# Identifying Misbehaving Greedy Nodes in IoT Networks
## 2 Related Work
In this case, it can occupy the channel at the first time slot without any waiting while the other devices wait to perform a CCA and detect that the channel is free before transmitting. In the second scenario Cw = 2, other devices in the nearby network can access the channel at the first time slot. This study only presents a mathematical analysis of the performance of the selfish node and the degradation of the throughput of other devices. However, there are no details on the methods for identifying greedy nodes within the IEEE 802.15.4 network. Moreover, the authors did not provide simulation parameters in terms of the number of packets sent or lost, the collision rate, or the battery level. These parameters are important to highlight in order to assess the difference in behaviour between legitimate and greedy nodes. In [15], the authors propose a nonparametric statistical approach to detect malicious filtering nodes in mobile networks assisted by network coding. The proposed method is based on the Kruskal-Wallis statistical test. Honest users represent a single population, and all control packets generated by that population are examined. The proposed scheme is based on ranks, in order to detect whether different sets of samples belong to the same probability distribution. Concerning the modelling of the legitimate and egoistic nodes, the authors suppose that the nodes of the network produce in a periodic time interval ratios rm (t) \(\in\) {0,1}. A given node is considered legitimate if its ratio rm (t) = 1, and selfish if it is rm (t) = 0. Knowing that legitimate nodes can provide a false report with a probability called \"pe\", due to channel errors, then those malicious nodes deliberately provide false reports with probability pf. The works [16; 17] are based on game of theory and fuzzy logic to detect greedy nodes and adopt a defence strategy in VANets networks. The authors propose a method combining the detection and identification of greedy nodes in VANets networks. This method is called GDVAN (Greedy Detection for VANETs). It uses linear regression to detect greedy behaviour attacks in the network and fuzzy logic to identify greedy nodes. Three metrics are used to identify selfish nodes, namely: number of connection attempts, node connection time, and average wait time between connections. In [18], the authors propose a countermeasure to misbehaviour attacks and optimized energy consumption. They propose a model of the IEEE 802.15.4 standard in the form of a dynamic game, where the different nodes of the network represent players able to assess the state of the game, then select and adapt their game strategies while optimizing energy consumption. Concretely, the dynamic MAC model proposed works exactly as the IEEE 802.15.4 standard in the normal state, but when the state of the game changes, the nodes individually change their game strategies by modifying their contention parameters to improve their energy consumption. It should be noted that the state of the game is evaluated only by two main parameters: the number of transmission failures, and the failure to execute the CCA (Clear Channel Assessment). Although the authors assure by the simulation results that this proposal allows the CSMA-CA and GTS mechanisms to be resilient to greedy behaviours in IoT networks, this method is likely to work badly in the case where a large number of nodes are saturated. Furthermore, this study does not in any way perform the greedy behaviour attack by a malicious node in order to demonstrate its impact on the WSN network before proposing this countermeasure. The simulation was performed in a probabilistic wireless network simulator based on MATLAB. The authors simulated four nodes, playing the native IEEE 802.15.4, while four other nodes are playing the proposed dynamic game. However, the proposed countermeasure solution was achieved without the presence of a greedy node. Therefore, this proposal may not work well when the IEEE 802.15.4 network is attacked by a misbehaving greedy node.