A novel selfish node detection based on reputation and game theory in Internet of Things

The Internet of Things (IoT) implies a new model and concept in the world of information and communication technology that the non-cooperation of some nodes in providing services to other nodes may result in the disconnection of some things with each other. Consequently, the proximity of such nodes is considerably reduced the network performance. In this paper, a multiphase method based on game theory and direct and indirect reputation has been designed to stimulate selfish and malicious nodes to cooperate in the Internet of Things, which starts with setting up nodes in the IoT network. In the first phase, nodes are grouped in clusters with cluster heads to collect data. Then, they play a dynamic and multi-person game when they promote their own data packet or others’ data packet in the second phase (multi-person game phase and data packet sending). The result of the game is applied to affect between nodes in the next phase. Nodes can pick their strategy when data packet forwarding in the third phase (direct and indirect reputation update). Nodes will update the neighboring node and the reputation table if they receive a confirmation message from the destination declaring that the data packet has a direct reputation. The results show that the accuracy of selfish node detection has increased by average of 8% and the false positive rate is 7% in comparison to comparative methods.