Research Article
Sparrow Search Algorithm-based Resource Management in Internet of Things (IoT)
@ARTICLE{10.4108/eai.13-5-2021.169915, author={S. Karthick and N. Gomathi}, title={Sparrow Search Algorithm-based Resource Management in Internet of Things (IoT)}, journal={EAI Endorsed Transactions on Energy Web}, volume={9}, number={37}, publisher={EAI}, journal_a={EW}, year={2021}, month={5}, keywords={Internet-of-Things (IoT), Resource Allocation, Sparrow Search Algorithm, Gateways, Optimization search space}, doi={10.4108/eai.13-5-2021.169915} }- S. Karthick
N. Gomathi
Year: 2021
Sparrow Search Algorithm-based Resource Management in Internet of Things (IoT)
EW
EAI
DOI: 10.4108/eai.13-5-2021.169915
Abstract
INTRODUCTION: The principle of Internet of Things (IoT) completely concentrates on integrating heterogeneous entities for establishing seamless cooperation among virtual and physical entities. IoT has facilitated a paradigm shift of the Internet from collaborating networks to physical world interaction. The IoT devices are potent in sensing, processing, communicating and data storing that are derived from the physical world. The majority of the IoT applications are considered to be pervasive in characteristics and face huge challenges due to unattended IoT devices and constrained resources.
OBJECTIVES: The proposed Sparrow Search Algorithm-based Resource Management (SSARM) targets on the potential assignment of resources to gateways in an IoT environment with better establishment in maintaining the tradeoff between intensification and diversification. It concentrates on the reduction of total data transmission cost in the IoT environment.
METHODS: In this paper, Sparrow Search Algorithm-based Resource Management (SSARM) is proposed based on the inspired by the foraging, group wisdom and anti-predation characteristics of sparrow for potential assignment of multiple resources to gateways in IoT. This SSARM balances the degree of exploitation and exploration in the optimization search space to an acceptable level.
RESULTS: The simulation results of this proposed SSARM confirmed better throughput of 23.82%, reduced delay of 18.21% and minimized energy consumption of 20.28% when compared to the existing schemes.
CONCLUSION: This SSARM offers better accuracy in stability, convergence rate, precision in searching and preventing the value of local point of optimality.
Copyright © 2021 S. Karthick et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
