Selfish node detection based on hierarchical game theory in IoT

Abstract

Cooperation 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%.

Selfish Node detection based on GA and Learning Automata in IoT

“…It simulated in core i7 processors, 370 M processors, 2.40 GHz of speed with a memory of 8 GB, Window 8.1 basic (64bit), and MATLAB 2018 software. The simulation results of the proposed method compared with the Game theorybased [21], PPS [25], and Trust management [26] protocols in evaluation metrics like throughput, average end-to-end delay, detection accuracy, false positive/negative rate, and energy consumption. The simulation performed 100 runs, and the simulation results have shown and indicated in different charts.…”

“…The fact that the proposed scheme has a slighter slope compared to the methods Game theory-based [21], PPS [25], and Trust management [26] protocols have shown in Table 3. The proposed method uses GA, and LA processes in each cluster to detect the selfish nodes.…”

“…The acknowledgment packet from destination learn the LA about the nodes status are cooperate or selfish. The performance of the method has been tested on the network and compared with Game theory-based [21], PPS [25], Trust management [26]. The results have shown that the proposed method can detect nodes in high accuracy and decreasing end-to-end delay and consumption of node resources (energy, battery, memory, etc.).…”

“…In the game theory methods, each node plays a role as players in the game, which are interacting with each other and design the game and its profits to send the data packets. These methods have a higher false positive rate, and the average end to end delay is more than other methods [19][20][21]. Another group of selfish nodes detection and recognition methods in the network is hybrid and specified methods.…”

Detection and Isolation of Selfish Nodes in MANET Using Collaborative Contact-Based Watchdog With Chimp-AODV

“…Here, the , , , values are calculated using Equations (6)(7)(8)(9). The 1 , 2 , 3 , 4 are chaotic values that can be calculated using Equation (5).…”