Abstract With the advantages of flexibility and mobility, unmanned aerial vehicles (UAVs) have been widely used in the wireless rechargeable sensor networks (WRSNs) to collect data and supply energy for ground sensor nodes. Due to the limited battery capacity of UAVs and the continuity requirement of WRSN, mobile unmanned vehicles (MUVs) are introduced as mobile charging stations to ensure the energy supply for UAVs and mitigate energy wastage. This paper investigates the problem of Joint Optimization Mission Allocation and Cooperative Trajectory Planning for data collection in WRSNs. The goal is to maximize the minimum energy efficiency by optimizing mission allocation including UAV trajectory and MUV travel. This problem is proved to be NP-hard and solved by two proposed algorithms. The first algorithm incorporates the clustering utilizing the K-Means algorithm and genetic algorithm. The second algorithm is a self-attention architecture based on the reinforcement learning framework and formulate an actor-critic algorithm for training. The simulation results show the feasibility and efficiency of the proposed algorithms, which achieve better performance. The first algorithm has more advantages when the distribution of sensor nodes is relatively concentrated; and the second algorithm may be more suitable when more comprehensive global path planning optimization is required.

This paper investigates the framework of wireless rechargeable sensor network (WRSN) assisted by multiple mobile unmanned vehicles (MUVs) and laser-charged unmanned aerial vehicles (UAVs). On the basis of framework, we cooperatively investigate the trajectory optimization of multi-UAVs and multi-MUVs for charging WRSN (TOUM) problem, whose goal aims at designing the optimal travel plan of UAVs and MUVs cooperatively to charge WRSN such that the remaining energy of each sensor in WRSN is greater than or equal to the threshold and the time consumption of UAV that takes the most time of all UAVs is minimized. The TOUM problem is proved NP-hard. To solve the TOUM problem, we first investigate the multiple UAVs-based TSP (MUTSP) problem to balance the charging tasks assigned to every UAV. Then, based on the MUTSP problem, we propose the TOUM algorithm (TOUMA) to design the detailed travel plan of UAVs and MUVs. We also present an algorithm named TOUM-DQN to make intelligent decisions about the travel plan of UAVs and MUVs by extracting valuable information from the network. The effectiveness of proposed algorithms is verified through extensive simulation experiments. The results demonstrate that the TOUMA algorithm outperforms the solar charging method, the base station charging method, and the TOUM-DQN algorithm in terms of time efficiency. Simultaneously, the experimental results show that the execution time of TOUM-DQN algorithm is significantly lower than TOUMA algorithm.

In the industrial Internet of Things (IIoT), edge computing is a pivotal power in enhancing system efficiency and responsiveness. However, traditional edge computing faces some challenges like poor flexibility in communication and susceptibility to blockages. Autonomous aerial vehicles (AAVs)-assisted edge computing can address these challenges due to their flexible deployment and strong Line of Sight (LoS) link capabilities. But it also confronts challenges like signal attenuation and resource constraints. To solve these problems, reconfigurable intelligent surface (RIS) emerges as a promising integration strategy to enhance network communication and computing capabilities. Integrating AAVs and RISs in complex dynamic edge computing system poses a notable challenge in achieving real-time and efficient decision-making. Digital twin (DT) technology is an advanced technology that establishes real-time mapping and interaction between the physical world and virtual models, thereby providing real-time status monitoring and precise offloading decisions for the system. Therefore, this article considers a novel DT-driven edge computing system supported by AAVs equipped with RIS in IIoT. In this system, we focus on the intelligent computation offloading problem, whose objective is to minimize the maximum execution time across all user devices (UDs). To tackle this nonconvex mixed-integer nonlinear optimization problem, we decompose it into the scheduling and offloading optimization problem and the allocation optimization problem. Then, we first propose a multitask reinforcement learning algorithm to solve the scheduling and offloading optimization problem by optimizing the AAV trajectories, UD offloading choices, and RIS phase shifts. Afterward, based on the solution of the scheduling and offloading optimization problem, we propose an alternating iterative algorithm to address the allocation optimization problem through optimizing the offloading ratio and resource allocation. Finally, through extensive simulation experiments, we validate the effectiveness and feasibility of our proposed solution.

Unmanned Aerial Vehicle (UAV) can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks (WSNs), which is crucial for providing seamless services and improving the performance in the next generation wireless networks. However, since the UAV are powered by batteries with limited energy capacity, the UAV can not complete data collection tasks of all sensors without energy replenishment when a large number of sensors are deployed over large monitoring areas. To overcome this problem, we study the Real-time Data Collection with Laser-charging UAV (RDCL) problem, where the UAV is utilized to collect data from a specified WSN and is recharged using Laser Beam Directors (LBDs). This problem aims to collect all sensory data from the WSN and transport it to the base station by optimizing the flight trajectory of UAV such that real-time data performance is ensured It has been proven that the RDCL problem is NP-hard. To address this, we initially focus on studying two sub-problems, the Trajectory Optimization of UAV for Data Collection (TODC) problem and the Charging Trajectory Optimization of UAV (CTO) problem, whose objectives are to find the optimal flight plans of UAV in the data collection areas and charging areas, respectively. Then we propose an approximation algorithm to solve each of them with the constant factor. Subsequently, we present an approximation algorithm that utilizes the solutions obtained from TODC and CTO problems to address the RDCL problem. Finally, the proposed algorithm is verified by extensive simulations.

