## 1 Introduction
### Related Work
For evaluation purposes, the authors present network simulations with several performance metrics, i.e., the packet delivery ratio, the network throughput, the energy consumption, and routing overhead. [PERSON] et al. have proposed the use of fuzzy and neuro-fuzzy inference methods for jamming detection in cluster-based WSN [25]. The cluster head determines the presence of jamming within its zone. For this task, the detection model considers the packet delivery ratio and received signal strength indicator (RSSI) as input variables. The proposed methods are evaluated by using the true detection ratio (TDR) and the analysis of variance (ANOVA) test. The paper by [PERSON] et al. [26] presents a review of denial of service attacks in WSN that causes resources depletion. For instance, a jamming attack may increase the energy consumption of the wireless nodes reducing the batteries' life. The review includes the definition of different categories of jamming attacks, i.e., constant jammer, deceptive jammer, random jammer, and reactive jammer. In addition, the authors provide a DoS defense taxonomy in WSN. For instance, the cluster head approach included in the taxonomy covers those methods where the main detection and defense mechanisms are tasks carried out by the cluster head, since it has larger capacities than other nodes. A game-theoretic mobility model is used to create a countermeasure against jamming attacks in the paper by [PERSON] [27] et al. The method is based on jamming avoidance following a a mobility pattern that minimizes the energy consumption and maximizes the network lifetime. For identifying a jamming affected region, timeouts for receiving and acknowledgment packets may indicate the presence of a jamming attack. Once the affected region has been identified, the involved nodes will follow a mobility model. The authorsuse several performance metrics to evaluate the proposed method: energy consumption of network, network overhead, and packet delivery ratio. A Bayesian detection scheme for physical layer attacks is proposed by [PERSON] et al. [28]. Their method determines the probability of a physical and MAC layer attack considering the packet delivery and receiving rate, the channel contention activity, and the packet delivery delay. In order to use this scheme, the variables' behaviour should be observed so that they can be used in the computation of the a priori probability distributions needed to estimate an attack's presence. Wireless networks play an essential role in achieving ubiquitous communication, where network devices embedded in the environment provide continuous connectivity and services, thus improving the quality of human life. However, due to wireless links' exposed nature, today's wireless networks can be easily attacked by jamming technology [7]. Jamming is different from regular network interference in that it involves the deliberate use of wireless signals in an attempt to disrupt communications. In contrast, interference refers to unintended forms of disruption. Two generic types of jamming are distinguished. The proactive jammer transmits jamming signals regardless of whether or not there is an ongoing transmission in the channel. The jammer or malicious node sends packets at random to try to interfere with the communications of the network of nodes in which it is immersed. There are three main types of proactive jamming: misleading, random, and constant [22]. A different kind of jamming begins the transmission of packets when it hears activity on the communications channel. This type consumes more energy because it must have constant monitoring of the network frequency. The advantage is that it is more difficult to detect this type of attack because you cannot know when the jammer node will transmit unless the nodes compromise their packets [29]. This work focuses on the affectation of a node and the routing protocol's reactive behaviour that can indicate a possible zone of affectation due to jamming.