UAV-Assisted MEC Architecture for Collaborative Task Offloading in Urban IoT Environment

TL;DR

This paper proposes a UAV-assisted MEC architecture for urban IoT environments, optimizing task offloading through novel algorithms to improve user satisfaction and provider profit with fewer UAVs.

Contribution

It introduces a joint IoT-UAV-ES association and UAV topology optimization framework using TMSC and K-means algorithms, addressing NP-hard challenges in urban MEC.

Findings

Outperforms benchmark schemes in served IoTs, user satisfaction, and profit.

Achieves these improvements with 25% fewer UAVs.

Demonstrates the effectiveness of the proposed algorithms through simulations.

Abstract

Mobile edge computing (MEC) is a promising technology to meet the increasing demands and computing limitations of complex Internet of Things (IoT) devices. However, implementing MEC in urban environments can be challenging due to factors like high device density, complex infrastructure, and limited network coverage. Network congestion and connectivity issues can adversely affect user satisfaction. Hence, in this article, we use unmanned aerial vehicle (UAV)-assisted collaborative MEC architecture to facilitate task offloading of IoT devices in urban environments. We utilize the combined capabilities of UAVs and ground edge servers (ESs) to maximize user satisfaction and thereby also maximize the service provider's (SP) profit. We design IoT task-offloading as joint IoT-UAV-ES association and UAV-network topology optimization problem. Due to NP-hard nature, we break the problem into two subproblems: offload strategy optimization and UAV topology optimization. We develop a Three-sided Matching with Size and Cyclic preference (TMSC) based task offloading algorithm to find stable association between IoTs, UAVs, and ESs to achieve system objective. We also propose a K-means based iterative algorithm to decide the minimum number of UAVs and their positions to provide offloading services to maximum IoTs in the system. Finally, we demonstrate the efficacy of the proposed task offloading scheme over benchmark schemes through simulation-based evaluation. The proposed scheme outperforms by 19%, 12%, and 25% on average in terms of percentage of served IoTs, average user satisfaction, and SP profit, respectively, with 25% lesser UAVs, making it an effective solution to support IoT task requirements in urban environments using UAV-assisted MEC architecture.