FSRW: fuzzy logic-based whale optimization algorithm for trust-aware routing in IoT-based healthcare.
Fuzzy logic
Healthcare
Internet of Things
Routing
Whale optimization algorithm
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
18 Jul 2024
18 Jul 2024
Historique:
received:
19
03
2024
accepted:
01
07
2024
medline:
19
7
2024
pubmed:
19
7
2024
entrez:
18
7
2024
Statut:
epublish
Résumé
The Internet of Things (IoT) is an extensive system of interrelated devices equipped with sensors to monitor and track real world objects, spanning several verticals, covering many different industries. The IoT's promise is capturing interest as its value in healthcare continues to grow, as it can overlay on top of challenges dealing with the rising burden of chronic disease management and an aging population. To address difficulties associated with IoT-enabled healthcare, we propose a secure routing protocol that combines a fuzzy logic system and the Whale Optimization Algorithm (WOA) hierarchically. The suggested method consists of two primary approaches: the fuzzy trust strategy and the WOA-inspired clustering methodology. The first methodology plays a critical role in determining the trustworthiness of connected IoT equipment. Furthermore, a WOA-based clustering framework is implemented. A fitness function assesses the likelihood of IoT devices acting as cluster heads. This formula considers factors such as centrality, range of communication, hop count, remaining energy, and trustworthiness. Compared with other algorithms, the proposed method outperformed them in terms of network lifespan, energy usage, and packet delivery ratio by 47%, 58%, and 17.7%, respectively.
Identifiants
pubmed: 39025873
doi: 10.1038/s41598-024-66392-4
pii: 10.1038/s41598-024-66392-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
16640Subventions
Organisme : Heilongjiang Province Philosophy and Social Science Research Planning Project under Grant
ID : 23TQD182
Organisme : National Natural Science Foundation of China
ID : LBH-Z23268
Informations de copyright
© 2024. The Author(s).
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