Joint Optimization of Area Coverage and Mobile-Edge Computing With Clustering for FANETs

Area coverage is one of the most common and important tasks for flying <italic>ad hoc</italic> networks (FANETs). The increasingly large scale of FANETs brings challenges in communication and coverage. Clustering is an effective technique for networking and management for large-scale <italic>ad hoc</italic> networks. Meanwhile, some applications, i.e., face recognition, need to perform intensive computation after unmanned aerial vehicles (UAVs) perform area coverage. Due to long response delay in transferring data to the cloud, it becomes a trend to use mobile-edge computing (MEC) for processing data in FANETs, which selects the node of rich computing resources, i.e., cluster head (CH), as MEC server, thus the delay performance of the edge node to the server is particularly critical. However, there is a conflict between area coverage efficiency and delay performance. Area coverage expects UAVs to spread as widely as possible, which may lead to a longer delay. In this article, we consider maximizing coverage efficiency under delay constraints. We define the coverage efficiency and propose an iterative coverage-efficient clustering algorithm (CECA) by applying penalty and block coordinate descent methods. Specifically, the CHs, positions and transmit powers are alternately optimized in each iteration. In addition, CECA can adjust delay constraints according to task requirements. Extensive simulation results show that our proposed approach is superior to other approaches in terms of coverage efficiency and delay.