classification confidence and regression quality, thereby minimizing false positive rates during training. Normative Focal Loss (NFL) employs a normalized joint assignment metric as continuous labels to effectively address sample imbalance. Experimental evaluations on underwater detection benchmarks reveal that our approach markedly enhances precision and recall, stabilizes gradient signals, and improves training efficiency. We also report training-side GPU memory/time/energy and edge-side memory and latency, confirming unchanged inference cost and reduced training resource usage. Our training enhancement strategy applies meticulously designed lightweight generic object detection models to the underwater domain. Without requiring complex modifications to the network architecture, it enables rapid training and facilitates the deployment and inference of high-precision models within resource-constrained IoT underwater devices.

Combining big data and deep learning can analyze large-scale breast cancer pathology images for auxiliary diagnosis. Furthermore, Whole Slide Images (WSIs) of breast cancer pathology offer detailed tissue feature information, which supports the accurate identification of malignant lesions. Current approaches combine Self-Supervised Learning (SSL) and Multiple Instance Learning (MIL) for WSI analysis, aiming to address the issues of billion-level pixels in a single WSI and the lack of precise annotations. However, pseudo-labels produced by SSL frequently lack accuracy, and MIL fails to effectively integrate global information at the WSI level, resulting in performance bottlenecks. This paper proposes the Multi-granularity Self-supervised Second-order MIL (MS2M) to tackle these issues. MS2M first achieves instance-level fine-grained feature learning through multi-granularity SSL and optimizes instance-level representations using bag-level labels within the MIL framework. Then, the transformer captures long-range dependencies between instances. When combined with second-order (covariance) pooling, it also captures high-order relational information. This process generates a robust bag-level representation. MS2M achieves accuracies of 0.9845 and 0.9719 on the CAMELYON16 and private breast cancer WSI datasets, respectively, outperforming existing methods.

Medical Mixed Reality (MR) has made significant progress in virtual surgery simulation and tumor teaching. This paper proposes a framework for pulmonary nodule attribute editing based on image feature consistency, achieving spatial alignment of multi-stage case data. To address the limitations of traditional time-image reconstruction, we design an adversarial siamese model architecture capable of synthesizing missing nodule images, completing temporal data, and fine-grained modeling of nodule growth. To tackle challenges such as deformation, background inconsistency, and attribute uncertainty in generated samples, we introduce a Denoising Diffusion Implicit Model (DDIM) and construct an attribute vector space for pathological feature editing. Additionally, we propose a separable image reconstruction strategy to enhance local feature stability. Extensive validation on the lung-specific LIDC-IDRI dataset demonstrates superior performance with SSIM of 97.5${\%}$ and LPIPS of 0.036. To further verify generalization capability, cross-organ testing on the liver-focused LiTS dataset achieves competitive results with SSIM of 85.0${\%}$ and LPIPS of 0.128. These outcomes provide strong technical support for high-fidelity virtual surgery and intelligent tumor teaching platforms.

The rapid advancement of Internet of Vehicles technology has led to massive growth in vehicular data generation, imposing strict computational demands for latency-sensitive applications. By offloading computational tasks to Road-side Units (RSUs), Vehicular Edge Computing (VEC) offers an efficient solution for those latency-sensitive applications. However, current task offloading schemes generally ignore the time-varying topology caused by vehicle mobility, which poses a risk of task interruption. Moreover, existing task offloading models primarily focus on serial tasks processing and fail to adequately account for the relationships among parallel tasks, leading to inefficient resource utilization and potential latency accumulation in multi-task VEC scenarios. To this end, we propose a mobility-awareCollaborativeTaskOffloading scheme forParallel tasks (CoTOP) in VEC. Integrated with vehicle mobility detection, a collaborative task offloading model is designed based on deep reinforcement learning, achieving effective coordination among RSUs to reduce task processing latency. Additionally, a task prioritization algorithm is incorporated to optimize resource allocation. Experimental results show that CoTOP significantly outperforms existing schemes in terms of task processing latency, energy consumption and completion ratio.

Underwater acoustic sensor networks (UASNs) are extensively utilized in various sectors, including aquaculture, naval operations, and oceanic disaster alert systems. The protection of UASNs, with a specific focus on internal threats, has become an increasing priority. Attacks originating from within the network, involving compromised legitimate nodes, can be more harmful and covert compared to external threats, such as communication interception, data decryption, and identity impersonation. Trust models, which serve as mechanisms for detecting internal threats through interaction data, have proven effective in enhancing UASN security. However, traditional trust models often face scalability issues, particularly in environments characterized by mobile underwater devices, diverse network conditions, and evolving attack strategies. To address these challenges, this work presents a novel trust management scheme based on attention-weighted federated deep reinforcement learning (AFRTM). The AFRTM overcomes the limitations of existing approaches by first improving the evidence quantification methods-encompassing both environmental and behavioral evidence-to better adapt to the uncertainty of underwater scenarios. Subsequently, the acquired trust evidence is input into the respective deep reinforcement learning (DRL)-driven local trust framework to achieve trust estimation and model development. Finally, the model's parameters are periodically aggregated and updated using an attention-weighted federated learning method, ensuring adaptability to changing conditions. The experimental findings demonstrate that the suggested approach delivers commendable outcomes in enhancing trust estimation precision and energy efficiency, and further providing a robust solution to the security challenges faced by UASNs.

Advancements in Wireless Buoy Network (WBN) have significantly accelerated the development of marine exploitation and monitoring, acting as a relay between underwater and surface networks in emerging 6G scenarios. Due to unstable maritime communication environment, it is a challenging issue to deploy the optimal data routing or collection strategies to ensure the collected data to be delivered to the target point. By employing the Software-Defined Networking (SDN) technology, this paper proposes the paradigm of Software-Defined WBN (SDWBN) to improve the network management efficiency and provide a platform to embed the Multi-Agent Reinforcement Learning (MARL) framework (for data routing intelligence), respectively. On account of the proposed SDWBN, this paper proposes a Reward Backpropagation-enabled Multi-Agent Deep Q-learning algorithm (RBMADQ)-based active routing scheme, which aims to assist buoys in making routing decisions and navigating the challenges posed by the dynamic and unstable communication environment. Further, this paper proposes a dual replay buffer-based training method, to enhance the convergence speed of the proposed RBMADQ-based routing scheme. Evaluation results demonstrate that the proposed routing scheme performs better compared with recent research products, with a higher packet delivery rate, lower network latency, and simultaneously, less communication overhead, etc.

Integrated sensing and communication (ISAC) is potentially viewed as a key driver in the ubiquitous service of future wireless systems. However, numerous challenges should be solved before the alluring benefits are enjoyed. Although enhancement of sensing capabilities inevitably leads to a reduction in the communication performance, the loss can be compensated through the emerging reconfigurable intelligent surface (RIS). Specifically, active RIS is more favored because the “multiplicative fading” effect existed in passive RIS is overcome, and the manipulation effectiveness of wireless environments can be further enhanced. To this end, this paper investigates the simultaneous optimization of the transmit beamforming at base station (BS) and the precoding at RIS. To fully explore the potential of the active RIS-enabled ISAC system, two antenna deployment strategies, i.e., separated deployment and shared deployment, are designed to maximize the communication users' sum-rate under the considered constraints including transmission power, probing power and reflection manipulation, etc. Furthermore, an effective algorithm combining fractional programming and semidefinite relaxation is employed to derive a sub-optimal solution through an alternating optimization framework due to the non-convex characteristic of the initial problem. Finally, the simulation results demonstrate that more superior transmit beampatterns can be achieved in the shared deployment in comparison with the separated deployment, and that the sum-rate's upper bound is significantly enhanced in the active RIS when comparing to traditional passive RIS.

We consider the efficient resource allocation task in the blockchain-based mobile edge computing (MEC) system that requires decentralized transaction management to validate transactions between edge servers (ESs) and mobile devices (MDs). In such task allocation process (where MDs' resources are limited and privacy-sensitive), it is a significant challenge to guarantee individual rationality with satisfactory system stability while enabling flexible task offloading under various locality constraints (e.g., communication distance, bandwidth and delay). In this paper, we formulate the target problem as a blockchain-assisted task-resource matching model, and then propose a near optimal locality-aware resource allocation mechanism over smart contract to enable automatic and efficient transactions in MEC system. More specifically, for the service agents selection, we design the preference-based selection strategy to get highest estimated profit. For the flexible task offloading, we develop the minimum delay task graph partitioning algorithm to determine the optimal task offloading solution for MD under different resource bundles. For the task-resource matching, we propose a task-resource matching game (based on potential game) with the second lowest cost strategy to determine the matching of task-resource and decide the price of resource bundle. For the transaction verification and block allocation, we propose a social welfare-driven consensus mechanism to enable verified transaction and fair block allocation in a reward-free way. Strict theoretical analysis and extensive simulations demonstrate that our mechanism guarantees individual rationality, Nash Equilibrium, and stable near optimal solution.

Critical node identification is essential for Underwater Acoustic Sensor Networks (UASNs) to ensure network connectivity and reliability. Existing methods identify critical nodes by evaluating their contributions to network connectivity and node communication count. However, these methods identify critical nodes inaccurately due to neglecting the influence of packet collisions, leading to unreliable network. Packet collisions disrupt connected links and cause communication failures, resulting in unreliable network connectivity and improper communication count. To this end, we propose the Collision-Aware Critical Node Identification Algorithm (CCNIA), which accounts for the impact of packet collisions to improve the accuracy of critical node identification and enhance network reliability. CCNIA identifies critical nodes with high connectivity, large collision probability, and heavy network load, through building the three following interdependent models. Specifically, Topological Connectivity Model (TCM) evaluates link reachability by analyzing connectivity and density within a node's local network. Based on TCM, Collision Probability Model (CPM) further ensures packet reliability by quantifying the impact of packet collisions on critical node identification. Through CPM's reliable packet transmissions, Network Load Model (NLM) assesses network efficiency by analyzing node occurrence count within global end-to-end communication paths. Experiments show that CCNIA outperforms existing methods across diverse network configurations, enhancing network reliability in terms of packet delivery ratio, delay, and energy efficiency.

Underwater Acoustic Sensor Networks (UASNs) have garnered considerable attention in recent years due to their widespread applications in both industrial and civilian domains, such as ocean exploration and environmental monitoring. This paper introduces an intelligent Multi-Agent Reinforcement Learning (MARL) algorithm to determine routing in UASNs with dynamic underwater environments adaptively. Initially, we model ocean currents and acoustic signal loss in underwater communication to perform the real-world characteristics of underwater routing. Based on software-defined networking (SDN) principles, we redefine the architecture of UASNs and propose an Adaptive Routing scheme for Software-Defined UASNs (ARSDU). Leveraging ARSDU, we propose the Reward Cooperative Distribution and Tracing Mechanism-enabled Multi-Agent Reinforcement Learning (RCDTM-MARL) algorithm to optimize routing decisions. The proposed RCDTM-MARL algorithm enhances the convergence speed of MARL by decomposing the reward function into independent-reward and interactive-reward, while incorporating an experience replay buffer to further accelerate convergence. Ultimately, the adaptive routing decision algorithm, based on RCDTM-MARL, adaptively determines the optimal routing path for UASNs. The evaluation results demonstrate that the proposed routing scheme outperforms recent research approaches, achieving superior underwater data routing decisions on multiple key performance metrics.

Routing-driven timely data collection in Underwater Acoustic Sensor Networks (UASNs) is crucial for marine environmental monitoring, disaster warning, and underwater resource exploration, etc. However, harsh underwater conditions, including high delays, limited bandwidth, and dynamic topologies, make efficient routing decisions challenging in UASNs. In this paper, we propose a smart interrupted routing scheme for UASNs to address dynamic underwater challenges. We first model underwater noise influences from real underwater routing features, e.g., turbulence and storms. We then propose a Software-Defined Networking (SDN)-based Interrupted Software-defined UASNs Reinforcement Learning (ISURL) framework, which ensures adaptive routing through dynamical failure handling (e.g., energy depletion of sensor nodes or link instability) and real-time interrupted recovery. Based on ISURL, we propose the MA-MAPPO algorithm, integrating multi-head attention mask mechanism with MAPPO to filter out infeasible actions and streamline training. Furthermore, to support interrupted data routing in UASNs, we introduce MA-MAPPO_i, MA-MAPPO with interrupted policy, to enable smart interrupted routing decisions in UASNs. The evaluations demonstrate that our proposed routing scheme achieves exact underwater data routing decisions with faster convergence speed and lower routing delays than existing approaches.

Vision-Language Models (VLMs), with their advantages in vision and language processing, exhibit immense potential in mobile intelligent systems. Integrating federated learning with parameter-efficient fine-tuning of VLMs helps address data heterogeneity challenges. However, existing methods mainly focus on task-specific patterns, neglecting the impact of general features, such as background information and low-quality data, which weakens the model's ability to generalize when handling data from different sources and dealing with fluctuations in quality. To tackle these challenges, we propose Inverse Feature Consistency Federated Unlearning (IFCFU) for VLM, comprising three components: 1) Feature Consistency Federated Learning (FCFL) aligns fine-tuned features with pre-trained features through constraints to ensure the preservation of general features; 2) Pseudo-label Low-quality Data Detection (PLDD) identifies potential low-quality data through model quality assessment and pseudo-label generation; 3) Inverse Feature Consistency Unlearning (IFCU) distances low-quality data features from optimal model features to eliminate the negative impact and restores training with pseudo-labels. Evaluations on StanfordCars show that FCFL increased accuracy by 4.97% and 29.88% under normal data and low-quality data configurations, respectively. PLDD identified over 90.00% of low-quality data, while IFCU improved the global model's accuracy by 4.43% with 80% low-quality data.

Large language models (LLMs) are revolutionizing various fields due to their powerful generation capabilities. However, their immense computational complexity poses significant challenges in resource consumption, inference latency, and data privacy for traditional cloud-centric deployments. Edge artificial intelligence (Edge-AI) offers promising LLMs deployment solutions by leveraging distributed resources at the network edge. However, existing approaches struggle to adapt to dynamic workloads and efficiently utilize heterogeneous resources in Mobile Edge Computing (MEC) environments. This paper proposes aDynamicBatching andAdaptivePartitioning (DyBAP) scheme for LLMs deployment, which utilizes ubiquitous geo-distributed resources via end-edge-cloud collaboration. Firstly, we formulate a collaboration deployment optimization problem to minimize inference latency and resource usage under heterogeneous resource and user requirements for latency and accuracy constraints, which is NP-hard. Secondly, to solve this, we develop a dynamic batch fusion optimization algorithm that optimizes the batch size of inference by utilizing the parallel processing power of computing units to balance the latency and resource usage. A block-aware partition optimization algorithm based on multi-agent reinforcement learning (MARL) is proposed for efficient transformer block allocation, integrating mobility awareness for optimal partitioning across dynamic network environments. Simulation results demonstrate the superiority of DyBAP over other benchmarks, reducing inference latency by 17.94% and saving 11.12% in memory resource consumption compared to the end-edge-cloud collaboration approaches.

Recent advances in network technologies and Multi Agent Reinforcement Learning (MARL) have accelerated the development of Autonomous Underwater Vehicle (AUV) cluster networks, enabling intelligent applications such as target tracking and cooperative target encirclement. However, existing MARL models are typically designed for single-task scenarios, limiting their scalability in real-world multi-task environments. To ad dress this, we propose a lightweight MARL framework capable of handling multiple AUV tasks with reduced reliance on underwater sampling. Specifically, a unified state space representation is constructed to support task generalization, while a hybrid online offline MARL training paradigm is introduced by leveraging the logical reasoning and sample generation capabilities of Large Language Models (LLMs). This reduces the demand for real-time data collection. Furthermore, a supervised pretraining strategy is incorporated to improve convergence and learning stability. Based on these components, we develop the Large Language Model-driven Hybrid online-offline MARL algorithm towards Multi-Task scenarios (LLM-HMT), which supports intelligent deployment of multi-task AUV cluster systems with minimal state representation, reduced sample requirements, and limited training iterations. Extensive experiments demonstrate that LLM HMT outperforms mainstream MARL baselines in convergence speed, task success rate, and resource efficiency, highlighting its potential for practical underwater applications.

Autonomous Underwater Vehicles (AUVs) in wireless cluster networks have shown great promise for ocean exploration, particularly in multi-target tracking, with Critical applications in both military and civilian purposes such as environmental monitoring and underwater resource exploration. This paper proposes a novel framework for smart underwater AUV wireless cluster networks by integrating Software-Defined Networking (SDN) and Multi-Agent Reinforcement Learning (MARL) to achieve efficient, scalable multi-target tracking in dynamic underwater environments. Specially, this paper introduces a Software-Defined Multi-Teacher-Student Reinforcement Learning (SD-TSRL) architecture that synergizes SDN's centralized control with MARL's adaptive decision-making, enabling intelligent communication and dynamic resource management. To further enhance learning efficiency, a reciprocal teacher-student mechanism is proposed, which optimizes resource allocation and communication during training. On account of the mechanism, this paper presents the Reciprocal Teacher-Student-Inspired Centralized (RTSIC) MARL algorithm, which improves both communication and computation resource utilization in AUV wireless cluster network. Experimental results demonstrate that the proposed approach significantly enhances tracking accuracy and network performance compared to existing methods, validating the effectiveness of SDN-MARL integration for advanced underwater wireless networks.

Vehicle-to-everything (V2X) networks face critical security challenges due to their dynamic nature, stringent latency requirements, and susceptibility to malicious attacks. Traditional trust management approaches often rely on centralized authorities or historical data, creating vulnerabilities and scalability limitations. This paper presents a new trust management system that leverages integrated sensing and communication (ISAC) technology and blockchain-based smart contracts to provide secure and decentralized trust evaluation in V2X networks. The proposed framework leverages real-time ISAC signal processing to compute five comprehensive trust metrics: behavior score, reputation score, safety score, uptime score, and response time score. These metrics are derived through advanced Kalman filtering and statistical anomaly detection applied to physical-layer measurements, enabling immediate detection of malicious activities that traditional approaches might miss. Trust records are securely stored and validated through smart contracts deployed on 5G base station blockchains, ensuring tamper-proof storage and automated policy enforcement. Numerical results demonstrate that the proposed protocol achieves faster trust convergence, higher communication reliability, significant reduction in false positive rates, improved detection accuracy, acceptable end-to-end latency, and lower computational overhead compared to state-of-the-art approaches.

Autonomous Underwater Vehicles (AUVs) provide a new prospect for data collection in underwater wireless sensor networks (UWSNs). For dynamic underwater environments, researchers typically apply deep reinforcement learning (DRL) to design multi-AUV collection schemes for UWSNs. However, these methods suffer from the following issues. 1)Overloaded observations. The importance of various observations for an AUV varies over time. Considering all observations equally complicates decision-making for subsequent actions. 2)Dynamic scale of AUVs and sensors. Once the number of AUVs or sensors is changed, traditional static neural networks require retraining, lacking scalability across diverse scenarios. To solve the above issues, we propose a Generalizable Attention-based Data collection scheme (GAMD) for Multi-AUV UWSNs, while enhancing AUVs’ collection efficiency. GAMD incorporates the attention mechanism with multi-agent DRL framework, which enables AUVs to prioritize observations more critical for action decisions. Moreover, we propose an adaptive information processing approach, enabling the AUV policy model to seamlessly adapt to various scenarios without retraining. Additionally, we develop a training paradigm with incremental complexity across different scale scenarios to simplify training process and accelerate convergence. Simulation results demonstrate that GAMD alleviates the training cost compared to the state-of-the-art methods, and simultaneously optimizes collection energy efficiency, collection time, and trajectory distance.

We consider the collaborative inference acceleration task via cloud-edge-end collaboration, which involves a series of tightly coupled decision-making steps, includingwhichDNN model to be selected,how muchto compress model,howto partition model, andwhereto offload partitioned submodels. In practical deployments, these decisions jointly affect both fine-tuning and inference performance, and must jointly account for such aspects as the model being used, the computational resources and local datasets available at each device, as well as network latencies, which significantly increases the complexity of optimizing the problem. Yet, no existing studies focus on such joint optimization problem for these tightly coupled decisions. In this paper, we model this problem as a multi-dimensional optimization problem, jointly optimizing collaborative inference and fine-tuning by selecting the DNN model, compression level, partition strategy, and computational resource allocation, with the objective of minimizing the overall energy consumption of the learning-inference process, subject to accuracy and latency constraints. To this end, we propose an algorithmic framework called JQODI combining a time-energy tree diagram to represent the learning process, a dynamic programming solution strategy, and a data-driven theoretical approach to predict the expected total number of training epochs that meet the accuracy requirements. We prove that JQODI approximates the optimal solution with polynomial complexity. Numerical results demonstrate that JQODI surpasses state-of-the-art methods in both energy efficiency and latency.

Mobile Edge Computing (MEC) decentralizes the network's computing and storage capabilities from centralized infrastructure to edge nodes located closer to end-users, enabling context-aware service deployment, low-latency service response, and efficient computation for mobile users. However, achieving energy-efficient service deployment while maintaining service delivery quality remains a significant challenge due to the wide geographic distribution of edge nodes, the dynamic variation of service workloads, and the differences between service deployment and delivery cycles. To address these challenges, we first design a feature encoding strategy and a self-attention-based encoder to extract contextual features, which are fused to support adaptive decision timescale regulation driven by service semantics and system load dynamics. Then, we propose a novel Dual-Timescale Energy-Efficient Service Deployment and Delivery (DT-EESD) framework integrated with hierarchical learning. The upper layer leverages an enhanced decision-making mechanism to optimize proactive service deployment and base station switching on a larger timescale, aiming to reduce long-term network costs. The lower layer employs a fine-grained real-time optimization approach to dynamically handle service delivery and resource allocation on a smaller timescale, effectively responding to dynamic service requests. By incorporating an expected reward-based learning mechanism, the framework efficiently handles the temporal coupling between deployment and delivery cycles. Extensive experiments demonstrate that DT-EESD outperforms baseline algorithms, achieving at least a 10.89% reduction in average system cost while also improving model convergence, reducing delay, and enhancing resource utilization.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology to enhance indoor wireless communication and sensing performance. However, the construction of reliable received signal strength (RSS)-based fingerprint databases for RIS-assisted indoor positioning remains an open challenge due to the lack of realistic and spatially consistent channel modeling methods. In this paper, we propose a novel method with open-source code for generating RIS-assisted RSS fingerprint databases. Our method captures the complex RIS-assisted multipath behaviors by extended cluster-based channel modeling and the physical and electromagnetic properties of RIS and transmitter (Tx). And the spatial consistency is incorporated when simulating the fingerprint data collection across neighboring positions. Moreover, an effective sorting algorithm is proposed to solve the online synchronization issue, a closed-form RIS phase configuration strategy is proposed to improve the localization accuracy, and the modeling method of mutual coupling (MC) effect is provided. Extensive simulations are conducted to evaluate the fingerprint database generated by the proposed method. And the positioning performance on the database using different algorithms is analyzed, providing valuable insights for the system design.

Scheduling-based Medium Access Control (MAC) protocols significantly augment the ability for simultaneous transmissions while mitigating signal interference, thereby elevating the bandwidth utilization efficiency crucial for data-collection-oriented underwater acoustic sensor networks (UASNs). However, existing scheduling-based MAC protocols for UASNs primarily analyze the interference between pairwise links or nodes, neglecting the cumulative interference effect fostered by the concurrent transmissions of multiple nodes. Besides, the existing multi-node interference models used in Radio Frequency (RF) based wireless networks only describe the total strength of cumulative interference signals and do not consider scenarios in which the interference from multiple nodes does not align at the receiver. This paper introduces an innovative interference model to extensively quantify the non-aligned overlapping interference from multiple packets on a single valid packet in segments. Based on this segmented interference model, we establish a transmission time constraint that can avoid excessive interference in high-interference segments and utilize low-interference segments for parallel reception, thereby enhancing the degree of time reuse. The optimization of transmission slot scheduling is modeled as a sequential decision-making process, wherein the action space is delineated by considering the constraints related to multi-packet interference. Furthermore, the Reinforced Packet Level Slot Scheduling (R-PLSS) algorithm leveraging the principles of approximate dynamic programming is proposed to allocate transmission slots within each frame. The simulation results demonstrate that the R-PLSS algorithm avoids the practical packet demodulation failure caused by excessive interference from multiple packets and can comprehensively improve the channel utilization efficiency.

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multisource domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a dual-reweighted Siamese contrastive learning network (DRSC) for cross-domain fault diagnosis with multisource domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis.

In unmanned aerial systems, especially in complex environments, accurately detecting tiny objects is crucial. Resizing images is a common strategy to improve detection accuracy, particularly for small objects. However, simply enlarging images significantly increases computational costs and the number of negative samples, severely degrading detection performance and limiting its applicability. This paper proposes a Dynamic Pooling Network (DPNet) for tiny object detection to mitigate these issues. DPNet employs a flexible down-sampling strategy by introducing a factor (df) to relax the fixed down-sampling process of the feature map to an adjustable one. Furthermore, we design a lightweight predictor to predict df for each input image, which will be used to decrease the resolution of feature map in backbone. Thus, we achieve input-aware down-sampling. We design an Adaptive Normalization Module (ANM) to make a unified detector well compatible with different dfs. At the same time, we also design a guidance loss to supervise the predictor’s training. DPNet realizes the dynamic allocation of computing resources to trade off detection accuracy and efficiency through this. Experiments on the TinyCOCO and TinyPerson datasets show that our DPNet can save over 35% and 25% GFLOPs, respectively, while maintaining comparable detection performance.The code will be made publicly available.

Mobile Edge Computing (MEC) is a promising technology for future 6G communication systems. However, the dynamic network environment and the selfish nature of devices pose challenges to task offloading. Therefore, it is very critical to design an effective cooperative offloading scheme in dynamic environments. In this paper, a social-assisted two-stage cooperative task offloading and resource allocation algorithm based on Stackelberg game and DRL (SAC-SDRL) is proposed to maximize the system utility. The problem is formulated as a mixed integer non-linear programming (MINLP) problem that jointly determined the edge server selection, and offloading rate, resource price, and resource allocation. To address this problem, two stage-solutions are introduced. In the first stage, given a fixed resource price and offloading rate, the edge server selection and resource allocation scheme based on hybrid actor-critic algorithm is designed to solve the problem of hybrid action space. In order to avoid invalid decision space, a clustering method based on social relationship and spectral clustering is developed. In the second stage, based on the obtained edge server selection and resource allocation decision, a dynamic pricing and offloading incentive scheme based on the Stackelberg game is proposed, in which the optimal resource price and optimal offloading rate can be determined with the proposed gradient-based iterative search method. Moreover, it is proved that the game can achieve the Stackelberg equilibrium. Finally, simulation results show that the proposed SAC-SDRL algorithm can achieve higher system utility compared with other concerned algorithms.

In the field of early automatic detection of smoke from forest fires, there is the issue of the small size and interference of smoke detection by clouds. The conventional NMS (Non-Maximum Suppression) requires manual adjustment of the threshold, which may result in missed or erroneous detection. This paper proposes a high-accuracy anti-interference forest fire smoke detection network for small objects. Firstly, a window feature extractor based on singular value decomposition (SVD-STR) is designed. This extractor is capable of extracting more representative features, of capturing small and inconspicuous features in the image, and of reducing the complexity and computation of the model. Secondly, a SinThreshold Screening Attention Mechanism (SinAttention) is proposed, which can filter interference information and enhance the discriminative power of the features, thereby facilitating the accurate recognition and distinction of smoke and clouds. Subsequently, a variational particle swarm soft suppression optimization (PGS) is proposed as a means of further enhancing the optimization effect. This is achieved by adjusting the suppression strategy and incorporating a Gaussian variational particle swarm algorithm. In conclusion, an IoT forest fire detection system based on PSSNet has been constructed. The experimental results demonstrate that the mAP50 value of the method is 98.2%, the value of mAP50-95 is 80.4%, and the FPS value is 35.7. These values are superior to those of current forest fire smoke detection methods and can be utilized for the precise detection of forest fire smoke, thereby providing technical support for forest ecological protection.

Underwater backscatter is an emerging passive communication technology powered by underwater acoustic energy, which has emerged as a promising solution to the underwater energy problem. However, the backscatter mechanism causes the reflected signal strength to undergo periodic ups and downs due to the phase cancellation effect when the receiving end is in motion. Additionally, during the signal retro-reflective process, the reflected array signals deviate from the optimal beam direction when there is movement at the receiving end, introducing nonlinear attenuation into the signal strength. This paper proposes GKCformer signal strength forecasting method for underwater backscatter systems. It is based on the original sequence and Gram angular field image modal design to add period information. It utilizes Transformer to rearrange and improve channel feature information, and Kolmogorov-Arnold Networks to make the fitting better. Experimental results show that the proposed model outperforms the classical model in mean squared error (MSE) and mean absolute error (MAE) metrics, which demonstrates its better performance in prediction accuracy.

The integrated access and backhaul (IAB) architecture is a candidate in the beyond fifth-generation (B5G) era to improve the network capacity and coverage extension. However, real-time changes in the transmission demands in this system bring a few technical challenges in terms of resource management. Aimed at this, the long-term throughput maximized subchannel assignment is investigated in this article. First, the cumulative achievable rate maximized subchannel assignment considered in the system is expressed as a nonlinear and nonconvex (NLNC) optimization problem under the constraints of dynamically changed transmission demands, real-time achievable rate, and available resources. Subsequently, the original problem is equivalently reformulated as a long-term throughput maximization problem within a Markov decision process (MDP) framework. A deep reinforcement learning (DRL)-based subchannel assignment framework using Transformer, named DSAF-Former, is designed to effectively capture long-range dependencies and contextual information of transmission demands. Additionally, the subchannel assignment strategy is dynamically manipulated. Finally, we conducted simulation experiments to assess the performance improvements of the proposed DSAF-Former in various scenarios with other baseline algorithms (deep Q-network, Q-Learning, etc.). Specifically, the average throughput and spectral efficiency (SE) is increased by 129.02% and 104%, respectively, when the Adam optimizer is selected in the proposed DSAF-Former.

Generative diffusion models deliver remarkable synthesis quality but remain impractical for resource-limited Internet of Things (IoT) devices due to their substantial computational and memory demands. To bridge this critical gap, we present a comprehensive, multi-stage optimization framework that systematically reduces model size while meticulously preserving generative fidelity. The framework integrates an efficient backbone architecture designed for inherent lightness, a sensitivity-guided fine-grained pruning strategy that strategically removes redundant parameters to achieve high sparsity, and a novel distribution-aware quantization algorithm based on Gaussian Mixture Models (GMMs) to compress weights and activations with minimal quality degradation. Extensive validation across multiple diffusion architectures (DDPM, DDIM, SGM) and diverse datasets demonstrates the framework’s strong generalizability, achieving up to 79% model sparsity while preserving generative fidelity. To showcase practical utility, we demonstrate that our framework produces a compressed model compatible with standard mobile deployment toolchains, realizing a significant reduction in the on-device memory footprint required for inference. This work offers a robust and generalizable methodology for enabling advanced generative AI on a wide spectrum of edge and IoT platforms. Code is available at: https://github.com/mitchell-cheng/compress_diffusion.

