A Novel Selfish Node Detection Based on Fuzzy System and Game Theory in Internet of Things

Internet of Things describes a situation in which a large number of devices (things) are connected through a number of sensors via Internet, and lack of cooperation of some nodes in providing service to other nodes might interrupt the connection of some things, degrading network efficiency. A multi-phase mechanism based on Game theory and direct/indirect fame has been designed to motivate the selfish and malicious nodes to cooperate in IoT, which begins by deploying nodes in the IoT network. In the first phase, the nodes are grouped into clusters with cluster-heads for data collection. In the second phase, a multiplayer and dynamic game is executed while forwarding their data packet or others’ data packet. Nodes can pick their strategy when data packet forwarding in the third phase (Fuzzy logic reputation). Nodes will determine the neighboring node reputation by using fuzzy system. The amount of reputation of each of the nodes has been realized and finally, with the help of second phase 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 cooperate in network activities otherwise the node will be isolated. The effectiveness of the proposed solution has been assessed and the parameters of non-cooperative node detection accuracy, positive and negative warning rates, network PDR, and average endto-end latency perform better compared to other previous methods.