Version 1
: Received: 14 October 2024 / Approved: 14 October 2024 / Online: 15 October 2024 (07:27:39 CEST)
How to cite:
Boopalan, V.; V, V. A Hybrid Improved Particle Swarm Optimization and Genetic Algorithm for Energy Efficient Task Offloading in Industrial IoT Edge Computing Network. Preprints2024, 2024101098. https://doi.org/10.20944/preprints202410.1098.v1
Boopalan, V.; V, V. A Hybrid Improved Particle Swarm Optimization and Genetic Algorithm for Energy Efficient Task Offloading in Industrial IoT Edge Computing Network. Preprints 2024, 2024101098. https://doi.org/10.20944/preprints202410.1098.v1
Boopalan, V.; V, V. A Hybrid Improved Particle Swarm Optimization and Genetic Algorithm for Energy Efficient Task Offloading in Industrial IoT Edge Computing Network. Preprints2024, 2024101098. https://doi.org/10.20944/preprints202410.1098.v1
APA Style
Boopalan, V., & V, V. (2024). A Hybrid Improved Particle Swarm Optimization and Genetic Algorithm for Energy Efficient Task Offloading in Industrial IoT Edge Computing Network. Preprints. https://doi.org/10.20944/preprints202410.1098.v1
Chicago/Turabian Style
Boopalan, V. and Vasudevan V. 2024 "A Hybrid Improved Particle Swarm Optimization and Genetic Algorithm for Energy Efficient Task Offloading in Industrial IoT Edge Computing Network" Preprints. https://doi.org/10.20944/preprints202410.1098.v1
Abstract
Mobile Edge Computing (MEC) is a growing concept that expands on cloud computing technology. It leverages edge infrastructure to efficiently manage computationally intensive and time-sensitive tasks. By tapping into the capabilities of 7G network infrastructure, MEC can effectively reduce latency by transferring computing tasks from edge devices to edge servers. As part of Industrial 4.0 revolution, the use of large number of IOT sensors, edge devices, and edge servers are increasing at rapid phase. To effectively process these sensor and device data with less power and low latency is a key challenge. Here comes the need for an effective task offloading strategy to edge servers. This strategy must meet the industrial automation requirements of reduced energy usage and faster processing. A solution based on an Improved Particle Swarm Optimization with Genetic Algorithm (IPSOGA) is designed. It harnesses the power of effective exploration using Particle Swarm Optimization (PSO) and maintain genetic diversity within the population using Genetic Algorithm (GA). This hybrid approach is powerful in solving complex optimization problems with the aims to effective resource allocation, faster task offloading decisions, and consequently reduce processing delays. The proposed IPSOGA is compared with popular metaheuristic algorithms like Genetic Algorithm (GA) and Simulated Annealing (SA), and it has been proven to be superior in task offloading strategy by effectively reducing processing delays, energy consumption, and optimizing resource allocation efficiently.
Keywords
industrial internet of things, improved particle swarm optimization with genetic algorithm, edge computing, optimization, task offloading, IoT, signal processing
Subject
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