The widespread adoption of IoT devices and the lack of standardized security measures have made IoT networks vulnerable to cyberattacks, particularly botnet intrusions. Machine learning methods can improve the detection performance of network attacks through effective statistical characterization of network traffic, but they tend to ignore network topology and temporal information, thus limiting the detection performance of potential botnet attacks. Graph neural network (GNN) methods are capable of extracting information about network topology and are currently widely used for network intrusion detection. However, most of the GNN-based methods mainly target static graphs or use transductive models that assume the network contains a fixed set of nodes or edges, which cannot cope with dynamic IoT environments. In this paper, we propose a hierarchical attention-based dynamic GNN algorithm (HADGA) for botnet detection in dynamic IoT networks. HADGA transforms network traffic into a dynamic graph and decouples spatiotemporal evolution through dual attention modules. Specifically, we propose a novel joint attention mechanism in the neighbor attention module, which is fused with GraphSAGE to generate spatial embeddings of nodes inductively. The temporal attention module captures the temporal evolution information of network traffic by flexibly weighting the historical representations of nodes. Experiments on BoT-IoT and TON-IoT datasets demonstrate HADGA’s superiority in dynamic IoT networks with variable topologies, achieving 97.6% and 99.9% accuracy, respectively, surpassing Anomal-E by 2.56% and 1.27%.

Monitoring renewable energy devices is crucial for the timely detection of faults and the improvement of system stability, making it a key component of the Internet of Things (IoT) ecosystem. However, existing intelligent algorithms and IoT methods rely on edge devices with limited computational capacity, which requires balancing real-time performance and accuracy. Additionally, most methods require initial training on large-scale data using cloud platforms or high-performance servers, leading to high initial costs. To address these challenges, we propose multimodal statistical prompt learning (MSPL) for new energy equipment defect recognition on IoT edge devices. This method enables rapid learning with only a few samples, avoiding the need for large-scale centralized training. Specifically, MSPL uses the pretrained contrastive language-image pretraining model as its backbone, leveraging text-based conceptual information to enhance the understanding of visual inputs. A statistical query module is implemented at the end of the backbone to extract distinctive features from the outputs, integrating these features with soft prompts to customize them for the defect recognition task. Since the learnable parameters are limited to soft prompts added at the end of the backbone, MSPL restricts gradient backpropagation to this point. This improves parameter and memory efficiency, making it more suitable for scenarios with limited computational capacity on IoT edge devices. Experimental results of MSPL on two renewable energy equipment defect datasets and edge devices indicate that it meets real-time processing requirements while maintaining high accuracy, outperforming other methods.

Currently, the Internet of Underwater Things (IoUT) plays an important role in ocean exploration, monitoring, and protection. However, it faces many security threats due to resource constraints, such as denial-of-service attacks. To overcome these challenges, a novel underwater network architecture that incorporates cloud computing and edge computing technologies, namely, the ocean-oriented cloud edge collaboration networks (O-OCECNs), is designed. O-OCECNs, while enhancing computational capabilities, still faces network threats, such as energy depletion attacks, data pollution attacks, etc. On this basis, a trust management mechanism called ETrust is proposed in this article, which uses ensemble learning to ensure network security. In the ETrust mechanism, nodes collect trust evidences by monitoring the behavior and communication results of other nodes and then deliver the evidences to the cluster head node. The cluster head node uploads the collected evidences to the edge server in its region to complete the trust value computation. Then, the edge server uploads the trust values to the cloud server, which utilizes the built-in learning to complete the trust evaluation. Finally, the cloud server outputs the trust evaluation value to the cluster head node and it adjusts the network topology. The experimental result demonstrates that the proposed scheme can detect malicious nodes with a trust evaluation accuracy of up to 98.55%. It also performs well in capturing selective forwarding attacks occurring in the network. Furthermore, the mechanism enhances network lifetime more effectively compared to existing schemes. Experimental result shows that our scheme maintains the average residual energy of devices at approximately 63%, compared to 50% with other methods.

Edge computing is fundamental to filling the various quality-of-service needs for Industrial Internet of Things (IIoT) applications. However, introducing edge computing to IIoT inevitably results in hybrid intrusion problems and fails to satisfy the security demands of IIoT. Fortunately, Lagrange coded computing has emerged as a low-complexity and low-overhead solution for resisting hybrid intrusions during data offloading and processing. However, how to make decentralized, accurate, and real-time encoding/offloading/decoding decisions remains challenging. This article designs a fully decentralized training and decision-making framework to address the joint secure data offloading and resource allocation problem against hybrid intrusions for dynamic and uncertain IIoT, attempting to minimize the long-run energy and delay costs while improving the data confidentiality, integrity, and availability. It is proposed a fully decentralized multiagent actor–critic-based secure data offloading (FM-SDO) algorithm to solve the secure data offloading subproblem, wherein each industrial end device utilizes its local information to learn and execute its policy independently. This algorithm improves the structure of actor and critic networks and designs a multiagent alternant updating mechanism to increase learning accuracy, convergence, and stability. Based on the received offloading decisions of each device, each edge server leverages the Lagrange multiplier approach and Karush–Kuhn–Tucker condition to make fast and decentralized resource allocation decisions. Finally, we employed an IIoT intelligent production line platform named iCandyBox to test the performance of the FM-SDO algorithm. Experiment results suggest that the FM-SDO algorithm effectively reduces the total energy and delay costs while increasing the capability of resisting hybrid intrusions.

Network intrusion detection (NID) is a critical area of research in network security. While deep learning based NID methods have recently achieved advanced detection performance, they often struggle with limited labeled traffic and the resource constraints of edge Internet of Things (IoT) devices. To address these challenges, we propose an advanced few-shot NID method using lightweight transfer learning (LTL), termed NID-LTL. Our approach begins by pre-training a detection model on the large-scale auxiliary dataset to learn universal representations of network traffic characteristics. Then, an automatic pruning strategy is crafted to prune the pre-trained model, which uses a kernel based nonlinear traffic feature selection algorithm to filter out the key information most relevant to the original traffic. Finally, the layer-wise knowledge distillation method is combined to transfer the useful knowledge learned by the pre-trained model to a lightweight student model. This method can not only quickly adapt to novel few-shot NID tasks, but also further compress the model size, reduce computational and storage overhead. Experimental results demonstrate that the proposed NID-LTL method has excellent classification performance with small model sizes, low parameter counts, and low floating point operations (FLOPs). Especially, in the 1-shot scenario, the NID-LTL method achieves 89.38% classification accuracy with only 1.41% of the parameters in the original model.

Anomaly detection is a fundamental component of intelligent monitoring in the Internet of Things (IoT), where accuracy, efficiency, and interpretability are critical requirements. However, existing methods often overlook the unique characteristics of IoT signals such as seasonality, trends, and irregular residual components, as well as the complex interactions among them. This oversight can lead to anomaly masking, increased false positives, and reduced interpretability in anomaly identification. Motivated by the effectiveness of disentangled representation learning, we propose TRAdetector, a novel disentangled reconstruction-based framework for IoT signals anomaly detection. TRAdetector explicitly models recurrent and consistent patterns, as well as irregular variations in the latent space by leveraging variational inference strategies, thereby enhancing probabilistic guidance in learning both regular and irregular temporal representations. A sparse coding strategy is incorporated within the latent space of the residual component to directly model inconsistent temporal fluctuations. Finally, a multihead cross-attention mechanism and a gated, decomposition-aware reconstruction strategy are designed to effectively model the complex interactions among different components. Extensive experiments show that our model achieves state-of-the-art performance on multiple benchmark datasets in terms of accuracy, efficiency, and interpretability.

The dynamic production environments and stringent quality of service (QoS) requirements in discrete manufacturing smart factories pose significant challenges to deploying edge intelligence networks. These networks must simultaneously satisfy critical QoS metrics while maintaining adaptability to fluctuations in resource availability and task priorities. To address the industrial demands for real-time responsiveness, lightweight design, and flexible deployment, this paper proposes an adaptive deployment optimization strategy for edge intelligent networks based on an improved K-means particle swarm optimization (IK-PSO) algorithm. The strategy incorporates dynamic clustering and weight adjustment mechanisms to optimize multiple performance metrics, including latency, throughput, reliability, and interference mitigation. Experimental results validate that the IK-PSO-based deployment optimization strategy rapidly converges to high-quality solutions across different scenarios and various factory complexities, significantly improving network performance. This study provides a practical and efficient solution for network deployment in smart factories, contributing to the ongoing development of intelligent production and resource management.

The advent of Multi-access Edge Computing (MEC) has empowered Internet of Things (IoT) devices and edge servers to deploy sophisticated Deep Neural Network (DNN) applications, enabling real-time inference. Many concurrent inference requests and intricate DNN models demand efficient multi-DNN inference in MEC networks. However, the resource-limited IoT device/edge server and expanding model size force models to be dynamically deployed, resulting in significant undesired energy consumption. In addition, parallel multi-DNN inference on the same device complicates the inference process due to the resource competition among models, increasing the inference latency. In this paper, we propose a Resource-aware and Dynamic DNN Deployment (R3D) scheme with the collaboration of end-edge-cloud. To mitigate resource competition and waste during multi-DNN parallel inference, we develop a Resource Adaptive Management (RAM) algorithm based on the Roofline model, which dynamically allocates resources by accounting for the impact of device-specific performance bottlenecks on inference latency. Additionally, we design a Deep Reinforcement Learning (DRL)-based online optimization algorithm that dynamically adjusts DNN deployment strategies to achieve fast and energy-efficient inference across heterogeneous devices. Experiment results demonstrate that R3D is applicable in MEC environments and performs well in terms of inference latency, resource utilization, and energy consumption.

Optical communication alignment between an unmanned aerial vehicle (UAV) and an autonomous underwater vehicle (AUV) is crucial for achieving cross sea-air optical communication. However, the high directivity of laser beams and sea surface fluctuations pose significant challenges to precise alignment. To address this issue, this paper first establishes a dynamic sea surface model and a cross sea-air optical communication channel model, utilizing the beam splitting method to determine the light spot center position on the UAV receiving plane. To achieve rapid and stable alignment, we propose an adaptive attention-based multi-agent deep reinforcement learning (MADRL) strategy with Koch Snowflake cluster structure for cross sea-air optical communication alignment (A2M-KS). This algorithm dynamically optimizes the evaluation network through an adaptive learning rate and employs an attention mechanism to enable agents to selectively focus on relevant information, thereby achieving rapid convergence and higher accuracy. Furthermore, by designing an attractive reward function, the UAV can rapidly and accurately track the light spot center, completing the alignment task with the AUV. Experimental results demonstrate that the A2M-KS algorithm outperforms baseline methods in terms of communication reliability, convergence speed, and accuracy.

The rapid evolution of the Internet of Underwater Things (IoUT) has led to the widespread adoption of autonomous underwater vehicle (AUV)-assisted underwater acoustic sensor networks (UASNs) for various applications such as marine environment monitoring and resource exploration. This article introduces an energy-balanced data collection scheme tailored for AUV-supported UASNs. The proposed scheme combines a node clustering method based on a social welfare function and an intelligent path planning strategy for the AUV. The node clustering approach integrates canopy and K-means algorithms for initial node clustering, followed by reclustering using an enhanced hierarchical clustering algorithm. To balance energy distribution, Atkinson's social welfare function is employed to select and rotate cluster heads (CHs) within each cluster. To address limited CH memory constraints, a lossless compression technique is introduced to reduce data storage requirements at the CHs. Moreover, the article introduces the use of the deep Q-network (DQN) technique for AUV path planning, considering multiple pertinent factors simultaneously. Simulation results demonstrate that the proposed data collection scheme effectively reduces energy consumption, prolongs network lifespan, and enhances data collection efficiency when compared to recent research endeavors.

The CNN-Transformer structure is widely applied to underwater image enhancement (UIE) tasks. However, previous studies have typically used structures similar to those in other image restoration scenarios, without specifically designing a unified structure that fully integrates the characteristics of convolution and Transformers for real-world underwater scenarios. Moreover, the inconsistency of color channel and spatial region attenuation of underwater images has not been given sufficient attention. To this end, this paper proposes a new UIE network, UMCTN, based on multi-kernel convolution Transformer. A multi-kernel convolution residual self-attention block (MCRA) was constructed. By designing a multi-kernel convolutional residual structure, it addresses the issue of critical feature information loss when convolution is applied to small-sized image patches in Transformers, which are widely adopted to reduce computational costs. It elegantly combines the characteristics of convolution and Transformers, endowing the network with a strong capability to capture both local and global dependencies. In addition, a feature fusion compensation module (FFCM) is proposed to supplement richer global perceptual features for MCRA, and It can effectively remove and restore spatial and color channels with more severe attenuation. Tests on the UIEB, UFO-120, and LSUI datasets show that UMCTN achieves better quantitative evaluation and visual performance compared to state-of-the-art (SOTA) schemes, with a maximum peak signal-to-noise ratio (PSNR) improvement of 1.93 dB. Detailed ablation studies validate the effectiveness of each component.

Monocular 3D object detection has garnered significant attention for its outstanding cost effectiveness compared with multi-sensor systems. However, previous work mainly acquires object 3D properties in a heuristic way, with less emphasis on the cues between objects. Inspired by the mechanisms of monocular vision, we propose MoVis, an innovative 3D object detection framework that skillfully combines object hierarchy and color sequence cues. Specifically, a decoupled Spatial Relationship Encoder (SRE) is designed to effectively feed back the high-level encoding results with object hierarchical relationships to low-level features. This method not only effectively reduces the computational overhead of multi-scale coding, but also significantly improves the detection accuracy of occluded objects by incorporating the hierarchical relationship between objects into multi-scale features. Moreover, to obtain more precise object depth information, an Object-level Depth Modulator (ODM) based on the concept of conditional random fields is designed, which employs color sequences. Ultimately, the results of the SRE and ODM are efficiently fused by our Spatial Context Processor (SCP) to accurately perceive the 3D attributes of the objects. Extensive experiments on the KITTI and Rope3D benchmarks show that MoVis achieves state-of-the-art performance. Our MoVis represents a progressive approach that emulates how human monocular vision utilizes monocular cues to perceive 3D scenes.

Deep reinforcement learning-based object detection approaches center around a pivotal concept: hierarchically scaling image segments that harbor more intricate details. Compared with the traditional object detection approaches, this approach significantly curbs the quantity of region proposals. This reduction holds paramount significance in curtailing the computational overhead. However, common deep reinforcement learning-based approaches suffer from a significant defect in terms of precision. This issue arises from inadequacies in representing image states appropriately and the unstable learning ability exhibited by the agent. To address these issues, we present the LHAR-RLD. First, we design the Low-dimensional RepVGG(LDR) feature extractor to reduce memory consumption and to reduce the difficulty of fitting downstream networks. Second, we propose the Hybrid DQN(HDQN) to enhance the agent's ability to determine the state-action of images in complex environments. Then, the Adaptive Dynamic Reward Function(ADR) is crafted to dynamically adjust the reward based on shifts within the agent's exploration environment. Finally, the ROI Align-based bounding box regression network (RABRNet) is proposed, which aims at further regressing the localization results of reinforcement learning to improve the detection precision. Our method accomplishes 74.4% mAP on the VOC2007, 76.2% mAP on the COCO2017, 75.2% Precision on the SF dataset, with 1.43G FLOPs. The precision outperforms the advanced deep reinforcement learning approaches and the computational cost is far lower than theirs and mainstream object detection methods. This method facilitates highly accurate object localization with minimal computational demands, which means it has notable applications on resource-constrained devices.

In underwater acoustic sensor networks (UASNs), source nodes serving as data centers hold significant commercial value and strategic importance, and the leakage of their location information may result in immeasurable negative consequences. Presently, the methods employed to protect the location privacy of source nodes within UASNs face challenges such as limited network security duration, high node energy consumption, and prolonged data transmission delays. Additionally, security research has predominantly focused on passive attacks, with insufficient provisions against active threats. To address these issues, this study proposes a polygonal phantom source position privacy protection algorithm based on a secure zone (PPSZ) in autonomous underwater vehicle (AUV)-aided UASNs. First, a polygonal secure zone is defined with the source node at its center. Phantom nodes are strategically selected from nodes situated outside this zone, leveraging the relative angles between nodes to deter passive attacks while mitigating data transmission delays. Next, the selection of relay nodes is optimized using the Q-learning algorithm, where each node adjusts its selection strategy based on real-time feedback, further lowering node energy consumption. Finally, auxiliary nodes are deployed using a nonuniform clustering strategy to collectively transmit interference signals, effectively disrupt active attacks, and ensure the secure transmission of source data. Simulation results demonstrate that the PPSZ algorithm can better balance the relationships among safety time, node energy consumption, and data transmission delays.

With the advancement of marine resource exploration and exploitation technologies, autonomous underwater vehicles (AUVs) have shown significant potential to perform underwater tasks, such as pipeline maintenance. However, traditional control algorithms, such as proportional-integral-derivative, sliding mode, and model predictive control, struggle to adapt to nonlinear and current-disturbed underwater environments, which impedes their accuracy and stability in path-tracking tasks. To address these challenges, this paper proposes a curiosity-driven distributional soft actor-critic framework. The framework leverages distributional soft actor-critic algorithms to control the navigation direction of the AUV and employs traditional proportional-integral control to maintain navigation speed, creating a stable, high-precision control strategy for complex underwater environments. Building on this framework, this paper further advances its capabilities through two key improvements. First, a curiosity-driven automatic entropy adjustment technique is designed to enhance the exploration of unknown states and the utilization of similar states, thus optimizing the accuracy of path tracking. Second, a dynamic prioritized experience replay mechanism is developed to prioritize learning samples based on time-differential errors and historical rewards, thereby improving learning efficiency and stability. Simulation experiments are conducted in two different underwater environments with and without ocean currents. Compared with reinforcement learning-based methods and traditional control algorithms, the proposed method has significant advantages in terms of accuracy, stability, and adaptability.

The multiple Autonomous Underwater Vehicle (AUV)-assisted cooperative system or the AUV-based Underwater Ad-hoc Networks (UAN) system has been considered as a highly-potential future in underwater data surveillance. In this paper, we propose grid-based distributed data collection architecture and define two categories of navigation modes. Based on the proposed data collection model, we propose MADAC, a scheme based on AUV-based UAN to cooperatively collect data from 6G-driven underwater wireless networks. We utilize the Software-Defined Networking (SDN) technique to re-organize the architecture of AUV-based UAN and propose software-defined actor-critic MARL framework. Based on the proposed MARL framework, we present the paradigm of MADDPG algorithm with optimal similarity attention mechanism (MADDPG-SA), to plan the paths for the AUV-based UAN, especially the cooperative underwater obstacle avoidance, the task distribution balancing, the Value of Information (VoI) are concurrently taken into account. In particular, the proposed MADDPG-SA improves the running efficiency of the proposed MADDPG-SA by encouraging the agent to learn from the similar and better-performance agent. The evaluation results demonstrate that the proposed MADAC can schedule the AUV-based UAN to perform efficient underwater data collection, reduce data collection time and energy consumption, and balance data collection tasks in the AUV-based UAN.

The rapid advancement of autonomous driving systems has created a pressing need for accurate and robust lane detection to ensure driving safety and reliability. However, lane detection still faces several critical challenges in real-world scenarios: 1) severe occlusions caused by urban traffic and complex road layouts; 2) the difficulty of handling sharp curves and large curvature variations; and 3) varying lighting conditions that blur or degrade lane markings. To address these challenges, we propose DLNet, a novel direction-aware feature integration framework that integrates both low-level geometric details and high-level semantic cues. In particular, the approach includes: (i) a Multi-Skip Feature Attention Block (MSFAB) to refine local lane features by adaptively fusing multi-scale representations, (ii) a Context-Aware Feature Pyramid Network (CAFPN) to enhance global context modeling under adverse conditions, and (iii) a Directional Lane IoU (DLIoU) loss function that explicitly encodes lane directionality and curvature, providing more accurate lane overlap estimation. Extensive experiments conducted on two benchmark datasets, CULane and CurveLanes, show DLNet achieves new state-of-the-art results, with${\mathrm {F}}{1_{50}}$and${\mathrm {F}}{1_{75}}$scores of 81.23% and 64.75% on CULane, an${\mathrm {F}}{1_{50}}$score of 86.51% on CurveLanes and a high F1 score of 97.62% on the TUSimple dataset. Moreover, the model maintains competitive computational efficiency at 18.8 GFLOPs while running at 74FPS, satisfying real-time requirements. The source code has been publicly released athttps://github.com/RDXiaoLu/DLNet.git

Wireless sensor networks (WSNs) are vital for addressing vehicle-related challenges information management, congestion, and safety in Intelligent Transportation Systems (ITS). Ensuring reliable communication is critical, particularly in urban environments where real-time data from roadside infrastructure enhances traffic flow efficiency and safety. This paper proposes R2Com, a reliable and resilient communication protocol for duty-cycled Software-Defined Wireless Sensor Networks (SDWSNs), specifically designed for urban traffic monitoring. By integrating reliable routing and adaptive duty cycling, R2Com ensures low-latency, energy-efficient data exchange between vehicle detection units and traffic control centers. The protocol leverages four attributes: direct trust, recommended trust, signal-to-interference noise ratio, and residual energy, considering their probability distributions to ensure reliability and resilience in the data plane communication. Secondly, the SDN controller calculates these attributes alongside the Expected Duty Cycled Wake-ups (EDC), enhancing reliability through flexible management and low latency. It then assigns communication strategies to each node through reliable nodes and limits the number of forwarding nodes per node to reduce packet duplication. Simulation results demonstrate that the proposed protocol significantly outperforms existing protocols in terms of average energy consumption, packet delivery ratio, average latency, network lifetime, communication overhead, packet success ratio, reliable coverage degree, coverage percentage, traffic density estimation accuracy, and intersection congestion levels.

In this study, we address the challenge of traversability analysis for autonomous vehicles in diverse environments, leveraging LiDAR sensors. We propose the Transformer-Voxel-Bird’s eye view (BEV) Network (TVBNet), a novel dual-branch framework designed to increase the accuracy and versatility of such analyses in both urban and off-road conditions. TVBNet first preprocesses raw point cloud data through voxelization and the generation of a BEV. It incorporates a Transformer network with a rotational attention mechanism to aggregate features from multiple point cloud frames, capturing long-range correlations both within and between point clouds. Additionally, a Swin Transformer extracts the relative positional relationships in the BEV projection, facilitating a comprehensive understanding of the scene. The fusion of data from both branches via a multisource feature fusion module, which employs a context aggregation mechanism based on a residual structure, allows for robust local to global contextual understanding. This approach not only improves the extraction of correlation features between 2D BEV and 3D voxel data but also demonstrates superior performance on the challenging off-road dataset RELLIS-3D and the urban dataset SemanticKITTI.

Multi-UAV autonomous cooperative air warfare is an important mode of future intelligent air warfare. However, due to the complexity and uncertainty of air combat situation information, how to accurately interpret the enemy’s sustained combat intent remains a major challenge. To address this problem, we propose a Context-Aware Adaptive Feature Fusion (CAAFF) method, which can effectively utilize the time-series data of battlefield situation for hierarchical feature fusion. Specifically, the input data is first subjected to dimensionality reduction processing and feature extraction by an encoder-decoder to provide high-quality low-dimensional feature representations for further feature fusion. Next, the middle layer captures attitude changes by aggregating information from neighboring nodes via a graph attention convolutional network (GACN), flexibly fusing the features of each node, and identifying complex relationships between nodes. Finally, the mechanism of stabilizing multi-attention with self-attention is used to integrate global information at the upper layer to construct an overall feature representation of the mission and realize local-to-global posture analysis. In order to enhance the interpretation of persistent operational intent, we utilize the context-aware module to construct contextual feature representations by combining current and historical state information, thus improving the depth and interpretability of mission understanding. Finally, we combine the CAAFF method with reinforcement learning and verify its performance through multiple experiments, demonstrating the applicability and effectiveness of the method in Multi-UAV cooperative air combat.

The complex submarine geography, variable ocean current dynamics, and challenging underwater communication conditions present significant technical challenges to the implementation of data collection systems for autonomous underwater vehicles (AUVs), which are crucial for the deployment of Internet of Underwater Things (IoUT) applications. Multi agent reinforcement learning (MARL), in which agents interact with their environment to obtain rewards by joint trial-and-error, has been thoroughly researched for the cooperation of intelligent vehicles. Nevertheless, the conventional MARL approaches for cooperative AUVs are challenging to apply due to the difficulties in information exchange and the time-varying submarine environment. Accordingly, this study proposes a novel deep reinforcement learning (DRL) approach named staged learning with distributed negotiation (SLDN). Compare to prevalent MARL approaches like centralized training with distribute execution (CTDE), the proposed method relies on staged and independent DRL and accomplishes multi-AUV cooperation tasks through staged negotiation and training, aiming to facilitate collaborative operations in dynamic underwater environments while significantly reducing communication costs. This method addresses the IoUT scenario of a cluster-structured hybrid underwater network that integrates acoustic and MI communications, and its superiority in terms of data collection capabilities is confirmed by simulations and comparative analysis.

Autonomous Underwater Vehicle (AUV)-assisted Underwater Acoustic Networks (UANs) are promising for complex ocean applications. In essence, an AUV-assisted UAN is still dominated by fixed nodes, and Time Division Multiple Access (TDMA)-based Medium Access Control (MAC) protocols have undisputed practicability in such fixed nodes-dominated UANs since they are simple and easy to deploy. However, AUV-assisted UANs may exist dynamic bidirectional data streams, while most existing protocols assume UANs have a unidirectional data stream, and their fixed scheduling sequence results in the long end-to-end delay in AUV-assisted UANs. In this paper, we first reveal a phenomenon between the data stream and the scheduling sequence, derived from real-world experiments: their consistent direction decreases the packet waiting delay but increases the slot length, and vice versa. To optimize the end-to-end delay, UANs with dynamic bidirectional data streams expect the MAC protocol to provide a flexible scheduling sequence. To this end, we propose a low-delay Adaptive Scheduling MAC protocol (AS-MAC) based on TDMA for AUV-assisted UANs. In AS-MAC, we analyze the relationship between scheduling sequence and data stream, extracting two significant factors: slot length and packet delay. Afterwards, we design Slot Length Model (SLM) and Packet Delay Model (PDM) to analyze the end-to-end delay of different data streams. Based on these two models, we present a Scheduling Sequence and Slot Length allocation Algorithm (SSSLA) to adaptively provide the minimum end-to-end delay for current bidirectional data streams. Extensive simulation results show that AS-MAC efficiently addresses severe queue congestion of the state-of-the-art protocols and reduces the end-to-end delay of different dynamic streams in various scenarios.

The Internet of Underwater Things (IoUT) has garnered significant interest due to its potential applications in monitoring underwater environments. However, the unique characteristics of acoustic communication, such as long propagation delays and high attenuation, present considerable obstacles for achieving efficient and dependable data transmission. Opportunistic routing is a crucial technique for enhancing packet delivery ratios by selecting a set of forwarding nodes and utilizing their cooperative forwarding to boost network throughput. Nevertheless, choosing an excessive number of forwarding nodes can lead to wasteful energy usage and extended communication delays. Moreover, the overlooked trustworthiness of forwarded nodes in most research works can undermine the effectiveness of opportunistic routing. Therefore, this study presents a novel trust opportunistic routing scheme that employs reinforcement learning to achieve resilience in constantly changing underwater settings. The combination of reinforcement learning and trust management enables the proposed opportunistic routing scheme to adapt to the unstable underwater environment and unknown malicious attacks. Initially, a method is introduced for measuring environmental fitness by considering multiple trust factors, including communication success rate, data reliability, and location dynamics. The proposed scheme then uses reinforcement learning to develop a reliable opportunistic routing method based on quantified state information. This component employs the obtained state to formulate action strategies and obtains reward values from environmental inputs. The reward update equation integrates these qualities to optimize the deployment of superior action strategies, finally achieving trust opportunistic routing for underwater data collection. Fundamental experimental results demonstrate that the proposed protocol performs exceptionally well in demanding underwater conditions, outperforming existing methods in packet transmission rate, energy efficiency, and end-to-end delay.

The underwater acoustic sensor network (UASN) is a pivotal paradigm within the underwater Internet of Things, where multi-hop forwarding-based underwater data routing is essential for information acquisition. However, the dynamic nature of underwater network topology and the instability of underwater acoustic communication pose significant challenges to achieving efficient and reliable data transmission. In light of unreliable underwater environments and potential malicious attacks, studying trusted routing strategies for UASNs is crucial. This study introduces a context-aware trust routing scheme (CADTR) based on deep reinforcement learning (DRL), which integrates real-time environmental state perception with AI-driven routing decisions, thereby enhancing the reliability and robustness of data routing in dynamic and potentially hostile underwater scenarios. Firstly, a unified trust evidence framework is developed to strengthen the support of evidence experience for subsequent trust decisions by mapping multi-dimensional trust evidence to a unified scale. This framework is tightly coupled with the DRL agent, allowing the agent to evaluate and update trust levels based on real-time evidence. Secondly, a dynamic topology perception model and an underwater acoustic communication perception model are constructed to enable real-time perception of the interactive experience context. These models provide continuous input to the DRL agent, enabling it to adapt to topological changes and communication conditions dynamically. This facilitates priority experience sampling during the training process of the routing decision model, indirectly boosting model training efficiency and decision accuracy. Finally, the DRL agent learns optimal routing policies by interacting with the environment, leveraging the trust evidence and perception models to make informed decisions. Experimental results demonstrate that the proposed CADTR algorithm significantly improves the overall performance of the routing strategy in terms of packet delivery rate, energy utilization efficiency, and data transmission delay compared to the benchmark algorithms.

In recent years, autonomous underwater vehicle (AUV) swarms are gradually becoming popular and have been widely promoted in ocean exploration or underwater tracking, etc. In this paper, we propose a multi-AUV cooperative underwater multi-target tracking algorithm especially when the real underwater factors are taken into account. We first give normally modelling approach for the underwater sonar-based detection and the ocean current interference on the target tracking process. Then, based on software-defined networking (SDN), we regard the AUV swarm as a underwater ad-hoc network and propose a hierarchical software-defined multi-AUV reinforcement learning (HSARL) architecture. Based on the proposed HSARL architecture, we propose the “Dynamic-Switching” mechanism, it includes “Dynamic-Switching Attention” and “Dynamic-Switching Resampling” mechanisms which accelerate the HSARL algorithm's convergence speed and effectively prevents it from getting stuck in a local optimum state. Additionally, we introduce the reward reshaping mechanism for further accelerating the convergence speed of the proposed HSARL algorithm in early phase. Finally, based on a proposed AUV classification method, we propose a cooperative tracking algorithm calledDynamic-Switching-BasedMARL (DSBM)-driven tracking algorithm. Evaluation results demonstrate that our proposed DSBM tracking algorithm can perform precise underwater multi-target tracking, comparing with many of recent research products in terms of various important metrics.

Currently, the source location privacy (SLP) becomes a hot research interest in network security of Underwater Acoustic Sensor Networks (UASNs), and existing schemes are mostly proposed for a given scenario. Introducing source location privacy technologies inevitably increase the energy consumption of nodes, while they are widely deployed in available studies, resulting in massive energy wastage. Therefore, an adaptive scheme for protecting source location privacy (APSLP) in UASNs is proposed. The APSLP scheme first analyzes the possible locations of the adversary by trust method. Then, considering the lagging nature of the trust method, which means that the adversary may not stay in locations given by trust method, a hidden Markov-based backtracking method is proposed and location privacy methods are functioned according to the backtracking result. The simulation shows that even though the security level of the APSLP scheme is not the largest, the efficiency is the highest, approximately an increase of 69.1$\%$and 10.3$\%$compared with two comparison algorithms, respectively.

