## 1 Introduction
In addition, the function of these kernels are to take dataset as input and transform it into the required form, with little computational cost even in very high-dimensional spaces. RBF kernel is the most popular kernel trick applied to non-linearly separable boundaries. Thus, the linear kernel works well if the datasets are linearly separable. However, the complexity of the RBF kernel grows with the size of the training. It is much easier to over-fit a complex model, because they have more hyperparameters to tune. This makes training of an RBF kernel SVM more computational costly and, furthermore, during prediction, the projection into \"infinite\" higher dimensional space where the data become linearly separable is more expensive than using LSVM [13; 14]. To overcome this problem, we study a Kernelized Linear SVM technique for correlation-based high dimensional data classification and anomaly detection in WSNs. This approach is a linear classifying technique that learns the \"best\" parameter settings. The focus of our classification approach is not on generalization but on the accuracy of our classification for WSN's link quality estimates, which are linearly separable. Our investigations show that the scattered plots of sensor node observations would tend to cluster around straight, non-horizontal lines and the sensed data are linearly correlated. This strategic technique and with a proposed data-driven framework for reliable link classification (see Figure 1) will provide insight into spatiotemporal correlations, high dimensional multi-category classification and anomaly detection, given an understanding of the distributive characteristic of WSNs. Prior studies of WSNs have observed that links have a wide range of PRR which can vary significantly overtime [15]. To determine whether the network behaves similarly or dynamically, we measured our packet reception rates on a \(4\times 5\) TelosB testbed. We borrowed an approach from [16] and used beacon-based measurement (BCM) to capture received beacon messages receptions/losses in bitmap denoted as \"1\" for reception and \"0\" for loss. From the bitmaps, we computed PRR, retransmission count and Conditional Probability Delivery Function (CPDF). We used PRR to measure link quality estimates and the estimates were classified in a similar approach used by [PERSON] et al. [17] as: Faulty Link with 0% PRR, Very Bad Link with \(PRR<10\%\), Bad Link with \(10\%\leq PRR<45\%\), Intermediate Link with \(45\%\leq PRR<75\%\), Good Link with \(75\%\leq PRR<90\%\) or Very Good Link with \(PRR\geq 90\%\). The contributions of this paper are threefold. Firstly, we designed a data-driven framework for reliable link classification that models KLSVM to reveal two phenomena: high dimensional multi-category classification and spatiotemporal correlations in the sensor network. The design framework also detects anomalies caused by noises. Secondly, we studied the KLSVM model in the context of machine learning. We applied KLSVM technique to model high dimensional data classification, capture data correlation and detect anomalies. Finally, from empirical packet link traces collected from the WSN to our experimental evaluation, we show that the KLSVM technique modeled high dimensional multi-category classification over a real dataset. The approach captured temporal and spatial data correlation in 2D and 3D plots from the sensed data. We show KLSVM algorithm detects anomalies within the sensor network and models accordingly to more accurate diagnosis results. To the best of our knowledge, this is the first attempt to introduce KLSVM to experimentally model the two phenomena of spatiotemporal data correlation and high dimensional multi-category data classification based on link quality estimations in the field of WSNs. In addition, the technique detects anomalies within WSN in a single generic algorithm, paving the way to a sizable number of possible domains which can benefit from this technique.