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Energy-balanced neuro-fuzzy dynamic clustering scheme for green & sustainable IoT based smart cities by Premkumar Chithaluru; Fadi Al-Turjman; Manoj Kumar; Thompson Stephan is a Environmental Science article available to read on EtoBox.

What is Energy-balanced neuro-fuzzy dynamic clustering scheme for green & sustainable IoT based smart cities about?

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

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Author

Premkumar Chithaluru; Fadi Al-Turjman; Manoj Kumar; Thompson Stephan

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Environmental Science (Physical Sciences)