## 5 The Proposed Solution  
### Classifier Setup  
The proposed interference detection method employs an SVM-based classifier, which processes input data composed of observations of the background RF noise on a specific IEEE 802.15.4 radio channel. The method is based on the basic assumption that when there is no transmission on a certain channel (and thus, there is an absence of intra-network interference), the devices can collect samples of the RF radiation and process the data to detect and classify eventual interferers, as well as assessing the eventuality of an IFC. This assumption nicely fits with the time-division multiple-access (TDMA) approach employed in ISWANs, since in these networks, the allocation of frequency-time resources for data transmission is known a priori; thus, a contiguous set of time slots on a specific channel can be reserved for spectrum sensing. The common hypothesis for spectrum sensing is that the classification can be done with a certain level of accuracy if the time window is sufficiently long for specific signal features to emerge. The proposed solution is designed to keep this detection time as short as possible. As shown in Figure 3, the first stage of the classifier employs a process of signal feature extraction, in which data are processed in order to extrapolate a number of signal features in the time and amplitude domains. The second stage of the classifier is composed of four SVMs, which perform a first decision stage, outputting single binary partial hard decisions with respect to the related interference scenarios. The different SVMs are hereby described:  
1. SVM-free channel: this SVM is trained to detect the presence of an IFC. 2. SVM-active network: targets the presence of an active IEEE 802.11 network occupying the related IEEE 802.15.4 PHY channel (i.e., an IEEE 802.11 access point with at least one associated terminal, generating uplink/downlink traffic). 3. SVM-silent network: targets a silent IEEE 802.11 network overlapping the specific channel. This is the case of an IEEE 802.11 access point with no associated terminal or an access point with associated terminals that are not generating data traffic in the observation time window. 4. SVM-microwave oven: detects the presence of RF leakage from a microwave-oven operating in close proximity to the radio node. Figure 3: Setup of the support vector machine (SVM)-based interference classifier. The outputs of the four SVM are represented by binary signals, \(S_{1}\), \(S_{2}\), \(S_{3}\) and \(S_{4}\), which have the value 1 (0) if the related decision is positive (negative). The binary decisions preformed by the SVMs are then processed by the logic decay shown in Figure 4. The logic function of the decay has been synthesized considering the cross-detection resilience of the single SVM. The final decision is composed of the four different classes listed in Table 1.  
It must be highlighted that the classification performed based on the observation of a single radio node of the network only has local validity. This is because radio devices located in different locations of an industrial plant may be subjected to different interference conditions. In this context, the proposed method allows the interference scenario to be captured for each of the deployed nodes, opening the possibility of mapping the different sources of interference in the space-frequency domain. Nevertheless, since the aforementioned classification scheme exhibits a computational complexity that is beyond the capabilities of COTS WSN nodes, the proposed implementation relies on a centralized classification in place of a distributed approach. This, in turn, means that while the classifier can be implemented in the IWSAN network manager, the spectrum sensing and feature extraction process can be carried out by IWSAN nodes.