Title: Energy optimisation model based on ant-mating optimisation for collaborative fog computing in internet of drones

Authors: Dillon Leong Lon Zan; Muhammad Umair Munir; Rafidah Md. Noor; Ismail Ahmedy; Rami Sihwail; Husam Ahmed Al Hamad

Addresses: Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia ' Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia ' Centre for Mobile Cloud Computing (C4MCCR), Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia ' Centre for Mobile Cloud Computing (C4MCCR), Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia ' College of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan ' College of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan

Abstract: Rapid advancements in drone technology have facilitated their deployment in various applications, including aerial surveys through the internet of drones (IoD). Given that IoD operations are resource-intensive, efficient management is crucial to avoid overloading drones and reducing power consumption. This study introduces an energy-aware task scheduling model for IoD operations within fog networks, enhancing resource allocation by offloading tasks from drones to fog devices. This method optimises drone data handling and significantly reduces IoD energy usage. We implemented the model in a simulated environment using an augmented version of iFogSim, with a focus on minimising energy expenditure in fog devices. Our findings reveal that the proposed ant-mating optimisation (AMO) algorithm markedly outperforms traditional genetic algorithms in efficiency, presenting a viable solution for energy optimisation in IoD systems.

Keywords: internet of drones; IoD; fog computing; energy-aware task scheduling; resource allocation; power consumption optimisation.

DOI: 10.1504/IJAHUC.2026.151267

International Journal of Ad Hoc and Ubiquitous Computing, 2026 Vol.51 No.1, pp.12 - 26

Received: 04 Sep 2024
Accepted: 11 Dec 2024
Published online: 20 Jan 2026 *

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