Increasing demands for versatile applications have spurred the rapid development of Unmanned Underwater Vehicle (UUV) networks. Nevertheless, multi-UUV movements exacerbates the spatial-temporal variability, leading to serious intermittent connectivity of underwater acoustic channel. Such phenomena challenge the identification of reliable paths for high-dynamic network routing. Existing routing protocols overlook the effects of UUV movements on forwarding path, typically selecting forwarders based solely on the current network state, which lead to instability in packet transmission. To address these challenges, we propose a Routing protocol based on Spatial-Temporal Graph model with Q-learning for multi-UUV networks (STGR), achieving high reliable and energy effective transmission. Specifically, a distributed Spatial-Temporal Graph model (STG) is proposed to depict the evolving variation characteristics (neighbor relationships, link quality, and connectivity duration) among underwater nodes over periodic intervals. Then we design a Q-learning-based forwarder selection algorithm integrated with STG to calculate reward function, ensuring adaptability to the ever-changing conditions. We have performed extensive simulations of STGR on the Aqua-Sim-tg platform and compared with the state-of-the-art routing protocols in terms of Packet Delivery Rate (PDR), latency, energy consumption and energy balance with different network settings. The results show that STGR yields 24.32 percent higher PDR on average than them in multi-UUV networks.

With the rapid development of underwater materials technology and underwater robot technology, human exploitation of marine resources has been increasingly advanced, which has given rise to various application scenarios for Autonomous Underwater Vehicle (AUV) cluster networks, such as cooperative data collection and target tracking. In this paper, we aim to explore how to utilize networking and swarm intelligence to improve the AUV cluster network’s target tracking performance in a time-saving manner. Specifically, on account of our previous work, we introduce an underwater interrupted mechanism and propose an Interrupted Software-Defined Multi-AUV Reinforcement Learning (ISD-MARL) architecture. For MARL algorithm in ISD-MARL, we propose a time-saving MARL algorithm, S-MADDPG, integrating our proposed action optimization model and action network loss function, to accelerate the convergence of the MARL algorithm. Furthermore, to further improve the AUV cluster network’s path planning performance during the target tracking, we propose an Interrupted Tracking Path Planning Scheme (ITPPS) for the AUV cluster network based on the proposed ISD-MARL and S-MADDPG. The evaluation results showcase that our proposed scheme can effectively plan the underwater target tracking path for the AUV cluster network in a shorter time and outperform various mainstream strategies in terms of convergence speed and training time, etc.

Recently, the rapid advancement of Multi-Agent Reinforcement Learning (MARL) has introduced a new paradigm for intelligent underwater target tracking within Autonomous Underwater Vehicle (AUV) cluster networks, enabling these networks to intelligently collaborate in target tracking. However, the limited scalability of MARL poses significant challenges to the performance of AUV cluster networks in tracking tasks. Specifically, MARL models trained on a fixed agents lose their effectiveness when the agent count changes, underscoring the critical need to enhance MARL’s scalability to accommodate an arbitrary number of agents. This paper addresses the pressing issue of MARL’s scalability in the context of AUV cluster network-based target tracking. Specifically, we propose an Elastic Software-Defined Multi-Agent Reinforcement Learning (ESD-MARL) architecture to enhance the scalability of AUV cluster networks. Moreover, we propose an Incremental Multi-Agent Reinforcement Learning algorithm based on Minimal Reward Participation (IMARL-MRP) that allows for the expansion of the agents without retraining. By integrating the ESD-MARL with the IMARL-MRP, we propose an elastic underwater target tracking scheme, achieving high-performance target tracking with enhanced scalability. Evaluation results demonstrate that the proposed approach effectively enhances the scalability of MARL, enabling the arbitrary expansion of the AUV cluster network, thus supporting scalable and efficient underwater target tracking.

With the rapid development of the industrial internet, the value of internal data is increasing. Federated learning, which can protect data privacy, is crucial in this context. However, it faces challenges such as device heterogeneity, data heterogeneity, and single point of failure in industrial internet scenarios. To address these, we propose the Blockchain-integrated Decentralized Asynchronous Federated Learning (BDAFL) algorithm. It leverages blockchain and the Raft consensus algorithm to decouple global model updates from a central server, using multiple servers to aggregate partial model parameters and mitigate single-point failure impacts. For device heterogeneity, BDAFL introduces a weighted mechanism based on dynamic waiting times and update frequencies. To handle data heterogeneity, it uses the Earth Mover’s Distance (EMD) to measure data distribution differences and adjusts local model parameter weights accordingly. Experimental results show that BDAFL improves model accuracy by 1.27% on MNIST, 0.99% on CIFAR-10 and 0.63% on a self-bulid bearing fault dataset compared to similar algorithms, and outperforms them in precision, recall, and F1 scores across all classification categories.

With advances in the edge computing (EC) and federated learning (FL) technologies in jointcloud, the edge FL service market has emerged recently and it requires trading edge resources between model requesters and data owners to complete FL tasks, which needs to incentivize sufficient data owners to participate in model training tasks. However, the limitations of resource trading and incentive design for edge FL service market have not been well addressed. In this paper, we propose a two-level blockchain-aided resource trading mechanism for encouraging appropriate edge servers to compete for dynamic FL tasks from the market while incentivizing data owners to participate in the FL tasks. At the upper level, we apply the deep learning-based reverse auction to model the dynamics of the task server selection process, with the aim of maximizing the total social welfare of the edge FL service market, where the edge server, as a seller, considers not only the data contribution of edge devices but also the cost of using blockchain when bidding. At the lower level, the edge servers offer rewards in exchange for the data owners’ participation, while the parameter aggregation is completed through the blockchain in a decentralized manner, which improves the FL’s robustness. Then, we utilize the Stackelberg game to model the dynamic process that the data owners compete for the servers’ revenue. We conduct extensive simulation experiments and the experimental results show that the proposed mechanism is able to get maximized social welfare and provide effective insights and strategies for the resource trading in the edge FL market to complete the federated training.

The archipelagic environment is an important feature of island distribution. The archipelago environment, an important feature of island distribution, allows for more wireless communication nodes to be deployed, which helps achieve large-scale network coverage at sea. This provides communication convenience for real-time and 3-dimensional oceanographic observations. On the other hand, air-sea-ground networking should overcome the complex communication conditions brought by the archipelago and shallow water. In this article, we propose an air-sea-ground integrated observation system based on ad hoc network. It combines underwater acoustic communications and LoRa communications to construct an air-sea-ground transmission system. The core equipment of this system includes the scientific instrument interface module (SIIM), LoRa relay node, and underwater acoustic communication (UAC) modem. The UAC Modem and LoRa used as communication modules are connected to various ocean observation sensors through the SIIM. Opportunistic routing and transmission control are implemented by embedded chip programming. The system has been tested on Zhairuoshan Island, Zhoushan Archipelagoes, China, and the transmission success rate exceeds $90 \%$. The preliminary verification proves the effectiveness of the proposed system, which is of great significance to marine scientific research, environmental protection, and marine economic development.

IoT-based wireless sensor networks (WSNs) face significant energy constraints, which can be alleviated by wireless power transfer (WPT) technology. Integrating WPT with WSNs creates wireless rechargeable sensor networks (WRSNs), where optimizing charging efficiency and scheduling is critical. This paper introduces an ISAC-facilitated optimal on-demand mobile charging scheme for IoT-based WRSNs (IOMSN) with three key components. First, it presents an ISAC-assisted prioritized charging queue, incorporating four attributes with probability distributions: residual energy, traffic load, MCV travel time, and direction angle. Second, it provides ISAC-driven estimations of MCV distance, speed, and location to enhance prioritization, thereby optimizing the charging route and potentially reducing travel costs. Third, a time-allocated partial charging model improves charging efficiency. Numerical results show that the proposed protocol outperforms cutting-edge protocols in energy usage efficiency, travel distance, charging delay, and service time.

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.

As a result of the development of the Internet of Underwater Things (IoUT), underwater connected devices generate a large volume of data with varying values and time sensitivity. Previous data collection strategies cannot accommodate the varying time requirements of various data types. To address the aforementioned issues, this article proposes a cooperative data collection method (MADC-DV) for multiple autonomous underwater vehicles (AUVs) based on local global deep$Q$learning (LG-DQN) and data value, which divides data into emergency and nonemergency and achieves hybrid data collection. First, the MAC protocol for communication between AUVs and clusters is designed to divide nonemergency data into high-value data and low-value data, with low-value data not needing to reply to ACK acknowledgment packets, thereby reducing the nonemergency data collection delay. Second, nonemergency data are collected cooperatively using multiple AUVs, and the LG-DQN approach is used to plan the paths for multiple AUV data collection in order to reduce the overall energy consumption of underwater wireless sensor networks (UWSNs). Finally, emergency data are collected using a multihop routing approach to assist in the collection. A routing method is proposed to compensate for the inability of AUVs to be applied to emergency data collection. The experimental results indicate that the method can improve the network life cycle by 18.7%, reduce the delay in the collection of nonemergency data by 40%, and reduce the delay in the collection of emergency data by 26.3%, thereby meeting the varying time requirements for different types of data.

Empowered by the large amounts of sensor data in the Industrial Internet of Things, data-driven fault diagnosis has a pivotal role in improving equipment reliability in harsh industrial environments. To enhance diagnostic performance under unknown operating conditions, transfer learning-based cross-domain fault diagnosis has been emerging. However, diagnostic models are prone to overfit to the source domain due to the lack of sample diversity when only a single-source domain is available. Moreover, significant domain shifts between the single-source domain and multiple unknown target domains may degrade the generalization performance on the unknown domains. To address these challenges, we propose a multipseudo domains augmented adversarial domain-invariant learning (MDA-AD) for cross-domain fault diagnosis. First, we design a multipseudo domain generator, where interdomain diversity constraints and manifold-semantic consistency constraints are implemented to avoid overfitting on the source domain by generating diverse and representative pseudo samples. Subsequently, to alleviate the domain shift, we design an adversarial domain-aware classifier that extracts domain-invariant features by introducing an adversarial paradigm between a feature extractor and a domain discriminator. Finally, to further enhance the diversity of the pseudo domains, we implement a diversity-consistency constrained domain-invariant training strategy. The experimental results, obtained through comparative studies, hyperparameter influence analysis, and visualization on two bearing data sets, affirm the superior diagnostic performance of MDA-AD in a single-source domain.

Constructing underwater acoustic sensor networks (UASNs) for data collection has gradually become an effective ocean exploration and exploitation method. However, the interference of the underwater environment and the limited capacity of underwater communication equipment increase the difficulty of information interaction, posing a challenge to secure data transmission strategies for UASNs. Therefore, this study proposes a safe and reliable data transmission scheme based on reinforcement learning-aided two-stage trust evaluation (RLTST) to overcome the problems mentioned above. This article proposes a distinct self-trust concept, different from traditional trust mechanisms. A node self-trust evaluation method based on Q-learning is designed in the first stage, which defects compromised nodes actively. In the second stage, the trustworthiness of data is calculated based on the real data received, followed by backtracking the transmission path of untrustworthy data to identify malicious nodes. Finally, the results show that our proposed scheme is more effective in malicious node detection and improves data collection reliability.

In the development of Internet of Underwater Things (IoUT), the unknown nature of the underwater environment is a challenging issue. In various domains related to IoUT, utilizing autonomous underwater vehicles (AUVs) for unmanned and autonomous missions has become an inevitable trend. Considering the particularity of underwater environments, this study proposes a hybrid-algorithm-based full coverage search approach to searching moving targets in unknown underwater environments. This approach combines the improved Voronoi clustering strategy, the improved artificial bee colony (ABC) algorithm, the improved line-of-sight (LOS) technique, and the artificial potential field (APF) method to enhance the efficiency of underwater full coverage search (FCS). First, the improved Voronoi clustering strategy is employed to partition the entire underwater region and allocate each part to an AUV. Second, to enhance the search capability of AUVs, a full-dimensional ABC algorithm with adaptive factor is designed to plan global paths for AUVs to search for targets, and the paths are further smoothed using the improved acrlong SLOS technique. During the navigation of the AUVs along the global paths, obstacles may be detected; thus, the APF method is utilized to dynamically plan local paths for AUVs to avoid obstacles. Experimental results demonstrate that the proposed approach significantly improves the efficiency of underwater FCS.

Based on the review of existing source location privacy protection technologies and research on underwater data transmission, numerous scholars have performed extensive work in the field of source location privacy protection. Further, current methods for protecting the source node location privacy in Internet of Underwater Things (IoUT), especially in underwater acoustic sensor networks (UASNs), suffer from several issues, including high data transmission energy consumption, short network lifespan, and inability to ensure data accuracy. Moreover, current common security research on UASNs considers only passive attacks and has fewer countermeasures for active attacks. To address these challenges, this article proposes a source location privacy protection algorithm based on polyhedral phantom routing in UASNs (PPR-USLP). First, the polyhedral phantom routing algorithm based on platonic solids is employed to introduce phantom nodes, which increases path diversity and protects the privacy of the source node, thereby thwarting passive attacks from adversaries and prolonging the life cycle of the network. Second, suitable relay nodes are selected by considering the peripheral status of the nodes, the data are collected by autonomous underwater vehicles (AUVs) to reduce the waiting time of sensor nodes and reduce the energy consumption of data transmission. Furthermore, to ensure data integrity and defend against active attacks from adversaries, this article combines error correction coding, which enhances network resilience and security while improving throughput and achieving load balancing. The simulation results demonstrate that PPR-USLP exhibits favorable performance in terms of energy consumption, safety time, and data accuracy, effectively safeguarding the privacy of the underwater source locations.

Data collection and transmission is the foundation for Internet of Underwater Things (IoUT) applications. Currently, quite a few autonomous underwater vehicle (AUV)-assisted data collection technologies have been proposed. Most of them concentrate on AUV path planning or multipath routing for data transmission, although MAC protocol design is crucial for reliable and secure data transmission in IoUT; hence, a MAC protocol based on parity group-graph coloring (PGGC-MAC) is investigated for underwater AUV-aided data collection. First, the AUV broadcasts path packets before data collection, informing sensor nodes of the path to travel in advance. Then, sensor nodes perform location update before AUV arrives, and collect the location information of neighbor nodes. Based on the position and the path information, the dynamic network topology is analyzed and the interference graph is obtained, which is used to assign working time slots for sensor nodes to transmit packets to the AUV. Finally, simulation results demonstrate that PGGC-MAC outperforms other related techniques in terms of network throughput, packet delivery ratio, energy usage, etc.

In underwater acoustic sensor networks (UASNs), the underwater sensors perform underwater data collection tasks, such as data collection and transmission at different locations in the monitoring area. To support cooperative underwater missions among the underwater sensor nodes, such as cooperative data delivery, one of the challenges is how to design smart underwater routing protocols that can guarantee safe, reliable, and energy-efficient data transfer among the underwater sensors. In this article, we introduce the paradigm of software-defined networking (SDN) and propose an SDN-based network framework for UASNs. Based on the proposed network framework, a$Q$-learning-assisted trust routing scheme for SDN-based UASNs (SDN-QLTR) is proposed. The proposed SDN-QLTR aims to seek for a secure routing path for executing underwater data transmission. Note that, in SDN-QLTR, effective trust evaluation methods are designed to resist malicious attacks initiated by nodes in UASNs. And SDN-QLTR integrates the advantages of SDN and reinforcement learning algorithm, can be flexibly applied in UASNs with dynamic features. Simulation results show that SDN-QLTR performs better in network lifetime, latency, and reliability.

Anomaly detection in multivariate time series (MTS) has been applied to various areas. Recent studies for detecting anomalies in high-dimensional data have yielded promising results. However, these methods are incapable of explicitly dealing with the complex contextual information that exists between features. In this paper, we present a novel unsupervised anomaly detection framework for MTS. We model the complex relationships of MTS using graph attention networks from the perspectives of time and features, respectively. Furthermore, our framework employs masked autoregressive flow for density estimation, which is then treated as an anomaly score, to identify anomalies. Extensive experiments show that our model outperforms baseline approaches in terms of accuracy on three publicly available datasets and accurately captures temporal and inter-feature relationships.

With the increase in the status of the ocean, the development of ocean resources is a priority, as is ocean security. Ocean security contains several aspects, such as equipment security, data security, military security, and so on. Location privacy protection of ocean equipment during data collection missions in unknown environments is investigated and a multiple autonomous underwater vehicles (AUVs) collaboration-assisted location privacy-preserving scheme is proposed in this thesis. In an unknown environment, several AUVs gather together to form a swarm to perform regional detection and defense missions. The swarm may face potential active and passive attacks, and the leader AUV is in the priority of protection. In this case, the follower AUVs deploy nodes in an unknown environment to make a rough sense. Then, the nodes build a Voronoi diagram based on their own perceptions to form a graph. Based on this graph, the AUV divides the nodes into regions by density-based spatial clustering of applications with noise (DBSCAN) and uses rapidly-exploring random trees (RRT) to plan the path in an unknown environment. Finally, the fake data along with the randomness of the moving follower AUV contribute to the location privacy-preserving during the regional detection and defense mission. Compared with the previous two schemes, the simulation results of the proposed method show some advantages in certain metrics.

The automatic modulation classification for surface and underwater sensors in the perception layer is crucial in the Internet of Underwater Things (IoUT), where Deep Learning (DL) is becoming an important tool to improve classification accuracy. This work focuses on the radio environment in the perception layer. The biggest challenge in popular DL-based methods is deploying the algorithm in edge devices with limited computing power. Network pruning has been found to be a critical and effective method for network lightweight and the improvement of resources, thus mitigating potential interference. While not studied in previous work, this paper fills the hole in algorithm deployment’s criterion selection and accuracy loss. Specifically, we develop a Convolutional Neural Network (CNN) based lightweight framework on distinguishing modulated signals from generated datasets (which is named DLocean) in different Signal-to-Noise Ratios (SNR). The performance of the lightweight framework is tested on the edge device. The experiments demonstrate that the proposed model compensates for accuracy and can successfully classify the modulation schemes with 93.4% accuracy at the SNR=5 dB. Our results also show that the proposed framework can improve performance without exceeding the original network complexity on edge device deployment.

Formation path planning of autonomous underwater vehicles (AUVs) entails establishing optimal collision-free routes over challenging underwater terrain while maintaining state coherence to preserve an intended formation, and path planning techniques have been the subject of significant study over the last decade, with swarm intelligence algorithms such as the sparrow search algorithm (SSA) being among the most commonly employed. However, the algorithms typically are constrained by the imbalanced adjustment between local development and global exploration, which reduces the optimization capability, and they are relatively understudied for the formation movement issues. Accordingly, this paper proposes a consensus-SSA based formation path planning (CSFPP) method, which applies an improved SSA for planning an optimal path, and then incorporates the path into a consensus algorithm that introduces an artificial potential field (APF) to enable collaborative formation movement. In the path planning phrase, the CSFPP employs an improved SSA which applies the golden search optimization (GSO) and an adaptive iteration approach to adjust the local development and global exploration in order to improve the overall optimization performance. Then in the formation control phrase, the CSFPP introduces a virtual point scheme for APF-based obstacle avoidance in order to navigate an AUV formation in an obstacle environment while maintaining the formation shape controlled by a consensus algorithm. The superiority of the proposed path planning capability is demonstrated by comparing the convergence performance of the improved SSA with the recent contributions; and simulations of formation movement in underwater space verify the feasibility of the proposed formation control method in the obstacle environment.

Edge computing has become increasingly important to fulfill the diversified Quality-of-Service (QoS) or Quality-of-Experience (QoE) demands for Industrial Internet of Things (IIoT) applications, such as machine condition monitoring, fault diagnosis, intelligent production scheduling, and production quality control. Due to the heterogeneity of IIoT systems, it is of urgent necessity to concentrate on the cloud–edge–end cooperative partial task offloading and resource allocation (CPTORA) problem for realizing workload balancing, efficient resource utilization, and better QoS/QoE of IIoT applications. However, the challenge lies in how to make real-time, accurate, decentralized task offloading (TO) and resource allocation (RA) decisions for dynamic and device-intensive IIoT. Therefore, this work examines the CPTORA problem for IIoT, aiming at minimizing its long-run overall delay and energy costs. To lower the problem complexity, this problem is decomposed into the TO subproblem and the RA subproblem. Then, an improved soft actor–critic-based decentralized multiagent deep reinforcement learning (MADRL) algorithm is proposed to address the TO subproblem, where each IIoT device can learn its globally optimal policy and make its decisions independently. This algorithm innovatively combines the divergence regularization, the distributional reinforcement learning, and the value function decomposition methods to improve convergence speed and accuracy of the existing MADRL methods. After receiving the TO decisions of every IIoT device, every edge server employs the Lagrange multiplier method and Karush–Kuhn–Tucker condition to solve its RA subproblem. The experimental results show that the proposed algorithm decreases the overall delay and energy costs more effectively, compared to the other state-of-the-art MADRL approaches.

Intelligent control of autonomous marine vehicles (AMVs) is one of the essential technologies for exploring marine resources. In the deep sea with a complicated exploration environment, collaboration between heterogeneous AMVs can maximize exploration efficiency by utilizing various functional benefits. Accordingly, this article proposes a method for collaborative path planning for heterogeneous AMVs that employs a fused metaheuristic algorithm for the underwater path planning of an autonomous underwater glider (AUG), and an adaptive surface path planning of an autonomous surface vehicle (ASV), respectively. The fused metaheuristic method balances global and local path explorations for underwater path planning by integrating the gray wolf optimizer (GWO) and equilibrium optimizer (EO), and it reduces the local optimum problem by using a conditional convergence factor; and the adaptive surface path planning approach considers the influence of ocean currents at various locations to guide the ASV collaboratively to track the AUG underwater in the horizontal plane. The fused metaheuristic algorithm has demonstrated superior convergence performance in simulations, which indicates that the proposed method has advantage in terms of underwater path planning for complex marine exploration.

Although semantic segmentation methods have made remarkable progress so far, their long inference process limits their use in practical applications. Recently, some two-branch and three-branch real-time segmentation networks have been proposed to improve segmentation accuracy by adding branches to extract spatial or border information. For the design of extracting spatial information branches, preserving high-resolution features or adding segmentation loss to guide spatial branches are commonly used methods to extract spatial information. However, these approaches are not the most efficient. To solve the problem, we design the spatial information extraction branch as an AutoEncoder structure, which allows us to extract the spatial structure and features of the image during the encoding and decoding process of the AutoEncoder. Border, semantic and spatial information are all helpful for segmentation tasks, and efficiently fusing these three kinds of information can obtain better feature representation compared to the fusion of two types of information in the dual-branch network. However, existing three-branch networks have yet to explore this aspect deeply. Therefore, this paper designs a new three-branch network based on this starting point. In addition, we also propose a feature fusion module called the Unified Multi-Feature Fusion module (UMF), which can fuse multiple features efficiently. Our method achieves a state-of-the-art trade-off between inference speed and accuracy on the Cityscapes, CamVid, and NightCity datasets. Specifically, BSSNet-T achieves 78.8% mIoU at 115.8 FPS on the Cityscapes dataset, 79.5% mIoU at 170.8 FPS on the CamVid dataset, and 52.6% mIoU at 172.3 FPS on the NightCity dataset. Code is available at https://github.com/SXQ-STUDY/BSSNet.

Data-driven approaches have gained great success in the field of rotating machinery fault diagnosis for its powerful feature representation capability. However, in most of the current studies, model training process requires massive fault data which are costly to gather or even unavailable in some extreme operating conditions. At the same time, structural relationships between samples are not fully exploited to facilitate the model performance. In response to these problems, a novel GHOAN for rotating machinery fault diagnosis is proposed in this study. Specifically, the proposed approach incorporates the advantages of improved graph attention network model and the multiorder neighborhood feature perception to achieve richer feature representation by aggregating features from multiple neighborhood domains. In this way, effective fault diagnosis may be achieved by using fewer training samples based on vibration signal analysis. The results of experiments conducted on two benchmarking datasets and a practical experimental platform show that the proposed GHOAN achieve superior performance.

In industrial Internet of Things, the combination of specific emitter identification (SEI) and key authenticated technologies can effectively resist spoofing attacks and improve system security. However, most existing SEI approaches extract features based on real valued operations and only work in static scenario. This motivated us to develop a novel SEI method tasked with: exploiting the high potential model for SEI based on the inphase/quadrature (I/Q) signal that is represented by complex number, and realizing rapid reconstruction of the model in the face of dynamic scenarios. To this end, in this article, we introduce a two-stage cross-domain identification model. First, a complex-valued separate residual network (CVSRN) with novel separate residual modules is proposed as the pretrained model. The CVSRN can automatically extract effective inherent features directly from raw signals in an end-to-end manner, which favors complex-valued signals that are found in two distinct signal paths. Second, three transfer strategies are proposed to achieve rapid construction of the target SEI model. They leverage the knowledge learned from the pretrained CVSRN to facilitate the recognition of a new but similar emitters. We benchmark our proposed approach against four state-of-the-art SEI methods on real-world data and exhibit that it is not only competitive but also able to cope with complex dynamic scenarios.

Automatically detecting human mental workload to prevent mental diseases is highly important. With the development of information technology, remote detection of mental workload is expected. The development of artificial intelligence and Internet of Things technology will also enable the identification of mental workload remotely based on human physiological signals. In this article, a method based on the spatial and time-frequency domains of electroencephalography (EEG) signals is proposed to improve the classification accuracy of mental workload. Moreover, a hybrid deep learning model is presented. First, the spatial domain features of different brain regions are proposed. Simultaneously, EEG time-frequency domain information is obtained based on wavelet transform. The spatial and time-frequency domain features are input into two types of deep learning models for mental workload classification. To validate the performance of the proposed method, the Simultaneous Task EEG Workload public database is used. Compared with the existing methods, the proposed approach shows higher classification accuracy. It provides a novel means of assessing mental workload.

Transportation big data (TBD) are increasingly combined with artificial intelligence to mine novel patterns and information due to the powerful representational capabilities of deep neural networks (DNNs), especially for anti-COVID19 applications. The distributed cloud-edge-vehicle training architecture has been applied to accelerate DNNs training while ensuring low latency and high privacy for TBD processing. However, multiple intelligent devices (e.g., intelligent vehicles, edge computing chips at base stations) and different networks in intelligent transportation systems lead to computing power and communication heterogeneity among distributed nodes. Existing parallel training mechanisms perform poorly on heterogeneous cloud-edge-vehicle clusters. The synchronous parallel mechanism may force fast workers to wait for the slowest worker for synchronization, thus wasting their computing power. The asynchronous mechanism has communication bottlenecks and can exacerbate the straggler problem, causing increased training iterations and even incorrect convergence. In this paper, we introduce a distributed training framework, Heter-Train. First, a communication-efficient semi-asynchronous parallel mechanism (SAP-SGD) is proposed, which can take full advantage of acceleration effect of asynchronous strategy on heterogeneous training and constrain the straggler problem by using global interval synchronization. Second, Considering the difference in node bandwidth, we design a solution for heterogeneous communication. Moreover, a novel weighted aggregation strategy is proposed to aggregate the model parameters with different versions. Finally, experimental results show that our proposed strategy can achieve up to$6.74 \times $speedups on training time, with almost no accuracy decrease.

In the process of data acquisition of underwater acoustic sensor networks (UASNs), the safety of the network is threatened by the disclosure of source node location information. So how to protect the security and privacy of source node location is the main challenge faced by UASN security. To realize this taeget, a hierarchical structure-based algorithm for protecting source location privacy (HSSLP) is proposed in this paper. Firstly, it is proposed to divide UASNs into dynamic and static layers based on Ekman drift model. Location privacy protection schemes suitable for source nodes located in different layers have been proposed separately. In the static layer, k-means clustering separates the nodes into groups, and the source node’s location privacy is protected using fake source node and phantom nodes, while auxiliary cluster head and sleep scheduling mechanism are used to save node energy. Nodes in the dynamic layer, whose positions are prone to change, are no longer clustered. The source node makes use of inducing nodes to take adversaries away from the source node, enhancing the privacy and security of the source node with minimal energy expenditure. Finally, autonomous underwater vehicles (AUV) need to support the cluster head in collecting data combined in the static layer and data uploaded in the dynamic layer. Based on the communication range of AUV, the network is segmented into areas, and when the AUV receives warning messages while traveling, it changes its route to lead the adversary to an area remote from the source node. Simulation results show that the proposed algorithm owns the capacity to balance the relationship between network security, transmission delay, and node energy consumption. To be more specific, the HSSLP algorithm improves the safety time by about 50$\%$, reduces the delay by about 20$\%$and saves the node energy by about 36$\%$as compared to the DIS-PLP algorithm.

Unauthorized underwater vehicles (UUVs) pose a serious threat to maritime security. To preserve maritime security, it is essential to pursue these UUVs. The majority of traditional pursuit methods are based on known environmental dynamics. However, the underwater environment is too complicated and unpredictable to describe these dynamics accurately. This study developed a novel online decision-making technique called multi-agent distributional soft actor-critic (MADA) to handle the issue of underwater cooperative pursuit. The method constructs a control-oriented framework based on multi-agent reinforcement learning that can map autonomous underwater vehicle (AUV) observations to pursuit actions. Multiple AUVs can combine to make prompt pursuit decisions. Then, the proposed method combines distributional soft actor-critic and curriculum learning to improve the success rates of multiple AUVs in pursuing UUVs. Experimental results show that the MADA can obtain a better cooperative pursuit strategy.

With the further development of Unmanned Aerial Vehicle (UAV) technologies, research on multi-UAV formations have also received more attention. Unmanned Aerial Vehicles (UAVs) cooperate with each other to form a formation group, which can give full play to the advantages that a single UAV does not have, and more capable of working in multi-task scenarios. Based on the Sierpinski fractal structure, this paper proposes a hybrid formation control architecture. To address the aggregation problem of UAV swarms, a multi-agent deep reinforcement learning (MADRL) method is used for aggregation control. To improve the efficiency, accuracy, effectiveness and scalability of MADRL in environments with a large number of UAVs, a multi-intelligent deep deterministic policy gradient method based on attention mechanism and adaptive accuracy (3A-MADDPG) is proposed. The method enables each agent to selectively focus on the information of other agents, with adaptive learning rate to dynamically learn its own critic network. The algorithm has a large learning rate in the early stage and converges quickly. The later stage of the algorithm has small learning rate and high accuracy, and combined with the fixed variable hybrid reward function designed in the paper, so the UAV can fly to the center of the expected region more accurately, making the whole cluster more accurate. Experiments show that the 3A algorithm proposed in the paper converges faster and achieves higher accuracy than the benchmark algorithm, whether it is the cluster aggregation formation or the separation and aggregation of sub-formations.

