## 7 Results  
### Global Classification Accuracy  
We tested the performance of the proposed algorithm by splitting each of the RSSI traces into several data chunks, with a length varying according to the tested sampling window, in the range of 50-500 ms. The data chunks were then processed in MATLAB, where we implemented the proposed classifier, including the feature extraction process and the four SVMs using the standard MATLAB SVM implementation with the Gaussian kernel, as well as the final design stage. For each test set, we calculated a detection accuracy metric by analyzing the outcome of the predicted interference source (according to Table 1) and comparing to the actual source determined during experiments. The detection accuracy was then simply calculated by dividing the number of correct classifications by the total number of classification rounds. In Table 2, we show the classification rates calculated for Locations A, B and C, including test data for all 16 IEEE 802.15.4 channels when the sampling widow is 250 ms; hence, chunks of 500 eight-bit RSSI samples are analyzed in each round of the test. It should be noted that the validity of the presented results is expected to be quite broad in nature as our dataset includes extensive traces from a broad range of scenarios including both controlled and uncontrolled IEEE 802.11 interference, spanning all of the IEEE 802.15.4 channels. A more in-depth discussion of the effects of a shorter or longer sampling window on the accuracy of the solution will be carried out in Section 8.1. In the following tables, we also include data about the distribution of misclassification in order to highlight which sources of interference were most likely to be misinterpreted by the classifier. As shown in Table 2, the classifier was able to determine the presence of a free IEEE 802.15.4 channel 91.2% of the time, and the primary source of misclassification was the IEEE 802.11 network, which was detected 6.6% of the time. This fact is mainly due to the similarity of RSSI traces originating from an IEEE 802.11 network with low data traffic or even no associated terminal with RSSI originated from background noise. The similarity becomes more prominent when the signals originating from an IEEE 802.11 network and received by the WSN node are weak, due to attenuation effects. The same effect can also be used to explain the IFC misclassification rate of 12.4% when the interference comes from an IEEE 802.11 network. Nevertheless, in both cases, the introduction of the second support vector machine targeting silent IEEE 802.11 networks together with the employed decision logic helped to ensure a full-spectrum average detection accuracy of 83.9% for IEEE 802.11, even with a 250 ms sampling window, which is significantly shorter than other approaches presented in the literature (e.g., [21; 26; 28]). The classifier shows a detection accuracy of 82.8% when the source of interference \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Channel Status** & \multicolumn{3}{c}{**Detected Interference Source**} \\ \hline  & **IFC** & **IEEE 802.11** & **Microwave Oven** & **Unknown** \\ \hline
**IFC** & 91.2\% & 6.6\% & 2.1\% & 0.1\% \\
**IEEE 802.11** & 12.4\% & 83.9\% & 1.4\% & 2.3\% \\
**Microwave Oven** & 0.8\% & 16.3\% & 82.8\% & 0.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average classification accuracy for the 250 ms sampling window calculated over all of the scenarios. IFC, interference-free channel. was an MWO, where the most likely misclassification output was IEEE 802.11 interference, due to the similarities between temporal features of IEEE 802.11 and RF leakage from MWO. In Section 7.2, we point out that the detection accuracy appears to be significantly higher than the average for a specific contiguous set of IEEE 802.15.4 channels. This gives insight about dynamic channel-sensing strategies for maximizing the classification rate for this class of interference.