Using efficiently autoregressive estimation in wireless sensor networks

Wireless sensor networks (WSNs) are widely deployed nowadays on a large variety of applications. The major goal of a WSN is to collect information about a set of phenomena. Such process is non trivial since batteries´ life is limited and thus wireless transmissions as well as computing operations must be minimized. A common task in WSNs is to estimate the sensed data and to spread the estimated samples over the network. Thus, time series estimation mechanisms are vital on this type of processes so as to reduce data transmission. In this paper, we assume a single-hop clustering mechanism in which sensor nodes are grouped into clusters and communicate with a sink through a single hop. We propose a couple of autoregressive mechanisms to predict local sensed samples in order to reduce wireless data communication. We compare our proposal with a model called EEE that has been previously proposed in the literature. We prove the efficiency of our algorithms with real samples publicly available and show that they outperform the EEE mechanism.