Edge Computing Enabled Energy-Efficient Multi-UAV Cooperative Target Search

Multiple unmanned aerial vehicles (UAVs) cooperative search have been widely adopted for surveillance and search-related applications. For a certain search area, UAVs may need to search it repeatedly to obtain a high-confidence result about the target distribution in the search area. However, the short battery life and moderate computational capability restrict UAVs to repeatedly execute the computation-intensive and energy-consuming search tasks. To address the issue, in this paper, we utilize edge computing to develop a continual and cooperative UAV search mechanism. Specifically, we first establish an edge computing enabled multi-UAV cooperative search framework, in which the mobility model of UAV, search task computing and offloading models are presented. An uncertainty minimization problem is then formulated, aiming to obtain a high-efficiency and high-confidence search result at the unpredictable uncertainty in search area. Considering that round-trip energy consumption, offloading decision-making, and trajectory planning may contribute to the reduction in uncertainty, we propose an uncertainty minimization-based cooperative target search (UMCTS) strategy. Finally, extensive simulation results validate that UMCTS can outperform the existing strategies and achieve at least 89% performance gain on average uncertainty. Based on the results, we also present a comprehensive analysis and discussion on how different parameters affect the search performance.