Analyzing Data Prediction in Wireless Sensor Networks

In various sensor network scenarios, data of low entropy is measured and transported towards a data-sink. For example, sensor networks monitor temperature, air pressure, or humidity. As periodic sensor measurements rarely change over time, a receiver can often predict them. Therefore, energy-expensive periodic radio transmission of the measurement can often be omitted. Instead, the node taking and sending a measurement S and the node receiving the measurement R agree on a model. This work states that model updates for data prediction in wireless sensor networks must take the reliability of underlying communication as well as computational overhead into account.