Behavioral modeling of Wireless Sensor nodes using meta-data

Abstract

Many Wireless Sensor Network systems are deployed under extreme conditions. Thus, it's important to maximize their lifespan by optimal use of network's resources. Battery lifespan of a node is a crucial resource that needs to be used carefully. Energy aware routing & scheduled sensing have been introduced for careful use of battery. It is necessary to optimize battery usage by predicting it's future behavior. This will lead the users to take early decisions, thus minimizing network downtime. Hence, we explore the possibility of using meta-data on each node, to represent and predict node behavior using machine learning models. In this research, we use node voltage level as an indicator of the energy used, as voltage is proportional to available energy. Node energy consumption is modeled and predicted by ARIMA models using these voltage readings. We also classify nodes as high, medium & low use, with respect to its current and future usage, thus allowing user to take early decisions maximizing network throughput (lifetime). After evaluating predicted node behavior against a created base set of behavioral classifications, we achieved a 80% accuracy rate. Using different and increasing window sizes, we evaluated the validity of our model. In these experiments, our prediction method produced highly accurate results for all considered prediction windows. By being able to predict a node's energy consumption behavior at a higher accuracy rate, WSN users can make optimization decisions beforehand to increase the network lifetime.