A Novel Selfish Node Detection Based on Fuzzy System and Game Theory in Internet of Things
الموضوعات : Fuzzy Optimization and Modeling JournalGholam Hossein Abdi 1 , Amir Hosein Refahi Sheikhani 2 , Sohrab Kordrostami 3 , Shahram Babaie 4
1 - Department of Applied Mathematics and Computer Science, Lahijan Branch, Islamic Azad
University, Lahijan, Iran
2 - Department of Applied Mathematics and Computer Science, Lahijan Branch, Islamic Azad
University, Lahijan, Iran
3 - Department of Applied Mathematics and Computer Science, Lahijan Branch, Islamic Azad
University, Lahijan, Iran
4 - Department Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz,
Iran.
الکلمات المفتاحية: fuzzy logic, Game Theory, Internet of Things (IoT), Selfish and malicious node, direct and indirect reputation,
ملخص المقالة :
Internet of Things describes a large number of devices (things) are connected through a number of sensors via Internet, and lack of some nodes cooperation in providing service to other nodes might interrupt the connection of some things, decreasing network efficiency. A multi-phase mechanism based on fuzzy logic and Game theory has been designed to recognize and motivate the selfish and malicious nodes to cooperate in IoT. In the first phase, the nodes are grouped into clusters with cluster-heads for data collection. In the second phase, a multiply and dynamic game is executed while forwarding their data packet or others’ data packet. Nodes can select their strategy when data packet forwarding in the third phase (Fuzzy logic). 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 simulation of the proposed method has been evaluated and the parameters of selfish node detection accuracy 5%, false positive rate 8% and packet delivery rate 12% are perform better than other previous methods. .
