Predict Energy Consumption of Trigger-Driven Sensor Network by Markov Chains

Abstract

Markov Model has been proved its feasibility of predicting the energy state of sensor nodes. Thus, user can monitor sensor nodes energy state in real-time without querying them frequently. However, a stationary state transition probability is required to apply Markov Model, which means the prediction is only applicable to schedule-driven sensor networks rather than trigger-driven sensor networks. In this paper, we will introduce how to use Markov Model to make prediction in trigger-driven sensor networks. By considering events distribution and query patterns, our proposed method managed to predict sensor node energy level information of trigger-driven sensor networks. Experimental results show that our proposed model is able to predict sensor node energy state accurately for trigger-driven sensor networks.