Mobile Underwater Acoustic Networks (UANs) leverage Autonomous Underwater Vehicles (AUVs) to enhance flexibility and mobility, playing an essential role in ocean research. Similar to static UANs, the Medium Access Control (MAC) protocol is still critical for mobile UANs to achieve efficient communication. However, mobile UANs are delaysensitive and suffer from low Signal-to-Noise Ratio (SNR), which presents significant challenges for the design of MAC protocols. As a hybrid technology combining spread spectrum and multicarrier modulation, Multi-Carrier Code Division Multiple Access (MC-CDMA) offers simple multi-path channel equalization and flexible multi-user access, aiding the MAC protocol in achieving robust communication in mobile UANs. Along this line, we propose an MC-CDMA-based MAC (MC-MAC) protocol, which considers both characteristics of mobile UANs and MC-CDMA to achieve efficient concurrent communication. Specifically, to adequately utilize the limited underwater communication resources, we design an adaptive node clustering algorithm, classifying nodes based on propagation distance, relative mobile velocity, data size, and data grade. Meanwhile, the algorithm determines non-random initial center nodes and adaptively decides the optimal number of clusters to decrease the computational complexity. Based on the clustering results, we present a manyobjective optimization algorithm, which jointly allocates specific spreading code length, spreading code number, subcarrier range, and transmission power to optimize throughput, delay, and energy consumption in mobile UANs. Extensive simulation results demonstrate that MC-MAC fully leverages the advantages of MC-CDMA, providing efficient concurrent communication with lower energy consumption for mobile UANs compared to stateof-the-art protocols.

Path planning is a basic requirement for Autonomous Underwater Vehicles (AUVs) to accomplish underwater missions. However, previous studies often have limitations, such as ignoring the basic condition that the AUV operates in an ocean current environment and discretizing its actions without considering the action space, which results in the simulation being far from the actual situation. To solve the above problems, this paper proposes a method of using a Parametrized Deep Q-Network (PDQN) to output hybrid actions for path planning, which can output a hybrid action space based on the current local observation, flexibly avoid obstacles under limited sensor observations, and realize the refined operation of AUV actions. According to the setup of the simulation environment, the AUV needs to visit multiple target nodes underwater and decelerate within the communication range of the nodes to have enough time to communicate with the nodes. The PDQN enables the AUV to easily learn the connection between the current state and discrete actions. It outputs the corresponding continuous actions based on the current discrete actions, which realizes a time-saving strategy of accelerating and then decelerating among the nodes. Meanwhile, we also utilize the actual current data and terrain data to restore the simulation environment as accurately as possible, and the simulation results prove the superiority and robustness of the algorithm.

Underwater acoustic sensor networks (UASNs) have been widely deployed in many areas, such as marine ranching, naval applications, and marine disaster warning systems. The security of UASNs, particularly insider threats, is of growing concern. Internal attacks carried out via compromised normal nodes are more damaging and stealthy than external attacks, such as signal stealing, data decryption, and identity forgery. As a security mechanism for internal threat detection based on interaction data, trust models have proven to enhance the security of UASNs. However, traditional trust models lack sufficient scalability when faced with movable underwater devices, heterogeneous network environments, and variable attack patterns. Therefore, in this paper, a novel trust model based on federated deep reinforcement learning is proposed for UASNs. First, the evidence acquisition mechanism, including communication, energy, and data evidence, is improved based on existing ones to better accommodate the topological dynamics of UASNs. Second, acquired trust evidence is fed into the corresponding deep reinforcement learning-based local trust model to accomplish trust prediction and model training. Finally, a federated learning-based update method periodically aggregates and updates the parameters of the local models. The experimental results prove that the proposed scheme exhibits satisfactory performance in terms of improving trust prediction accuracy and energy efficiency.

The internet of things (IoT) and wireless sensor networks (WSNs) face an energy shortage challenge that could be overcome by the novel wireless power transfer (WPT) technology. The combination of WSNs and WPT is known as wireless rechargeable sensor networks (WRSNs), with the charging efficiency and charging scheduling being the primary concerns. Therefore, this paper proposes a probabilistic on-demand charging scheduling for integrated sensing and communication (ISAC)-assisted WRSNs with multiple mobile charging vehicles (MCVs) that addresses three parts. First, it considers the four attributes with their probability distributions to balance the charging load on each MCV. The attributes are residual energy of charging node, distance from MCV to charging node, degree of charging node, and charging node betweenness centrality. Second, it considers the efficient charging factor strategy to partially charge network nodes. Finally, it employs the ISAC concept to efficiently utilize the wireless resources to reduce the traveling cost of each MCV and to avoid the charging conflicts between them. The simulation results show that the proposed protocol outperforms cutting-edge protocols in terms of energy usage efficiency, charging delay, charging coverage, survival rate, travel distance, queue length, and service time.

Unmanned Aerial Vehicle (UAV)-enabled mobile edge computing has been proposed as an efficient task-offloading solution for user equipments (UEs). Nevertheless, the presence of heterogeneous UAVs makes centralized navigation policies impractical. Decentralized navigation policies also face significant challenges in knowledge sharing among heterogeneous UAVs. To address this, we present the soft hierarchical deep reinforcement learning network (SHDRLN) and dual-end federated reinforcement learning (DFRL) as a decentralized navigation policy solution. It enhances overall task-offloading energy efficiency for UAVs while facilitating knowledge sharing. Specifically, SHDRLN, a hierarchical DRL network based on maximum entropy learning, reduces policy differences among UAVs by abstracting atomic actions into generic skills. Simultaneously, it maximizes the average efficiency of all UAVs, optimizing coverage for UEs and minimizing task-offloading waiting time. DFRL, a federated learning (FL) algorithm, aggregates policy knowledge at the cloud server and filters it at the UAV end, enabling adaptive learning of navigation policy knowledge suitable for the UAV's performance parameters. Extensive simulations demonstrate that the proposed solution not only outperforms other baseline algorithms in overall energy efficiency but also achieves more stable navigation policy learning under different levels of heterogeneity of different UAV performance parameters.

With the rapid development of underwater robots, underwater communication techniques, etc., the Autonomous Underwater Vehicle (AUV) cluster network has emerged as a candidate paradigm to perform underwater civil and military applications, e.g., underwater target tracking. In this paper, we focus on how to utilize networking and multi-agent artificial intelligence technique to improve underwater target tracking. In particular, to improve the flexibility and scalability of the AUV cluster network, we employ Software-Defined Networking (SDN) and Centralized Training with Decentralized Execution (CTDE)-based Multi-Agent Reinforcement Learning (MARL) technologies, to propose a Hierarchical Software-Defined Multiple AUVs Reinforcement Learning (HSD-MARL) framework. For the MARL mechanism in HSD-MARL, we propose an advantage-attention mechanism and present the architecture of Multi-AUV Advantage-Attention Actor-Critic (MA-A3C), to address slow convergence and poor scalability issues on the AUV cluster network of large-scale. Further, to improve the utilization rate of advantage samples especially when the MA-A3C is utilized to perform AUV cluster network-based underwater tracking, we propose an ‘advantage resampling’ method based on experience replay buffer. Evaluation results showcase that our proposed approaches can perform exact underwater target tracking based on AUV cluster network systems and outperform some recent research products in terms of convergence speed, tracking accuracy, etc.

Performing a high-capacity Medium Access Control (MAC) protocol suffers from low bandwidth and long propagation delay in Underwater Acoustic Networks (UANs). Non-Orthogonal Multiple Access (NOMA) is a promising technology to assist MAC protocols in overcoming the above restrictions and improving UANs’ capacity. It enables multiple users to access the same frequency-time resource based on power or code differences of user classes. However, UANs lack MAC research employing NOMA’s physical advantages. Most existing NOMA-based MAC protocols are designed for terrestrial networks, which are inapplicable to UANs. In classifying users, they ignore the effects of harsh marine environments on acoustic channels and fail to decrease channels’ interference, resulting in conflicting communications. Moreover, such unreasonable classification results further affect resource allocation, leading to low transmission rate and high energy consumption in UANs. In this paper, we propose a Hybrid NOMA-based MAC protocol (HN-MAC) to achieve efficient concurrent communication for UANs. Specifically, HN-MAC combines power-domain and code-domain NOMA to classify users and allocate communication resources. For the user classification, we propose an Adaptive Clustering Algorithm (ACA), which dynamically determines the clusters’ number and classifies users based on channel gain and channel correlation under multipath conditions. In this way, HN-MAC decreases interference among multiple users in various ocean scenarios. During the resource allocation, we formulate a joint allocation problem of transmission power and codebook based on the clustering result to optimize transmission rate and energy consumption. Further, a genetic algorithm is proposed to solve the allocation problem by considering resource constraints. Simulation results show that HN-MAC provides more stable concurrent communications with less resource consumption than the state-of-the-art protocols in various UANs.

One of the key techniques for future wireless network is full-duplex-enabled millimeter wave integrated access and backhaul network underlaying device-to-device communication, which is a 3GPP-inspired comprehensive paradigm for higher spectral efficiency and lower latency. However, the multi-user interference (MUI) and residual self-interference (RSI) become the major bottleneck before the commercial application of the system. To this end, we investigate the sub-channel allocation problem for this networking paradigm. To maximize the overall achievable rate under the considerations of MUI and RSI, the sub-channel allocation problem is firstly formulated as an integer nonlinear programming problem, which is intractable to search an optimal solution in polynomial time. Secondly, a coalition formation based sub-channel allocation (CFSA) algorithm is proposed, where the final partition of the sub-channel coalition is iteratively formed by the concurrent link players according to the two defined switching criterions. Thirdly, the properties of the proposed CFSA algorithm are analyzed from the perspectives of Nash stability and uniform convergence. Fourthly, the proposed CFSA algorithm is compared with other reference algorithms through abundant simulations, and superiorities including effectiveness, convergence and sub-optimality of the proposed CFSA algorithm are demonstrated through the kernel indicators.

Remaining useful life (RUL) prediction of aero-engines is one of the important issues in research related to engine health management. Although deep learning has made great progress in fault diagnosis research, successful training of deep learning models is very time-consuming and difficult to meet the real-time requirements of online RUL prediction applications. Broad learning systems (BLS) provide an alternative to deep learning networks with low computational resource requirements, fast training time, and incremental scalability. Based on the typical BLS, we propose a new lightweight multiscale BLS (MSBLS). Considering that RUL is influenced by the working condition factor, the discrete wavelet transform is used to generate multiresolution components, and then feature nodes are extracted on top of the components. An elastic net regularization technique is used to constrain the output weights of the nodes, preserving the significant nodes, and finally obtaining a more sparse MSBLS. Experiments are conducted using the NASA publicly available commercial modular aero-propulsion system simulation (C-MAPSS) dataset and the N-CMAPSS dataset, and our proposed MSBLS not only improves the accuracy of RUL prediction but also has a very short training time compared with the latest research methods nowadays.

Integrated sensing and communication (ISAC) has become a promising technology for future communication system. In this paper, we consider a millimeter wave system over high mobility scenario, and propose a novel simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) aided ISAC scheme. To improve the communication service of the in-vehicle user and simultaneously track and sense the vehicle with the help of nearby roadside units (RSUs), a STAR-RIS is equipped on the outside surface of the vehicle to transmit and reflect the signal from the base station (BS). Firstly, an efficient transmission structure for the ISAC scheme is designed. Then, the time-frequency selective BS-RIS-RSUs channel model are characterized. Based on the estimated cascaded channel parameters (i.e., the delays, the Doppler frequency shifts, the angles of arrivals, and the angles of departure of the scattering paths) of the BS-RIS-RSUs links, the vehicle localization and its velocity can be acquired. With the help of sensing results, the reflection and refraction phase shifts of the STAR-RIS are designed for performance enhancememt. Moreover, the trade-off design for sensing and communication is proposed by optimizing the energy splitting factors of the STAR-RIS. Finally, simulation results are provided to validate the feasibility and effectiveness of our proposed STAR-RIS aided ISAC scheme.

Cell-free (CF) massive MIMO is considered one of the key technologies for 6G to achieve high spectral efficiency (SE) and ultralow latency. However, as the number of users increases, pilot contamination becomes more serious, and the optimal SE can not be achieved when the number of users exceeds the access points (APs). Therefore, we study the CF massive MIMO-NOMA system. Specifically, we design a user clustering algorithm based on the average Signal to Interference plus Noise Ratio (SINR), using orthogonal pilots between different clusters, and different users in the cluster using the same pilot, thereby reducing pilot contamination. Then we propose a flexible power allocation problem to maximize the system SE while taking into account user fairness. We model the problem as a Markov Decision Process (MDP) and then solve it using the asynchronous advantage actor-critic (A3C) algorithm in deep reinforcement learning. Simulation results show that the proposed A3C based power allocation scheme in CF massive MIMO-NOMA outperforms the baseline schemes in terms of fairness and SE.

The internet of things (IoT) based wireless sensor networks (WSNs) face an energy shortage challenge that could be overcome by the novel wireless power transfer (WPT) technology. The combination of WSNs and WPT is known as wireless rechargeable sensor networks (WRSNs), with the charging efficiency and charging scheduling being the primary concerns. Therefore, this paper proposes a probabilistic on-demand charging scheduling for integrated sensing and communication (ISAC)-assisted WRSNs with multiple mobile charging vehicles (MCVs) that addresses three parts. First, it considers the four attributes with their probability distributions to balance the charging load on each MCV. The distributions are residual energy of charging node, distance from MCV to charging node, degree of charging node, and charging node betweenness centrality. Second, it considers the efficient charging factor strategy to partially charge network nodes. Finally, it employs the ISAC concept to efficiently utilize the wireless resources to reduce the traveling cost of each MCV and to avoid the charging conflicts between them. The simulation results show that the proposed protocol outperforms cutting-edge protocols in terms of energy usage efficiency, charging delay, survival rate, and travel distance.

The orthogonal time frequency space (OTFS) technique is an innovative modulation scheme that provides significant advantages in terms of channel delay and Doppler shifts. In this work, we study the sparse delay and Doppler channel estimation problem for OTFS and consider the impact of inheriting initial and iterative parameters on adjacent estimated channel corresponding to previous OTFS transmitted blocks. We propose a parameter-inherited sparse Bayesian learning (SBL) channel estimation algorithm based on unitary approximate message passing (UAMP). Simulation results show that compared to the state-of-art SBL-based algorithms, the proposed algorithm has faster convergence speed and higher accuracy. Furthermore, by exploiting the block circulant matrix with circulant blocks (BCCB) matrix property, we replace the matrix multiplication with two-dimensional (2D) fast Fourier transform (FFT), which leads to a low complexity.

Accident severity prediction is a hot topic of research aimed at ensuring road safety as well as taking precautionary measures for anticipated future road crashes. In the past decades, both classical statistical methods and machine learning algorithms have been used to predict traffic crash severity. However, most of these models suffer from several drawbacks including low accuracy, and lack of interpretability for people. To address these issues, this paper proposed a hybrid of Balanced Bagging Classification (BBC) and Light Gradient Boosting Machine (LGBM) to improve the accuracy of crash severity prediction and eliminate the issues of bias and variance. To the best of the author’s knowledge, this is one of the pioneer studies which explores the application of BBC-LGBM to predict traffic crash severity. On the accident dataset of Great Britain (UK) from 2013 to 2019, the proposed model has demonstrated better performance when compared with other models such as Gaussian Naïve Bayes (GNB), Support vector machines (SVM), and Random Forest (RF). More specifically, the proposed model managed to achieve better performance among all metrics for the testing dataset (accuracy = 77.7%, precision = 75%, recall = 73%, F1-Score = 68%). Moreover, permutation importance is used to interpret the results and analyze the importance of each factor influencing crash severity. The accuracy-enhanced model is significant to several stakeholders including drivers for early alarm and government departments, insurance companies, and even hospitals for the services concerned about human lives and property damage in road crashes.

Due to the poisonousness, explosiveness, and diffuseness of some continuous objects (e.g., toxic gas, nuclear radiation, and industrial dust), continuous object tracking has a pivotal role in protecting the safety of the people, especially in hazardous industries. To improve production safety, the Industrial Internet of Things (IIoT) has become a promising technology for continuous object tracking. However, IIoT can hardly satisfy the requirements of both energy efficiency and tracking accuracy due to diffusion characteristics, redundant packets, unnecessary awakened nodes, etc. To address these challenges, we propose a two-stage continuous object predictive tracking scheme based on a state transition model (TCOT-STM). First, the predictive tracking process of TCOT-STM is partitioned into two stages to determine wake-up regions where the future continuous objects are located. Considering the high diffusion speed in the tracking process, stage I tracking is designed by communication range calibration and global wake-up region establishing. To eliminate the redundant boundary nodes in the tracking process, stage II tracking is designed by intercluster gap eliminating, virtual node generating, and local wake-up region establishing. Then, a state transition model (STM) based on finite state machines is designed to awaken nodes selectively. Finally, with the STM and the wake-up regions determined by two-stage tracking, the potential boundary nodes are proactively awakened for predictive tracking. Simulation results demonstrate that the proposed TCOT-STM can reduce energy consumption and communication cost while improving tracking accuracy.

Underwater acoustic sensor networks (UASNs) are data driven, and the data generation is infeasible without source nodes, sensors, and equipment deployed underwater. However, underwater acoustic communication between nodes is especially vulnerable to malicious attacks, which could cause the source data packets to be tracked and indirectly expose the locations of source nodes. Once the source node is located, the security of the network and the monitored object will be considered threatened. A collision-free transmission-based source location privacy protection algorithm in UASNs under time slot allocation (CFTSLP-TSA) is proposed in this article. First, we select suitable fake source nodes to generate fake data packets, aiming at concealing the traffic of the source data packets. Then, different transmission time slots are arranged for the source and the fake data packets, in order to avoid interference in the transmission between one another. In addition, a handshake-based relay node selection strategy is presented. This not only makes the paths more diverse but also requires a higher requirement for the attacker to track the flow of source packets while the source packets are transmitted without collisions. The performance of the simulation shows that the CFTSLP-TSA produces both a greater source location privacy protection level and a better data packet delivery rate compared with the other state-of-the-art schemes.

Owing to the characteristics of underwater communication, such as limited bandwidth, low transmission speed, and long delivery delay, it is significantly challenging to address trust management in the Internet of Underwater Things (IoUT). In the process of trust calculation, trust judgments between nodes may be conflicting based on the obtained diverse trust evidences. However, in existing studies, the analysis of trust-conflict adjudication is nonexhaustive and lacking in detail. Therefore, a controversy-adjudication method is proposed in this study to handle conflict recommendations, and a novel trust management mechanism is further investigated based on the controversy-adjudication method, including three phases: 1) trust calculation; 2) trust recommendation; and 3) trust evaluation. First, trust evidences, e.g., packet delivery ratio, end-to-end packet transmission latency, and residual energy, are collected to calculate trust for trustees. In addition, for the trustor without sufficient trust evidences, recommendations are required and an incentive mechanism is proposed based on the prisoner’s dilemma to encourage neighbors to participate in trust recommendation. Finally, trust values of trustees are obtained by executing trust evaluation based on the controversy-adjudication mechanism. Simulation results demonstrate that the proposed trust management mechanism outperforms existing related works in terms of accuracy and robustness against unreliable IoUT.

In the Internet of Underwater Things (IoUT), nodes are usually deployed randomly. Effective discovery of randomly deployed neighbor nodes is the basis for network topology self-configuration, data routing, transmission, etc. Especially in the IoUT with the directional transmission, how to efficiently discover neighbors is a major challenge to be solved at present. Hence, in this study, the neighbor discovery problem is investigated. The proposed algorithm consists of two parts: 1) a basic quorum system-based neighbor discovery (QSND) algorithm and 2) an adaptive reinforcement learning-based neighbor discovery (RLND) algorithm. First, a directional transceiver beam scanning sequence is designed adopting a C-torus quorum system to complete the initial neighbor discovery. Then, a reinforcement learning-based adaptive beam adjustment method is investigated to adjust the number of directional beams to be scanned based on neighbor recommendations and prior knowledge, thereby reducing the number of time slots expected to be required for neighbor discovery. Finally, simulation results demonstrate that QSND and RLND outperforms other related algorithms in terms of neighbor discovery rate, neighbor discovery delay, energy usage, etc.

The Internet of Underwater Things (IoUT) has attracted a lot of attention because of its promising applications in underwater environmental monitoring; however, the characteristics of acoustic communication, e.g., long propagation delay and high attenuation, pose great challenges for efficient and reliable underwater data transmission. Currently, opportunistic routing is regarded as a key technology to improve the packet delivery ratio, because it can dynamically choose several forwarding nodes and leverage their cooperative forwarding to increase network throughput. However, choosing an excessive number of forwarding nodes may result in energy waste and lengthy communication delays. Therefore, an opportunistic routing based on directional transmission (ORDT) is studied to improve packet delivery timeliness and reliability and lower energy consumption. ORDT mainly contains three phases: 1) forwarding area division; 2) candidate forwarder selection; and 3) candidate forwarder coordination. The forwarding region is first established based on directional transmission. Only neighbors located in the forwarding region can forward packets. Following that, candidate forwarders in the forwarding region are chosen depending on their forwarding capability. Additionally, the chosen candidate’s time of holding packets is specified so that they might collaborate to reduce redundant data transmission and transmit packets to gateway nodes. Then, an autonomous underwater vehicle (AUV) is employed to gather packets from gateway nodes. Simulation findings demonstrate that ORDT performs better than existing opportunistic routing in terms of packet delivery success rate, transmission latency, and energy usage.

As a core component of the Internet of Vehicles, reasoning about the trajectory of pedestrians or vehicles in complex road conditions plays a critical role in autonomous driving and socially aware robotic navigation. Most existing methods do not adequately consider the effects of heterogeneous traffic agents. Toward this end, we propose the traffic trajectory prediction algorithm based on the convolutional attention network (TraGCAN) to predict the trajectories of heterogeneous traffic agents in dense traffic. The algorithm of the proposed method examines the behavior of different traffic agents in terms of both time and space dimensions to identify their movement patterns and interactions. We construct the spatial relationship of traffic agents as a graph structure and introduce a graph convolutional network to extract spatial interactions. In addition, we design a spatial attention mechanism to adaptively calculate weights for all spatial interactions to capture different influences from neighboring agents. To improve the accuracy of trajectory prediction, the algorithm considers the influence of the heterogeneous characteristics of traffic agents on their motion behaviors. We evaluated the performance of the proposed TraGCAN on heterogeneous traffic data sets, and the results demonstrate that the error of TraGCAN is reduced by 15% compared to existing methods.

Accurately estimating the state of equipment plays an important role in ensuring the efficient operation of Industrial 4.0 systems. This article focuses on monitoring the operating state and detecting the faults of beam pumping units under the condition of heavy noise within the Industrial Internet of Things. On the one hand, the equipment operating state monitoring system designed in this article uses an acceleration sensor, the signal of which contains considerable noise that greatly reduces the motion state estimation accuracy. On the other hand, the complexity of the indicator diagrams of beam pumping units makes it difficult to extract features, which limits the ability to improve the fault detection accuracy. To overcome these issues, first, a period estimation method based on self-checking that employs acceleration data is proposed to effectively overcome the influence of complex noise on the estimated data period; second, a denoising method based on a physical model is proposed to effectively reduce the influence of complex noise on the acceleration-based displacement estimation; and third, a method for detecting the faults of beam pumping units based on edge intelligence is proposed to effectively improve the fault detection accuracy while maintaining a low computational demand. Extensive experiments on real data verify the effectiveness of the proposed method. To the best of our knowledge, this is the first work to discuss the impact of the quality of data on the performance of fault detection of beam pump units.

Node localization is one of the basic requirements in various Internet of Things applications. Among a wide range of localization schemes, the range-free localization algorithm is promising as a cost-effective technique. However, the localization accuracy of this technique is susceptible to various anisotropy factors, such as the existence of holes, nonuniform node distribution, and dynamic radio propagation pattern. To this end, an accurate range-free localization model using extreme learning machine (ELM) and ring-shaped salp swarm algorithm (SSA) is proposed for anisotropic wireless sensor networks. First, the integer hop count between two adjacent nodes is quantized as a real number according to the Jaccard coefficient of their shared neighbor nodes. Second, exploiting the strong generalization and fast learning speed of ELM, a distance mapping model based on the modified real hop count is developed for solving anisotropic signal attenuation. Third, the coordinate calculation of normal nodes is formulated as a minimum problem by taking into account the weighted squared error of estimated distance, and the bounding box method is utilized to initialize the possible location boundary area of normal nodes. Finally, the SSA based on the ring-shaped topology is designed to compute the coordinates of normal nodes. Extensive simulations on several network topologies are conducted with the effect of multiple anisotropic factors. Experimental results show that the proposed algorithm is superior to other developed ones not only in localization accuracy but also in robustness against network anisotropy.

Underwater acoustic sensor networks (UASNs) are effective instruments for monitoring marine environments and surveying seabed resources, it is important to improve their security protection, including source location privacy protection. Numerous strategies have been presented by researchers to strengthen location privacy, however, the majority of these plans have expensive energy costs. Therefore, a backbone-network-construction-based multiautonomous underwater vehicle (AUV) collaboration source location privacy protection (BNCSLP) algorithm has been enhanced to address this issue. First, the nodes in the network are split into various clusters, and an entire network is segmented into various regions. The backbone network is built using clusters that house the source. To prevent the adversary’s tracking, the AUV alternately chooses alternative backbone nodes and relays the source and fake data. Then, the cluster head determines whether to update the clusters by calculating how similar the data are with nearby clusters. The nearest neighbor technique is used by the updated cluster to anticipate the data and to reduce the energy utilization of forwarding the data packets, resulting in that there is less chance of data packets being intercepted and the source location being revealed. Finally, the AUV replans the trajectory, which shortens the AUV’s traveling path because only fewer cluster heads need to be accessed, decreasing the time it takes for data to be transmitted.

The position of the source is sensitive and critical information in underwater acoustic sensor networks (UASNs). In this study, a network coding-based scheme called the stratified source location privacy protection scheme (SSLP-NC) with autonomous underwater vehicle (AUV) is suggested for a strong adversary that can decode data. First, for the adversary with passive attacks, several fake source selection algorithms are suggested for two circumstances where the source is in the shallow and deep sea, respectively. Each node then utilizes a pseudo-random number generator to create sequences on a regular basis so that the key data can be delivered to the sink without interference. Then, for the adversary with the active attack, the node encrypts the source and fake data using the pre-existing pseudo-random number sequence as an encoding vector to thwart the adversary’s decryption. Further, this work develops a relay node selection approach for transmitting the encoded data, which increases the variety of the data transmission pathways. Finally, this study includes a hole avoidance strategy that uses nodes or an AUV to address the potential hole issue. The simulation demonstrates that the SSLP-NC successfully fends off an adversary that can decode data packets, and performs better than the EECOR and DBR-MAC algorithms in terms of network safe time and packet delivery rate.

Acoustic ranging is required to obtain the location of underwater mobile vehicles in the Internet of Underwater Things (IoUT). As seawater is an inhomogeneous medium, the sound velocity in the ocean is not constant, thereby causing sound waves to deviate from a straight line of propagation and bend. Thus, the travel time of the sound wave from the transmitter to receiver cannot be directly converted to a range value using a linear relationship as is done in the case of wireless radio ranging in the air. Therefore, the concept of effective sound velocity was introduced to account for the differences between the sound velocities at a transmitter and receiver. This enables conversion of the travel time to slant distance via a linear relationship. However, the existing methodologies for computing effective sound velocities are computationally intensive, which hinders the use of acoustic ranging in small underwater mobile vehicles operating in the deep sea. This study aimed to resolve this problem by developing an effective, computationally less demanding algorithm that could improve the precision of acoustic ranging on such platforms. The proposed algorithm converts global integrals into local integrals that correspond to depth variation ranges, thereby reducing the amount of integral calculation. The performance of the proposed algorithm is evaluated using extensive simulations and real deep-sea experimental data sets obtained at a depth of 3000 m. The results verify the efficacy of the algorithm in realizing real-time underwater acoustic ranging in deep sea. The proposed algorithm can improve the accuracy of real-time effective sound velocity measurement by small underwater mobile vehicles, and subsequently realize low-complexity underwater acoustic ranging in the deep-sea IoUT networks.

Due to the harsh deployment environment of the underwater coustic sensor networks (UASNs), a reliable and energy-saving routing algorithm has always been an important challenge and a hot topic. A Nonuniform clustering (NC) algorithm is designed first in which clusters are generated according to different node densities. Based on NC, the backbone of the underwater acoustic sensor network is formed in UASNs. To guarantee the reliability of data transmission of the backbone network, an NC routing algorithm based on a virtual gravitational potential field (NC_RVGPF) is proposed. This algorithm: 1) establishes a virtual gravitational potential field model to allow data transmission by 3-D underwater nodes; 2) designs the virtual gravitational potential energy by combining the transmission distance between the nodes, the residual energy of the nodes, and other parameters; and 3) selects the path of the highest average potential energy as being the optimal path of data transmission. The simulation results show that compared with the classical routing algorithm, the NC_RVGPF algorithm has higher transmission efficiency, less energy consumption, and can more effectively extend the network’s lifetime.

Underwater acoustic communication networks (UACNs) have been widely utilized in recent years because of the growing interest in interactive information in the deep ocean. Compared with traditional radio wireless networks, UACNs are characterized by complex and dynamic 3-D network topology and longer signal propagation delay. Therefore, recent studies on UACNs usually apply dynamic routes in data communication to adapt to complex UACN structure. However, the approaches are hardly adequate for UACN scenarios with high-traffic requirements. This is because dynamic routing methods, such as opportunistic routing, usually require external contention costs to control the routing paths, which leads to decreased network throughput. Accordingly, this article focuses on enabling high-speed acoustic communications for underwater application scenarios with high-traffic requirements, and proposes an underwater data transmission method using the multichannel full-duplex (FD) communication technique. The proposed method applies the underwater orthogonal frequency division multiple access (OFDM) technique, that uses collision-free channels to relay data in multihop routes for simultaneous transmission and reception. Unlike the traditional communication methods of UACNs with dynamic routing, the proposed one uses static routes when transporting data over hop-by-hop paths to achieve stable and uninterrupted FD communication. The approach also includes a directional forwarding-based route request method and an elimination-based channel evaluation proposal, which purpose to reduce the overheads from exploring routes and enable collision-free FD communications. The performance analysis of the proposed method is under simulated conditions of high-traffic UACN scenarios, and the results show that it has superior performance compare to the classical underwater data transmission methods.

In Mobile Edge Computing (MEC) networks, dynamic service migration can support service continuity and reduce user-perceived delay. However, service migration in MEC networks faces significant challenges due to the uncertainty in future traffic demands, the distributed architecture of MEC networks, high operating costs and the dynamism of network resources. Digital Twins (DT), which achieve the mapping of physical entities to virtual digital models in cyberspace, provide new perspectives for intelligent and efficient service provisioning in MEC networks. In this paper, we propose a traffic prediction-assisted federated deep reinforcement learning scheme to efficiently migrate services and improve the cost efficiency of DT-enabled MEC networks. Specifically, to address the coupled spatio-temporal dependencies of mobile traffic and the imbalance in traffic data, a Multi-order Spatio-temporal information integration-based distributed Traffic Prediction (MSTP) scheme is proposed, which achieves high-accuracy mobile traffic prediction at a low cost. Then, we propose a Federated Cooperative cost-efficient Service Migration (FCSM) algorithm that adaptively adjusts service migration strategies in a distributed manner to respond to future traffic demands. Moreover, a theoretical model is developed to analyze the convergence of FCSM and derive the upper bound of the time-average squared gradient norm. Finally, extensive simulations demonstrate that the proposed schemes achieve excellent traffic prediction performance, enhance users’ Quality of Service (QoS), and significantly reduce the system cost of MEC networks.

