DIFFERENTIAL EVOLUTION FRAMEWORK TO IMPROVE THE NETWORK LIFETIME OF IOT-MANETS
Abstract
In the dynamic landscape of Internet of Things Mobile Ad Hoc Networks (IOT-MANETs), optimizing the network lifetime is paramount for sustained and efficient operation. The research begins by recognizing the inherent complexities of IOT-MANETs and the inadequacies of current methodologies. The identified research gap revolves around the lack of a comprehensive framework specifically tailored to optimize network lifetime in these dynamic environments. To bridge this gap, the proposed methodology leverages the powerful optimization capabilities of Differential Evolution—a nature-inspired algorithm that mimics the process of natural selection. This research endeavors to address the pressing challenge of enhancing the longevity of IOT-MANETs by proposing a novel framework based on Differential Evolution (DE). The DE-based framework employs a systematic approach to adaptively optimize network parameters, considering factors such as energy consumption, routing efficiency, and communication reliability. The methodology integrates seamlessly with the inherent characteristics of IOT-MANETs, ensuring adaptability to changing network dynamics. Rigorous simulations and experiments validate the effectiveness of the proposed framework, demonstrating substantial improvements in network lifetime compared to existing methods. The results underscore the significance of the DE-based framework in substantially extending the operational lifespan of IOT-MANETs. This research contributes a valuable tool to the arsenal of solutions for enhancing the sustainability and efficiency of IoT-based mobile ad hoc networks, paving the way for more resilient and long-lasting deployments.

Authors
Suresh Chandrasekaran
Kalaignar Karunanidhi Institute of Technology, India

Keywords
Internet of Things, Mobile Adhoc Networks, Network Lifetime Optimization, Differential Evolution, IoT-MANETs
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Published By :
ICTACT
Published In :
ICTACT Journal on Communication Technology
( Volume: 14 , Issue: 4 , Pages: 3079 - 3084 )
Date of Publication :
December 2023
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104
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