A novel fractional kookaburra crayfish optimization algorithm for trusted routing based blockchain in wireless sensor network

In a Wireless Sensor Network (WSN), the routing procedure is complex and supports data transmission to base stations. However, routing attacks can significantly compromise or disrupt the functionality of WSNs. Furthermore, the majority of routing algorithms are impractical due to the difficulty of e...

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Veröffentlicht in:Knowledge-based systems Jg. 323; S. 113812
Hauptverfasser: Lizzy Nesa Bagyam, M., Maheswari, S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 19.07.2025
Schlagworte:
Blockchain
Crayfish optimization algorithm
Fractional kookaburra crayfish optimization
Kookaburra optimization algorithm
Wireless sensor network
Wireless sensor network
Fractional kookaburra crayfish optimization
Kookaburra optimization algorithm
Crayfish optimization algorithm
Blockchain
ISSN:0950-7051
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Zusammenfassung:In a Wireless Sensor Network (WSN), the routing procedure is complex and supports data transmission to base stations. However, routing attacks can significantly compromise or disrupt the functionality of WSNs. Furthermore, the majority of routing algorithms are impractical due to the difficulty of effectively determining the reliability of routing nodes. Hence, Fractional Kookaburra Crayfish Optimization (FKCO) is introduced for trusted routing-based blockchain in WSNs. The WSN is first simulated, and then different factors are taken into consideration to build the node data structure. Afterwards, CH selection is carried out based on a hybrid Kookaburra Crayfish Optimization Algorithm (KCOA) considering multi-objectives. The devised KCOA is an incorporation of the Kookaburra Optimization Algorithm (KOA) and Crayfish Optimization Algorithm (COA). Subsequently, blockchain-based routing network-based next-hop selection is performed using FKCO based on link reliability, energy prediction, delay, and distance. The FKCO is the amalgamation of the Fractional Concept (FC) with the proposed KCOA. Furthermore, Deep Q-Network (DQN) is used for energy prediction. Further, the FKCO is analyzed for its efficiency by considering three performance metrics, a delay of 0.600 s, energy of 0.400 J, and distance of 48.068 m.
ISSN:0950-7051
DOI:10.1016/j.knosys.2025.113812