A Novel Genetic Trajectory Planning Algorithm With Variable Population Size for Multi-UAV-Assisted Mobile Edge Computing System

Muhammad Asim,Saima Hassan,Samir Brahim Belhaouari,Wali Khan Mashwani

doi:10.1109/access.2021.3111318

Abstract

This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system, where multiple UAVs (variable number of UAVs) are deployed to serve Internet of Things devices (IoTDs). We aim to minimize the sum of hovering and flying energies of UAVs by optimizing the trajectories of UAVs. The problem is very complicated as we have to consider the deployment of stop points (SPs), the association between UAVs and SPs, and the order of SPs for UAVs. To solve the problem, this paper proposed a novel genetic trajectory planning algorithm with variable population size (GTPA-VP), which consists of two phases. In the first phase, a genetic algorithm (GA) with a variable population size is used to update the deployment of SPs. Accordingly, a multi-chrome GA is adopted to find the association between UAVs and SPs, an optimal number of UAVs, and the optimal order of SPs for UAVs. The effectiveness of the proposed GTPA-VP is demonstrated through several experiments on a set of ten instances with up to 200 IoTDs. It is evident from the experimental results that the proposed GTPA-VP outperforms the benchmark algorithms in terms of the energy consumption of the system.

Highlights

With the development of mobile communication systems, a huge number of resource-intensive and latency-sensitive applications are emerging, such as virtual reality, and online gaming [1]
A genetic algorithm (GA) trajectory planning algorithm with variable population size (GTPA-VP) is proposed, which consists of two phases
We used ⇑, ⇓, and to show that GTPA-VP performs significantly better than, worse than, and similar to its competitors

Summary

Introduction

With the development of mobile communication systems, a huge number of resource-intensive and latency-sensitive applications are emerging, such as virtual reality, and online gaming [1] Such applications are usually sensitive to latency and require huge computational resources. Mobile edge computing (MEC) is a promising technology to address the above-mentioned issue It can provide service with low latency and high reliability for IoTDs. It can provide service with low latency and high reliability for IoTDs It can execute tasks of IoTDs at the nearby edge cloud and sends back the results to IoTDs [1]. Due to the shorter physical distance between MEC’s server/edge cloud and IoTDs, it consumes less energy as compared to mobile cloud computing. It cannot provide timely services during natural disasters as the terrestrial communication link may be broken/lost

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Results

Conclusion