An Optimized Bio-inspired Localization Routing Technique for Sustainable IIoT Networks & Green Cities
Publication Name
Sustainable Cities and Society
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.
Open Access Status
This publication is not available as open access
Volume
97
Article Number
104722