Energy Efficient and Balanced Task Assignment Strategy for Multi-UAV Patrol Inspection System in Mobile Edge Computing Network

TL;DR

This paper presents a novel energy-efficient and balanced task assignment and trajectory design strategy for multi-UAV patrol inspection in mobile edge computing networks, optimizing task distribution and energy use.

Contribution

It introduces a new clustering method and a hybrid optimization approach to balance UAV tasks and minimize energy consumption in patrol inspection scenarios.

Findings

Balanced task assignment reduces UAVs' task completion time

Energy-efficient trajectory design lowers total energy consumption

Proposed method outperforms conventional approaches in simulations

Abstract

This paper considers a patrol inspection scenario where multiple unmanned aerial vehicles (UAVs) are adopted to traverse multiple predetermined cruise points for data collection. The UAVs are connected to cellular networks and they would offload the collected data to the ground base stations (GBSs) for data processing within the constrained duration. This paper proposes a balanced task assignment strategy among patrol UAVs and an energy-efficient trajectory design method. Through jointly optimizing the cruise point assignment, communication scheduling, computational allocation, and UAV trajectory, a novel solution can be obtained to balance the multiple UAVs' task completion time and minimize the total energy consumption. Firstly, we propose a novel clustering method that considers geometry topology, communication rate, and offload volume; it can determine each UAV's cruise points and balance the UAVs' patrol task. Secondly, a hybrid Time-Energy traveling salesman problem is formulated to analyze the cruise point traversal sequence, and the energy-efficient UAV trajectory can be designed by adopting the successive convex approximation (SCA) technique and block coordinate descent (BCD) scheme. The numerical results demonstrate that the proposed balanced task assignment strategy can efficiently balance the multiple UAVs' tasks. Moreover, the min-max task completion time and total energy consumption performance of the proposed solution outperform that of the current conventional approach.