Javanmardi, Saeed, Sakellari, Georgia ORCID: 0000-0001-7238-8700 , Shojafar, Mohammad and Caruso, Antonio (2024) Why it does not work? Metaheuristic task allocation approaches in fog-enabled Internet of Drones. Simulation Modelling Practice and Theory:102913. ISSN 1569-190X (doi:https://doi.org/10.1016/j.simpat.2024.102913)

PDF (AAM) 45971_SAKELLARI_Why_it_does_not_work_Metaheuristic_task_allocation_approaches.pdf - Accepted Version Restricted to Repository staff only until 22 February 2025. Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (263kB) | Request a copy

Abstract

Several scenarios that use the Internet of Drones (IoD) networks require a Fog paradigm, where the Fog devices, provide time-sensitive functionality such as task allocation, scheduling, and resource optimization. The problem of efficient task allocation/scheduling is critical for optimising Fog-enabled Internet of Drones performance. In recent years, many articles have employed meta-heuristic approaches for task scheduling/allocation in Fog-enabled IoT-based scenarios, focusing on network usage and delay, but neglecting execution time. While promising in the academic area, metaheuristic have many limitations in real-time environments due to their high execution time, resource-intensive nature, increased time complexity, and inherent uncertainty in achieving optimal solutions, as supported by empirical studies, case studies, and benchmarking data. We propose a task allocation method named F-DTA that is used as the fitness function of two metaheuristic approaches: Particle Swarm Optimization (PSO) and The Krill Herd Algorithm (KHA). We compare our proposed method by simulation using the iFogSim2 simulator, keeping all the settings the same for a fair evaluation and only focus on the execution time. The results confirm its superior performance in execution time, compared to the metaheuristics.

Item Type:	Article
Uncontrolled Keywords:	IoT-Fognetworks; fog-enabled internet of drones; task allocation; metaheuristic algorithms; execution time
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	17 May 2024 09:06
URI:	http://gala.gre.ac.uk/id/eprint/45971

Actions (login required)

View Item

Downloads