The forthcoming 6G networks are expected to provide a vision of overlapping aerial-ground-underwater wireless networks. Meanwhile, the rapid development of the Internet of Underwater Things (IoUTs) brings forth many categories of Autonomous Underwater Vehicle (AUV)-assisted Underwater Wireless Networks (UWNs). In this paper, we argue that the AUV-assisted UWNs can be intelligently utilized to track underwater pollution. To perform smart underwater pollution tracking, we propose the paradigm of AUV flock-based networking system and Software-Defined Networking (SDN)-enabled AUV flock Networking System (SDN-AUVNS). We introduce the concept of Mobile Edge Computing (MEC) into the control of SDN-AUVNS and propose the upgrade of the control plane of the SDN-AUVNS to with the multi-tier edge computing ability. By the proposed system architecture, we adopt the artificial potential field theory to construct the network controlling model. And we present the underwater tracking model for SDN-AUVNS, especially for the underwater pollution equipotential line of a particular concentration. Furthermore, to provide accurate path planning for the equipotential line tracking, we utilize the linearizability mechanism to optimize and revise the control input for the SDN-AUVNS. Lastly, we give a fast united control algorithm that can intelligently schedule the SDN-AUVNS to track underwater pollution equipotential lines. In particular, we propose a smart approach with the name of ’Inverse Distance Weighting’ to optimize the detection sample of the SDN-AUVNS. Evaluation results indicate that our proposal is able to track/survey the equipotential lines within a satisfactory error.

Underwater image enhancement (UIE) is an essential task for intelligent environment perception in underwater remote visual sensing scenarios. However, the computing power of mobile platforms limits the usage of larger-scale models. In this paper, we propose a lightweight encoder-decoder architecture (UIENet) to enhance underwater images from visual sensors. We also involve the architecture into a generative adversarial model (UIEGAN) against a supervised discriminator to further perfect its corrective capabilities for the photo-realistic images with more global appearance and local details. The multi-resolution counterparts are embedded into the generator to diversify the feature representation of the original inputs. Further, UIEGAN guides the spatial attention module and the channel attention module to jointly enhance the global-local connection of the image. We evaluate the proposed method on benchmark datasets of UIEB and UFO-120 and report better performance than the state-of-the-art schemes, exceeding 11.15% and 12.85% on peak signal-to-noise ratio (PSNR) than the baselines of these datasets. Besides, by testing on the UIEB challenge, URPC and SQUID datasets without any reference images, our scheme outperforms the other methods on evaluation metrics to validate its generalization performance, and meanwhile uses a series of ablation study demonstrates the effectiveness of the functional modules.

Service uncertainty modeling is an important problem of manufacturing service composition optimization, this article proposes a cloud manufacturing service composition optimization framework based on cloud-edge collaboration considering manufacturing service uncertainty. In the proposed framework, on the edge side, a model parameters estimation method of the manufacturing services' uncertainty is proposed based on Gaussian mixture regression; while on the cloud side, an intelligent evolutionary algorithm is adopted to effectively optimize the manufacturing service composition. Since the Gaussian mixture distribution is used to approximate the service availability distribution, the service uncertainty can be modeled adaptively. Compared with the previous optimization methods of manufacturing service composition with uncertainty based on the deterministic parameter models, the method proposed in this article can model the uncertainty of service more effectively, thus obtain better service composition solutions. Extensive experimental results prove the effectiveness of the algorithm.

Specific emitter identification (SEI) is a technology that extracts subtle features from signals sent by emitters to identify different individuals. It can effectively improve the security of the Industrial Internet of Things (IIoT) by acting on the physical layer of the internet. Recent research on SEI has focused on deep learning (DL) models that can automatically learn effective inherent emitter features from raw signals. Nevertheless, training popular DL models is computationally expensive because of the numerous hyperparameters and nonscalable structures. This limits the application of DL-based SEI models in certain practical IIoT scenarios. To address this concern, we propose an adaptive broad learning (ABL) method to build a lightweight SEI model. In the proposed model, the raw signal samples are mapped to feature nodes, and the emitters are denoted as the output nodes. The hidden nodes are directly connected to the output nodes by a broad network. Through this flat structure, the size and calculation amount of the model can be effectively reduced. To further economize the computational cost, we designed an adaptive node expansion strategy for rapidly obtaining the optimal hyperparameters of the models. The results of experiments on real-world data prove the superiority of ABL over popular state-of-the-art DL-based SEI models.

With the rapid development of edge intelligence (EI) and machine learning (ML), the applications of Cyber-Physical Systems (CPS) have been discovered in all aspects of the life world. As one of its most essential branches, Medical CPS (MCPS) determines human health and medical treatment in the Internet of Everything (IOE) era. Knowledge sharing is the critical point of MCPS and has also been humanity's best dream through the ages. This paper explores a novel knowledge-sharing model in MCPS and takes a pulmonary nodule detection task as a significant case for building an Unet-based mask generator. A Classification-guided Module (CGM)-based discriminator with knowledge from EMRs is set against a generator to offer a promising result for each mask from the inexperienced participant of federated ML. After an iterative communication between the federated server and its clients for knowledge sharing, the segmented sub-image owns a coincident attribute distribution with that of the EMRs from the experts. Besides, the adversarial network augment the data to normalize the data distribution for all the clients as a remission for none independent identically distributed (non-IID) data problem. We implement a detection framework on the simulated EI environment following an existing adaptive synchronization strategy based on data sharing and median loss function. On 1304 scans of the merged dataset, our proposed framework can help boost the detection performance for most of the existing methods of pulmonary nodule detection.

The exploitation/utilization of marine resources and the rapid development of urbanization along coastal cities result in serious marine pollution, especially underwater diffusion pollution. It is a non-trivial task to detect the source of diffusion pollution, such that the disadvantageous effect of the pollution can be reduced. With the vision of 6G framework, we employ Autonomous Underwater Vehicle (AUV) flock and introduce the concept of AUV-based network. In particular, we utilize the Software-Defined Networking (SDN) technique to update the controllability of the AUV-based network, leading to the paradigm of SDN-enabled multi-AUVs network Intelligent Transportation Systems (SDNA-ITS). For SDNA-ITS, we utilize artificial potential field theories to model the control model. To optimize the system output, we introduce the graph-based Soft Actor-Critic (SAC) algorithm, i.e., a category of Multi-Agent Reinforcement Learning (MARL) mechanism where each AUV can be regarded as a node in a graph. In particular, we improve the optimization model based on Centralized Training Decentralized Execution (CTDE) architecture with the assistance of the SDN controller, by which each AUV can efficiently adjust its speed towards the diffusion source. Further, to achieve exact path planning for detecting the diffusion source, a dynamic detection scheme is proposed to output the united control policy to schedule the SDNA-ITS dynamically. Simulation results demonstrate that our approaches are available to detect the underwater diffusion source when the actual scenario is taken into account and perform better than some recent research products.

To improve the reliability of autonomous vehicles, open-set fault diagnosis is indispensable to jointly detect known and unknown faults, in which unknown faults only appear in the testing set. However, in learning the representations for open-set diagnosis, the extracted representations lack hierarchy to preserve high-level and genuine representations, and the final representations utilized for diagnosing lack distinctiveness to separate unknowns from knowns. In addition, in the stage of testing, the open-set diagnosis models are error-prone when unknowns are similar to knowns. Motivated by these challenges, we propose a Multi-hop Attentive Graph Variational Autoencoder (MAGVA) model for open-set fault diagnosis in this paper. First, a multi-hop attentive graph convolutional network is developed to adaptively extract hierarchical representations and eliminate unknown fault misidentification. Then, to avoid unknown faults occupying the same region as known faults and identify known faults, structural representation constraints are designed by jointly conducting reconstruction with an intra-class constraint and classification with an inter-class constraint. Finally, combining the distinguishable representations learned by MAGVA, a generative distance-based open-set diagnosis algorithm is proposed, in which the procedures of estimating class-conditional distributions are designed, and a relative generative distance is then presented to derive diagnosis results under the class-conditional distributions. Experiments on three commonly used bearing datasets for vehicles demonstrate that the proposed MAGVA consistently outperforms the compared models in open-set, closed-set, and unknown fault diagnosis.

As a prototype of the underwater Internet of Things-enabled maritime transportation systems, multi-Autonomous Underwater Vehicle (AUV)-based Underwater Wireless Networks (UWNs) have become an important research topic due to their distribution and robustness. In this paper, the concept of multi-AUV-based UWNs is first defined, where AUV is regarded as a network node, and communication among the AUVs is the potential network links. Then, to improve network scalability and controllability, a paradigm of Software Defined multi-AUV-based UWNs (SD-UWNs) is proposed, where the Software Defined Network (SDN) technique is used to upgrade the UWN architecture by directing intelligent network functions. Topology and artificial potential field theories are applied to construct a network control model for the SD-UWNs. Based on the efficient data sharing ability of the SD-UWNs, an early warning obstacle avoidance-enabled path planning scheme is proposed to guarantee safe sailing of the SD-UWNs, where comprehensive obstacle avoidance scenarios are taken into account. Simulation results demonstrate that the proposed method is effective in planning the cooperative operation for the SD-UWNs and is capable of performing accurate and reliable obstacle avoidance tasks.

With the increasing popularity of cab services such as Didi and Uber, cities are faced with the challenge of high carbon emissions and traffic congestion. Ride-sharing services, as a novel green mode of transportation, have emerged as a key technology in smart transportation for addressing these problems. The implementation of ride-sharing is predicated on an accurate ridership demand forecasting model, which can effectively prevent vehicle resource waste, alleviate traffic congestion, and reduce carbon emissions. In this paper, a periodic attentional graph convolutional spatio-temporal network model (PAG-TSN) is proposed to predict regional ridership demand. Specifically, the model is trained using a large amount of GPS data and user demand data collected by the travel service provider. PAG-TSN consists of two parts: the bicomponent attention graph convolution model (BAT-GCN) and the periodic attentional gated recurrent unit model (PA-GRU). The former uses GCN to extract spatial features from pointwise and edgewise graphs; the latter uses the spatial feature vectors extracted from the former with external information as input, and uses GRU to extract temporal features from feature data of different periods, and finally uses attention mechanism and POI requirement correlation to integrate the extracted spatio-temporal information to derive prediction results. Extensive experiments and evaluations on the CD2Date and XA2Date datasets show that PAG-TSN outperforms other baseline models in accurately predicting regional ridership demand, with MAPE and RMSE values of 0.1147 and 5.56, respectively.

The rapid development of intelligent underwater devices promotes marine exploitation activities, including marine resource exploitation, marine target tracking, etc. This work will present how to utilize the Autonomous Underwater Vehicle (AUV) swarm or multi-AUVs system to track the underwater diffusion pollution, especially the equipotential line of particular concentration. Different from most of the current research, in this work, we take the AUV swam as a network system and utilize the Software-Defined Networking (SDN) technique to optimize the network architecture, constructing an SDN-enabled AUV network Intelligent Transportation Systems (ITS). With the centralized management ability of the SDN technique, we propose the software-defined Centralized Training Decentralized Execution (CTDE) architecture based on the graph-based Soft Actor-Critic (SAC) algorithm to optimize the system control and management. To improve the computing and training efficiency, we embed the self-attention mechanism into the critic network construction, leading to a self-attention-based SAC algorithm. Evaluation results demonstrate that our proposed approach is able to exactly track the equipotential lines of a particular concentration in many categories (with different types of equipotential lines (including the shape, noise, and diffusion value)) of underwater diffusion fields. Meanwhile, our proposed approaches outperform some classical schemes in system awards, tracking errors, etc.

As an emerging multi-submersible system, Human Occupied Vehicle (HOV) under a convoy of a set of Autonomous Underwater Vehicles (AUVs) is regarded as the future framework for underwater exploration. In this work, to improve the interoperability and communication efficiency of the multi-submersible formations, we treat the multi-submersible system as a paradigm of the underwater Internet of Vehicle (IoV) and show how to utilize the Software-Defined Networking (SDN) technique to optimize the system architecture. With the assistance of SDN, we consider the ocean current factors and propose an artificial flow potential field algorithm that combines the artificial potential field algorithm and the gradient descent algorithm, to plan the path for the multi-submersible system. In particular, to improve the safety and efficiency of path planning, we propose a dual leader-follower algorithm-based escort formation obstacle avoidance mechanism for dealing with all categories of obstacle avoidance situations. Simulation tests show that the proposed scheme performs better in data delivery among the multi-submersible system, at a lower energy cost. And it shows high stability and strong practicability in multi-submersible formation control and path planning, respectively.

Boundary tracking of sea continuous objects (e.g., oil spills and radioactive waste) is a challenging task that can be tackled viaunderwater acoustic sensor networks. Existing methods operate by selecting sensor nodes in the proximity of the boundary, and tend to over- or underestimate the actual boundary of the continuous object. In this article, a boundary tracking algorithm termedfeasible region search for continuous objects(FRSCO) is proposed. To determine a feasible region where the actual boundary lies, the proposed method first bounds the continuous object inside a minimum elliptical boundary. Within the minimum boundary ellipse, cell partition is performed through a binary tree structure, from which a set of backbone cells is selected. These roughly localize the feasible region. By constructing and deconstructing convex hulls of nodes in each backbone cell, the feasible region location uncertainty is further narrowed down. Virtual nodes are introduced in the final feasible region to determine the boundary nodes – by applying the principle of maximum entropy. Similar to virtual nodes, the selected boundary nodes do not need to correspond to the actual sensor nodes in the proximity of the boundary. Results from realistic testbed experiments and simulations show that the FRSCO exhibits effective tracking accuracy.

In recent years, the increasing number of industrial infrastructure security incidents around the world has drawn public attention to industrial control networks (ICNs) security issues. Fault diagnosis of industrial devices is an indispensable part of the security system in ICNs. The mainstream fault diagnosis models rely on long-term training and massive fault data, which results in the inability to update the model effectively and timely when the environment changes. Thus, some researchers focus on developing cross-domain industrial fault diagnosis methods. However, they usually presume that the samples of the target and source domains share the same fault mode sets, and existing prior knowledge concerning the label spaces of these two domains. These are difficult to satisfy in actual ICNs. To respond to these challenges, we develop atransferability-measured adversarial adaptation network(TAAN) to identify unknown classes without prior knowledge. It embeds the hybrid transferability estimation into an adversarial domain adaptive network to weigh the contribution of each sample. In this way, TAAN can properly classify samples in a public label space by selectively aligning source and target samples with high transferability. The experimental results obtained using two diagnosis datasets prove that the developed TAAN can achieve satisfactory diagnostic accuracy by effectively bridging the distribution discrepancy under various working conditions.

Driven by privacy concerns and the promise of Deep Learning, researchers have devoted significant effort to exploring the applicability of Machine Learning (ML). In the domains of communication, network, and service management, ML-based decision-making solutions are eagerly sought to replace traditional model-driven approaches, addressing the growing complexity and heterogeneity of modern systems. In this context, Federated Learning (FL) has gained increasing interest as a decentralized approach that overcomes the limitations of centralized systems for data analysis.

Security assessment (SA) system is crucial to ensure the production safety of a smart factory with rapid development of artificial intelligence. In this article, we propose a novel SA framework. Different from the conventional static monitoring systems based on traditional sensing technologies, the proposed framework can automatically detect objects via visual sensing. We use a skeleton-based graph convolutional network to generate action vocabulary for the intermediate representations of action-to-action cooccurrence relations. These representations are encoded into the sequential interaction models to form the interaction representations. Integrating the states of molten steel levels as the reference labels, the sequential representations are fed into a recurrent neural network model with multilayer gated recurrent units (GRUs) to capture the key interactions leading to the accidents, in which an attention mechanism is used to reweight the actions and eliminate the invalid interactions. The predicted labels and the hidden states of the scenes are passing among multilayer GRUs. Finally, we optimize the global output to dynamically assess the security by calculating a joint objective function with a regularized cross-entropy loss. On the self-collected dataset from our partner Iron and Steel company and on-line video clips, the proposed framework performs better than the existing SA schemes.

Solar insecticidal lamps Internet of things (SIL-IoTs) has a long-term application trend, because it makes agricultural pest control more environmentally friendly and intelligent. However, the increase of lamps deployed has resulted in the challenge about maintenance burden. In this paper, we therefore provide a constrained SIL Deployment Problem under Coverage and Maintenance Cost considerations, referred to as cSILDP-CMC, where the positions used to deploy SIL nodes are a limited set of weighted Candidate Locations (CL) located on the ridges. A novel method is proposed to quantify the maintenance cost of each CL based on their comprehensive weight of coverage and maintenance cost considerations. Then we formulate the cSILDP-CMC and propose an Iterative Deployment Method (IDM) to solve the defined optimization problem. Finally, the experimental results show that our proposal equips better performance in terms of deployment cost, total comprehensive weight and coverage uniformity compared with the other four peer algorithms.

The Internet of Things (IoT) technology has expanded network space by interconnected devices, which has been widely used in various fields, such as environmental monitoring, object tracking, risk warning, etc. Due to insufficient computing capacity, limited battery life, and unreliable communication environment in IoT, unmanned aerial vehicle (UAV)-enabled edge computing has been recently utilized to provide enhanced coverage and efficient computational support in the scenarios with sparse or unreliable ground infrastructure, such as disaster rescue, emergency response, military fields, etc. However, UAV-enabled edge computing faces many challenges, such as low offloading efficiency, high energy consumption, high complexity, etc. In this article, a distributed computation offloading scheme is proposed to provide computational support to large-scale IoT nodes and optimize the energy efficiency of multiple UAVs. First, to provide accurate and efficient computational support, a real-time intelligent positioning algorithm is designed to obtain the precise location information of IoT nodes. Then, a distributed computation offloading and path planning algorithm is presented, which jointly optimizes the computation offloading of large-scale IoT nodes and trajectory planning of multiple UAVs to reduce the energy consumption of UAVs. Furthermore, we develop a closed-form theoretical analysis model to demonstrate that the algorithm enables a performance guarantee related to energy efficiency. Finally, extensive simulations have been conducted and show that the proposed scheme can greatly improve the system utility and energy efficiency.

The massive growth in interconnected devices from a multiplicity of networks goes hand-in-hand with the emergence of the Internet of Things (IoT) paradigm. As a critical component of the IoT, sensor networks have become ubiquitous and widely used in various application domains. However, open-ended wireless communication brings severe threats to user privacy and security. Attackers from outside the network can trace back along the data stream to capture the source node, which poses a significant threat to the privacy of the data source. A feasible defense method is to interfere with the attacker’s tracking process through forged data streams. However, the related traditional solutions generally have shortcomings in terms of balancing security and efficiency. Therefore, this article proposes a pseudopacket scheduling algorithm (PPSA), which aims at reasonably regulating the process of pseudopacket generation to interfere with the adversary’s tracking to the data source. The algorithm comprises three phases. First, the sink node performs geographic information acquisition and neighbor node discovery with a flood-based method. Then, the sink node uses a self-adapting proxy selection method to construct backbone routes with both randomness and low latency to receive actual packets. Finally, the nodes on both sides of the backbone routes follow a pseudopacket scheduling strategy to interfere with the adversary’s tracking of the source locations. The experimental results showcase that our proposed scheme effectively controls the additional energy consumption and transmission delays within acceptable ranges while ensuring adequate location privacy.

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.

As the research topics in ocean emerge, underwater acoustic sensor networks (UASNs) have become ever more relevant. Consequently, challenges arise with the security and privacy of the UASNs. Compared to the active attacks, the characteristics of passive attacks are more difficult to discriminate. Thus, the focus of this study is on the passive attacks in UASNs, where a push-based probabilistic method for source location privacy protection (PP-SLPP) is proposed. The fake packet technology and the multipath technology are utilized in the PP-SLPP scheme to counter the passive attacks, so as to protect the source location privacy in UASNs. Moreover, the Ekman drift current model is employed to simulate the underwater environment. And the mean shift algorithm and the k-means algorithm are adopted in the dynamic layer and static layer of the Ekman drift current model, respectively, to increase the stability of the clusters. Finally, an autonomous underwater vehicle (AUV) swarm is implemented to collect data in clusters. Through the comparison with existing data collection schemes in UASNs, the simulation results have demonstrated that the PP-SLPP scheme can achieve a longer safety period, with a minor compromise of energy consumption and delay.

Location information plays an important role in many applications of the Internet of Things (IoT). The low cost and ease of scalability of range-free localization algorithms have attracted the attention of many researchers, but the performance of many localization algorithms available in the literature varies greatly in different networks. Specifically, algorithms designed for anisotropic networks may not perform well in isotropic networks, and vice versa. To improve localization accuracy in both isotropic and anisotropic networks, a novel range-free localization algorithm named LSAE is proposed in this article, oriented to the network positioning without ranging over the IoT. The proposed algorithm utilizes the known information in the network, namely, the hop counts and distances between anchor nodes, to train the stacked autoencoders (SAE) model. In this way, it achieves accurate prediction of the distances between unknown nodes and anchor nodes. To further improve the localization accuracy, the disadvantage of the least square method is analyzed, and a novel coordinate estimation method based on the statistical results of distance estimation errors is proposed. We conducted a huge number of numerical simulations with and without the impact of multiple anisotropic factors in three different types of networks. The results indicate that the proposed algorithm outperforms other state-of-the-art algorithms treating the impact of multiple anisotropic factors, and demonstrates the high accuracy and robustness.

Pairwise ranging between nodes plays a crucial role in Internet-of-Underwater-Things (IoUT) networks, and it typically impacts the overall performance of such networks. As the sound speed depends on several parameters, time-of-flight-based techniques cannot work well under varying sound speeds in actual underwater conditions. Pairwise ranging algorithms that consider stratification should be studied to improve ranging accuracy. However, there is a tradeoff between underwater acoustic ranging accuracy and number of calculations. This makes it challenging to implement underwater acoustic ranging algorithms in IoUT networks. In this article, we propose an underwater acoustic horizontal ranging algorithm that rapidly and accurately estimates the horizontal range from a sender to a receiver under an arbitrary sound-speed profile in the deep sea. Simulation results show that the proposed algorithm accurately calculates the horizontal range with a low computational complexity. We validate the performance of the proposed algorithm using the data collected during an ultrashort baseline precision test in the South China Sea.

Demand response is an effective way for ensuring safety and stabilization of power grid by maintaining the balance between the supply and the demand of power grid, and this article focuses on using electric water heaters for demand response. In addition to considering comfort and price factors as did in previous works, this article considers the overshoot temperature and its influence on demand response. First, a theoretical model of the heating and cooling processes of the electric water heater is established; second, the demand response process using electric water heaters is analyzed, including the influences of the physical parameters and the settings of electric water heaters on the demand response process; third, a model is established considering the demand response requirement, the comfort of owners of electric water heaters, and the electricity price, simultaneously; fourth, an optimization method based on deep reinforcement learning is proposed for demand response using electric water heaters. Meanwhile, the influence of parameters on the results of demand response is discussed in details. Experimental results show the effectiveness of the proposed method.

The papers in this special section focus on artificial intelligence-enabled software defined industrial networks. With the development of intelligent manufacturing, new manufacturing modes such as personalized customization and networked collaboration have been widely developed. These new manufacturing modes require frequent data exchanges between manufacturing machines and industrial information systems through the networks, and dynamically change according to the variations of orders, business, and environments, which cannot be supported in traditional manufacturing modes that focus on local and fixed processes. The current industrial network architecture cannot meet the needs of the aforementioned upcoming manufacturing mode. For example, there are many industrial network protocols, forming a complex industrial heterogeneous network, which seriously affects the interconnections between the underlying devices and the upper layer application systems. In addition, the layering information technology (IT) networks and the operation technology (OT) networks in the factory have hindered the developments of the industrial networks and intelligent manufacturing. There is an urgent need to build a flat, efficient, and flexible industrial network to support the new manufacturing modes.

The development of theindustrial Internet of Thingsparadigm brings forth the possibility of a significant transformation within the manufacturing industry. This paradigm is based on sensing large amounts of data, so that it can be employed by intelligent control systems (i.e.,artificial intelligencealgorithms) eliciting optimal decisions in real time. Ensuring the accuracy and reliability of the intelligent wireless sensing and control system pipeline is crucial toward achieving this goal. Nevertheless, the presence of noise in actual wireless transmission processes considerably affects the quality of the sensed data. Typically, noise and anomalies present in the data are very difficult to distinguish from each other. Conventional anomaly-detection techniques generate many error reports, which cause the control systems to issue incorrect responses that hinder the industrial production. In this article, a novel solution is proposed to denoise data while simultaneously preserving the actual anomalies. The proposed approach operates by measuring both the neighbor and background contrasts in computing a noise score. The trust level of each data point is then calculated through a correlation measure to purge spurious data. Extensive experiments on real datasets demonstrate that the proposed approach yields effective performance, as compared to existing methods, and it meets the requirements of low latency—facilitating the normal operation of the monitored control systems.

With the explosive growth of devices and tasks deployed in the industrial Internet of Things (IIoT), the lack of interconnection and collaboration between devices leads to poor timeliness and security in IIoT resource scheduling. This article focuses on the issue of adaptive scheduling of resources in large-scale IIoT. First, a collaborative terminal-edge IIoT architecture is designed, which introduces blockchain and AI technology to support dynamic resource scheduling in untrustworthy environments. Then, a smart contract-based multidimensional resource transaction model is developed to improve the efficiency and security of resource scheduling by establishing a credit-based consensus mechanism. Distributed transaction learning resource scheduling algorithm is further proposed to implement resource-adaptive scheduling between devices in IIoT. Extensive simulation experiments are conducted to evaluate the proposed method with respect to several performance aspects covering the scheduling decision delay, transaction generation ratio, and security. The obtained results demonstrate that the comprehensive scheduling performance of the proposed method outperforms other existing algorithms.

Obstructive sleep apnea (OSA) syndrome is a common sleep disorder and a key cause of cardiovascular and cerebrovascular diseases that seriously affect the lives and health of people. The development of Internet of Medical Things (IoMT) has enabled the remote diagnosis of OSA. The physiological signals of human sleep are sent to the cloud or medical facilities through Internet of Things, after which diagnostic models are employed for OSA detection. In order to improve the detection accuracy of OSA, in this study, a novel OSA detection system based on manually generated features and utilizing a parallel heterogeneous deep learning model in the context of IoMT is proposed, and the accuracy of the proposed diagnostic model is investigated. The OSA recognition scheme used in our model is based on short-term heart rate variability (HRV) signals extracted from ECG signals. First, the HRV signals and the linear and nonlinear features of HRV are combined into a one-dimensional (1-D) sequence. Simultaneously, a two-dimensional (2-D) HRV time-frequency spectrum image is obtained. The 1-D data sequences and 2-D images are coded in different branches of the proposed deep learning network for OSA diagnosis. To validate the performance of the proposed scheme, the Physionet Apnea-ECG public database is used. The proposed scheme outperforms the existing methods in terms of accuracy and provides a novel direction for OSA recognition.

Rescue assignments strategy are crucial for multiple Autonomous Underwater Vehicle (multi-AUV) systems in three dimensional (3-D) complex underwater environments. Considering the requirements of rescue missions, multi-AUV systems need to be cost-effective, fast-rescuing, and less concerned about the relationship between rescue missions. The real-time rescue plays a vital role in the multi-AUV system with the characteristics mentioned above. In this paper, we propose an efficient Reward acting on Reinforcement Learning and Particle Swarm Optimization (R-RLPSO), to provide a strategy of real-time rescue assignment for the multi-AUV system in the 3-D underwater environment. This strategy consists of the following three parts. Firstly, we present a reward-based real-time rescue assignment algorithm. Secondly, we propose an Attraction Rescue Area containing a Rescue Area. For the waypoints in each Attraction Rescue Area, the reward is calculated by a linear reward function. Thirdly, to speed up the convergence of the R-RLPSO and mark the rescue states of Attraction Rescue Area and rescue area, we develop a Reward Coefficient based on the reward of all Attraction Rescue Areas and Rescue Areas. Finally, simulation results show that the system based on R-RLPSO is more cost-effective and time-saving than that of based on comparison algorithms ISOM and IACO.

The coming 6G communication technology introduces the possibility of practice underwater Internet of Things (UIoT) applications with high-speed and reliable underwater communications. Among them, the cooperative coverage path planning (CPP) with autonomous underwater vehicles (AUVs) is a promising approach for enabling deep ocean exploration. The cooperative CPP in underwater is challenged by several marine factors, typically the harsh underwater communication environment, which brings difficulties in sharing the coverage progresses of AUVs and the environmental information such as the seafloor bathymetry, obstacles, etc. Accordingly, this paper proposes a novel CPP method based on 6G enabled cooperative AUVs, named the Pac-AUV. As the name suggests, the method is based on the mechanism of the Ms. Pac-Man game, where AUV-assisted subsea exploration is considered as cooperative Pac-Men sharing the Pac-Dots distributed on the seafloor. The Pac-AUV involves two steps in cooperative CPP. One is a dot-spreading-based mission assignment (DMA), which is discretely performed by each AUV and requires support from reliable underwater communication in sharing the Pac-Dots. The other step is virtual attraction-based coverage path planning (V-CPP), which adopts the virtual attraction force from the Pac-Dots, results in low computational complexities in generating the coverage paths and avoids obstacles. Simulations are performed to demonstrate the performance of the Pac-AUV, and the results prove the advantages of cooperative and balanced CPP executions.

Underwater acoustic sensor networks (UASNs) have been widely promoted for developing various categories of marine applications, where the sensor nodes cooperate to complete specific tasks. Given the fact that the sensor nodes are unattended while continuously exposed to harsh environments, an associatedtrust modelplays a significant role in node trustworthiness evaluation and defective node detection, such as the case of adverse attacks on the network. However, the existing trust models only evaluate the communication behavior and the energy of the sensor nodes, ignoring the effects of underwater environmental noise on trust reliability. Further, most trust models are designed with arbitraty weighted trust metrics, causing inevitable evaluation errors. To achieve the accurate calculation of node trust, we propose a new anomaly and attack resilient trust model, based on the isolation forest. We refer to this model asITrust. The proposed ITrust model consists of two phases: trust metrics specifics and defective node detection. In the first phase, the trust dataset is integrated from four types of trust metrics: communication trust, data trust, energy trust, and environment trust. In the second stage, trust is evaluated with the obtained trust dataset using the isolation forest algorithm. Simulation results demonstrate that the proposed ITrust can detect defective nodes effectively, and achieves higher detection accuracy than that of the existing trust models in a noisy environment.

Underwater acoustic sensor networks (UASNs) have been widely applied in marine scenarios, such as offshore exploration, auxiliary navigation and marine military. Due to the limitations in communication, computation, and storage of underwater sensor nodes, traditional security mechanisms are not applicable to UASNs. Recently, various trust models have been investigated as effective tools towards improving the security of UASNs. However, the existing trust models lack flexible trust update rules, particularly when facing the inevitable dynamic fluctuations in the underwater environment and a wide spectrum of potential attack modes. In this study, a novel trust update mechanism for UASNs based on reinforcement learning (TUMRL) is proposed. The scheme is developed in three phases. First, an environment model is designed to quantify the impact of underwater fluctuations in the sensor data, which assists in updating the trust scores. Then, the definition of key degree is given; in the process of trust update, nodes with higher key degree react more sensitively to malicious attacks, thereby better protecting important nodes in the network. Finally, a novel trust update mechanism based on reinforcement learning is presented, to withstand changing attack modes while achieving efficient trust update. The experimental results prove that our proposed scheme has satisfactory performance in improving trust update efficiency and network security.

