Optimizing Communication and Computation for Multi-UAV Information Gathering Applications
This work addresses energy efficiency for multi-UAV networks in applications like target tracking and area mapping, but it is incremental as it builds on existing clustering and optimization methods.
The paper tackles the problem of limited energy in multi-UAV systems by optimizing the trade-off between communication and computational energy, proposing a mixed-integer optimization formulation with hierarchical clustering and data aggregation. Simulation results show it significantly saves energy compared to a baseline without these schemes.
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.