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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.

The diffusion of toxic gas, biochemical material, and radio-active contamination, known as continuous objects, endangers the safe production of petrochemical and nuclear industry. Industrial wireless sensor networks are a new paradigm, which shows great potential in monitoring evolving hazardous phenomena in unfriendly industrial field. To prolong the lifetime of IWSNs, existing research focuses on energy-efficient boundary nodes selection. However, sensor state cannot be scheduled proactively due to the difficulty of predicting the spatiotemporal evolution of diffusive hazard. In this article, we propose a predictive boundary tracking algorithm based on motion behavior learning (MBLPT) for continuous objects in IWSNs. Considering the multiple behavior patterns exhibited by continuous objects, MBLPB establishes data-driven models for motion state recognition, and then utilizes Bayesian model averaging for future boundary prediction. The prediction of MBLPT provides the knowledge for establishing a wake-up zone, in which sleeping nodes are activated in advance to participate in tracking the upcoming boundary. Simulation results demonstrate that MBLPB achieves superior energy efficiency under acceptable tracking accuracy.

The paper proposes a novel visual question answering (VQA)-based online teaching effect evaluation model. Based on the text interaction between teacher and students, we give a guide-attention (GA) model to discover the directive clues. Combining the self-attention (SA) models, we reweight the vital feature to locate the critical information on the whiteboard and students’ faces and further recognize their content and facial expressions. Three branches of information are encoded into the feature vectors to be fed into a bidirectional GRU network. With the real labels of the students’ answers annotated by two teachers and the predicted labels from the text and facial expression feedback, we train the chained network. Experiment reports a couple of competitive performance in the 2-class and 5-class tasks on the self-collected dataset, respectively. &nbsp

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 Pseudo-Packet Scheduling Algorithm (PPSA), which aims at reasonably regulating the process of pseudo-packet 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 pseudo-packet 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.

Smart agriculture enables the efficiency and intelligence of production in physical farm management. Though promising, due to the limitation of the existing data collection methods, it still encounters few challenges required to be considered. Mobile crowd sensing (MCS) embeds three beneficial characteristics: 1) cost-effectiveness; 2) scalability; and 3) mobility and robustness. With the Internet of Things becoming a reality, smartphones are widely becoming available even in remote areas. Hence, both the MCS characteristics and the plug-and-play widely available infrastructure provide huge opportunities for MCS-enabled smart agriculture, opening up several new opportunities at the application level. In this article, we extensively evaluate agriculture mobile crowd sensing (AMCS) and provide insights for agricultural data collection schemes. In addition, we offer a comparative study with the existing agriculture data collection solutions and conclude that AMCS has significant benefits in terms of flexibility, collecting implicit data, and low-cost requirements. However, we note that AMCSs may still possess limitations regarding data integrity and quality to be considered a future work. To this end, we perform a detailed analysis of the challenges and opportunities that concerns MCS-enabled agriculture by putting forward seven potential applications of AMCS-enabled agriculture. Finally, we propose general research based on agricultural characteristics and discuss a special case based on the solar insecticidal lamp maintenance problem.

Due to the flammability, explosibility and toxicity of continuous objects (e.g. chemical gas, oil spill, radioactive waste) in petrochemical and nuclear industry, boundary tracking of continuous objects is becoming a critical issue for industrial wireless sensor networks (IWSNs) to achieve disaster reduction. In this article, we fully exploit the collective intelligence and machine learning capability of senor nodes, and propose a continuous objects boundary tracking algorithm for IWSNs. The proposed algorithm first determines an upper bound of the event region covered by continuous objects. A full binary tree-based partition is performed within the event region for coarse-grained boundary area mapping. To study the irregularity of continuous objects in detail, the boundary tracking problem is further transformed into a binary classification problem. A hierarchical soft margin support vector machines training strategy is designed to address the binary classification problem in a distributed fashion. Simulation results demonstrate that the proposed algorithm achieves high tracking accuracy with less boundary nodes. Without additional fault-tolerant mechanisms, the proposed algorithm is inherently robust to the false sensor readings reported by low proportion of faulty nodes, which is expected in harsh industrial environments.

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 multi-path 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.

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 paper focuses on using electric water heaters for demand response. In addition to considering comfort and price factors as did in previous works, this paper 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.

Pair-wise 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. Pair-wise 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 paper, 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.

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 paper proposes a dynamic collaborative charging algorithm that 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 – able to charge the underpowered mobile nodes, and terrestrial charging vehicles, 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 sub-algorithms: a charging algorithm for mobile nodes and a charging algorithm for static nodes. The charging algorithm for mobile nodes is designed so that each underpowered mobile node can be charged by a dedicated aerial charging vehicle. 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 firefly algorithm. In addition, the charging algorithm for static nodes ensures that the terrestrial charging vehicles traverse their routes, charging static nodes as they proceed. This traversing process was formulated as a multi-objective 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.

