HBMFTEFR: Design of a Hybrid Bioinspired Model for Fault-Tolerant Energy Harvesting Networks via Fuzzy Rule Checks

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Design of a typical energy harvesting model for fault tolerant wireless sensor networks
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Published: Dec 10, 2022
DOI: https://doi.org/10.17762/ijritcc.v10i1s.5821
Keywords:
Energy, Harvesting, Fault, Tolerance, Bioinspired, Communication, GA, PSO, ACO, TLBO, Fuzzy

Main Article Content

Jaya Dipti Lal
Assistant Professor, Electronics & Tc Department, S.G.S.I.T.S. Indore (MP), PhD Scholar, Oriental University Indore (M.P.)
Dolly Thankachan
Associate Professor and Head, Department of Electrical and Electronics Engineering Oriental University Indore (M.P.)

Abstract

Designing energy harvesting networks requires modelling of energy distribution under different real-time network conditions. These networks showcase better energy efficiency, but are affected by internal & external faults, which increase energy consumption of affected nodes. Due to this probability of node failure, and network failure increases, which reduces QoS (Quality of Service) for the network deployment. To overcome this issue, various fault tolerance & mitigation models are proposed by researchers, but these models require large training datasets & real-time samples for efficient operation. This increases computational complexity, storage cost & end-to-end processing delay of the network, which reduces its QoS performance under real-time use cases. To mitigate these issues, this text proposes design of a hybrid bioinspired model for fault-tolerant energy harvesting networks via fuzzy rule checks. The proposed model initially uses a Genetic Algorithm (GA) to cluster nodes depending upon their residual energy & distance metrics. Clustered nodes are processed via Particle Swarm Optimization (PSO) that assists in deploying a fault-tolerant & energy-harvesting process. The PSO model is further augmented via use of a hybrid Ant Colony Optimization (ACO) Model with Teacher Learner Based Optimization (TLBO), which assists in value-based fault prediction & mitigation operations. All bioinspired models are trained-once during initial network deployment, and then evaluated subsequently for each communication request. After a pre-set number of communications are done, the model re-evaluates average QoS performance, and incrementally reconfigures selected solutions. Due to this incremental tuning, the model is observed to consume lower energy, and showcases lower complexity when compared with other state-of-the-art models. Upon evaluation it was observed that the proposed model showcases 15.4% lower energy consumption, 8.5% faster communication response, 9.2% better throughput, and 1.5% better packet delivery ratio (PDR), when compared with recently proposed energy harvesting models. The proposed model also showcased better fault prediction & mitigation performance when compared with its counterparts, thereby making it useful for a wide variety of real-time network deployments.

Article Details

How to Cite
Lal, J. D. ., & Thankachan, D. . (2022). HBMFTEFR: Design of a Hybrid Bioinspired Model for Fault-Tolerant Energy Harvesting Networks via Fuzzy Rule Checks. International Journal on Recent and Innovation Trends in Computing and Communication, 10(1s), 166–181. https://doi.org/10.17762/ijritcc.v10i1s.5821
Issue
Vol. 10 No. 1s (2022): Special Issue on Enhancement and Innovations in Exploring Engineering
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Articles

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