Unmanned Collaborative Intelligent Edge Computing Task Scheduling

Abstract

UAV-assisted (Unmanned aerial vehicle, UAV) mobile edge computing (Mobile Edge Computing, MEC) has the characteristics of flexible deployment and low network latency. The introduction of mobile edge computing in UAV networks is expected to provide low latency, ultra-reliable, and highly robust network services, which can greatly improve user service experience and is a very promising technology in future mobile communication networks. First, this article introduces the background of MEC technology and unmanned application scenarios, uses the edge computing server carried by drones to provide users with computing services, and analyzes the necessity of introducing MEC into UAV networks. Secondly, it is analyzed that unmanned edge computing networks need to allocate resources reasonably for task scheduling. Therefore, this paper studies the priority task scheduling problem of intelligent edge computing in unmanned collaborative scenarios, transforms integer nonlinear programming problem into mixed integer linear programming problem, and proposes a priority task scheduling scheme based on multi-factor linear optimization. This scheme is solved by combining the branch and bound method and the cutting plane method, and the results are obtained through the MATLAB simulation experiment; the simulation results show that compared with the Max-Min algorithm and the particle swarm optimization(PSO) algorithm, this algorithm can more effectively shorten the total task completion time and total energy consumption reflect the priority of the task and improve the degree of load balance; In addition, this task scheduling makes the computing resources on UAV better distributed to each task, and makes UAV have better load balance.