In recent days, Internet of things (IoT) technology received more consideration from both academia and industry. IoT is a system that combines several computing devices, and digital tools offered by means of exclusive identifiers, which have capacity to transport data over a network through optimal path. Thus, dependable and secure IoT communication and correlation is necessary for appropriate operation of IoT network and the best strategy to attain a robustness security in an IoT network is to permit and generate trusted path between the nodes. An existing IoT models experienced a numerous decisive problems, namely cluster based trust techniques for IoT networks and attack identification of IoT trusted schemes from the malicious nodes, like poor Internet service providers. In this paper, Tversky hillenger distance-based Fuzzy Local Information C-Means clustering (Tversky hillenger distance-based FLICM clustering) approach is developed for Cluster Head Selection (CHS) in IoT routing. Accordingly, the developed Tversky hillenger distance-based FLICM clustering is newly designed by integrating tversky index, hillenger distance and FLICM clustering model. Moreover, Rider Foraging Optimization (RFO) algorithm is also employed in order to perform secure routing process. Besides, the developed Tversky hillenger distance-based FLICM model achieves better performance with respect to throughput, delay and energy consumption of 0.4390, 0.5882s, and 0.5614J.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.