## 5 Conclusions  
Generally, LSVM has the ability to minimize training error and maximize the margin over dataset, whose labels are included as supplemental variables in the optimization problem. While other classification methods focus on conditional probabilities, LSVM targets estimating the decision boundary directly. The LSVM technique performs classification into two classes. However, to perform a high dimensional classification for many classes, the multi-category classification approach using a kernel trick is required.  
The Linear kernel trick transforms linearly non-separable data points into higher dimensional feature space where the data points cannot be linearly separated. Kernels are major strength to all SVM systems with small to moderate number of data. Linear kernel is the least common choice, though a powerful feature classifier machine that can be used to design linear functions based on the task at hand.  
In this study, we designed a data-driven framework for reliable link classification that models KLSVM to produce stable and consistent results. We present the model based on the real dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Tag**1 & **PRR**2 & **RC**3 & **Chnl**3 & **TL**3 & **Description** \\ \hline
1 & 352 & 311 & 16 & Node 7 to Node 1 & The link exhibits a busty traffic pattern with frequent long packet losses. \\ \hline
2 & 446 & 174 & 12 & Node 5 to Node 3 & The link exhibits a busty traffic pattern with frequent long packet losses. Notably, after the 780 th transmission only 11 packets were delivered. \\ \hline
3 & 272 & 108 & 12 & Node 7 to Node 3 & The link exhibits a busty traffic pattern with frequent long packet losses. Notably, after the 780 th transmission only 5 packets were delivered. \\ \hline
4 & 248 & 101 & 12 & Node 6 to Node 3 & The link exhibits a busty traffic pattern with frequent long packet losses. Notably, after the 779 th transmission only 3 packets were delivered. \\ \hline \hline \end{tabular}  
* In Figure 8 all the outliers are tagged sequentially. 2 The outliers are in the range of “Bad Link” quality estimation. They are ignored because they are not reliable for end-to-end communication. 3 Abbreviations as used in the table: RC (Retransmission Count), Chnl (Channel) and TL (Transmission Link).  
\end{table}
Table 2: Diagnosis results of the outliers.  
Our approach modeled and captured the two phenomena--data correlation and high dimensional data classification. Thus, we detected a faulty link and captured four outliers with extremely inconsistent behavior within the network. The technique has a good performance on link quality estimation accuracy.  
For this research article: Writing--Original Draft Preparation was done by [PERSON]; Writing--Review & Editing also was done by [PERSON]; under the supervision of [PERSON] and [PERSON].  
This research received no external funding.  
We could like to thank all the referees for their constructive comments and helpful suggestions. In addition, special thanks to [PERSON] and [PERSON] for their valuable inputs on TinyOS and Matlab, respectively. Thank you [PERSON] for your valuable inputs on SVM. Finally, thank you Dr. [PERSON] for proofreading this paper.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SVM & Support Vector Machines \\ LSVM & Linear SVM \\ KLSVM & Kernelized Linear Support Vector Machine \\ SVC & Support Vector Classifier \\ WSN & Wireless Sensor Network \\ MEMS & Micro Electromechanical Systems \\ PRR & Packet Reception Rate \\ BCM & Beacon-based Measurement \\ NGMI & New Generation Mobile Internet Research Laboratory \\ CPDF & Conditional Probability Delivery Function \\ EMD & Earth Mover's Distance \\ \end{tabular}