# Identifying Misbehaving Greedy Nodes in IoT Networks
## 2 Related Work  
While the greedy behaviour attack has been widely studied in IEEE 802.11 wireless networks, little work has been done in IEEE 802.15.4-based WSNs. Indeed, several techniques and methods for greedy node detection have been proposed in IEEE 802.11 wireless networks, including game-theoretic, fuzzy logic-based schemes; however, to the best of our knowledge, there is almost no work regarding greedy node detection and identification in IoT networks. We have selected the most recent works in the literature to present in this section. Works [10; 11; 12; 13] focus on the characterization and modelling of the greedy behaviour attack. The authors propose in [10] a model of greedy behaviour in wireless Ad Hoc networks. The greedy node increases its bandwidth by randomly choosing a backoff value within a range bounded by a misbehaviour threshold. This threshold represents a bandwidth limit that the greedy node must not exceed in order to avoid performance degradation. In order to prevent other nodes from sending their packets, the greedy node causes collisions with its neighbours' frames except for the next-hop frames of its current flow. In [11], the authors have studied the impact of greedy nodes in the IEEE802.11 WLAN network by combining timed automata and Discrete-Time Markov Chains (DTMC). The study proposes a formal approach for modelling CSMA-CA in IEEE802.11 networks containing greedy nodes. In order to monopolize the transmission channel, the greedy node modifies the Distributed Coordination Function (DCF) procedure. It does not execute the backoff procedure under any circumstances, due to the removing of the condition y = DIFS from the WaitingUntilTransmit and WirelessMediumSensing steps. Therefore, if the greedy node finds the channel free after carrying out a CCA, it immediately starts sending its packets without delaying the DIFS (DCF InterFrame Space). Likewise, if the channel has been detected busy, the greedy node does not execute a backoff and continues to sense the medium at each timeslot until it finds it free. If the packet from the greedy node collides with another, and transmission fails, the greedy node resumes its selfish behaviour by returning directly to the procedure WaitingUnitTransmit without observing any backoff. This study demonstrates that this behaviour negatively impacts the traffic of all nodes, including the compromised nodes themselves. In [12], the authors propose a UPPAAL model of the greedy node in a WSN network using the IEEE 802.15.4e standard and star topology. Authors were particularly interested in the beacon-activated mode contention access period of slotted CSMA-CA. The proposed model consists of two automates, namely, Medium and Node. The first models the state of the wireless medium, while the second describes the behaviour of nodes transmitting frames during CAP. The greedy node is modelled by falsifying the following parameters: the length of the initial conflict window (CW0), the minimum value of BE (minBe), and the maximum value of BE (maxBe). The authors propose in [13] the modelling of a greedy node in WSN networks based on unslotted IEEE 802.15.4 CSMA/CA, using the UPPAAL model-checker. The greedy node tries to monopolize the transmission channel by reducing its backoff, and by increasing its attempts to access the medium. The authors of [14] consider the greedy behaviour attack in IEEE 802.15.4 CSMA-CA. They propose the use of a Markov chain model to demonstrate the impact of this attack on the network throughput performance degradation. For this purpose, the authors represent the CSMA-CA protocol as a discrete-time Markov chain. This study presents the results of the mathematical analysis of the CSMA-CA throughput with an exponential random backoff. In order to show the effects of the selective backoff attack on the network, two scenarios are possible. The first assumes that the greedy node sets its contention window parameter Cw to 1.