Vector-based data prediction model for wireless sensor networks

Abstract

Wireless sensor networks WSNs present a new way of data-stream sources in which data is received periodically from different sensors; resulting in a large amount of data accumulated over a short period. WSNs have limited resources in which fine-detailed data streams lead to an exhaustive energy consumption of the sensor nodes. In this paper, we extend the work presented in Samara 2015 and propose a data prediction model that is built within the sensor node and used by the Sink to predict the future readings of the sensor node. The purpose of the proposed model is to exempt the sensor node from sending a large amount of data in order to reduce the energy consumption of the sensor's battery. We manage to formulate the prediction model as a line equation through two n-dimensional vectors. Results showed that the proposed model will be able to achieve a better error rate and an accurate fitting for the data compared with the linear regression.