## 1 Introduction  
The miniaturization of computing and sensing technologies enables the development of tiny, low-power, and inexpensive sensors, actuators, and controllers. These are sensing systems that typically interact closely with the physical world and are designed to perform a limited number of dedicated functions. Sensing is a technique used to gather information about a physical object or process, including the occurrence of events (for example, changes in state such as a drop in temperature or pressure). A device performing such a sensing task is called a sensor [1]. Sensors link the physical with the digital world by capturing and revealing real-world phenomena and converting these into a form that can be processed, stored, and acted upon [2]. Wireless Sensor networks (WSNs) are composed of cooperating sensor nodes that can perceive the environment to monitor physical phenomena and events of interest [3]. The dynamic environment makes the wireless communications vulnerable to distortion. The exposure of WSN to these external factors limits their performance due to their computing, storage and energy constraints. The fast development of information from users and their applications; their networks and the cloud [4] have generated information diversification and complex relationship among information data [5]. Internet of Things that will connect the Internet with any kind of devices, living beings, and things will be the largest source of data in the near future. Wireless Sensor Networks are expected to have a key role in the realization of the future Internet of Things. Understanding the data deeply (the scale, structure, dimension, and correlation) guides the development of networks and information. Data correlation can solve the problems in data clustering, personal query and social network prediction [5; 6]. Empirical WSN data correlations may exist in sensor network in three forms: correlation existing between sensor node observations on its history (temporal correlation), correlation existing between sensor node observation and neighboring nodes observation (spatial correlation), and correlation existing among attributes of sensor nodes (attribute correlation) [7]. Apart from the three basic types of data correlations mentioned, considering the temporal and spatial correlations together forms spatiotemporal correlations [8]. The combined correlation increases the detection rate of events on real dataset [9]. A major challenge for sensor nodes is sending sensed data to their sink in a reliable and energy efficient way, due to their limited resources in transmission, storage, computational memory and energy [10]. With the advancement in microelectromechanical systems (MEMS) technology, it is possible to deploy large scale WSNs, which introduce many data to be processed, transmitted and received. Transmitting all data back to a base station for processing and making inferences is impossible due to the sensor bandwidth constraints [11]. In addition, other challenges arise such as faulty node and outlier detection in target large-scale networks. The utilization of machine learning seems to be one of the most convenient solutions for detecting anomaly in WSNs. Discovering an anomaly is a major principle to ensure a mundane functioning of WSNs [2]. Anomaly detection in WSNs is a consequential aspect of data analysis in order to identify data items which does not conform to an expected pattern in a dataset. It is paramount that the anomaly of sensor node is detected to obtain precise information to consequently make efficacious decisions by accumulated information. For anomaly detection and utilization of data correlation for high dimensional classification in WSNs, there is a need for applying Support Vector Machine (SVM) with a kernel trick technique to mitigate such challenges. Some of the common types of kernel tricks applied are: Linear kernel, Polynomial Kernel, Radial Basic Function (RBF) kernel and Sigmoid Kernel [12]. These kernels are used to map dataset into high dimensionality feature space.