Path Prediction Method for Effective Sensor Filtering in Sensor Registry System

The Internet of Things (IoT) has emerged and several issues have arisen in the area such as sensor registration and management, semantic interpretation and processing, and sensor searching and filtering in Wireless Sensor Networks (WSNs). Also, as the number of sensors in an IoT environment increases significantly, sensor filtering becomes more important. Many sensor filtering techniques have been researched. However most of them do not consider real-time searching and efficiency of mobile networks. In this paper, we suggest a path prediction approach for effective sensor filtering in Sensor Registry System (SRS). SRS is a sensor platform to register and manage sensor information for sensor filtering. We also propose a method for learning and predicting user paths based on the Collective Behavior Pattern. To improve prediction accuracy, we consider a time feature to measure weights and predict a path. We implement the method and the implementation and its evaluation confirm the improvement of time and accuracy for processing sensor information.