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

TL;DR

This paper presents JOLT, a novel framework that jointly optimizes UAV deployment and trajectory in IRS-assisted IoT data collection to minimize energy consumption, using advanced algorithms to overcome complex non-linear programming challenges.

Contribution

The paper introduces JOLT, combining AWOA and ERSOM algorithms for joint deployment and trajectory optimization in UAV-IRS IoT systems, addressing non-convex MINLP challenges.

Findings

JOLT significantly reduces energy consumption in UAV-IoT systems.

The proposed algorithms outperform traditional methods in deployment and trajectory optimization.

Extensive experiments validate the effectiveness of the joint optimization framework.

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.