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• W4383105428 endingPage "104722" @default.
• W4383105428 startingPage "104722" @default.
• W4383105428 abstract "The industrial Internet of Things (IIoTs) network life is shortened due to sensor node (SN) energy limitations and computational capability. As a result, optimum node location estimation and efficient energy usage are two critical IIoT requirements. This work reduces energy consumption by performing node localization and cluster-based routing using an improved evolutionary algorithm called Cat Swarm Optimization (CSO). First, the CSO method is used to optimize the bio-inspired node’s location. Second, to conserve SN energy in the IIoT network, a cluster-based routing technique is used. The objective function is defined as minimizing the average distance between the cluster and its SNs while selecting the most energy-efficient Cluster Head (CH). In terms of fitness value, the Improved CSO (ICSO) algorithm outperforms the Particle Swarm Optimization (PSO) algorithm. In this paper, real-time test-bed analysis was used to investigate the performance of both node localization and energy-efficient clustering. When it comes to achieving sustainable IIoT and green cities, the findings show that ICSO outperforms in terms of convergence rate and network lifetime." @default.
• W4383105428 created "2023-07-05" @default.
• W4383105428 creator A5048113544 @default.
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• W4383105428 date "2023-10-01" @default.
• W4383105428 modified "2023-10-12" @default.
• W4383105428 title "An Optimized Bio-inspired Localization Routing Technique for Sustainable IIoT Networks & Green Cities" @default.
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• W4383105428 doi "https://doi.org/10.1016/j.scs.2023.104722" @default.
• W4383105428 hasPublicationYear "2023" @default.
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