Examples of application:

In many practical applications of wireless sensor networks, the sensor nodes are required to report approximation of their readings at regular time intervals. For these applications, it has been shown that time series prediction techniques provide an effective way to reduce the communication effort while guaranteeing user-specified accuracy requirements on collected data. Achievable communication savings offered by time series prediction however strongly depend on the type of signal sensed, and a priori choice of a prediction technique is not trivial in practice. We propose an online and lightweight algorithm dubbed adaptive model selection (AMS), that allows sensor nodes to autonomously determine the best performing model among a set of candidate models. Experimental results obtained on the basis of four-teen real-world sensor time series demonstrate the efficiency and versatility of the proposed framework in reducing the communication effort between sensor nodes. 1. Wireless sensors networks (WSN)- Applications In a WSN, sensor nodes self-organize and wirelessly communicate their readings to recipi-ents such as databases or controllers