Trajectory Optimisation for UAV Data Collection in IoT-Based WSN: A Lévy Flight-Based Approach

Abstract

In large-scale deployments, the Internet of things (IoT) and wireless sensor networks (WSNs) often face challenges in transmitting collected data to the base station due to limited network coverage. Unmanned aerial vehicles (UAVs) can extend this coverage by flying to remote WSN areas and communicating with aggregator nodes (CH-nodes) to retrieve data. Designing UAV-assisted data collection systems therefore requires a careful consideration of both UAV and WSN constraints. This article proposes an energy-efficient approach for UAV-based data collection in IoT/WSNs. The problem is formulated to jointly optimise system cost and energy consumption while accounting for communication power, mission duration, and data importance. The solution proceeds in two steps. First, aggregator nodes are selected using clustering based on residual energy and inter-node distances to minimise system costs. Second, the UAV trajectory is generated using a Lévy flight strategy that follows the positions of the selected aggregators. Although this trajectory may be slightly longer than that produced by a deterministic TSP route, it increases the amount of collected data and prolongs both UAV and WSN lifetime by ensuring timely visits to distant cluster heads. Simulation results confirm the efficiency and robustness of the proposed method compared with existing solutions.