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

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 occasionally blocked by trees or high-rise buildings. An intelligent reflecting surface (IRS) to improve channel quality smartly signal via large number low-cost passive reflective elements. This article aims minimize energy consumption system jointly optimizing deployment and trajectory UAV. The problem is formulated mixed-integer-and-nonlinear programming (MINLP), which challenging address traditional solution, because solution easily fall into local optimal. To this issue, we propose joint optimization framework (JOLT), where an adaptive whale algorithm (AWOA) optimize UAV, elastic ring self-organizing map (ERSOM) introduced Specifically, AWOA, variable-length population strategy find optimal stop points, nonlinear parameter $a$ partial mutation rule are balance exploration exploitation. In ERSOM, competitive neural network also learn UAV learning, structure presented avoid intersection. Extensive experiments carried out show effectiveness proposed JOLT framework.