Intelligent Sensing in Dynamic Environments Using Markov Decision Process

Abstract

In a network of low-powered wireless sensors, it is essential to capture as many environmental events as possible while still preserving the battery life of the sensor node. This paper focuses on a real-time learning algorithm to extend the lifetime of a sensor node to sense and transmit environmental events. A common method that is generally adopted in ad-hoc sensor networks is to periodically put the sensor nodes to sleep. The purpose of the learning algorithm is to couple the sensor's sleeping behavior to the natural statistics of the environment hence that it can be in optimal harmony with changes in the environment, the sensors can sleep when steady environment and stay awake when turbu..