Due to the flammability, explosiveness and toxicity of continuous objects (e.g., chemical gas, oil spill, radioactive waste) in the petrochemical and nuclear industries, boundary tracking of continuous objects is a critical issue for industrial wireless sensor networks (IWSNs). In this article, we propose a continuous object boundary tracking algorithm for IWSNs – which fully exploits the collective intelligence and machine learning capability within the sensor nodes. The proposed algorithm first determines an upper bound of the event region covered by the continuous objects. A binary tree-based partition is performed within the event region, obtaining a coarse-grained boundary area mapping. To study the irregularity of continuous objects in detail, the boundary tracking problem is then transformed into a binary classification problem; ahierarchical soft margin support vector machinetraining strategy is designed to address the binary classification problem in a distributed fashion. Simulation results demonstrate that the proposed algorithm shows a reduction in the number of nodes required for boundary tracking by at least 50 percent. Without additional fault-tolerant mechanisms, the proposed algorithm is inherently robust to false sensor readings, even for high ratios of faulty nodes ($\approx 9\%$).

The diffusion of toxic gas, biochemical material, and radio-active contamination – known as continuous objects – endangers the safe production of the petrochemical and nuclear industries. To mitigate these well known hazards, the new paradigm ofindustrial wireless sensor networks(IWSNs) shows great potential in monitoring evolving hazardous phenomena in unfriendly industrial fields. In order to prolong the lifetime of these networks, existing research focuses on energy-efficient boundary nodes selection. However, sensor state cannot be scheduled proactively, due to the difficulty in predicting the spatiotemporal evolution of diffusive hazards. In this article, we propose amotion behavior learning predictive tracking(MBLPT) algorithm for continuous objects in IWSNs. Considering the relatively unpredictable patterns exhibited by continuous objects, the MBLPT uses a data-driven approach for motion state recognition, and then utilizesBayesian model averaging(BMA) for future boundary prediction. The prediction of the MBLPT provides the knowledge for establishing a wake-up zone, in which standby nodes are activated in advance to participate in tracking the upcoming boundary. Simulation results demonstrate that the MBLPB achieves superior energy efficiency while keeping effective tracking accuracy.

With the rapid development of edge computing, a new paradigm has formed for providing the nearest end service close to the data source. However, insufficient supply of resources makes edge computing devices vulnerable to attacks, especially sensitive to resource-consuming attacks. This article first designs system function module, aiming to deal with resource-consuming attacks based on the general three-layer architecture of edge computing. Combined with the mean field game, an anti-attack model is designed to transform the security defense problem of large terminal-edge-cloud devices into the mean field countermeasure problem, and the self-organizing neural network is used to approximate the mean field coupling equation. On this basis, a distributed AI-driven resource-consuming attack security defense (ARASD) algorithm is designed to obtain the optimal solution for devices security interaction, thereby improving the system’s anti-attack ability. Finally, the effectiveness of the self-organizing neural network is verified through numerical simulation, and the parameters such as the number of initial terminal-edge-cloud devices and the number of iterations of different security defense algorithms are evaluated. The results show that the ARASD algorithm can achieve better resistance to resource-consuming attacks than other state-of-the-art algorithms in a large-scale edge computing architecture.

This article develops a robust global strategy for identifying the linear parameter varying (LPV) errors-in-variables (EIVs) systems subjected to randomly missing observations and outliers. The parameter interpolated LPV autoregressive exogenous model with an uncertain/noisy input is investigated and a nonlinear state-space model is considered for the input generation model (IGM). The parameters estimation of the LPV EIV systems with nonideal observations is realized using the expectation–maximization algorithm which is particular effective for the incomplete data issue. To ensure the robustness in the identification, the Student’s t-distribution which is characterized by its adjustable degree of freedom, is used to handle the measurement non-normality. Since the posterior distributions of the latent states in the IGM are also involved in the identification process and they are difficult to calculate directly, the particle filter is introduced to recursively approximate them instead. Finally, the verification examples are given to demonstrate the effectiveness of the developed strategy.

As an effective security mechanism, trust models have been proposed to estimate the reliability of the individual nodes in Underwater Acoustic Sensor Networks (UASNs) during adverse attacks. However, existing trust models neglect the relative importance of the different nodes within the network topology. Further, few trust models study the effects of defective recommendation trust filtering. In this work, we propose an adaptive trust model based on the Long Short-Term Memory (LSTM) network model for UASNs, which we term LTrust. The LTrust is composed of two stages: trust data collection and trust evaluation. In the first stage, the characteristics of the network topology are leveraged towards evaluating direct trust evidence, by aggregating the communication trust and environment trust metrics; a defective recommendation filtering method is designed for broadcasting accurate trust recommendations among the nodes. In the second stage, an adaptive trust model is designed based on the LSTM model, to identify anomalous nodes by evaluating their trust value. The LTrust model has been tested under both hybrid attack and single-mode attack scenarios. Simulation results demonstrate that the LTrust achieves effective performance, as compared to other approaches proposed in the literature, in terms of trust value, accuracy and error rate.

In recent years, developments in data collection schemes based on multipleautonomous underwater vehicles(AUVs) are facilitating the realization of the so-calledunderwater acoustic sensor networks(UASNs). As yet, the lack of suitable collaboration mechanisms among multiple AUVs, which are based on functional or resource distributions, prevents effective information sharing and yields increased data collection delays, thus reducing the capacity of the networks. In this article, to address these shortcomings, we propose astate prediction-based data collection(SPDC) algorithm for UASNs. The principle of operation is as follows. First, some cluster pairs named observation clusters obtain and exchange the state information about AUVs between the adjacent subregions. Based on the shared information, the AUVs predict each other’s status and adjust their data collection areas. Then, the AUVs use a heuristic strategy to complete the path planning based on the updated access area. Finally, a scheduling data forwarding mechanism reduces the diving number of the AUVs, by reasonably allocating the overlapped data unloading intervals between the AUVs and a mobile sink. Experimental results prove that the proposed algorithm shows satisfactory performance in reducing data collection delays and in improving the total network lifetime.

Industrial Internet of Things inevitably leads to the implementation of highly data-intensive devices, where the associated sensing nodes accelerate the energy consumption rate, which ultimately produces an energy bottleneck. To address this issue, this article proposes adynamic collaborative charging algorithmthat acts on both the mobile nodes and the static nodes in a sensing node network. The proposed scheme is to design a collaborative group of charging robots that can rendezvous with the sensing nodes. The group includes aerial charging vehicles (ACVs)—able to charge the underpowered mobile nodes, and terrestrial charging vehicles (TCVs), which charge their targeted static nodes. The aim of this study is to optimize the charging effect and the energy cost in the rendezvous process. This approach consists of two subalgorithms: 1) a charging algorithm for mobile nodes (CAMNs) and 2) a charging algorithm for static nodes (CASNs). The CAMNs is designed so that each underpowered mobile node can be charged by a dedicated ACV. For this purpose, a deep learning model is trained to divide the underpowered mobile nodes into appropriate clusters, each of which is equipped with a mobile base station. The rendezvous process is then constructed as a mixed continuous/discrete optimization problem, which is solved by using the firefly algorithm. In addition, the CASNs ensures that the TCVs traverse their routes, charging static nodes as they proceed. This traversing process was formulated as a multiobjective optimization problem, solved by using genetic algorithm. Through various experiments and case studies, the results have demonstrated both the feasibility and the efficiency of the proposed algorithms.

As a result of the increasing deployment of Industrial-Internet-of-Things (IIoT) architectures, large volumes of multidimensional data are continuously generated. An important issue with these data is that higher dimensionality increases the degree of fragmentation. Furthermore, data sets collected by IIoT nodes often display outliers, which are usually caused by anomalous events or errors. These outliers contain considerable valuable information, which prevent the normal operation of the system. Thus, methodologies are able to quantify the obtained information to protect the high priority IIoT nodes, are crucial. This study aims at developing such a method driven by sixth-generation (6G) networks. The proposed algorithm uses a multidimensional data relationship diagram to characterize the spatiotemporal correlations among heterogeneous data. Then, an autoregressive exogenous model is used to eliminate the effects of noise on sensor data, and to help in detecting anomalies. Finally, the algorithm produces a Cumulative Coefficient of Value (CCoV), to identify high-value sensing devices and enable massive Internet of Things (IoT) with 6G-using the characteristic patterns hidden within the data. The experimental results demonstrate that the proposed method can effectively handle the effects of the ubiquitous interference noise in complex industrial environments. Moreover, the method yields effective anomaly detection and compensates for some of the shortcomings in traditional methods.

The Internet of Things (IoT) is a network of everything. In IoT, the charging problem of devices is an issue that needs urgent attention. Most previous studies about wireless charging algorithms are based on the ideal conditions, which may be not suitable for the actual scene. Therefore, focusing on that nodes are evenly distributed, which is a type of ideal conditions, this article proposes a mobile charging algorithm based on multicharger cooperation (MCCA) for IoT with the random deployment of nodes. The MCCA, which is based on a new uneven cluster method, mainly includes three parts. First, based on the relationship between the number of charging requests and the number of chargers, the base station chooses an appropriate charging request threshold and the number of chargers. Then, multiple chargers perform the collaborative charging scheduling based on the energy requirement of each cluster and the distance between clusters. Finally, the base station adjusts the charging sequence of each charger if there exists a conflict. In the end, the MCCA is verified by MATLAB and compared with various algorithms. The simulation results show that the MCCA can effectively balance the energy consumption, reduce the number of nonfunctional nodes, improve the charging efficiency, and extend the network lifetime.

The Industrial Internet of Things (IIoT) involves the use of large numbers of sensing nodes, which should meet the requirements for industrial use, such as real-time performance monitoring and high reliability and stability. However, owing to single-station allocation, inappropriate mobile charger (MC) allocation and unreasonable route planning, conventional methods may lead to local blockages, incomplete charging coverage, and high energy consumption because of additional movement. Hence, we propose a multistation-based collaborative charging strategy, termed MCCS, to overcome these problems. In MCCS, the energy sources are static charging stations. Furthermore, MCs that consist of primary and senior chargers act as the transmission media. Specifically, the senior chargers, which are charged by the stations, transmit energy to the primary chargers, which then transmit energy to the sensor nodes. The following steps are involved in MCCS. To begin with, MCCS divides the sensor nodes into various categories based on a self-organizing feature mapping neural network in order to ensure appropriate primary charger allocation. Next, a genetic algorithm is used to generate the optimal routes for the MCs. Finally, MCCS allocates the senior chargers and sets up the charging stations. Simulations were conducted to evaluate MCCS, which exhibited better performance in terms of efficiency, energy consumed in movement, and charging energy loss as compared with existing strategies.

Underwater acoustic sensor networks (UASNs) have been proposed as a promising way in supporting the Underwater-Internet-of-Things (UIoT) applications. However, to guarantee and improve Quality of Service (QoS), they are still facing great challenges especially when it comes to enabling reliable data communication for the UIoT applications and meanwhile protecting the marine ecosystems for sustainable underwater monitoring and exploration; this is because the acoustic signals used by UASNs can do harmful interference to vocalizing marine mammals. Therefore, the article proposed an ecologically friendly data transmission scheme for UASNs, which adopts an interference-aware opportunistic route discovery method and a frequency-division multiplexing (FDM)-based full-duplex communication scheme. First, in the route discovery phase, a virtual-void zone scheme, an FDM-based channel allocation approach and a Bayesian network-based mammal avoidance strategy is introduced to establish interference-free routing paths for enabling reliable and environmentally friendly data transmissions. Then during the data transmission phase, an FDM-based full-duplex communication technique is adopted to enable high-speed data flow from the seabed to the surface. Extensive simulations indicate that the proposed scheme has excellent advantages in terms of QoS while also taking marine mammals into account since the signal interference to both underwater nodes and nearby vocalizing mammals is significantly mitigated.

Using unmanned aerial vehicles (UAVs) for equipment condition monitoring is an important application of Industrial Internet of Things (IIoT), and the limited energy is the key factor to restrict the application of UAV. In order to reduce the computational load for intelligence computing of UAV, this article proposes a cloud edge collaborative intelligent method for object detection, and applies it to insulator string recognition defect detection in the power IIoT. First, the impact of the extremely large aspect ratio of object on the detection accuracy and the computational load is analyzed, then the cloud edge collaborative intelligent method for insulator string detection and defect recognition is presented, in which on the UAV side a low cost method is proposed for estimating possible directions of insulator strings, and on the cloud side, an effective method is proposed for insulator string defect detection. The experimental results show the effectiveness of the proposed algorithm. To the best knowledge of us, this article is the first work to analyze the impact of the extremely large aspect ratio of insulator string on the detection accuracy and the computational load.

Solar insecticidal lamps (SILs) play a vital role in green prevention and control of pests. By embedding SILs in wireless sensor networks (WSNs), we establish a novel agricultural Internet of Things (IoT), referred to as the SIL-IoTs. In practice, the deployment of SIL nodes is determined by the geographical characteristics of an actual farmland, the constraints on the locations of SIL nodes, and the radio-wave propagation in a complex agricultural environment. In this article, we mainly focus on the constrained SIL deployment problem (cSILDP) in a mixed-crop farmland, where the locations used to deploy SIL nodes are a limited set of candidates located on the ridges. We formulate the cSILDP in this Scenario as a connected set cover (CSC) problem and propose a hole-aware node deployment method (HANDM) based on the greedy algorithm to solve the constrained optimization problem. The HANDM is a two-phase method. In the first phase, a novel deployment strategy is utilized to guarantee only a single coverage hole in each iteration, based on which a set of suboptimal locations is found for the deployment of SIL nodes. In the second phase, according to the operations of deletion and fusion, the optimal locations are obtained to meet the requirements on complete coverage and connectivity. Experimental results show that our proposed method achieves better performance than the peer algorithms, specifically in terms of deployment cost.

The fifth-generation of wireless communication (5G) is a promising paradigm toward massive interconnectivity within Internet-of-Things (IoT) networks. However, because the data traffic throughput sharply increases with the number of IoT devices, a tremendous burden on the backhaul links and core networks results. With this in mind, mobile edge caching is an effective method that can relieve stress of the backhaul links, while decreasing the service latency. The purpose of this study is to analyze the problem of jointly optimizing cooperative edge caching and radio resource allocation in 5G-enabled massive IoT networks. For that, a joint optimization long-term nonlinear integer programming problem is posed. This class of problems is known to be NP-hard; thus, to reduce the problem complexity, a divide and conquer scheme will be applied—the task at hand will be divided into two subproblems: 1) cooperative edge caching and 2) radio resource allocation. The cooperative edge caching subproblem is formulated as a constrained Markov decision process. Herein, a deep reinforcement learning method to optimize the caching decisions for all the edge nodes. Then, based on the resulting optimal caching decisions, the radio resource allocation subproblem for each edge node is posed as an NLIP problem, and an improved branch-and-bound method is proposed to yield the optimal radio resource allocation decisions for each edge node. Extensive simulations were performed to confirm that the proposed methods have the capability of enhancing the content caching hit ratio, while lessening the content retrieving delays for 5G-enabled massive IoT networks—improving over various baseline algorithms.

Escorting a moving object in a subsea environment with cooperative autonomous underwater vehicles (AUVs) is a typical subject in Underwater Internet of Things (UIoT) applications. It involves two issues that should be studied. First, a mobile task assignment method is required to lead the AUVs to the escorting positions; then, a formation control scheme should be utilized to safely escort the moving object to the destination. Accordingly, in this article, a comprehensive target escorting mechanism called the cooperative-control-based underwater target estimating mechanism (CUTE) is proposed, which includes a belief-function-method-based self-organizing map algorithm for task assignment and an artificial potential field-based formation control method. The task assignment method aims to establish smooth routes from the AUVs to the escort position while flexibly avoiding obstacles, and the formation control method aims to improve the monitoring coverage on the escort route by rotating the formation structure while following the moving object. Simulations show that the proposed CUTE method may be very practical in underwater target escorting scenarios.

The continuously increasing number of connected smart devices has led to the emergence of a crucial fault detection challenge to the Internet of Things (IoT). In this study, we aim to identify a method for the effective detection of faults in IoT devices. An IoT network model is first established, and a data edge verification mechanism based on blockchain is proposed; the blockchain is used to ensure that the data cannot be tampered with, and their accuracy is verified using the edge. Finally, a data set accuracy weighted random forest based on particle swarm optimization is proposed. The simulation results demonstrate that the proposed detection algorithm is both effective and efficient.

Data are widely collected via the IoT for machine learning tasks in in-home health monitoring applications and mislabeled training data lead to unreliable machine learning models in in-home health monitoring. Researchers have proposed a wide arrangement of algorithms to deal with mislabeled training data, in which one straightforward and effective solution is to directly filter noise from training data so that the negative effects of mislabeled data can be minimized. In essence, noise filtering might be a suboptimal solution because the mislabeled data are not completely useless. The features and distributions of mislabeled data are still useful for learning, especially when training data are insufficient. In this work, we propose a novel framework to learn from mislabeled training data through ambiguous learning (LeMAL). LeMAL mainly consists of two parts. First, it converts the original training data to ambiguous data. Second, an ambiguous learning algorithm is applied to the ambiguous data. In this work, we propose a novel distance-based ambiguous learning algorithm so that the ambiguous data can be used in a better way. Finally, we demonstrate that LeMAL can effectively improve learning performance over existing noise filtering methods.

Depression and anxiety are a couple of pernicious mental states, which may affect lifestyle and quality and even become the primary causes of disability worldwide. Hence, daily monitoring of the mental states is significant for avoiding possible injury. Dynamics of the human blood vascular system convey significant information on recording the emotion and the mental state, which can be monitored via photoplethysmography (PPG). It is one of the best schemes with the advantages of nonintrusiveness and low cost. Conventional approaches for PPG signal analysis usually depend on handcrafted feature extraction and classification, thus resulting in a lack of feature discrimination and difficulties in generalization. In this article, we propose an attentive deep recurrent architecture called Arousal-valence Networks (ArvaNets), which benefits from graph convolutional networks and recurrent neural networks. Our approach overcomes the limitations of previous methods by automatically extracting the learnable spatial representations from a rigorous custom data set as semantic motifs to infer immediate emotions, which are mapped to a 2-D arousal-valence coordinate system. Finally, we exploit long short-term memory (LSTM) units to output the mental states by incorporating the temporal factor. During the entire inference, we propose a spatiotemporal attention mechanism based on correlation fractal dimensions (CFDs) and time-averaged wall shear stress (TAWSS) to capture and stress the key subtle motifs for performance optimization. Experimental results demonstrate the proposed architecture has enough competitiveness in the tasks of emotion and mental-state recognition for daily monitoring.

Illegally forged signals in automatic identification system (AIS) pose a threat to maritime traffic safety management. In this paper, a multi-level sparse representation based identification (MSRI) algorithm is proposed for specific emitter identification (SEI) in the AIS. The MSRI innovatively combines neural networks with sparse representation based classification (SRC). Channel attention mechanism is introduced to a multi-scale convolutional neural network (CNN) for extracting hidden features in the signal. These extracted features are divided into shallow and deep features according to the depth of the network layer they are extracted from. The original AIS signals and the two-level features are spliced together to form a multi-level dictionary. Subsequently, a sparse representation based identification is performed on the decorrelated multi-level dictionary using the principal components analysis (PCA) method. The proposed MSRI is evaluated on a dataset composed of real-world AIS signals, and compared with the state-of-the-art identification algorithms. The evaluation is based on several factors including computational complexity, number of training samples, and number of emitters. Numerical results indicate that the proposed algorithm can identify emitters with higher accuracy and requires lower training time compared to other methods. Given more than 15 training samples at each emitter, the MSRI can identify nine emitters with an accuracy higher than 90%.

In this article, we propose a hierarchical data collection scheme, toward the realization of unmanned aerial vehicle (UAV)-aided industrial wireless sensor networks. The particular application is that of agricultural monitoring. For that, we propose the use of hybrid compressed sampling through exact and greedy approaches. With the exact approach-to model the energy-optimal formulation-an improved linear programming formulation of the minimum cost flow problem was utilized. The greedy approach is based on a proposed balance factor parameter, consisting of data sparsity, and distance from cluster head to normal nodes. To improve node clustering efficiency, a hierarchical data collection scheme is implemented, by which nodes in different layers are adaptively clustered, and the UAV can be scheduled to perform energy-efficient data collection. Simulation results show that our method can effectively collect the data and plan the path for the UAV at a low energy cost.

The data in industrial informatics may be high-dimensional and mislabeled. Irrelevant or noisy features pose a significant challenge to the detection of high-dimensional mislabeling. The traditional method usually adopts a two-step solution, first finding the relevant subspace and then using it for mislabeling detection. This two-step method struggles to provide the optimal mislabeling detection performance, since it separates the procedures of feature selection and label error detection. To solve this problem, in this article, we integrate the two steps and propose a sequential ensemble noise filter (SENF). In the SENF, relevant features are selected and used to generate a noise score for each instance. Continuously, these noise scores guide feature selection in the regression learning. Thus, the SENF falls in the scope of sequential ensemble learning. We evaluate our approach on several benchmark datasets with high dimensionality and much label noise. It is shown that the SENF is significantly better than other existing label noise detection methods.

The existing intelligent fault diagnosis methods of rotor-bearing system mainly focus on vibration analysis under steady operation, which has low adaptability to new scenes. In this article, a new framework for rotor-bearing system fault diagnosis under varying working conditions is proposed by using modified convolutional neural network (CNN) with transfer learning. First, infrared thermal images are collected and used to characterize the health condition of rotor-bearing system. Second, modified CNN is developed by introducing stochastic pooling and Leaky rectified linear unit to overcome the training problems in classical CNN. Finally, parameter transfer is used to enable the source modified CNN to adapt to the target domain, which solves the problem of limited available training data in the target domain. The proposed method is applied to analyze thermal images of rotor-bearing system collected under different working conditions. The results show that the proposed method outperforms other cutting edge methods in fault diagnosis of rotor-bearing system.

With the development of the industrial Internet of Things and the advancements in wireless sensor networking technologies, the smart grid based on edge computing now is regarded as being essential for real-time monitoring and automatic control of the electricity generation and distribution. In this article, we propose a highly efficient energy control framework supported by edge computing to reduce energy waste and increase the benefit for industrial users. To this end, battery energy storage systems (BESSs) are currently being employed to store energy for stability of supply and quality of power. The optimal load patterns and corresponding energy storage capacities of the BESSs can be obtained through the framework, according to the energy market and the historical load data of industrial users. However, computing these requires considering the tradeoff between equipment cost, time-of-use electricity price, running expenses, and other related factors, which would be an NP-hard problem. To address this challenge, we also propose an adaptive mixed differential evolution algorithm with a novel mutation strategy. Experiments on real-world data demonstrate the effectiveness of the proposed algorithm and framework.

This paper proposes a two-way multi-ringed forest (TMR-Forest) to estimating the malignancy of the pulmonary nodules for false positive reduction (FPR). Based on our previous work of deep decision framework, named MR-Forest, we generate a growing path mode on predefined pseudo-timeline of L time slots to build pseudo-spatiotemporal features. It synchronously works with FPR based on MR-Forest to help predict the labels from a dynamic perspective. Concretely, Mask R-CNN is first used to recommend the bounding boxes of ROIs and classify their pathological features. Afterward, hierarchical attribute matching is introduced to obtain the input ROIs' attribute layouts and select the candidates for their growing path generation. The selected ROIs can replace the fixed-sized ROIs' fitting results at different time slots for data augmentation. A two-stage counterfactual path elimination is used to screen out the input paths of the cascade forest. Finally, a simple label selection strategy is executed to output the predicted label to point out the input nodule's malignancy. On 1034 scans of the merged dataset, the framework can report more accurate malignancy labels to achieve a better CPM score of 0.912, which exceeds those of MR-Forest and 3DDCNNs about 2.8% and 4.7%, respectively.

With the rapid development of urbanization, enormous amounts of vehicular services have been emerging and challenge both the architectures and protocols of the Internet of Vehicles. The high-speed mobility features of nodes in the vehicular networks changes the network topology frequently, resulting in low routing efficiency, and higher packet loss. In this article, we utilize software-defined networking (SDN) technology to decouple the network control plane from the data forwarding plane, and divide the vehicular networks into three functional layers: data, control, application layers. Based on the proposed network architecture, we propose an adaptive traffic engineering (TE) mechanism to guarantee the V2V continuous traffic in vehicular networks with high-speed mobile vehicles or dynamic network topology. In particular, the proposed TE is based on a proposed active network measurement mechanism under the assistance of the centralized management ability of the SDN technique. The proposed active network measurement approach is a greedy approach where the next hop determination for the measurement packet takes multiple link reliability factors (e.g., the delay, the length, the packet error rate, the neighbors, etc.) into account. Then, we utilize the artificial bee colony (ABC) algorithm to optimize the TE mechanism that can be deployed and executed in the SDN controller. By the proposed TE mechanism, multiple candidate end-to-end paths can be concurrently measured, and the optimal data forwarding path can be adaptively switched. Simulation results demonstrate that our approach performs better than some recent research outcomes, especially in the aspect of performing reliable data forwarding (almost 5% better than the compared objects).

The rapid development of industrial wireless sensor networks (IWSNs) promotes the development of intelligent agricultural monitoring systems. As one of the most challenging issues, it is indispensable to improve the lifetime or sustainability of IWSNs based agricultural monitoring systems. In this paper, we employ UAV as the wireless charging component to improve the availability of IWSNs based agricultural monitoring systems. We propose a UAV-embedded IWSN architecture where IWSN is regarded as the data sensing or collecting component, and UAV serves as the data relay and charging component. We focus on the length constrained energy-optimal charging path planning issues for the UAV-aided IWSN based agricultural monitoring system, where the length of the charging path and the entire residual energy for the entire network are concurrently taken into account. We formulate the problem by utilizing linear programming (LP) technique, and propose an improved version of the max-min ant system (MMAS) algorithm, an adaptive optimization approach to plan the charging path for the UAV. Our adaptive approach can dynamically optimize the ratio of pheromone concentration and the state transfer function during the iteration. Simulation results demonstrate that our proposal is more efficient than some novel approaches in energy charging efficiency, network utilization ratio/lifetime.

Network embedding is an important method to learn low-dimensional vector representations of nodes in networks, which has wide-ranging applications in network analysis such as link prediction. Most existing network embedding models focus on the unsigned networks with only positive links. However, networks should have both positive and negative links in practical applications such as the trust and distrust relationships in social networks. It is certain that there are different properties between positive links and negative links, which means the network embedding models designed for unsigned networks are not suitable for signed networks. In this paper, we propose SNE-DML, a signed network embedding model with dynamic metric learning. The model learns positive and negative distance metrics respectively in the training process. We conduct sign prediction experiments on three datasets and compare with seven baselines including three signed network embedding models and four state-of-the-art unsigned network embedding models. The experimental results show the effectiveness of our model.

The research of underwater acoustic sensor networks (UASNs) has gained much attention because of its wide applications, such as environmental monitoring and seabed oil exploration, etc. However, underwater acoustic communication has certain specific characteristics, such as low transmission rate, high delay, and limited energy, which have challenged the data transmission of UASNs. This paper is dedicated to solving the problem of transmission collisions between sensor nodes at the MAC layer in UASNs. A Collision-free MAC protocol for UASNs is proposed, which is a global TDMA-based MAC protocol and optimizes the quorum system based on the network topology to reduce unnecessary time slot allocation and improve the channel utilization. Compared with previous MAC protocol, the result of simulation has shown the superior performance of the proposed MAC protocol both in terms of reducing latency and saving energy consumption.

The Internet-of-Things (IoT) system is a novel networking technology that connects smart communication devices through Internet-enabled infrastructure to enhance wireless communications. The explosive growth of IoT devices connectivity has increased energy consumption drastically that raises economic and physical environment concerns. To meet the challenges posed by high energy consumption, energy efficiency has become an urgent need for IoT networks recently. This article examines resource allocation and formulates the joint optimization problem for power allocation and user selection subject to the maximum transmit power and different Quality-of-Service (QoS) requirements, to achieve an improved energy efficiency performance in IoT networks under channel uncertainty. Furthermore, the formulated optimization problem is mixed-integer nonlinear programming (MINLP) with no practical solutions. Due to the nonconvexity and NP-hardness of the MINLP problem, the primal optimization problem is transformed into a convex problem and solved optimally for power allocation and user selection, by applying the Lagrangian dual decomposition method and the Kuhn-Munkres algorithm, respectively. An efficient joint iterative algorithm is proposed to maximize energy efficiency performance with guaranteed convergence within few numbers of iterations. The numerical results validate the robustness of the proposed algorithm and significantly show its superior performance as compared with the baseline algorithms.

Internet of Things (IoT) is an emerging networking paradigm that enhances smart device communications through Internet-enabled systems. Due to massive IoT devices connectivity with economic and greenhouse emission effects, the energy-efficiency poses critical concerns. Under imperfect channel state information (CSI), this article investigates joint optimization of user selection, power allocation, and the number of activated base station (BS) antennas of multiple IoT devices considering the transmit power and different Quality-of-Service (QoS) requirements in combinatorial mode to maximize energy-efficiency. The optimization problem formulated is a nonconvex mixed-integer nonlinear programming, which is NP-hard with no practical solution. The primal optimization problem is transformed into a tractable convex optimization problem and separated into inner and outer loop subproblems. This article proposes a joint energy-efficient iterative algorithm, which utilizes a successive convex approximation technique and the Lagrangian dual decomposition method to achieve near-optimal solutions with guaranteed convergence. The simulation results are provided to evaluate the proposed algorithm and its significant performance gain over the baseline algorithms in terms of energy-efficiency maximization.

As an emergent Internet-of-Underwater-Things (IoUT) system, the underwater wireless networks (UWNs), especially the autonomous underwater vehicle (AUV)-based UWNs are considered to be future of deep-sea exploration. Instead of underwater exploring or data collection based on an independent AUV, the multi-AUVs cooperative system or the AUV flock-based UWNs perform more efficiently and accurately in some particular underwater exploring tasks. In this article, we focus on improving the scalability or controllability of the AUV flock-based UWNs and utilize the paradigm of software-defined networking (SDN) to improve the flexibility and controllability of the AUV flock-based UWNs. With the proposed SDN-enabled architecture for the AUV flock-based UWNs, the UWNs are divided into three layers, and the data transmission, synchronization, and collection among the AUVs are implemented by the proposed software-defined beacon and control frameworks. By the centralized management feature of SDN, we define the concept of AUV flock and the united control model based on the artificial potential field theory. Then, we propose an exact path planning scheme for the AUV flock, especially when potential underwater obstacles or “no-go” areas are taken into account. We will show how an SDN controller can be a director/leader for the AUV flock-based UWNs to perform an exact underwater path planning mission. The simulation results show that our proposal is efficient in managing the operation of the AUV flock, especially the proposal SDN controller-guided path planning scheme performs more efficiently than the normal-distributed path planning scheme.

