Mejorando la recolección de datos en Redes de Sensores con estimación en series de tiempo
(Improving data aggregation in Wireless Sensor Networks with time series estimation)
Karen Miranda (email@example.com)1, Victor Ramos (firstname.lastname@example.org)2
1Universidad Autónoma Metropolitana - Cuajimalpa2Universidad Autónoma Metropolitana - Iztapalapa
This paper appears in: Revista IEEE América Latina
Publication Date: May 2016
Volume: 14, Issue: 5
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 a 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 for 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 compare three mechanisms based on the autoregressive, moving average, and normalized least mean square models to predict local sensed samples in order to reduce wireless data communication. We prove the efficiency
of these algorithms with real samples publicly available and show how suitable they are for processes of different nature.
Wireless sensor networks, autoregressive processes, moving average processes, least mean square estimation, data aggregation
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