Selfish node Detection Based on Fuzzy Logic and Harris Hawks Optimization Algorithm in IoT Networks

Abstract

The Internet of things describes a network of physical things for example, "things"that are connected with the sensors, software, and other technologies to connect and exchange data with other devices and systems via the Internet. In this type of network, the nodes communicate with each other because of the low radio range by step by step with the help of each other until they reach their destination, but there are nodes in the network that do not cooperate with other nodes in the network, which are called "selfish nodes". In this paper, we try to detect selfish nodes based on a hybrid approach to increase the performance of our network. The proposed method consists of three stages: in the first stage, with the help of the Harris hawk operation, we try to set up the cluster and select head cluster; in the second stage, the sink investigates the existence or nonexistence of selfish nodes in the network by considering the general parameters of the network; and in the event of a selfish node in the network, it informs the head clusters to check the cluster members and recognize the selfish node. In the third stage, with the help of fuzzy logic, the amount of reputation of each of the nodes has been realized, and finally, with the help of fusion of head clusters and fuzzy logic, each node is decided to be cooperate or selfish nodes, and in case of head clusters and fuzzy logic in some cases, the opportunity node will be reestablished to participate in network activities otherwise the node will be isolated. The results show that the accuracy of selfish node detection has increased by an average of 12% and the false positive rate is 8% in comparison to existing methods.