## 2 Related Works
[18], the energy consumption is reduced by putting the node in sleep mode since the the node consumes more power when in transmission mode than when in sleep mode. However, the problem of jamming attacks can be approached in different ways. [PERSON], [PERSON] et al. [20] have used an exponentially weighted moving average method to detect jamming using the packet inter-arrival time of the packets received from the sensor nodes. The authors in study [27] propose a technique based on clustering approach and timestamp which made a contribution to the grouping of sensor nodes and the timestamp calculated from one node to another node. A PDR and RSSI metrics are used by [PERSON], [PERSON] et al. [28] to detect jamming with two methods: fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS). On the other hand, [PERSON], [PERSON] et al. [29] propose two approaches for cluster-based WSN. The first is based on detecting the level of maliciousness of the nodes using a certification module for the defense of the network. The second is based on monitoring, using fuzzy logic to discover which nodes are being affected by a jammer. On the other hand, [PERSON], [PERSON] [30] proposes a security method against the jammer based on the Bit Error Rate (BER) and Signal to Noise Ratio (SNR) metrics. The above provides greater readability of the message in the legitimate receiver and finds an energy distribution that favors the transmission of packets. There are several ways to detect the presence of intentional jamming interference in a WSN. An overview of detection techniques is presented in Table 2. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Detection Technique** & **Metrics** & **Description** \\ \hline PDR with consistency checks [31] & PDR, packet sending ratio (PSR) and sensing time & Low PDR leads to the detection of jamming. Consistency checks are used. \\ \hline Exponentially weighted moving average (EWMA) [20] & Inter arrival time (IAT) & Statistical method using inter arrival times of packets. \\ \hline Fuzzy interference system [14] & SNR: packet dropped per terminal & This is followed by a confirmatory check and a 2-means clustering of neighborhood nodes. \\ \hline Ant system [32] & Hops, energy, distance, SNR, BER, PDR & The Ant collects data from various routes with which a destination is reached, indicating if there is a jammer. \\ \hline Jammed-area mapping protocol [33] & Clear channel assessment (CCA) & The number of unsuccessful attempts to capture wireless channel is counted; if the count is greater than 10, a jammer is detected. \\ \hline Channel surfing and spatial retreat [11] & CSMA, ambient noise levels & By measuring ambient noise levels, the detection is conducted at either MAC layer using CSMA or physical level. \\ \hline Reactive detection [34] & BER, RSSI & The RSSI of each bit is observed on reception. A high RSSI means that there is a jammer. \\ \hline Trigger nodes identification [35] & Curvilinear component analysis (CCA) & Techniques are integrated: the group testing, the disk cover and the clique-based clustering. \\ \hline Fighting implicit jamming (FIJI) [36] & Delay throughput & While non-jammed, the clients are unaffected, but while being jammed, the clients receive maximum throughput. \\ \hline \end{tabular}
\end{table} 
Table 2: Overview of detection techniques. We become aware of the presence of jamming because the WSN begins to behave differently, acquiring readings altered to normal, thus showing the influence of each type of jamming with the behavior of each of the performance metrics. In this work, these anomalies in the network performance metrics are referred as _symptons_. The symptoms are manifestations that indicate that the state of health of a WSN is not in normal conditions, such as a very low level of PDR, or a very high level of energy consumed. In Table 3, we present some of these relationships between jamming and metrics.