Energy-balanced neuro-fuzzy dynamic clustering scheme for green & sustainable IoT based smart cities. (March 2023)

• Record Type: Journal Article
• Authors: Chithaluru, Premkumar; Al-Turjman, Fadi; Kumar, Manoj; Stephan, Thompson
• Is Part Of: Sustainable cities and society. Volume 90(2023)
• Publication Date: 2023-03
• Subjects: IoT -- QoS -- Sustainable -- Dynamic clustering -- Neuro-fuzzy -- Green communication protocols -- Smart city; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2022.104366
• Languages: English
• ISSNs: 2210-6707

Abstract

Abstract: The Internet of Things (IoT) is a pervasive computing technology that provides solutions to critical sustainable smart city applications. Each sustainable application has its own set of requirements, including energy efficiency, Quality of Service (QoS), hardware, and software resources. Even though green IoT devices operate in a resource-constrained environment. Monitoring, recognizing, and responding to activities that entail continuous access to timely information in a partially or fully distributed ecosystem is a difficult task. To overcome the challenges of resource management in the IoT, we proposed an energy-efficient Dynamic Clustering Routing (DCR) protocol using a neuro-fuzzy technique for restricting the resources of IoT devices. The proposed protocol uses a dynamic self-organizing neural network to create dynamic clusters in a network. The test-bed analysis is for computing the real-time event detection and clustering sensor nodes using TinyOS. The simulation result shows that the proposed protocol achieved a significant gain over peer-competing well-known green communication routing protocols like Low-energy Adaptive Clustering Hierarchy (LEACH) and Low-energy Adaptive Clustering Hierarchy-Centralized (LEACH-C). The proposed model results show that using neuro-fuzzy logic is effective for sustainable IoT devices and green smart city applications in terms of resource management and dynamic clustering. The result analysis shows that the proposed protocol shows an average 35% significant gain on the First Node Dies (FND), Last Node Dies (LND), the number of packets sent to CH & BS, network convergence time, network overhead, and average packet delay to compare with the LEACH and LEACH-C. Highlights: An energy-efficient dynamic clustering based on a neuro-fuzzy technique. Cluster formation using a dynamic self-organizing network. An Energy-efficient resource management technique for optimizing the nodes' energy. A Computational intelligence-assisted clustered routing mechanism.