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Secured cross‐layer cross‐domain routing in dense wireless sensor network: A new hybrid based clustering approach
Author(s) -
Lahane Shivaji R.,
Jariwala Krupa N.
Publication year - 2021
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22438
Subject(s) - computer science , cluster analysis , wireless sensor network , energy consumption , routing protocol , overhead (engineering) , routing (electronic design automation) , throughput , node (physics) , firefly algorithm , network layer , computer network , layer (electronics) , wireless , algorithm , artificial intelligence , engineering , telecommunications , chemistry , structural engineering , organic chemistry , particle swarm optimization , electrical engineering , operating system
In wireless sensor network (WSN), increasing the network life span remains as a crucial challenge yet to be resolved. The modeling of effectual methods is necessary for conserving the scarce energy resources in WSN. To overcome such issues, cross‐layer protocols are exploited, which concerns routing the messages with increased lifetime. This study introduces a new cross‐layer design routing model under a clustering‐based approach. More importantly, the cluster head is optimally selected by a new hybrid algorithm termed as moth flame integrated dragonfly algorithm. Moreover, the optimal selection of cluster head is carried out based on parameters such as energy consumption, delay, distance, throughput, security, and overhead. Finally, the supremacy of the presented model is proved over existing models in terms of alive node analysis and network lifetime analysis. The experimental outcomes show that the proposed algorithm for test case 3 has accomplished a higher value of 66.229, which is 29.07%, 13.33%, 26.36%, and 9.67% better than conventional ant lion optimisation approach, grouped grey wolf search optimisation, firefly replaced position update in da, and alpha wolf‐assisted whale optimization algorithm, respectively, for median case scenario.