In smart cities, urban intelligent transportation systems (ITSs) are highly anticipated to improve transportation efficiency, decrease traffic congestion, and promote sustainable transportation development. However, the ITS-based transportation network may fail as a result of a traffic congestion which is considered as one of the challenging issues in large-scale smart cities. The possible traffic congestion on the link is with spatiotemporal features and may vary over time. Nevertheless, the continuous or delay-sensitive traffic flow is always requested in smart cities even under serious traffic congestion. In this article, we will prove such spatiotemporal features of traffic congestion can be forecasted, and the path for the delay-sensitive urban traffic can be accurately planed before the traffic is started. Our main contributions can be summarized as follows: 1) we employ the software-defined networking (SDN) technology to improve the scalability of ITS in smart cities and propose a grid-based model to quantify the traffic-congestion probability of the transportation network; 2) we propose a polynomial-time solvable algorithm to recognize the grids that affect the traffic-congestion probability of the network links or paths; and 3) we utilize the time-expanded network technology to expand the time slots in the spatial dimension and propose a polynomial-time path planning algorithm that can seek for a congestion-aware path to schedule the traffic within a given time threshold.

The rapid development of Internet-of-Things technologies (such as edge computing) has promoted the development of numerous emerging urban applications, particularly smart transportation. As an anticipated aspect of smart transportation, bike-sharing systems have recently been deployed in many cities and are considered an efficient way to address the issue of “the last mile.” In a bike-sharing system, the supply and demand of shared bikes at each bike station frequently change over time. Consequently, one of the most challenging issues of a bike-sharing system is predicting the required number of shared bikes at each station. In this article, we take the real aspects of a bike-sharing system into account, e.g., the high complexity, nonlinearity, and uncertainty of the traffic flow, and propose a hybrid edge-computing-based machine learning model. Notably, our proposed model, which combines a self-organizing mapping network with a regression tree (RT), is applied to predict the bicycle demand of a certain station through the following steps: 1) the proposed model adopts self-organization mapping to assemble the original samples in the form of clusters and 2) each cluster is then built as an RT to forecast the required number of bikes at each station. Experiments based on real data from the Washington and London bike-sharing systems show that our proposed method achieves a higher prediction accuracy and better generalization than previous approaches.

Solar insecticidal lamp (SIL) is a green prevention and control technology for pests. With the development of wireless sensor networks (WSNs), the combination of SILs and WSNs forms a novel agricultural Internet of Things-SIL Internet of Things (SIL-IoTs). However, the complex geographical characteristic of actual farmland has a great impact on SIL deployment. In this article, we study the SIL deployment problem (SILDP) with characteristics of full coverage, penetrable obstacles, irregular boundary, and partition structure. According to the partition structure caused by natural physiognomy feature, the actual farmland is divided into many subareas by ridges, and each subarea can be considered as a separate partition. Then, we formulate the SILDP in the scenario with the partition structure as the quadratic assignment problem. After that, we propose two deployment methods based on the genetic algorithm to address the SILDP. These two methods are the same in optimization objectives, but different in deployment sequence. The experimental results show that the proposed deployment methods equips better performance in terms of deployment cost compared with the other six peer algorithms.

The hop count matrices (HCMs) are very helpful in obtaining the location information of sensing nodes in Internet of Things (IoT). However, in some scenarios, the HCMs cannot be completely observed due to abnormal termination of the flooding process, or some of the entries are contaminated by false information in external malicious attacks. Therefore, it is very important to recover the missing HCMs. However, to the best of our knowledge, there is no specific algorithm used in the current research to recover the HCMs, which would cause the positioning accuracy to be seriously deteriorated. In this article, for the scenarios of the entries partially observed in the HCMs, the HCMs recovery schemes, namely, HCMR-NBC and HCMR-MC, are proposed. The former, HCMR-NBC, is to learn the internal relations of different sensing node pairs in the HCMs. It is a simple and fast approach which utilizes the feature with a single dimension to predict the missing hop count values between the sensing nodes. The latter, HCMR-MC, is to transform the problem of the matrices recovery to the one of matrices completion. Compared with the previous SVT and BLMC algorithms, the proposed algorithms have great advantages in terms of the reconstruction performance and the computation complexity.

As an essential step in quality control, fabric defect detection plays an important role in the textile manufacturing industry. The traditional manual detection method is inaccurate and incurs a high cost; as a result, it is gradually being replaced by deep learning algorithms based on cloud computing. However, a high data transmission latency between end devices and the cloud has a significant impact on textile production efficiency. In contrast, edge computing, which provides services near end devices by deploying network, computing and storage facilities at the edge of the Internet, can effectively solve the above-mentioned problem. In this article, we propose a deep-learning-based fabric defect detection method for edge computing scenarios. First, this article modifies the structure of DenseNet to better suit a resource-constrained edge computing scenario. To better assess the proposed model, an optimized cross-entropy loss function is also formulated. Afterward, six feasible expansion schemes are utilized to enhance the data set according to the characteristics of various defects in fabric samples. To balance the distribution of samples, proportions of various defect types are used to determine the number of enhancements. Finally, a fabric defect detection system is established to test the performance of the optimized model used on edge devices in a real-world textile industry scenario. Experimental results demonstrate that compared with the conventional convolutional neural network (CNN), the proposed optimized model attains an average improvement of 18% in the area under the curve (AUC) metric for 11 defects. Data transmission is reduced by approximately 50% and latency is reduced by 32% in the Cambricon 1H8 platform compared with a cloud platform.

Health knowledge graph provides an ideal technical means to integrate heterogeneous data resources and enhance knowledge-based services. There are many challenges for the construction of health knowledge graph such as complex concepts and relationships, various medical standards, heterogeneous data structures, poor data quality, highly accurate and interpretable services, etc. In this paper, firstly, we propose Health Knowledge Graph Builder (HKGB), an end-to-end platform which could be used to construct disease-specific and extensible health knowledge graphs from multiple sources. Secondly, we analyze the capabilities and requirements of clinicians, design the tasks to involve the clinicians and implement a clinician-in-the-loop toolset to integrate the clinicians prior knowledge into the construction of health knowledge graphs. Thirdly, we design an extensible mechanism to add new diseases to an existing knowledge graph. Fourthly, we present a quantitative effort estimation algorithm to quantitatively evaluate the effort of clinicians during the construction, and use it to calculate the workloads such as 44.27 person days for knee osteoarthritis domain. Finally, we have developed several knowledge graph based tools to facilitate real applications.

Driven by the emerging advanced information and communication technologies, e.g., artificial intelligence, 5G wireless communications, big data analytics, etc., industrial Internet serves as a key enabling technology to realize intelligent manufacturing, and has been attracting considerable attentions from academia and industry. However, the traditional industrial networks can hardly satisfy the quality of service (QoS) requirements for some mission-critical industrial applications (e.g., fault detection, advanced control, remote monitoring, predictive maintenance, etc.) due to network heterogeneity, traffic congestion, dynamic end-to-end latency, reliability issues, and so on. The emerging software-defined networking (SDN) has been considered as a promising architecture to improve the QoS of industrial applications by flexibly decoupling the control and data planes to control the network behaviours centrally. Owing to economy and policy considerations, a realistic solution is to incrementally deploy SDN in industrial networks instead of fully replacing traditional industrial routers with SDN-enabled switches. In this article, we consider a hybrid Industrial network consisting of conventional routers (e.g., running OSPF protocol) and SDN-enabled switches (e.g., running OpenFlow protocol), and propose an intelligent QoS-aware forwarding strategy to improve the QoS of industrial applications, by utilizing a single path minimum cost forwarding scheme and a K-path partition algorithm for multipath forwarding. Simulation results demonstrate that the proposed scheme not only guarantees the QoS requirements of industrial services, but also efficiently utilizes bandwidth resources by balancing traffic load in the SDN/OSPF hybrid industrial Internet.

Among several new technologies, such as social and cognitive mobile computing, wireless sensor networks (WSNs) constitute the founding pillar of the industrial Internet of Things. These networks are expected to play an increasingly important role in our daily lives. Social and cognitive mobile computing requires the sharing of data recorded by sensor nodes. However, the data can be vulnerable to attacks. It is of utmost importance to protect the users privacy while ensuring the security of the WSNs. This investigation is focused on the source-location privacy (SLP) of WSNs. This article proposes a dynamic multipath privacy-preserving routing (DMPPR) scheme based on multiple sinks for protecting the privacy. Different from single sink schemes, the technique of using multiple sink nodes to protect SLP is discussed in this article. Furthermore, a packet-slicing transmission scheme that generates a large number of dynamic routings based on multiple sink nodes is adopted for transmitting the packets. Local adversaries are considered, and to cope with these adversaries, a transmission loop, constructed using real and fake packets, is proposed to confuse the adversaries during the source detection process. The aim is to break the sociality between the sensor nodes. Simulations performed in MATLAB show that the proposed method outperforms similar existing schemes in terms of the secure time, adversary's capture probability, and node utilization ratio. Moreover, the DMPPR scheme also reduces energy consumption by allowing more nodes in the nonhotspot areas to participate in the packet transmission process.

Edge computing brings data storage closer to the location where it is needed. Therefore, the edge devices, especially smart industrial edge devices, require higher storage systems. NAND flash memory has the advantages of small size, high speed, and strong shock resistance, which is widely used in various storage systems, providing a good choice for edge devices. NAND flash has unique physical characteristics, such as “out-of-place updates” and “prewrite erasure,” therefore, the traditional address mapping methods require improvement. This article presents a novel demand-based page-level address mapping algorithm called DPAM. The goal of DPAM is to provide efficient address translation by using a smaller address mapping table. Due to the high service cost of block-level address mapping and hybrid address mapping, a page-level address mapping scheme is proposed. The algorithm is implemented and tested on the flash simulation platform FlashSim. The results indicate that our algorithm provides improvements of 7.11% for the hit ratio and 7% for the number of block erasures compared with other approaches.

Poisonous pollutants produced in chemical, plastics, or nuclear power industry are easy to leak and result in a large-scale hazardous event region. Recently, industrial wireless sensor networks (IWSNs) are intended to provide situational awareness in industry site and thus hold the promise of profiling the event region. However, low-cost nodes in IWSNs are prone to fail due to prolonged exposure to harsh environment. This article targets the detection of hazardous event region for IWSNs with faulty nodes. A fault-tolerant event region detection algorithm named TPE-FTED is proposed to formulate faulty nodes identification as a trajectory pattern extraction problem. Through online learning of probabilistic model, each node characterizes the distribution of sensing values under different sensing states. A specific set of probabilistic models can be formed as a trajectory which indicates something special happens. Based on the implicit knowledge from generated trajectories, TPE-FTED conducts pattern matching and checks spatiotemporal constraint to identify the declaration of faulty nodes. Simulation results demonstrate that TPE-FTED achieves low false alarm rate as well as high detection accuracy.

In this paper, a high-availability data collection scheme based on multiple autonomous underwater vehicles (AUVs) (HAMA) is proposed to improve the performance of the sensor network and guarantee the high availability of the data collection service. Multi-AUVs move in the network and their trajectory is predefined. The nodes near the trajectory of an AUV directly send their data to the AUV while the others transmit data to nodes that are closer to the trajectory. Malfunction discovery and repair mechanisms are applied to ensure that the network operates appropriately when an AUV fails to communicate with the nodes while collecting data. Compared with existing methods, the proposed HAMA method increases the packet delivery ratio and the network lifetime.

As a candidate trust management scheme, trust models based on the cloud theory are always taken into account when detecting malicious attacks in Underwater Acoustic Sensor Networks (UASNs). To evaluate the trust values of nodes accurately, the evidence of trust ought to be collected frequently. As a result, continual trust update results in excessive energy consumption or premature death of some sensor nodes that are close to the trust cloud node. To address the above issues, in this paper, we propose an Energy-balanced Trust Cloud Migration scheme (ETCM) for UASNs, which consists of Destination Node Determination (DND), trust cloud migration and trust cloud update. Particularly, DND is performed hierarchically to obtain the destination node for trust cloud migration by selecting the candidate destination clusters, determining the destination cluster and the destination node, respectively. First, the candidate destination clusters are selected based on the distribution for the overall residual energy in UASNs using the simulated annealing algorithm. Then, an indicator of Cluster Ability (CA) is proposed to seek the destination cluster. Particularly, to calculate CA, the improved standardized Euclidean distance formula is employed to evaluate the connectivity between clusters and their neighboring clusters. Finally, on the basis of the defined node density reachability and the residual energy, the Node Ability (NA) is presented to indicate the capacity of nodes for trust cloud storage, calculation and update. The destination node in the destination cluster can be determined as the new trust cloud node using the NA. Simulation results demonstrate that the proposed ETCM scheme can balance energy consumption, increase node survival ratio and prolong lifetime effectively.

Large-scale Cranes often need to run in a long-term working time, and result in various potential structural health risks. To reduce these risks, we herein design structural health monitoring system of the crane (SHMC for short) with Narrow Band IoT (NB-IoT). Firstly, we design a monitoring terminal for the SHMC system, wherethe single-chip microcomputer controls the electronic device to collect external signals to monitor the crane information in real time. Secondly, we employ NB-IoT based the wireless transmission module for network bandwidth-saving and long-term running. Then, we design a core platform, which parses and calculates the information uploaded by the terminal, and stores it in the database. Our system can monitor the running status of cranes in real time, and timely diagnose the fault information of crane to ensure their safe operation. Also the implementation of our design with apache storm cluster, Kafka server and Alibaba EC2 web server is showed in this study.

The intelligent treatment of urban garbage is an important component of creating a smart city and also solves several problems associated with urban garbage. Many traditional garbage cans are widely distributed, resulting in a waste of human and material resources, untimely government. Therefore, in this paper, we propose an intelligent system based on edge computing and the narrow-band Internet of things (NB-IoT) for monitoring smart trash cans (STCs). The deployed intelligent garbage cans are distributed throughout the city and are equipped with a variety of sensors, including a compression sensor, a location sensor, an infrared sensor, and an alarm sensor. The data sent from the smart bins are preprocessed through edge nodes for data classification and priority transmission, which reduces the required network transmission bandwidth and the computational tasks at the centralized data center. The NB-IoT is a narrow-band communication technology with low power consumption, wide coverage, low cost, and large capacity. The experimental results show that the proposed STC system shows good system performance, and allows for intelligent management of garbage in smart cities.

The Internet of Things (IoT) that allows connectivity of network devices embedded with sensors undergoes severe data exchange interference as the unlicensed spectrum band becomes overcrowded. By applying cognitive radio (CR) capabilities to IoT, a novel cognitive radio IoT (CR-IoT) network arises as a promising solution to tackle the spectrum scarcity problem in conventional IoT network. CR is a form of wireless communication whereby a radio is dynamically programmed and configured to detect available spectrum channels. This enhances the spectrum utilization efficiency of radio frequency while avoiding interference and overcrowding to other users. Energy efficiency in CR-IoT network must be carefully formulated since the sensor nodes consume significant energy to support CR operations, such as in dynamic spectrum sensing and switching. In this paper, we study channel spectrum sensing to boost energy efficiency in clustered CR-IoT networks. We propose a two-way information exchange dynamic spectrum sensing algorithms to improve energy efficiency for data transmission in licensed channels. In addition, the concern of the energy consumption in dynamic spectrum sensing and switching, we propose an energy efficient optimal transmit power allocation technique to enhance the dynamic spectrum sensing and data throughput. Simulation results validate that the proposed dynamic spectrum sensing technique can significantly reduce the energy consumption in CR-IoT networks.

Wireless sensor networks (WSNs) are an important core of the Industrial Internet of Things (IIoT). Wireless rechargeable sensor networks (WRSNs) are sensor networks that are charged by mobile chargers (MCs), and can achieve self-sufficiency. Therefore, the development of WRSNs has begun to attract widespread attention in recent years. Most of the existing energy replenishment algorithms for MCs use one or more MCs to serve the whole network in WRSNs. However, a single MC is not suitable for large-scale network environments, and multiple MCs make the network cost too high. Thus, this paper proposes a collaborative charging algorithm based on network density clustering (CCA-NDC) in WRSNs. This algorithm uses the mean-shift algorithm based on density to cluster, and then the mother wireless charger vehicle (MWCV) carries multiple sub wireless charger vehicles (SWCVs) to charge the nodes in each cluster by using a gradient descent optimization algorithm. The experimental results confirm that the proposed algorithm can effectively replenish the energy of the network and make the network more stable.

Edge computing is proposed to bridge mobile devices with cloud computing data centers in the era of mobile big data, as an intermediate level of computing power. One important issue in edge computing is how to improve performance for mobile devices. Current systems utilize cache in multicore systems to reduce memory access cost with an acceptable hardware cost. However, existing cache management policies are unable to maximize cache value in the newly developed hybrid memory platform that combines phase-change memory and dynamic random-access memory. In this paper, we propose maximizes cache value (MCV), an efficient cache management policy, which MCV to minimize memory access cost in a hybrid main memory platform for edge computing. Extensive simulation studies indicate that this strategy can improve performance in hybrid main memory-based edge computing for mobile devices.

In the era of the Internet of Things, edge computing-based wearable sensors are rapidly emerging for smart health. The collection, storage, and retrieval of data are the key components of wearable sensors. Therefore, it is important to optimize data storage and retrieval. Phase change memory (PRAM) is a kind of phase change memory that is widely used as a new storage medium. It has the characteristics of nonvolatility, high-density storage. However, it has the disadvantages of asymmetry in reading and writing and limited life. In recent years, PRAM and DRAM were combined into PDRAM as a hybrid memory architecture, to solve the problems caused by PRAM. This paper proposes a new cache policy named hybrid-LRU to adapt PDRAM. Hybrid-LRU uses two different LRU cache policies to distinguish PRAM and DRAM as two different storage mediums. The experimental results show that the hybrid-LRU cache policy improves the performance by 4.2%, and reduces the utilization rate of PRAM in PDRAM by 11.8%. In addition, the energy consumption of writing and reading can be reduced to 87.8%.

Due to their limited functionality, ubiquitous connected devices in the Internet of Things rely heavily on the computational and storage resources of the cloud. However, mainstream cloud systems always require high network bandwidth and cannot satisfy the delay requirement of real-time applications. Therefore, a new paradigm called multiaccess edge computing has emerged to offload the computation and storage needs of end user devices to the edge cloud servers located in the radio access networks of 5G mobile networks. In this paper, we study and compare three load sharing schemes, namely, no sharing, random sharing, and least loaded sharing, which exploit the collaboration between clustered servers in different degrees. We develop computationally efficient analytical models to evaluate the performance of these schemes. These models are validated by simulation, and then used to compare the performances of the three load sharing schemes under various system parameters. Comparison results show that the least loaded sharing scheme is most suitable to fully exploit the collaboration between the servers and achieve load balance among them. It contributes to reducing the blocking probability and waiting time experienced by users.

To provide satisfactory Quality of Service (QoS) on the move, efficient mobility management is indispensable to provide mobile users with seamless and ubiquitous wireless connectivity. However, both the conventional centralized mobility architecture and the upcoming distributed mobility management face fundamental challenges such as sub-optimal routing, scalability, and so on. The emerging software-defined networking (SDN) architecture can efficiently manage network operations, and accordingly provides a new direction to address the challenges in mobility management. In this paper, we propose an SDN-based Mobility Management (SDN-MM) scheme to support seamless Intro/Inter domain handover with route optimization. SDN-MM decouples mobility management and packet forwarding functions by installing route optimizing and mobility control logics in an SDN controller, but exempting it from traffic redirecting. In SDN-MM, a comprehensive set of signaling operations are designed in order to provide transparent and efficient mobility support for ongoing sessions in each handover scenario, which prevents packet loss and tunneling overhead, and accordingly provide improved QoS to mobile users. For data communications, an SDN controller in SDN-MM pre-calculates the optimal end-to-end route before a handover, and decides whether to migrate traffic to the route by balancing the performance gain and the signaling overhead, which greatly improves bandwidth resource utilization. Finally, we develop a novel analytical model to evaluate the performance of SDN-MM, including signaling overhead, handover latency, and packet delivery cost. The simulation results have been provided to demonstrate that the proposed SDN-MM can greatly improve handover performance and maintain high resource utilization efficiency as well.

Both limited main memory size and memory interference are considered as the major bottlenecks in virtualization environments. Memory deduplication, detecting pages with same content and being shared into one single copy, reduces memory requirements; memory partition, allocating unique colors for each virtual machine according to page color, reduces memory interference among virtual machines to improve performance. In this paper, we propose a coordinate memory deduplication and partition approach named CMDP to reduce memory requirement and interference simultaneously for improving performance in virtualization. Moreover, CMDP adopts a lightweight page behavior-based memory deduplication approach named BMD to reduce futile page comparison overhead meanwhile to detect page sharing opportunities efficiently. And a virtual machine based memory partition called VMMP is added into CMDP to reduce interference among virtual machines. According to page color, VMMP allocates unique page colors to applications, virtual machines and hypervisor. The experimental results show that CMDP can efficiently improve performance (by about 15.8 percent) meanwhile accommodate more virtual machines concurrently.

The papers in this special section focus on edge computing and the challenges that exist for artificial intelligence applications. Edge computing has the advantages of real-time response and less network demand for computing closer to the edge of the network, while bridging the physical and digital worlds. The core of the edge computing is to provide the edge intelligent service. Therefore, edge artificial intelligence is becoming a popular trend for the future, such as intelligent sound box, and so on. Edge artificial intelligence combines edge computing with artificial intelligence, while taking both advantages. However, there are some problems, which need to be solved for the edge artificial intelligence. Addresses these issues and examines future areas of development in this area.

The advent of underwater acoustic sensor networks (UASNs) has enhanced marine environmental monitoring, auxiliary navigation, and marine military defense. One of the core functions of UASNs is data collection. However, current underwater data collection schemes generally encounter problems such as high energy consumption and high latency. Furthermore, the application of multiple autonomous underwater vehicles (AUVs) has contributed to more problems of task assignment and load balancing. This leads to significant failure in data collections and controlling of spontaneous emergencies. To address these problems, a district partition-based data collection algorithm with event dynamic competition in UASNs has been proposed. In this algorithm, the value of information of the packet determines the priority of its transmission to the cluster head. The navigation position of the mobile sink and the area under the responsibility of each AUV are determined by the spatial region division. The path of the AUV in the subregion is then planned using reinforcement learning. Subsequently, the dynamic competition of multiple AUVs is used to handle emergency tasks. The simulation demonstrates that our proposed algorithm significantly reduces energy consumption to guarantee load balancing while reducing end-to-end transmission delay.

In Cognitive Radio Networks (CRNs), the available channels for the unlicensed Secondary Users (SUs) may be varying. When SUs want to communicate with each other, they must first access the same channel simultaneously. The process of accessing the same channel is referred to as a rendezvous process, by which SUs can exchange control information for establishing data transmission link. Channel Hoping (CH) is one of the most representative techniques for letting SUs rendezvous with each other. At the beginning of each time slot, SUs access available channels according to their CH Sequences (CHSs) generated by the CH algorithm. In our previous work, we have proposed a Heterogeneous Radio Rendezvous (HRR) algorithm to address the rendezvous problem for heterogeneous CRNs, where SUs may be equipped with different numbers of radios. In this paper, we propose an Enhanced HRR (EHRR) algorithm, which can further shorten the length of period for the CHSs. Compared with the HRR algorithm, the EHRR algorithm lowers the upper bounds of Maximum Time To Rendezvous (MTTR). Moreover, the upper bounds of MTTR for the EHRR algorithm are derived by theoretical analysis. In addition, the performance of the EHRR algorithm in terms of MTTR is evaluated by simulation. Simulation results show the superiority of the EHRR algorithm compared with the HRR algorithm in terms of MTTR.

Cognitive Radio (CR) technique has been proposed for improving spectrum efficiency by dynamic spectrum access. In Cognitive Radio Networks (CRNs), unlicensed Secondary Users (SUs) with CR can utilize licensed spectrum without interfering licensed Primary Users (PUs). For effectively avoiding interference with licensed PUs and malicious attacks from jammers, a two-stage Learning-based Optimal Channel Selection (LOCS) algorithm for unlicensed SUs in distributed heterogeneous CRNs is proposed in this paper. The LOCS algorithm enables SUs to obtain real states of the licensed channels without knowing their information. Hence, SUs using LOCS algorithm can efficiently avoid collision and attack with PUs and jammers. Besides, the LOCS algorithm considers hardware limitation of the SUs, i.e., SUs can only sense and access parts of the license spectrum during any given time. SUs can select the optimal channels for spectrum sensing and data transmission by using the LOCS algorithm. Simulation results show the efficiency of our proposed algorithm in terms of collision and attack avoidance.

In the smart cities, sharing electric vehicles have many advantages such as environmental protection, low carbon emission, and high efficiency, which will greatly facilitate human life. However, whether the shared electric vehicle can be charged in time directly affects the users' experience. Based on this fact, we study that how to deploy the charging piles in the parking station of the shared electric vehicle, and propose a charging pile deployment model. First, the number of shared electric vehicles that check out from parking stations is predicted based on the random forest algorithm. Then, a mobile model of the shared electric vehicle is established. Based on the mobile model, the number of shared electric vehicles that check in within the target period is calculated and whether they reach the starvation state are determined. After that, the number of charging piles to be deployed is determined. Finally, the effects of hunger rate, battery capacity and the distance between parking stations on the number of charging piles were simulated by experiments. The experimental results verify the validity and feasibility of the proposed model. The prediction model proposed in this paper can provide a certain decision basis for the sharing of electric vehicle charging pile planning.

An exciting network called smart IoT has great potential to improve the level of our daily activities and the communication. Source location privacy is one of the critical problems in the wireless sensor network (WSN). Privacy protections, especially source location protection, prevent sensor nodes from revealing valuable information about targets. In this paper, we first discuss about the current security architecture and attack modes. Then we propose a scheme based on cloud for protecting source location, which is named CPSLP. This proposed CPSLP scheme transforms the location of the hotspot to cause an obvious traffic inconsistency. We adopt multiple sinks to change the destination of packet randomly in each transmission. The intermediate node makes routing path more varied. The simulation results demonstrate that our scheme can confuse the detection of adversary and reduce the capture probability.

Energy constraint is a critical issue in the development of wireless sensor networks (WSNs) because sensor nodes are generally powered by batteries. Recently, wireless rechargeable sensor networks (WRSNs), which introduce wireless mobile chargers (MCs) to replenish energy for nodes, have been proposed to resolve the root cause of energy limitations in WSNs. However, existing wireless charging algorithms cannot fully leverage the mobility of MCs because unity between the energy replenishment process and mobile data collection has yet to be realized. Thus, in this paper, a joint energy replenishment and data collection algorithm for WRSNs is proposed. In this algorithm, the network is divided into multiple clusters based on a K-means algorithm. Two MCs visit the anchor point in each cluster by moving along the shortest Hamiltonian cycle in opposite directions. The positions of anchor points are calculated by the base station (BS) based on the energy distribution in each cluster. A spare MC is assigned to the network in case either of the two MCs depletes its energy before reaching the BS. After the two MCs' current tours are over, a semi-Markov model is proposed for energy prediction so anchor points can be updated in the next round. Simulation results demonstrate the semi-Markov-based energy prediction model is highly precise, and the proposed algorithm can replenish energy for network energy effectively.

An intelligent manhole cover management system (IMCS) is one of the most important basic platforms in a smart city to prevent frequent manhole cover accidents. Manhole cover displacement, loss, and damage pose threats to personal safety, which is contrary to the aim of smart cities. This paper proposes an edge computing-based IMCS for smart cities. A unique radio frequency identification tag with tilt and vibration sensors is used for each manhole cover, and a Narrowband Internet of Things is adopted for communication. Meanwhile, edge computing servers interact with corresponding management personnel through mobile devices based on the collected information. A demonstration application of the proposed IMCS in the Xiasha District of Hangzhou, China, showed its high efficiency. It efficiently reduced the average repair time, which could improve the security for both people and manhole covers.

Both safety and energy conservation are very important advantages of smart cities. Namely, the city street lamp is correlated with both safety and energy conservation. Therefore, a street lamp is an indispensable part of the smart cities. However, current street lamps have lack of smart characteristics, which increases both danger and energy consumption. In order to address these problems, a smart street lamp (SSL) based on the fog computing for smarter cities is proposed in this paper. The advantages of the proposed SSL are as follows: 1) fine management, because every street lamp can be operated independently; 2) dynamic brightness adjustment, all street lamps can be adjusted dynamically; and 3) autonomous alarm on abnormal states, each street lamp can report the abnormal status independently, such as broken, stolen, and so on. The experimental results showed that the proposed SSL can improve the energy efficiency and reduce danger.

With the rapid development of the 5G network and Internet of Things (IoT), lots of mobile and IoT devices generate massive amounts of multisource heterogeneous data. Effective processing of such data becomes an urgent problem. However, traditional centralised models of cloud computing are challenging to process multisource heterogeneous data effectively. Mobile edge computing (MEC) emerges as a new technology to optimise applications or cloud computing systems. However, the features of MEC such as content perception, real‐time computing, and parallel processing make the data security and privacy issues that exist in the cloud computing environment more prominent. Protecting sensitive data through traditional encryption is a very secure method, but this will make it impossible for the MEC to calculate the encrypted data. The fully homomorphic encryption (FHE) overcomes this limitation. FHE can be used to compute ciphertext directly. Therefore, we propose a ciphertext arithmetic operation that implements data with integer homomorphic encryption to ensure data privacy and computability. Our scheme refers to the integer operation rules of complement, addition, subtraction, multiplication, and division. First, we use Boolean polynomials (BP) of containing logical AND, XOR operations to represent the rulers. Second, we convert the BP into homomorphic polynomials (HP) to perform ciphertext operations. Then, we optimise our scheme. We divide the ciphertext vector of integer encryption into subvectors of length 2 and increase the length of private key of FHE to support the 3‐multiplication level additional. We test our optimised scheme in DGHV and CMNT. In the number of ciphertext refreshes, the optimised scheme is reduced by 2/3 compared to the original scheme, and the time overhead of our scheme is reduced by 1/3. We also examine our scheme in CNT of without bootstrapping. The time overhead of optimised scheme over DGHV and CMNT is close to the original scheme over CNT.

Channel Hopping (CH) is a representative technique to solve the rendezvous problem for Cognitive Radio Networks (CRNs). Multiple radios technique were utilized in several latest researches on CH owing to the fact that it can significantly reduce the Time-To-Rendezvous (TTR) while the cost of the device is low. However, the radios of one unlicensed Secondary User (SU) may access same channel at the same time for most of the existing multi-radio CH protocols, which is a waste of energy. Moreover, the number of radios for the SUs is implicitly assumed same or must be more than one, which is unrealistic for heterogeneous CRNs. In this paper, an energy-efficient CH protocol, Hybrid Radio Rendezvous (HRR) protocol is proposed to address the above issues. Furthermore, theoretical analysis is presented to derive the upper bound on the Maximum TTR (MTTR) for the HRR protocol. In addition, the theoretical analysis is corroborated by extensive simulations while the simulation results show that the HRR protocol outperforms the state- of-the-art CH protocols in terms of the TTR and the energy efficiency.

