Dynamic Bargain Game Theory in the Internet of Things for Data Trustworthiness.

Smart home and smart building systems based on the Internet of Things (IoT) in smart cities currently suffer from security issues. In particular, data trustworthiness and efficiency are two major concerns in Internet of Things (IoT)-based Wireless Sensor Networks (WSN). Various approaches, such as routing methods, intrusion detection, and path selection, have been applied to improve the security and efficiency of real-time networks. Path selection and malicious node discovery provide better solutions in terms of security and efficiency. This study proposed the Dynamic Bargaining Game (DBG) method for node selection and data transfer, to increase the data trustworthiness and efficiency. The data trustworthiness and efficiency are considered in the Pareto optimal solution to select the node, and the bargaining method assigns the disagreement measure to the nodes to eliminate the malicious nodes from the node selection. The DBG method performs the search process in a distributed manner that helps to find an effective solution for the dynamic networks. In this study, the data trustworthiness was measured based on the node used for data transmission and throughput was measured to analyze the efficiency. An SF attack was simulated in the network and the packet delivery ratio was measured to test the resilience of the DBG and existing methods. The results of the packet delivery ratio showed that the DBG method has higher resilience than the existing methods in a dynamic network. Moreover, for 100 nodes, the DBG method has higher data trustworthiness of 98% and throughput of 398 Mbps, whereas the existing fuzzy cross entropy method has data trustworthiness of 94% and a throughput of 334 Mbps.