Abstract
The Internet of Things (IoTs) is becoming ubiquitous in our everyday lives, implying that more technologies will generate data. IoT devices use sensors to monitor various attributes of the environment such as temperature, humidity, light, etc. These sensors produce data periodically and storing this massive data in a database is becoming a huge challenge in the data storage infrastructure. Prior research has proposed compression algorithms and signature techniques to reduce data storage but do not specify how the data patterns are defined. Since similar patterns are exhibited everyday by the environment, this data generates the same information from everyday sensing. Therefore, in this study, we propose a system that stores data models rather than storing raw data points. Instead of storing each data point at a time, we develop and store data models with the corresponding time periods that captures the behavior of the sensor data. This helps in reducing data storage requirements. The data models developed are mathematical polynomial models that fit a sample data set. In addition, we propose a sensor database structure that addresses the issues of data redundancy as well as temporal constraints in the database.