Optimizing Communication and Computation for Multi-UAV Information Gathering Applications

TL;DR

This paper presents a mixed-integer optimization approach to balance communication and computation energy in multi-UAV networks, enhancing energy efficiency for tasks like target tracking and area mapping.

Contribution

It introduces a novel hierarchical clustering-based framework with data aggregation to optimize energy use in multi-UAV systems.

Findings

Significant energy savings over baseline strategies.

Effective trade-off between communication and computational energy.

Improved system lifetime through optimized data routing.

Abstract

Mobile agent networks, such as multi-UAV systems, are constrained by limited resources. In particular, limited energy affects system performance directly, such as system lifetime. It has been demonstrated in the wireless sensor network literature that the communication energy consumption dominates the computational and the sensing energy consumption. Hence, the lifetime of the multi-UAV systems can be extended significantly by optimizing the amount of communication data, at the expense of increasing computational cost. In this work, we aim at attaining an optimal trade-off between the communication and the computational energy. Specifically, we propose a mixed-integer optimization formulation for a multi-hop hierarchical clustering-based self-organizing UAV network incorporating data aggregation, to obtain an energy-efficient information routing scheme. The proposed framework is tested on two applications, namely target tracking and area mapping. Based on simulation results, our method can significantly save energy compared to a baseline strategy, where there is no data aggregation and clustering scheme.