Energy Efficiency and Total Mission Completion Time Tradeoff in Multiple UAVs-Mounted IRS-Assisted Data Collection System

UAV-mounted intelligent reflecting surface (IRS) helps address the line-of-sight (LoS) blockage between sensor nodes (SNs) and the fusion center (FC) in Internet of Things (IoT). This paper considers an IoT assisted by multiple UAVs-mounted IRS (U-IRS), where the data from ground SNs are transmitted to the FC. In practice, energy efficiency (EE) and mission completion time are crucial metrics for evaluating system performance and operational costs. Recognizing their importance during data collection, we formulate a multi-objective optimization problem to maximize EE and minimize total mission completion time simultaneously. To characterize this tradeoff while considering optimization objective consistency, we construct an optimization problem that minimizes the weighted sum of the total mission completion time and the reciprocal of EE. Due to the non-convex nature of the formulated problem, obtaining optimal solutions is generally challenging. To tackle this issue, we decompose it into three sub-problems: UAV-SN association, number of reflecting elements allocation, and UAV trajectory optimization. An iterative algorithm combining genetic algorithm, CS-BJ algorithm, and successive convex approximation technique is proposed to solve these sub-problems. Simulation results demonstrate that when the transmitted data amount is 10 and 30 Mbits, compared to the static collection benchmark (the UAV hovers directly above each SN), the EE of the proposed method improves by more than 10.4% and 5.2%, while the total mission completion time is reduced by more than 5.4% and 3.3%, respectively.