Selfish node detection based on hierarchical game theory in IoT

Abstract

AbstractCooperation between nodes is an effective technology for network throughput in the Internet of Things. The nodes that do not cooperate with other nodes in the network are called selfish and malicious nodes. Selfish nodes use the facilities of other nodes of the network for raising their interests. But malicious nodes tend to damage the facilities of the network and abuse it. According to reviews of the previous studies, in this paper, a mechanism is proposed for detecting the selfish and malicious nodes based on reputation and game theory. The proposed method includes three phases of setup and clustering, sending data and playing the multi-person game, and update and detecting the selfish and malicious nodes. The process of setup and clustering algorithm are run in the first phase. In the second phase, the nodes of each cluster cooperate with each other in order to execute an infinite repeated game while forwarding their own or neighbor nodes’ data packets. In the third phase, each node monitors the operation of its neighbor nodes for sending the data packets, and the process of cooperation is analyzed for determining the selfish or malicious nodes which forwarded the data packets with delay or even not sent them. The other nodes reduce the reputation of the nodes which does not cooperate with them, and they do not cooperate with the selfish and malicious nodes, as punishment. So, selfish and malicious nodes are stimulated to cooperate. The results of simulation suggest that the detection accuracy of the selfish and malicious nodes has been increased by an average of 12% compared with the existing methods, and the false-positive rate has been decreased by 8%.