Revenue Optimization of a UAV-Fog Collaborative Framework for Remote Data Collection Services

Abstract

Unmanned aerial vehicles (UAVs) can provide remote data collection services with quality of service guarantees. The typical application fields include geographic information systems, such as topological survey and natural disasters and hazards monitoring. In the bad geographic environment, wireless communication performance of UAVs cannot be guaranteed. Therefore, the efficiency of remote data collection cannot be guaranteed. This paper proposes a collaborative framework of UAVs and fog computing for remote data collection. Our goal is to maximize the revenue of UAVs with the support of fog computing, so we need to find the optimal computation resources allocation and task assignment scheme. This is a mixed integer nonlinear programming problem. The block coordinate descent method is used to solve this problem, which allows the original problem to be divided into the optimal task assignment sub-problem and the optimal computation resource allocation sub-problem. The greedy algorithm, heuristic algorithm and brute force algorithm are proposed to solve the optimal task assignment sub-problem. The convex optimization analysis method is used to solve the optimal resource allocation sub-problem. The genetic algorithm is used as a benchmark to compare with the heuristic-based block coordinate descent algorithm. The numerical simulation and network simulator based-simulation results show that the proposed UAV-Fog collaborative data collection problem can be efficiently solved by the block coordinate descent algorithm based on the heuristic strategy.