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.

Traffic flow prediction is essential for intelligent transportation systems, yet privacy concerns and limited cross-regional data sharing hinder accurate modeling of global traffic patterns. This paper proposes a Federated Graph Neural Network with Equivalent Hypergraph (FGNNEH) framework to address these challenges by preserving privacy and enhancing cross-client collaboration. FGNNEH consists of two key stages. First, local traffic networks are transformed into high-dimensional hypernodes through an integrated process of backbone network extraction, kernel matrix analysis, and multilayer perceptrons. The backbone network extraction simplifies graph structures by isolating critical nodes and edges based on topological centrality, ensuring computational efficiency while retaining key spatial dependencies. Kernel matrix analysis captures complex nonlinear correlations among traffic flow features, including spatial-temporal dependencies and region-specific dynamics, enabling more effective feature representation. The multilayer perceptrons further fuse these features into robust hypernode embeddings that encapsulate both structural and traffic flow characteristics. Second, a global hypergraph construction mechanism is introduced to optimize inter-client collaboration. This mechanism employs an iterative performance feedback loop to dynamically add or remove edges between hypernodes, addressing the issue of lost inter-client connections and enabling effective cross-regional information exchange. Together, these components reconstruct a global traffic model that balances local privacy with holistic accuracy. Experiments on real-world traffic datasets, including PeMSD4, METR-LA and Guangzhou, demonstrate that FGNNEH outperforms existing methods in prediction accuracy, computational efficiency, and scalability.

Aquatic environments confront mounting threats from diverse sources, among which the persistent migration of continuous objects (e.g., chemical contaminants) is a primary concern. While advances have been made in tracking these entities using underwater acoustic sensor networks (UASNs), full-scale monitoring is challenged by unpredictable sensor deployments. Though Autonomous underwater vehicles show promise, their prohibitive costs and operational complexities limit their broad adoption. To tackle these issues, this article introduces a novel search strategy named virtual mobile agent-assisted continuous object tracking (VMA-COT). Drawing inspiration from binary tree structures, VMA-COT refines the search from the entire network to targeted hierarchical boundary mapping cells. Each cell encompasses a Section of the object’s boundary. A designated node within each cell evaluates information entropy at specified locations using a meticulously designed search sequence. By utilizing a feedback mechanism, grounded in the information entropy and the search sequence, VMA-COT progressively pinpoints the boundary. This methodology can be likened to a central node dispatching mobile agents in each cell. These agents, limited by a specific step range, adjust their trajectories for precise boundary delineation. Empirical tests and simulations demonstrate the effectiveness of VMA-COT, highlighting its efficiency, accuracy, and boundary node utilization.

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.

Tracking continuous objects in the ocean, such as oil spills, is a significant challenge, and one that underwater acoustic sensor networks (UASNs) are well suited to address. While prevalent approaches often employ complex, multiphase processes to delineate the boundaries of these objects, enabling effective monitoring, they may not be ideally suited for time-sensitive situations, where a rapid response often outweighs the need for extreme precision. To address this gap, we introduce a streamlined boundary tracking algorithm, the centroid computation of feasible region for continuous object tracking (CCFR-COT), intended for unmanned surface vessel (USV)-assisted UASNs. This algorithm utilizes a fundamental full binary tree-structured network configuration along with two of its variations, designed to handle cases where continuous objects disperse in either free space or two semi-infinite spaces. The chosen network configuration aids in the selection of boundary mapping cells, thereby sketching a coarse-grained feasible region that envelops the actual boundary. Sequentially visiting these cells, the USV systematically constructs and deconstructs convex hulls of sensor nodes to progressively reduce location uncertainty within the feasible region. Only the centroid of the final feasible region in each boundary mapping cell is output for boundary fitting. Both experimental and simulated results validate the streamlined design of the CCFR-COT, which ensures timely tracking results and provides a balance between rapid response and tracking accuracy.

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.

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.

Frequent activities in marine energy exploration and transportation have led to the ongoing presence of continuous objects, such as oil spills and radioactive waste, in the ocean. This article focuses on enhancing the understanding of these objects’ boundaries for accurate assessment of their shape, coverage, and evolution. We introduce a continuous object tracking algorithm named JGL-COT, based on joint global-local binary tree topological transformations and specifically designed for underwater acoustic sensor networks. The contribution of JGL-COT lies in its ability to leverage the correlation between the morphologies of a continuous object's boundaries over time, alternating between global and local binary tree topological transformations. When the present boundary features a strong resemblance to its previous form, JGL-COT switches to a local transformation by establishing a semi-infinite region. Otherwise, it transitions to a global transformation. Following this, JGL-COT chooses a group of binary tree-structured cells for boundary mapping, creating virtual boundary nodes as sampling points for boundary fitting. Building upon graph theory, we derive the lower bound on the effectiveness and time complexity of the proposed joint global and local binary tree topological transformations when applied to object boundary tracking. Experiments in both realistic and simulated settings confirm that JGL-COT provides highly accurate tracking and significantly reduces network energy consumption.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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%.