A Continuum Approach for Collaborative Task Processing in UAV MEC Networks

TL;DR

This paper introduces a novel distributed approach for collaborative task processing in UAV mobile edge computing networks, enabling adaptive positioning of MEC UAVs to improve throughput in dynamic environments.

Contribution

It proposes a new distributed solution with workload estimation and optimization for adaptive MEC UAV placement, enhancing system throughput in UAV networks.

Findings

Achieves up to 28% throughput improvement over non-adaptive methods.

Effectively estimates workload distribution in dynamic UAV environments.

Demonstrates robustness with realistic UAV mobility models.

Abstract

Unmanned aerial vehicles (UAVs) are becoming a viable platform for sensing and estimation in a wide variety of applications including disaster response, search and rescue, and security monitoring. These sensing UAVs have limited battery and computational capabilities, and thus must offload their data so it can be processed to provide actionable intelligence. We consider a compute platform consisting of a limited number of highly-resourced UAVs that act as mobile edge computing (MEC) servers to process the workload on premises. We propose a novel distributed solution to the collaborative processing problem that adaptively positions the MEC UAVs in response to the changing workload that arises both from the sensing UAVs' mobility and the task generation. Our solution consists of two key building blocks: (1) an efficient workload estimation process by which the UAVs estimate the task field - a continuous approximation of the number of tasks to be processed at each location in the airspace, and (2) a distributed optimization method by which the UAVs partition the task field so as to maximize the system throughput. We evaluate our proposed solution using realistic models of surveillance UAV mobility and show that our method achieves up to 28% improvement in throughput over a non-adaptive baseline approach.