Unmanned Aerial Vehicles (UAVs) have attacted much attention in the field of wireless communication due to its agility and altitude. UAVs can be used as low-altitude aerial base stations (UAV-BSs) to provide communication services for ground devices (GDs) in various scenarios, such as emergency communication and traffic offloading in hotspots. However, due to the limited communication ranges and high prices of commercial UAV-BSs, covering a target area all the time with sufficient UAVs is quite challenging, especially under dynamic environment. We need to design the trajectory of the UAV-BSs to optimize system performance. Most existing works focus on the energy-efficient coverage and throughput maximization but ignore the fairness of communication service, especially the fairness at user-level. Besides, reinforcement learning is suitable for solving decision problems in dynamic environments. However, most existing works use centralized Deep Reinforcement Learning (DRL) approaches. Due to the scalability and low time complexity, a distributed DRL approach is more suitable for multiple UAV-BSs communication system in dynamic environment. Unlike previous works, we characterize the fairness at user-level based on proportional fairness scheduling and formulate a weighted-throughput maximization problem via designing UAV-BSs' trajectory. Then we model the dynamic deployment problem of UAV-BSs as a Markov game and propose a multi-agent deep reinforcement learning-based distributed UAV-BSs control approach named MAUC. MAUC approach adopts the framework of centralized training with distributed execution. Simulation results show that the MAUC can improve fairness of communication service by sacrificing a small amount of throughput.

Submarine pipelines are among the most important methods of marine oil transportation, and they are widely used in offshore oil exploitation. They can be efficiently monitored by deploying sensor nodes conforming underwater acoustic sensor networks (UASNs), located near to oil well, so that these nodes can sense, broadcast, and receive data surveying the status of the submarine infrastructure timely manner. Inevitably, UASNs may experience security problems, owing to the open nature of the underwater environment. This paper proposes a new source location privacy protection scheme based on an offshore oil acquisition platform, to address these security issues, particularly the case of source location protection. In the proposed scheme, nodes are defined as pipeline and environment locations, and are allocated to different clusters. The concept of forming anonymous cluster by exchanging the identities of a source and a cluster head is introduced to select fake sources. In addition, a data fragmentation method is developed to reduce the energy consumption of fake packets transmission. Particularly, we propose the deployment of disguised AUVs to collect data from fake sources, to conceal and protect the location of the actual sources. Simulation results show that the safety time of the network is increased by about 6%. Due to the adoption of the data fragmentation method, the energy consumption of nodes is also decreased.

Object detection based on point clouds has been widely used for autonomous driving, although how to improve its detection accuracy remains a significant challenge. Foreground points are more critical for 3D object detection than background points; however, most current detection frameworks cannot effectively preserve foreground points. Therefore, this work proposes a hybrid attention-based 3D object detection method with differential point clouds, which we name HA-RCNN. The method differentiates the foreground points from the background ones to preserve the critical information of foreground points. Extensive experiments conducted on the KITTI dataset show that the model outperforms the state-of-the-art methods, especially in recognizing large objects such as cars and cyclists.

As an enhanced system for marine monitoring and autonomous navigation, the space-based automatic identification system (AIS) has attracted extensive attention and become a hot topic for research. However, it encounters the problem of Doppler shift stemmed from the relative motion between satellites and ships, which leads to performance degradation. To circumvent this issue, in this correspondence, we propose an improved Doppler shift estimation algorithm for down-link signals of space-based AIS, utilizing the calculation of the autocorrelation and ratio on the Rice factor. Specifically, the addressed method is robust to the non-Gaussian noise. Further, the suggested approach has low complexity compared with the existing algorithm. Finally, numerical simulations, such as the standard deviation of the estimated Doppler shift versus signal-to-noise ratio, Rice factor and non-Gaussian noise, and the complexity comparisons, are carried out to validate the theoretical analysis, and demonstrate the superior performance of the proposed estimation approach.

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.

The Industrial Internet of Things (IIoT) is one of the main catalysts towards the realization of the Industry 4.0 paradigm, thus it is regarded as an essential element in future industrial systems – which can assist in reducing energy consumption and in enhancing product life-cycle management. In this study, an industrial multi-energy scheduling framework (IMSF) is proposed, with the aim of optimizing the usage of renewable energy and reducing the energy costs. The proposed method addresses the management of multi-energy flows in industrial integrated energy systems – incorporating multi-energy storage, renewable energy generation, energy conversion, and energy trading in a synchronous manner. The method considers the typical energy load of the industrial users, the energy price of the national grid and the trading platform, and the trade-off between investment costs and benefits from the various sub-systems. As this results in a complex system of systems, an artificial intelligence method is proposed to treat the problem, using reinforcement learning based differential evolution (RLDE), that can determine the optimal mutation strategy and associated parameters in an adaptive way. Case studies on real-world data evidence the effectiveness of the IMSF and the RLDE algorithm in reducing the energy costs in industrial environments.

