Sensor Data Analytics

Sensor analytics is the statistical analysis of data that is created by wired or wireless sensors. Modern sensors and information technologies make it possible to continuously collect sensor data, which is typically obtained as real-time and real-valued numerical data. Examples include home monitoring system, which can be equipped with numerous sensors that produce data from environment. Though the data gathering systems are becoming relatively mature, a lot of innovative research needs to be done on knowledge discovery from these huge repositories of data in order to predict future behavior and possibly build better control systems. This research focuses on developing knowledge discovery methodologies mainly for real-valued data generated from a variety of home or industry appliances such as resource control systems, security systems or early warning systems.

Recent Publications:

Real-Time Analysis of a Sensor’s Data for Automated Decision Making in an IoT-Based Smart Home
Nida Saddaf Khan, Sayeed Ghani and Sajjad Haider

IoT devices frequently generate large volumes of streaming data and in order to take advantage of this data, their temporal patterns must be learned and identified. Streaming data analysis has become popular after being successfully used in many applications including forecasting electricity load, stock market prices, weather conditions, etc. Artificial Neural Networks (ANNs) have been successfully utilized in understanding the embedded interesting patterns/behaviors in the data and forecasting the future values based on it. One such pattern is modelled and learned in the present study to identify the occurrence of a specific pattern in a Water Management System (WMS). This prediction aids in making an automatic decision support system, to switch OFF a hydraulic suction pump at the appropriate time. Three types of ANN, namely Multi-Input Multi-Output (MIMO), Multi-Input Single-Output (MISO), and Recurrent Neural Network (RNN) have been compared, for multi-step-ahead forecasting, on a sensor’s streaming data. Experiments have shown that RNN has the best performance among three models and based on its prediction, a system can be implemented to make the best decision with 86% accuracy.