A hybrid ANFIS reptile optimization algorithm for energy-efficient inter-cluster routing in internet of things-enabled wireless sensor networks

• Published in: Peer-to-peer networking and applications Vol. 16; no. 2; pp. 1049 - 1068
• Main Authors: Vazhuthi, P. Paruthi Ilam, Prasanth, A., Manikandan, S. P., Sowndarya, K. K. Devi
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2023; Springer Nature B.V
• Subjects: Algorithms; Clustering; Communications Engineering; Computer Communication Networks; Energy consumption; Energy efficiency; Engineering; Fault diagnosis; Hybrid systems; Industrial applications; Industrial development; Industry 4.0; Information Systems and Communication Service; Internet of Things; Networks; Nodes; Optimization; Optimization algorithms; Reptiles; Residual energy; Routing (telecommunications); Signal,Image and Speech Processing; Wireless sensor networks; Wireless sensor network; Internet of things; Inter-cluster routing; Fault detection; Clustering
• ISSN: 1936-6442; 1936-6450
• DOI: 10.1007/s12083-023-01458-0

In recent decades, the Internet of Things-enabled Wireless Sensor Networks (IWSN) plays a dominant role in the evolution of industry 4.0 by developing numerous WSN-assisted IoT applications. The energy efficiency of the deployed nodes will be the major concern for IWSN owing to the restricted battery resources. The clustering is widely utilized as an adequate approach for designing energy-efficient IWSN. Nonetheless, anonymous actions like a frequent change of inter-cluster routing, path identification, and occurrence of the faulty node have a direct influence on the energy consumption and network stability of IWSN. In this paper, a novel energy-efficient inter-cluster routing and fault management has been proposed to prolong the Quality of Services of IWSN. Primarily, the proposed system implements the Hybrid ANFIS Reptile Optimization Algorithm for identifying the optimal route from cluster to sink. Afterward, the Tuned supervision-based fault diagnosis strategy can be carried out to diagnose the different faults like sensing fault, residual energy fault, and communication fault in IWSN. The evaluation of the proposed system is computed using 1000 nodes with two distinct sink positions. Finally, the performance results validate that the proposed model achieves a lesser energy consumption of 0.01 J than the existing inter-cluster routing algorithms.