In recent years, underwater acoustic sensor networks (UASNs) have been widely investigated for ocean environmental monitoring, offshore exploration, and marine military. The core function of UASNs is to collect data for related operations. A number of factors make the monitoring challenging; ocean thermoclines may affect the communication of the underwater nodes and gliders, reducing their communication range at varying depth; moreover, the node movement caused by Ekman drifting effect can significantly interfere with the data transmissions. Thus, these factors are regarded essential towards characterizing the ocean environment. To address these challenges, a sleep-scheduling-based hierarchical data collection algorithm (SSHDCA) for underwater gliders is designed. The UASN is split into multiple virtual cubes, where the nodes in different virtual cubes sleep and work alternately to save energy. Then, the SSHDCA divides the network into a dynamic layer and a static layer. In the dynamic layer, a virtual-cube-based multi-hop method is leveraged to transmit data packets to the central area. In the static layer, an improved density-based clustering technique is applied to assign each node to an appropriate cluster, while the underwater gliders collect data from the cluster heads. Further, to reduce energy consumption, the SSHDCA compresses key and non-key data, reducing the size of the packets. Simulation results have shown that the proposed algorithm is effective in reducing the path length of the gliders and the average energy consumption of the nodes, while increasing the remaining operational life of the whole network.

Intelligent data collection is a key component in underwater acoustic sensor networks, and it plays an important role in seabed environment monitoring, marine resource detection and marine disaster early warning. Owing to the particularities of the underwater environment, such as reduced infrastructure and noisy communication channels, the data collected by underwater nodes are more efficiently transmitted to a control center on the surface by way of an autonomous underwater vehicle (AUV). However, with the increasing complexity of the underwater tasks, using a single AUV for data collection cannot meet the requirements of low latency and low power consumption. To solve this problem, a multi-AUV collaborative data collection algorithm that reduces the load of data collection task on a single AUV is proposed. The algorithm is divided into two stages: multi-AUV task allocation and Q-learning-based AUV path planning. The data transmission of the clusters is regarded as a set of different tasks, which are assigned to the AUVs for completion. Subsequently, path planning is performed to guide the AUVs, so that the tasks are completed promptly and at a reduced cost. Simulation results show that the proposed algorithm can leverage the energy consumption of a network and extend its lifetime. The performance of the proposed algorithm in energy consumption is increased by about 10%, and the delay of data collection is also significantly reduced.

Using deep reinforcement learning to solve traffic signal control problems is a research hotspot in the intelligent transportation field. Researchers have recently proposed various solutions based on deep reinforcement learning methods for intelligent transportation problems. However, most signal control optimization takes the maximization of traffic capacity as the optimization goal, ignoring the concerns of pedestrians at intersections. To address this issue, we propose a pedestrian-considered deep reinforcement learning traffic signal control method. The method combines a reinforcement learning network and traffic signal control strategy with traffic efficiency and safety aspects. At the same time, the waiting time of pedestrians and vehicles passing through the intersection is considered, and the Discrete Traffic State Encoding (DTSE) method is applied and improved to define the more comprehensive states and rewards. In the training of the neural network, the multi-process operation method is adopted, and multiple environments are run for training simultaneously to improve the model’s training efficiency. Finally, extensive simulation experiments are conducted on actual intersection scenarios using the simulation software Simulation of Urban Mobility (SUMO). The results show that compared to Dueling DQN, the waiting time due to our method decreased by 58.76% and the number of people waiting decreased by 51.54%. The proposed method can reduce both the number of people waiting and the waiting time at intersections.

The aging of the global population and increasing incidence of chronic diseases have exacerbated social problems such as the shortage of medical resources and increased medical costs. There is a growing interest in medical monitoring services based on wireless body area network (WBAN). In WBAN, due to limitations on processing capacity, battery, and storage capacity of the sensors, it is difficult to guarantee the low-latency, high-reliability requirements, and better user experience of medical monitoring services. Therefore, a novel data fusion method of WBAN for medical monitoring services is proposed in this study. According to the redundancy and complementarity of WBAN data collection in time or space, the proposed method realizes the fusion of single source and multisource data in time and space, which obtains a consistent interpretation and description of the measured object, as well as more effective collection results than a single sensor. Furthermore, to meet the requirements of real-time of medical monitoring services, a hierarchical data fusion model based on edge computing is proposed. In this model, to realize the load balance of the entire system and maximize system utility, different types of data fusion tasks are scheduled for execution on the sensor, sink node, or edge node. The simulation results show that the proposed method effectively improves the accuracy and reliability of WBAN data collection when the algorithm execution time is acceptable and meets the real-time requirements of medical monitoring services.

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.

Due to the harsh deployment environment of the Industrial Internet of Things, the reliability of data transmission has always been an important challenge and a hot topic in Industrial Internet of Things (IIoT). Based on a multiple sink node network model, we propose a reliable routing algorithm called SFPAG-R, which is based on sealed first-price auction games. The algorithm offers a route discovery process and data transmission mechanism using the multi-sink network structure. The classic sealed first-price auction game is improved, and we propose a sealed first-price auction game model based on standard normal distribution, in order to ensure that the auction nodes can obtain higher returns. Based on an analysis of the factors affecting reliable data transmission in a large-scale, industrial IoT system, a data forwarding auction game model is established. Finally, to address the issue of selfish nodes, a dynamic revenue control method is proposed to promote cooperation between nodes. The simulation results indicate that the SFPAG-R algorithm proposed in this paper can reliably reduce the average amount of hops that occur during data transmission, ensure a high rate of successful data transmission, balance network energy consumption, and prolong the network's life cycle.