Joint Latency-Energy Cost Optimization for Asynchronous UAV-Assisted Mobile Edge Computing in Cognitive Emergency Network

In the emergency scenario, we propose cognitive based and unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) network (cognitive UAVMN) to solve the problem of insufficient spectral and computing resources. In the cognitive UAVMN, the effect of sensing duration is considered to balance the tradeoff between sensing, transmitting and computing for cognitive users and MEC servers. Based on this, an optimization problem is formulated to minimize the total system cost by jointly optimizing the position of UAV, transmitting power of ground users, spectrum sensing duration, computing frequency of ground device and UAV and offloading probability. We also analyze effect of spectrum sensing performance to the system cost in task transmitting and computing. The simulation results indicate that the proposed cognitive UAVMN decreases system cost with limited resource when compared to other schemes.