Peer-to-Peer Networking and Applications

Erschienen in:

12.02.2024

Network lifetime optimization and route selection strategy towards energy enrichment in wireless body area networks

verfasst von: R. Pradeep, G. Kavithaa

Erschienen in: Peer-to-Peer Networking and Applications | Ausgabe 3/2024

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Abstract

Der Artikel untersucht die Herausforderungen und Lösungen zur Optimierung der Netzwerklebensdauer und Routenauswahl in drahtlosen Body Area Networks (WBANs). Es unterstreicht die Notwendigkeit effizienter Routing-Protokolle und Energiemanagement-Strategien, um die Beschränkungen batteriebetriebener Sensorknoten zu beheben. Die Forschung führt einen metaheuristischen Ansatz namens EFBOA ein, der darauf abzielt, den Energieverbrauch zu senken und die Lebensdauer von WBANs zu verlängern. Zusätzlich wird in der Studie das EEMR-Protokoll zur optimalen Routenauswahl mittels der DLH-LD-Technik vorgestellt. Die vorgeschlagenen Methoden werden durch Simulationen validiert und zeigen eine Leistungsverbesserung von 95% gegenüber bestehenden Protokollen. Der Artikel diskutiert auch die Beschränkungen und zukünftigen Richtungen für die Verbesserung des vorgeschlagenen Ansatzes.

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Abstract

Medical breakthroughs are currently being made to widen human beings' existence. For the vast majority of online medical care applications, Wireless Body Area Networks (WBANs) have emerged as an intriguing and important invention. Route loss and obstacles in offering essential information are significant criteria that drain power from the battery source and impair battery lifespan. By employing Optimal K-Means Clustering (OKMC), all body sensor nodes are positioned in tandem to construct cluster head selection. This research explores a novel metaheuristic method for strengthening network lifetime optimization and a sophisticated route selection strategy incorporating the Energy Enrichment Multi-Hop Routing (EEMR) protocol. The EEMR is intended to operate in a pair of phases. The Enhanced Flower Bee Optimisation Algorithm (EFBOA) is laid out as the initial phase, with the key objective of augmenting the network lifetime of the WBAN by laying down a network of clusters. The next phase employs Dynamic Local Hunting and Location Discarding (DLH-LD) to figure out the fastest route among all possible paths. The results of this analysis are validated and implemented in real-time using MATLAB and the Network Simulator. Various protocols, such as Mobility-supporting Adaptive Threshold-based Thermal-aware Energy-efficient Multi-hop Protocol (M-ATTEMPT), Even Energy Utilization Convention Routing (EECR), and Energy Mindful Posterior Routing (EMPR), are being compared to parameters such as path loss, throughput rate, energy consumption, cost estimation, and network lifetime. The outcomes revealed that, in comparison with conventional approaches, the suggested EEMR performs substantially better than existing routing protocols, spanning a range of performance indicators. Additionally, the research and simulation findings show that the suggested protocol is 30% more energy efficient than existing protocols, extending the life of the network. The numerical results exhibit an extensive performance enhancement of 95% in network throughput rate.

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Titel: Network lifetime optimization and route selection strategy towards energy enrichment in wireless body area networks
verfasst von: R. Pradeep
G. Kavithaa
Publikationsdatum: 12.02.2024
Verlag: Springer US
Erschienen in: Peer-to-Peer Networking and Applications / Ausgabe 3/2024
Print ISSN: 1936-6442
Elektronische ISSN: 1936-6450
DOI: https://doi.org/10.1007/s12083-023-01612-8

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Intrusion detection system extended CNN and artificial bee colony optimization in wireless sensor networks

Edge-assisted and energy-efficient access control for dynamic users group in smart grids

Cluster based hybrid optimization and kronecker gradient factored approximate optimum path curvature network for energy efficiency routing in WSN

An improved salp swarm algorithm for solving node coverage optimization problem in WSN

Lightweight privacy-enhanced secure data sharing scheme for smart grid

Security-aware IoT botnet attack detection framework using dilated and cascaded deep learning mechanism with conditional adversarial autoencoder-based features