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A novel simulated annealing trajectory optimization algorithm in an autonomous UAVs-empowered MFC system for medical internet of things devices

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Abstract

This article investigates a new autonomous mobile fog computing (MFC) system empowered by multiple unmanned aerial vehicles (UAVs) in order to serve medical Internet of Things devices (MIoTDs) efficiently. The aim of this article is to reduce the energy consumption of the UAVs-empowered MFC system by designing UAVs’ trajectories. To construct the trajectories of UAVs, we need to consider not only the order of SPs but also the association among UAVs, SPs, and MIoTDs. The above-mentioned problem is very complicated and is difficult to be handled via applying traditional techniques, as it is NP-hard, nonlinear, non-convex, and mixed-integer. To handle this problem, we propose a novel simulated annealing trajectory optimization algorithm (SATOA), which handles the problem in three phases. First, the deployment (i.e., number and locations) of stop points (SPs) is updated and produced randomly using variable population sizes. Accordingly, MIoTDs are associated with SPs and extra SPs are removed. Finally, a novel simulated annealing algorithm is proposed to optimize UAVs’ association with SPs as well as their trajectories. The performance of SATOA is demonstrated by performing various experiments on nine instances with 40 to 200 MIoTDs. The simulation results show that the proposed SATOA outperforms other compared state-of-the-art algorithms in terms of saving energy consumption.

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Data Availability

The data used to support the findings of this study are available from the authors upon request.

Abbreviations

UAV:

Unmanned Aerial Vehicle

QoS:

Quality of Service

ACO:

Ant Colony Optimization

MFC:

Mobile Fog Computing

TSP:

Travelling Salesman Problem

MIoTD:

Medical Internet of Things Devices

SATOA:

SA Trajectory Optimization Algorithm

EC:

Energy Consumption

MEC:

Mobile Edge Computing

IoT:

Internet of Things

SA:

Simulated Annealing

ISA:

Improved SA

DEC:

Differential Evolution Clustering

SATOA-W:

SATOA without Remove Operator

TS:

Tabu Search

GA:

Genetic Algorithm

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Acknowledgements

The author would like to thank Prince Sultan University for their support. Also, the studies at St. Petersburg State University of Telecommunications. prof. M.A. Bonch-Bruevich were supported by the Ministry of Science and High Education of the Russian Federation by the grant 075-15-2022-1137.

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Authors and Affiliations

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China

    Muhammad Asim, Chen Junhong & Liu Wenyin

  2. EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia

    Muhammad Asim & Ahmed A. Abd El-Latif

  3. Expertise Centre for Digital Media, Hasselt University, Hasselt, Belgium

    Chen Junhong

  4. Department of Telecommunication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 193232, Saint Petersburg, Russia

    Ammar Muthanna

  5. RUDN University, 6 Miklukho-Maklaya St, 117198, Moscow, Russian Federation

    Ammar Muthanna

  6. School of Software Engineering, South China University of Technology, Guangzhou, People’s Republic of China

    Siraj Khan

  7. Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom, 32511, Egypt

    Ahmed A. Abd El-Latif

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Asim, M., Junhong, C., Muthanna, A. et al. A novel simulated annealing trajectory optimization algorithm in an autonomous UAVs-empowered MFC system for medical internet of things devices. Wireless Netw 29, 3163–3176 (2023). https://doi.org/10.1007/s11276-023-03370-0

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