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Load Balancing of Energy Cloud using Wind Driven and Firefly Algorithms in Internet of Everything

Priya RM, S, Bhattacharya, S, Maddikunta, PKR, Somayaji, SRK, Lakshmanna, K, Kaluri, R, Hussien, A and Gadekallu, TR (2020) Load Balancing of Energy Cloud using Wind Driven and Firefly Algorithms in Internet of Everything. Journal of Parallel and Distributed Computing, 142. pp. 16-26. ISSN 0743-7315

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The smart applications dominating the planet in the present day and age, have innovatively progressed to deploy Internet of Things (IoT) based systems and related infrastructures in all spectrums of life. Since, variety of applications are being developed using this IoT paradigm, there is an immense necessity for storing data, processing them to get meaningful information and render suitable services to the end-users. The “thing” in this decade is not only a smart sensor or a device; it can be any physical or household object, a smart device or a mobile. With the ever increasing rise in population and smart device usage in every sphere of life, when all of such “thing”s generates data, there is a chance of huge data traffic in the internet. This could be handled only by integrating “Internet of Everything (IoE)” paradigm with a completely diversified technology - Cloud Computing. In order to handle this heavy flow of data traffic and process the same to generate meaningful information, various services in the global environment are utilized. Hence the primary focus revolves in integrating these two diversified paradigm shifts to develop intelligent information processing systems. Energy Efficient Cloud Based Internet of Everything (EECloudIoE) architecture is proposed in this study, which acts as an initial step in integrating these two wide areas thereby providing valuable services to the end users. The utilization of energy is optimized by clustering the various IoT network using Wind Driven Optimization Algorithm. Next, an optimized Cluster Head (CH) is chosen for each cluster, using Firefly Algorithm resulting in reduced data traffic in comparison to other non-clustering schemes. The proposed clustering of IoE is further compared with the widely used state of the art techniques like Artificial Bee Colony (ABC) algorithm, Genetic Algorithm (GA) and Adaptive Gravitational Search algorithm (AGSA). The results justify the superiority of the proposed methodology outperforming the existing approaches with an increased -life-time and reduction in traffic.

Item Type: Article
Uncontrolled Keywords: 0805 Distributed Computing, 0803 Computer Software
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Civil Engineering & Built Environment
Publisher: Elsevier
Date Deposited: 02 Mar 2020 11:42
Last Modified: 04 Sep 2021 07:49
URI: https://researchonline.ljmu.ac.uk/id/eprint/12357
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