Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System

• URL: http://arxiv.org/abs/2210.15203v1
• Date: Thu, 27 Oct 2022 06:27:40 GMT
• Title: Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System
• Authors: Li Dong, Zhibin Liu, Feibo Jiang, Kezhi Wang
• Abstract summary: Unmanned aerial vehicles (UAVs) can be applied in many Internet of Things (IoT) systems. The UAV-IoT wireless channels may be occasionally blocked by trees or high-rise buildings. This article aims to minimize the energy consumption of the system by jointly optimizing the deployment and trajectory of the UAV.
• Score: 25.32139119893323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unmanned aerial vehicles (UAVs) can be applied in many Internet of Things (IoT) systems, e.g., smart farms, as a data collection platform. However, the UAV-IoT wireless channels may be occasionally blocked by trees or high-rise buildings. An intelligent reflecting surface (IRS) can be applied to improve the wireless channel quality by smartly reflecting the signal via a large number of low-cost passive reflective elements. This article aims to minimize the energy consumption of the system by jointly optimizing the deployment and trajectory of the UAV. The problem is formulated as a mixed-integer-and-nonlinear programming (MINLP), which is challenging to address by the traditional solution, because the solution may easily fall into the local optimal. To address this issue, we propose a joint optimization framework of deployment and trajectory (JOLT), where an adaptive whale optimization algorithm (AWOA) is applied to optimize the deployment of the UAV, and an elastic ring self-organizing map (ERSOM) is introduced to optimize the trajectory of the UAV. Specifically, in AWOA, a variable-length population strategy is applied to find the optimal number of stop points, and a nonlinear parameter a and a partial mutation rule are introduced to balance the exploration and exploitation. In ERSOM, a competitive neural network is also introduced to learn the trajectory of the UAV by competitive learning, and a ring structure is presented to avoid the trajectory intersection. Extensive experiments are carried out to show the effectiveness of the proposed JOLT framework.

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