Wireless Rechargeable Sensor Networks (WRSNs) have been widely utilized and have played an important role in many surveillance application scenarios. The optimization of the charging process is beneficial for guaranteeing continuous coverage and enhancing the charging efficiency of WRSNs. And there are several influence factors of the charging process, like the sensors’ battery consumption mode, the chargers’ charging pattern and the environmental factors, which should be considered into the charging model. Based on the charging model via assigning sensors’ charging priority weights, we introduce the spatio-temporal optimization for charging scheduling (STO-CS) Problem in WRSNs for the goals of meeting the on-demand charging requirements and saving the charging consumption. We prove the NP-hardness of the problem and propose two algorithms to solve it. The first algorithm is based on two-phase dynamic programming and is proved to find the optimal solution when the charging ability is sufficient; the second algorithm adopts the clustering idea with K-Means Algorithm which has better time complexity. A series of simulation experiments are performed to compare the performance of the proposed algorithms in terms of the charging cost and the running time, whose results are analyzed to conclude that they can be applied to the application scenarios with the accuracy requirements and the real-time requirements respectively.

The development of wireless energy transmission technology has significantly propelled the advancement of wireless rechargeable sensor networks (WRSNs). Energy constraint is one of the most critical challenges in application of WRSNs. Integrating unmanned aerial vehicle (UAV) with wireless energy transmission technology has emerged as a promising approach to overcome the energy constraint problem in WRSNs, leveraging the advantages of UAV such as flexibility and maneuverability. In this paper, we consider the system of WRSN assisted by UAV and mobile utility vehicle (MUV), where the UAV serves as a mobile charger for replenishing energy of sensors and the MUV serves as a mobile base station for replacing the battery of UAV with insufficient energy. In the system, we focus on minimizing the death time of sensors and optimizing the energy consumption of UAV. To address this problem, a multi-objective deep Q-network (DQN) algorithm is employed, where the UAV makes online charging scheduling decisions based on real-time network status and utilizes experience replay for optimization. Experimental results demonstrate that the proposed algorithm significantly reduces the sensors’ death time and effectively decreases the energy consumption of UAV. Specially, the performance of proposed algorithm outperforms the three other classical algorithms: genetic algorithm, greedy algorithm, and Q-learning algorithm.

Edge computing is a computational paradigm that brings resources closer to the network edge, such as base stations or gateways, in order to provide quick and efficient computing services for mobile devices while relieving pressure on the core network. However, the current computing power of edge servers are insufficient to handle the high number of tasks generated by access devices. Additionally, some mobile devices may not fully utilize their computing resources. To maximize the use of resources, we propose a novel edge computing system architecture consisting of a resource-constrained edge server and three computing groups. Tasks from each group can be offloaded to either the edge server or the corresponding computing group for execution. We focus on optimizing the computation offloading of devices to minimize the maximum overall task processing latency in the system. This problem is proved to be NP-hard. To solve it, we propose a DQN-based resource utilization task scheduling (DQNRTS) algorithm that has two desirable characteristics: 1) it effectively utilizes the computing resources in the system and 2) it uses deep reinforcement learning to make intelligent scheduling decisions based on system state information. Experimental results demonstrate that the DQNRTS algorithm is capable of reducing the processing latency of the system by converging to optimal solutions.

This paper introduces two new coverage problems in mission-driven camera sensor networks, namely the target temporal effective-sensing coverage with non-adjustable cameras (TEC-NC) problem and the target-temporal effective-sensing coverage with adjustable cameras (TEC-AC) problem. Given a mission period, the objective of the problems is to find a sleep-wakeup schedule of the camera sensor nodes such that the overall target-temporal coverage is maximized. We formally introduce a method called Identifiability Test to check if a target with a face direction is effectively-covered by a camera sensor, and prove the problems are NP-hard. For TEC-NC, we propose a 2-approximation algorithm and two heuristic algorithms. We also design a greedy strategy which can be combined with our solutions for TEC-NC to solve TEC-AC. The simulation results indicate the quality of the outputs of our algorithms are much better than that of the existing alternative as well as close to the theoretical optimum on average.

Barrier coverage is an important issue in wireless sensor network. In wireless camera sensor networks, the cameras take the images or videos of target objects, the position and angle of camera sensor impact on the sense range. Therefore, the barrier coverage problem in camera sensor network is different from scalar sensor network. In this paper, based on the definition of full-view coverage, we focus on the Minimum Camera Barrier Coverage Problem (MCBCP) in wireless camera sensor networks in which the camera sensors are deployed randomly in a target field. Firstly, we partition the target field into disjoint subregions which are full-view-covered regions or not-full-view-covered regions. Then we model the full-view-covered regions and their relationship as a weighted directed graph. Based on the graph, we propose an algorithm to find a feasible solution for the MCBCP problem. We also proved the correctness of the solution for the MCBCP problem. Furthermore, we propose an optimal algorithm for the MCBCP problem. Finally, simulation results demonstrate that our algorithm outperforms the existing algorithm.