Recent breakthroughs in wireless technologies have greatly spurred the emergence of industrial wireless sensor networks (IWSNs). To facilitate the adaptation of IWSNs to industrial applications, concerns about networks' full coverage and connectivity must be addressed to fulfill reliability and real-time requirements. Although connected target coverage (CTC) algorithms in general sensor networks have been extensively studied, little attention has been paid to reveal both the applicability and limitations of different coverage strategies from an industrial viewpoint. In this paper, we analyze characteristics of four recent energy-efficient coverage strategies by carefully choosing four representative connected coverage algorithms: 1) communication weighted greedy cover; 2) optimized connected coverage heuristic; 3) overlapped target and connected coverage; and 4) adjustable range set covers. Through a detailed comparison in terms of network lifetime, coverage time, average energy consumption, ratio of dead nodes, etc., characteristics of basic design ideas used to optimize coverage and network connectivity of IWSNs are embodied. Various network parameters are simulated in a noisy environment to obtain the optimal network coverage. The most appropriate industrial field for each algorithm is also described based on coverage properties. Our study aims to provide IWSNs designers with useful insights to choose an appropriate coverage strategy and achieve expected performance indicators in different industrial applications.

The increasing demand on the main memory capacity is one of the main big data challenges. Dynamic random access memory (DRAM) does not represent the best choice for a main memory, due to high power consumption and low density. However, the nonvolatile memory, such as the phase-change memory (PCM), represents an additional choice because of the low power consumption and high-density characteristic. Nevertheless, the high access latency and limited write endurance have disabled the PCM to replace the DRAM currently. Therefore, a hybrid memory, which combines both the DRAM and the PCM, has become a good alternative to the traditional DRAM memory. Both DRAM and PCM disadvantages are challenges for the hybrid memory. In this paper, a dynamic adaptive replacement policy (DARP) in the shared last-level cache for the DRAM/PCM hybrid main memory is proposed. The DARP distinguishes the cache data into the PCM data and the DRAM data, then, the algorithm adopts different replacement policies for each data type. Specifically, for the PCM data, the least recently used (LRU) replacement policy is adopted, and for the DRAM data, the DARP is employed according to the process behavior. Experimental results have shown that the DARP improved the memory access efficiency by 25.4%.

Underwater acoustic sensor networks (UASNs) are susceptible to a large number of security threats, e.g., jamming attacks at the physical layer, collision attacks at the data link layer, and DoS attacks at the network layer. Because of the communication, computation, and storage constraints of underwater sensor nodes, traditional security mechanisms, e.g., encryption algorithms, are not suitable for UASNs. A trust model has been recently suggested as an effective security mechanism for open environments such as terrestrial wireless sensor networks (TWSNs), and considerable research has been done on modeling and managing trust relationships among sensor nodes. However, the trust models proposed for TWSNs cannot be directly used in a UASN due to its unique characteristics such as unreliable acoustic channel, dynamic network structure, and weak link connectivity. In this paper, we propose a novel trust model based on cloud theory (TMC) for UASNs. The objective of TMC is to solve uncertainty and fuzziness of trust based on cloud theory, which ultimately improves trust evaluation accuracy. Moreover, simulation results demonstrate that our algorithm outperforms other related works in terms of detection ratio of malicious nodes, successful packet delivery ratio, and network lifetime.

With the rapid development of marine technology, underwater sensor networks (UWSNs) are gradually evolving from research to practice in recent years. Practicability and reliability are two major concerns for routing protocols in UWSNs. As localization is not necessary in depth-based routing protocol (DBR), it has an outstanding practicability than other geographic routing protocols. However, the reliability is not well ensured. In this paper, we propose an innovative depth-based routing with network coding improving routing reliability while preserving the intrinsic distributed manner of DBR and introducing little time delay and energy cost. Moreover, a simple analytical performance model where ideal MAC is assumed is proposed to derive the analytical delivery ratio for our DBR-NC and DBR protocols. This analytical model is validated by simulation results. The extensive simulation results show that the proposed DBR-NC protocol outperforms (over 15%) the state of art DBR protocols in terms of packet delivery ratio. We also show that our DBR-NC will not introduce much extra delay and energy consumptions.

Traditional task solution schemes in Wireless Sensor Networks (WSNs) are mainly not suitable for complicated task processing due to high energy consumption and long processing delay. In this paper, we proposed an energy efficient Complicated Task Solution scheme for real-time task processing based on node Cooperation (CTSC), which consists of two main phases: task grouping and task allocation. In the task grouping phase, complicated tasks are divided into different groups based on task graph. In the task allocation phase, based on node cooperation, different group tasks are allocated to different nodes by using bid invitation. Thus, multiple tasks can be processed in parallel, which ultimately reduces task processing delay and limits communication overheads. Simulation results show that CTSC is much more suitable for large scale WSNs. In addition, CTSC outperforms related works in terms of shorter response time of task processing and less energy consumption.

Secondary Users (SUs) have capability to sense available licensed spectrum in Cognitive Radio Networks (CRNs). Hence, SUs can opportunistically access to the licensed spectrum without disturbing Primary Users (PUs). In this paper, a novel network architecture is proposed to reduce the production cost and the energy consumption for CRNs. The proposed network architecture is based on the spectral requirement of Secondary Users (SUs). In the proposed network architecture, only parts of SUs are equipped with Cognitive Radio (CR) module. In addition, a minimum number of SUs are selected to sense available licensed spectrum, which aims at reducing the energy consumption further. The minimum number of SUs selection problem is formulated as a non-linear programming problem under the constrains of energy efficiency and the real-time available spectrum information. However, the non-linear programming problem is a NP-hard problem. Hence, a distributed heuristic algorithm is proposed to calculate the near-optimal solution. The simulation results demonstrate that the proposed heuristic algorithm in the proposed network architecture outperforms the random algorithm in the proposed network architecture and traditional Cognitive Radio Ad Hoc Networks (CRAHNs) in energy efficiency.

A new generation memory, Non-Volatile Memory (NVM), such as Phase-Change Memory (PCM), has been adopted together with DRAM in the main memory to form the hybrid main memory for low energy consumption and high capacity. The biggest challenge of hybrid memory is how to decrease the average memory access cost for the higher cost of NVM's read/write operation. Currently, most researches are based on migration. However, the page migration itself is a high cost operation. And the migration based policy produces many migration operations, which induces high cost in memory access. Therefore, in order to decrease the cost, we present a virtual page behavior based page management policy (VBPM) in this paper. According to the virtual pages' behavior, we allocate virtual pages into DRAM or PCM physical pages correspondingly. The whole process is migration independent. The experimental results show our VBPM decreases the average memory access time by 24%, moreover, VBPM improves real-time performance in critical path.

With the development of wireless communication, industrial wireless sensor networks (IWSNs) plays an important role in monitoring and control systems. In this paper, we extend the application of IWSNs into High Frequency Surface-Wave Radar (HFSWR) system. The traditional antenna is replaced by mobile IWSNs. In combination of the application precondition of super-directivity in HF band and circular topology of IWSNs, a super- directivity synthesis method is presented for designing super-directivity array. In this method, the dominance of external noise is ensured by constraining the Ratio of External to Internal Noise (REIN) of the array, and the desired side lobe level is achieved by implementing linear constraint. By using this method, the highest directivity will be achieved in certain conditions. Using the designed super directive circular array as sub-arrays, the compact receive antenna array is constructed, the purpose of miniaturization is achieved. Simulation verifies that the proposed method is correct and effective, the validity of the proposed method has been proved.

Internet of Things (IoT) is a fast-growing research area which spans various technological fields, including computer science, electronic engineering, mobile and wireless communications, embedded systems, etc. Many technologies serve as the building blocks of this new paradigm, such as wireless sensor networks, RFID, cloud services, machine-to-machine interfaces, and so on. IoT will allow billions of objects in the physical world as well as virtual environments to exchange data with each other in an autonomous way so as to create smart environments such as automotive, healthcare, logistics, environmental monitoring, and many others. However, IoT introduces new challenges for the security of systems and processes and the privacy of individuals. Protecting the information in IoT is a complex and difficult task. IoT requires global connectivity and accessibility, which means that anyone can access in anytime and anyway, and that the number of attack vectors available to malicious attackers might become staggering. Furthermore, the inherent complexity of the IoT, where multiple heterogeneous entities located in different contexts can exchange information with each other, further complicates the design and deployment of efficient, interoperable, and scalable security mechanisms. Ubiquitous and cloud computing also increase the urgency of the privacy leakage problem. As a result, there is an increasing demand for development of new security and privacy approaches to guarantee the security, privacy, integrity, and availability of resources in IoTs. Traditional security countermeasures cannot be directly used in IoTs because of the different standards and communication stacks involved. Moreover, the large number of interconnected devices in IoTs introduces scalability issues. Therefore, new and novel security and privacy methods, architectures, and solutions are needed to deal with security threats in IoTs. In this special issue, we are delighted to present a selection of nine papers, which, in our opinion, will contribute to the enhancement of knowledge in security and privacy research for IoTs. The collection of high-quality research papers provides a view on the latest research advances on security and privacy methods, architectures, and solutions in IoTs. The contributions of these papers are outlined in the succeeding text. In the first paper, A new authentication protocol for healthcare applications using wireless medical sensor networks with user anonymity, Xiong Li, Jianwei Niu, Saru Kumari, Junguo Liao, Wei Liang, and Muhammad Khurram Khan adopt the biometrics as the third authentication factor and propose a new authentication protocol to guarantee secure communication and protect the user privacy for healthcare application using WMSNs with user anonymity. In the proposed protocol, a wrong password detection mechanism is designed to reduce unnecessary computation and communication costs. In the second paper, Fusion: coalesced confidential storage and communication framework for the IoT, instead of developing independent security solutions, Ibrahim Ethem Bagci, Shahid Raza, Utz Roedig, and Thiemo Voigt present Fusion to address both the communication and storage security. The paper demonstrates that compared with performing traditional cryptographic operations separately, using the combined solution is much safer and more energy efficient. In the third paper, A changeable personal identification number-based keystroke dynamics authentication system on smart phones, Ting-Yi Chang, Cheng-Jung Tsai, Wang-Jui Tsai, Chun-Cheng Peng, and Han-Sing Wu propose a novel keystroke dynamics-based authentication (KDA) system to protect security of smart phones. Compared with the traditional KDA system, in the proposed new KDA system, the personal identification number codes of the subscribers can be well protected, and the users can change their personal identification number codes and passwords anytime without extra retraining. With the wide use of smart mobile devices, task collaborations among mobile devices are becoming ubiquitous and important. The security issues can be well guaranteed if the tasks can be effectively balanced. Therefore, in the fourth paper, SAFE-CROWD: secure task allocation for collaborative mobile social network, Xiaochen Fan, Panlong Yang, Qingyu Li, Dawei Liu, Chaocan Xiang, and Yonggang Zhao propose “SAFE-CROWD”, which is a secure task-allocation scheme. Using SAFE-CROWD, the tasks can be securely and collaboratively completed among mobile devices. In the fifth paper, ShoVAT: Shodan-based vulnerability assessment tool for Internet-facing services, Béla Genge and Cǎlin Enǎchescu propose a novel tool called Shodan-based vulnerability assessment tool (ShoVAT) to guarantee the automated vulnerability assessment of Internet-facing services. Based on the indexing capabilities of Shodan search engine, ShoVAT first finds services and then reconstructs key vulnerability identifiers. Finally, the vulnerabilities are obtained using National Vulnerability Database. The experiment results show that 3922 vulnerabilities are found on 1501 services in 12 different institutions. In the sixth paper, Distributed flood attack detection mechanism using artificial neural network in wireless mesh networks, Muhammad Altaf Khan, Shafiullah Khan, Bilal Shams, and Jaime Lloret propose an artificial neural network-based technique to detect distributed flooding attacks in multi-hop wireless mesh networks. The proposed scheme is named as the distributed flood attack detector. The distributed flood attack detector is designed to be implemented at mesh gateway in wireless mesh network. By using artificial neural networks, the network traffic can be divided into different categories, and thus, the flood attacks can be detected. In the seventh paper, Toward a flexible and fine-grained access control framework for infrastructure as a service clouds, Bo Li, Jianxin Li, Lu Liu, and Chao Zhou propose a flexible and fine-grained access control framework, named IaaS-oriented Hybrid Access Control (iHAC), to ensure that the resources cannot be illegally accessed or used. iHAC consists of three main parts: an IaaS-oriented Hybrid Access Control model, a VM-centric access control approach, and a VMM-enabled network access control mechanism. The simulation results show that iHAC can efficiently make correct access control decisions with acceptable performance overhead. In the eighth paper, An intrusion detection method for wireless sensor network based on mathematical morphology, Yanwen Wang, Xiaoling Wu, and Hainan Chen propose an innovative intrusion detection method called granulometric size distribution (GSD) method based on mathematical morphology to detect malicious attack in IoTs. If the number of active nodes in a wireless sensor network is fixed, the GSD curves are similar. Therefore, malicious nodes can be efficiently detected based on the abnormal GSD. In the last of the presented papers, A secure energy-efficient access control scheme for wireless sensor networks based on elliptic curve cryptography, Yuanyuan Zhang, Neeraj Kumar, Jianhua Chen, and Joel J. P. C. Rodrigues propose a secure energy-efficient access-control scheme for wireless sensor networks based on elliptic curve cryptography. The algorithm is explained in detail, and a variety of malicious attacks are simulated to evaluate the performance of the proposed algorithm. To summarize, we believe that this special issue will contribute to enhancing knowledge in security and privacy research in IoT in particular. In addition, we also hope that the presented results will stimulate further research in the important areas of information and network security. We also want to thank the editor-in-chief of the Security and Communication Networks journal, the leading researchers contributing to the special issue, and excellent reviewers for their great help and support that made this special issue possible.

In this paper, an improved localization method named three-uniform-linear-array localization is proposed for patient track systems. Three receivers adopting a smart antenna technique cooperate with each other to locate the patients using the angulation positioning method. In order to be able to track patients in environment with high patient density, a high-resolution direction-of-arrival (DOA) estimation algorithm for the coexistence of noncircular and circular signals is proposed. First, the maximal and common noncircularity rated signals are preliminarily estimated. Second, based on the noise space block matrix, the DOAs of these signals are re-estimated with high accuracy. Then, the covariance matrix of the maximal and common noncircularity rated signals is reconstructed. The contributions of these signals are eliminated after performing a subtraction operation on the covariance matrix of the received data and only those of circular signals remain. Finally, the DOAs of circular signals are obtained. Results of simulations and real tests demonstrate the effectiveness and performance of the proposed algorithm.

Network architectures based on mobile devices and wireless communications present several constraints (e.g., processor, energy storage, bandwidth, etc.) that affect the overall network performance. Cooperation strategies have been considered as a solution to address these network limitations. In the presence of unstable network infrastructures, mobile nodes cooperate with each other, forwarding data and performing other specific network functionalities. This article proposes a generalized incentive-based cooperation solution for mobile services and applications called MobiCoop. This reputation-based scheme includes an application framework for mobile applications that uses a Web service to handle all the nodes reputation and network permissions. The main goal of MobiCoop is to provide Internet services to mobile devices without network connectivity through cooperation with neighbor devices. The article includes a performance evaluation study of MobiCoop considering both a real scenario (using a prototype) and a simulation-based study. Results show that the proposed approach provides network connectivity independency to users with mobile apps when Internet connectivity is unavailable. Then, it is concluded that MobiCoop improved significantly the overall system performance and the service provided for a given mobile application.

Abstract Localization is an essential and major issue for underwater acoustic sensor networks (UASNs). Almost all the applications in UASNs are closely related to the locations of sensors. In this paper, we propose a multi‐anchor nodes collaborative localization (MANCL) algorithm, a three‐dimensional (3D) localization scheme using anchor nodes and upgrade anchor nodes within two hops for UASNs. The MANCL algorithm divides the whole localization process into four sub‐processes: unknown node localization process, iterative location estimation process, improved 3D Euclidean distance estimation process, and 3D DV‐hop distance estimation process based on two‐hop anchor nodes. In the third sub‐process, we propose a communication mechanism and a vote mechanism to determine the temporary coordinates of unknown nodes. In the fourth sub‐process, we use two‐hop anchor nodes to help localize unknown nodes. We also evaluate and compare the proposed algorithm with a large‐scale localization algorithm through simulations. Results show that the proposed MANCL algorithm can perform better with regard to localization ratio, average localization error, and energy consumption in UASNs. Copyright © 2014 John Wiley & Sons, Ltd.

As the compensation and extension of static wireless sensor nodes, wearable helmets can build regional early warning network of personnel security. In this paper, a wearable helmet is presented towards early warning of leaking toxic gas in large-scale petrochemical plants for protecting the lives and safety of workers better.

Recently, wireless sensor networks (WSNs) have been widely used in many applications, such as Smart Grid. However, it is generally known that WSNs are energy limited, which makes WSNs vulnerable to malicious attacks. Among these malicious attacks, a sinkhole attack is the most destructive one, since only one sinkhole node can attract surrounding nodes with unfaithful routing information, and it executes severe malicious attacks, e.g. the selective forwarding attack. In addition, a sinkhole node can cause a large amount of energy wastes of surrounding nodes, which results in abnormal energy hole in WSNs. Thus, it is necessary to design an effective mechanism to detect the sinkhole attack. In this paper, we propose a novel Intrusion Detection Algorithm based on neighbor information against Sinkhole Attack (IDASA). Different from traditional intrusion detection algorithms, IDASA takes full advantage of neighbor information of sensor nodes to detect sinkhole nodes. In addition, we evaluate IDASA in terms of malicious node detection accuracy, packet loss rate, energy consumption and network throughput in MATLAB. Simulation results show that the performance of IDASA is better than that of other related algorithms.

The uncertainty and extreme large scale of cloud resources make task scheduling very difficult which affects the user quality of experience and probably result in a waste of cloud resources and energy consumption. Moreover, some resources stay in an unusable state for extended time. To take into account these problems a cloud resource evaluation model is proposed, termed Entropy Optimization Evaluation and ant colony clustering Model (EOEACCM). The model releases long-term unavailable resources to save energy. First, by mean of the entropy increasing minimum principle, the proposed model can maximize the system utilization and balance profits of both cloud resource providers and users. As a consequence, it can shorten task completion time. Secondly, the model narrows the task scheduling size and achieves optimal scheduling by clustering. To make the model more suitable for the dynamics of cloud resources, the model design improves pheromone update policies by fixing total path length in each function cycle when clustering by the ant colony algorithm. Evaluation of results using EOEACCM demonstrate that it may be applicable for resource management strategies for migration and release, an application which can effectively save energy. The proposed model was evaluated by simulation. Experiment results showed the positive effect of user satisfaction from entropy optimization, as well as scheduling time from clustering. Moreover, when the scale of tasks was large, this clustering algorithm performed much better than others. The clustering model also demonstrated better adaptability when some cloud resources were joined or terminated.

Underwater acoustic sensor networks (UASNs) have been widely used in many applications where a variable number of sensor nodes collaborate with each other to perform monitoring tasks. A trust model plays an important role in realizing collaborations of sensor nodes. Although many trust models have been proposed for terrestrial wireless sensor networks (TWSNs) in recent years, it is not feasible to directly use these trust models in UASNs due to unreliable underwater communication channel and mobile network environment. To achieve accurate and energy efficient trust evaluation in UASNs, an attack-resistant trust model based on multidimensional trust metrics (ARTMM) is proposed in this paper. The ARTMM mainly consists of three types of trust metrics, which are link trust, data trust, and node trust. During the process of trust calculation, unreliability of communication channel and mobility of underwater environment are carefully analyzed. Simulation results demonstrate that the proposed trust model is quite suitable for mobile underwater environment. In addition, the performance of the ARTMM is clearly better than that of conventional trust models in terms of both evaluation accuracy and energy consumption.

Trust models have been recently suggested as an effective security mechanism for Wireless Sensor Networks (WSNs). Considerable research has been done on modeling trust. However, most current research work only takes communication behavior into account to calculate sensor nodes' trust value, which is not enough for trust evaluation due to the widespread malicious attacks. In this paper, we propose an Efficient Distributed Trust Model (EDTM) for WSNs. First, according to the number of packets received by sensor nodes, direct trust and recommendation trust are selectively calculated. Then, communication trust, energy trust and data trust are considered during the calculation of direct trust. Furthermore, trust reliability and familiarity are defined to improve the accuracy of recommendation trust. The proposed EDTM can evaluate trustworthiness of sensor nodes more precisely and prevent the security breaches more effectively. Simulation results show that EDTM outperforms other similar models, e.g., NBBTE trust model.

Abstract In this paper, we propose a cross‐layer optimized geographic node‐disjoint multipath routing algorithm, that is, two‐phase geographic greedy forwarding plus. To optimize the system as a whole, our algorithm is designed on the basis of multiple layers' interactions, taking into account the following. First is the physical layer, where sensor nodes are developed to scavenge the energy from environment, that is, node rechargeable operation (a kind of idle charging process to nodes). Each node can adjust its transmission power depending on its current energy level (the main object for nodes with energy harvesting is to avoid the routing hole when implementing the routing algorithm). Second is the sleep scheduling layer, where an energy‐balanced sleep scheduling scheme, that is, duty cycle (a kind of node sleep schedule that aims at putting the idle listening nodes in the network into sleep state such that the nodes will be awake only when they are needed), and energy‐consumption‐based connectedk‐neighborhood is applied to allow sensor nodes to have enough time to recharge energy, which takes nodes' current energy level as the parameter to dynamically schedule nodes to be active or asleep. Third is the routing layer, in which a forwarding node chooses the next‐hop node based on 2‐hop neighbor information rather than 1‐hop. Performance of two‐phase geographic greedy forwarding plus algorithm is evaluated under three different forwarding policies, to meet different application requirements. Our extensive simulations show that by cross‐layer optimization, more shorter paths are found, resulting in shorter average path length, yet without causing much energy consumption. On top of these, a considerable increase of the network sleep rate is achieved. Copyright © 2014 John Wiley & Sons, Ltd.

The recent years realize a progressive transition where fixed computing reached maturity and the mobility age started to thrive. Nowadays, another transition from the mobility age to the “Internet of Everything” (IoE) is taking place. In the IoE vision, several types of quotidian objects will be able to communicate over the Internet. As a result, it is expected that within a decade, IoE will have an economic value of $14.4 trillion, as the number of devices connected to the Internet continues to increase exponentially. The support for security services in these emerging resource-constrained devices is considered a challenge but needs to take into account from the very early stages of the wireless network inception. This paper proposes a network-based intrusion detection system (IDS) for IPv6-enabled wireless sensor networks. The proposed IDS is used to detect security attacks based on traffic signatures and abnormal behaviors.

The growing gap between microprocessor speed and DRAM speed is a major problem that computer designers are facing. In order to narrow the gap, it is necessary to improve DRAM's speed and throughput. Moreover, on multi-core platforms, DRAM memory shared by all cores usually suffers from the memory contention and interference problem, which can cause serious performance degradation and unfairness among parallel running threads. To address these problems, this paper proposes techniques to take both advantages of partitioning cores, threads and memory banks into groups to reduce interference among different groups and grouping the memory accesses of the same row together to reduce cache miss rate. A memory optimization framework combined thread scheduling with memory scheduling (CTMS) is proposed in this paper, which simultaneously minimizes memory access schedule length, memory access time and reduce interference to maximize performance for multi-core systems. Experimental results show CTMS is 12.6% shorter in memory access time, while improving 11.8% throughput on average. Moreover, CTMS also saves 5.8% of the energy consumption.

Abstract Traditional security schemes developed for sensor networks are not suitable for cluster‐based wireless sensor networks (WSNs) because of their susceptibility to Denial of Service (DoS) attacks. In this paper, we provide a security scheme against DoS attacks (SSAD) in cluster‐based WSNs. The scheme establishes trust management with energy character, which leads nodes to elect trusted cluster heads. Furthermore, a new type of vice cluster head node is proposed to detect betrayed cluster heads. Theoretical analyses and simulation results show that SSAD can prevent and detect malicious nodes with high probability of success. Copyright © 2011 John Wiley & Sons, Ltd.

In Mobile Wireless Sensor Networks (MWSNs), location information of nodes is the basis of many applications. Compared with that in a static network, the localization issue in a mobile network is more difficult due to uncertainty of node positions, thus much more challenges are brought to MWSNs. In this paper, we propose three localization models to evaluate performances of DV-hop localization algorithm in MWSNs. We compare the performance of three localization models, DV-hop+RWP, DV-hop+RD and DV-hop+RPGM, with that of DV-hop. Simulation results show that, DV-hop+RD has higher localization accuracy and lower energy consumption than others. However, when the number of nodes increases, more unknown nodes in DV-hop+RWP can be localized than that in DV-hop+RD. The localization success and localization accuracy of DV-hop+RPGM, which is similar to that of DV-hop, is lower than DV-hop+RWP.

Accurately locating unknown nodes is a critical issue in the study of wireless sensor networks (WSNs). Many localization approaches have been proposed based on anchor nodes, which are assumed to know their locations by manual placement or additional equipments such as global positioning system. However, none of these approaches can work properly under the adversarial scenario. In this paper, we propose a novel scheme called two-step secure localization (TSSL) stand against many typical malicious attacks, e.g. wormhole attack and location spoofing attack. TSSL detects malicious nodes step by step. First, anchor nodes collaborate with each other to identify suspicious nodes by checking their coordinates, identities and time of sending information. Then, by using a modified mesh generation scheme, malicious nodes are isolated and the WSN is divided into areas with different trust grades. Finally, a novel localization algorithm based on the arrival time difference of localization information is adopted to calculate locations of unknown nodes. Simulation results show that the TSSL detects malicious nodes effectively and the localization algorithm accomplishes localization with high localization accuracy.

Owing to wireless communication's broadcast nature, wireless sensor networks (WSNs) are vulnerable to denial‐of‐service (DoS) attacks. It is of great importance to design an efficient intrusion detection scheme (IDS) for WSNs. In this study, the authors propose a novel IDS based on energy prediction (IDSEP) in cluster‐based WSNs. The main idea of IDSEP is to detect malicious nodes based on energy consumption of sensor nodes. Sensor nodes with abnormal energy consumption are identified as malicious ones. Furthermore, IDSEP is designed to differentiate categories of ongoing DoS attacks based on energy consumption thresholds. The simulation results show that IDSEP detects and recognises malicious nodes effectively.

ABSTRACT In wireless sensor networks (WSNs), many applications require sensor nodes to obtain their locations. Now, the main idea in most existing localization algorithms has been that a mobile anchor node (e.g., global positioning system‐equipped nodes) broadcasts its coordinates to help other unknown nodes to localize themselves while moving according to a specified trajectory. This method not only reduces the cost of WSNs but also gets high localization accuracy. In this case, a basic problem is that the path planning of the mobile anchor node should move along the trajectory to minimize the localization error and to localize the unknown nodes. In this paper, we propose a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT) in WSNs. LMAT algorithm uses a mobile anchor node to move according to trilateration trajectory in deployment area and broadcasts its current position periodically. Simulation results show that the performance of our LMAT algorithm is better than that of other similar algorithms. Copyright © 2011 John Wiley & Sons, Ltd.

Localization is a vital foundation in Wireless Sensor Networks (WSNs). However, most previous localization methods assume an idealistic radio propagation model that is far from reality. This will lead to inaccurate localization, since unknown nodes cannot receive enough location messages under the radio irregularity model. In our previous work “LMAT”, we solved the path planning problem of the mobile anchor node without taking into account radio irregularity. This paper further studies how localization performance is affected by radio irregularity. In order to improve localization accuracy, the anchor node's radio range is adjusted to guarantee that all unknown nodes can receive sufficient localization information. Furthermore, it points out the relationship between degree of irregularity (DOI) and communication distance, and the impact of radio irregularity on message receiving probability in presence of 2-hop localization. Finally, simulation results show that, compared with 1-hop localization algorithm, 2-hop localization along with a good trajectory reduces average localization error.

Currently, in Wireless Sensor Networks (WSNs), the main idea in most localization algorithms has been that a mobile anchor node, e.g., GPS-equipped (Global Positioning System) nodes, broadcasts its coordinates to locate unknown nodes. In this case, a basic problem is the path planning of the mobile anchor node which should move along the trajectory to minimize the localization error and locate the unknown nodes. In this paper, we propose a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT). LMAT algorithm uses a mobile anchor node to move according to the equilateral triangle trajectory in deployment area. Simulation results show that the performance of our LMAT algorithm is better than that of other similar algorithms, e.g., SPIRAL, SCAN, DOUBLE SCAN and HILBERT algorithms.

To solve the secure localization problems, a number of secure localization schemes have been developed at present. However, most of these techniques cannot survive collusion attacks where a majority of malicious nodes launch colluding attacks. In this paper, we introduce a new collusion attack model called Collaborative Collusion Attack Model (CCAM) and propose a novel scheme called Two-Step Format Detection (TSFD) that is well suited to WSN which is a resource constrained environment. The TSFD has reasonable and acceptable communication cost and algorithm complexity. Through simulations, we compare the performance of TSFD with other secure localization schemes and show that TSFD has more efficient and resilient performance.

Abstract A heterogeneous distributed sensor network (HDSN) is a type of distributed sensor network where sensors with different deployment groups and different functional types participate at the same time. In other words, the sensors are divided into different deployment groups according to different types of data transmissions, but they cooperate with each other within and out of their respective groups. However, in traditional heterogeneous sensor networks, the classification is based on transmission range, energy level, computation ability, and sensing range. Taking this model into account, we propose a secure group association authentication mechanism using one‐way accumulator which ensures that: before collaborating for a particular task, any pair of nodes in the same deployment group can verify the legitimacy of group association of each other. Secure addition and deletion of sensors are also supported in this approach. In addition, a policy‐based sensor addition procedure is also suggested. For secure handling of disconnected nodes of a group, we use an efficient pairwise key derivation scheme to resist any adversary's attempt. Along with proposing our mechanism, we also discuss the characteristics of HDSN, its scopes, applicability, future, and challenges. The efficiency of our security management approach is also demonstrated with performance evaluation and analysis. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract The key problem of location service in indoor sensor networks is to quickly and precisely acquire the position information of mobile nodes. Due to resource limitation of the sensor nodes, some of the traditional positioning algorithms, such as two‐phase positioning (TPP) algorithm, are too complicated to be implemented and they cannot provide the real‐time localization of the mobile node. We analyze the localization error, which is produced when one tries to estimate the mobile node using trilateration method in the localization process. We draw the conclusion that the localization error is the least when three reference nodes form an equilateral triangle. Therefore, we improve the TPP algorithm and propose reference node selection algorithm based on trilateration (RNST), which can provide real‐time localization service for the mobile nodes. Our proposed algorithm is verified by the simulation experiment. Based on the analysis of the acquired data and comparison with that of the TPP algorithm, we conclude that our algorithm can meet real‐time localization requirement of the mobile nodes in an indoor environment, and make the localization error less than that of the traditional algorithm; therefore our proposed algorithm can effectively solve the real‐time localization problem of the mobile nodes in indoor sensor networks. Copyright © 2008 John Wiley & Sons, Ltd.

The greatest benefit of Webit is without a doubt that it enables a standard connection to non-PC devices using the Internet. Since the use of Webit opens a new method for maintaining and supervising non-PC devices, it has become a helpful tool for users to control and manage devices remotely. Webit actualizes the connection between non-PC devices and Internet, thus all kinds of devices around us may be controlled and accessed over the Internet through a standard Web browser. In this paper, we introduce the reasons that why provide Internet connectivity for non-PC devices. We present the architecture of Webit that can support Internet connectivity for non-PC devices. We also present how to design and implement it and finally compare the results of Webit's performance and other similar Internet servers.