Unmanned aerial vehicles (UAVs) have been widely devoted to mobile edge computing (MEC) systems that have limited resources to provide high-quality computing and communication services for Internet of Things (IoT) ter...
详细信息
Unmanned aerial vehicles (UAVs) have been widely devoted to mobile edge computing (MEC) systems that have limited resources to provide high-quality computing and communication services for Internet of Things (IoT) terminals. Energy-efficient computation and resource allocation are key issues for the sustainable operation of the above-mentioned systems. The 3d deployment optimization of multi-UAVs is also crucial to maximizing the system's computation efficiency. In this paper, we discuss a multi-UAV-enabled MEC system. To maximize the computation efficiency of the terminal system, we consider jointly optimizing the terminal's CPU frequency, transmission power, offloading correlation decision, and the 3d position and beamwidth of the UAV. Since the original problem is a mixed-integer nonlinear programming (MINLP) problem with a fractional structure, which is difficult to solve directly. Based on dinkelbach's method, convex optimization theory, and greedy strategy, we simplify the mathematical model and propose a four-stage alternating iterative computation efficiency maximization algorithm(FICEM) to solve the problem. The simulation results indicate that the algorithm converges fast in various network scenarios and that its computation efficiency is better than that of the baseline algorithm. In addition, the simulation results also manifest the effect of different network parameters on the computation efficiency of the terminal system.
Compared with the traditional two-dimensional (2d) deployment form, three-dimensional (3d) deployment of sensor network has greater research significance and practical potential to satisfy the detecting needs of targe...
详细信息
ISBN:
(纸本)9781467313988
Compared with the traditional two-dimensional (2d) deployment form, three-dimensional (3d) deployment of sensor network has greater research significance and practical potential to satisfy the detecting needs of targets with complex properties. In this paper, a method for 3d deployment optimization of sensor network based on an improved Particle Swarm optimization (PSO) algorithm is proposed. Many factors such as coverage scale, detection probability and resource utilization are synthetically considered to optimize the sensor network's overall detection performance. To evaluate the network's performance, four indexes are presented and the 3ddeployment space is divided into different height levels. Accordingly, the mathematical model is formulated by weighting the performance indexes and height levels due to their importance degrees. In order to solve the optimization problem, an algorithm called WCPSO is carried out, which has a dynamic inertia weight and adaptable acceleration constants. Verified by the simulation results, the presented3d deployment optimization method effectively improves the sensor network's detection performance. The method in this paper can provide guidance and technical reference in future application of relevant research.
暂无评论