Original scientific paper
https://doi.org/10.24138/jcomss.v15i1.561
A Kalman Filter Process for Energy Optimization in WSNs
Imad Iala
; LRIT Associated Unit to the CNRST-URAC N29, Faculty of Sciences, University Mohammed V, Morocco
Imane Dbibih
; LRIT Associated Unit to the CNRST-URAC N29, Faculty of Sciences, University Mohammed V, Morocco
Ouadoudi Zytoune
; ENCG, University Ibn tofail Kenitra, Morocco
Mohammed Rziza
; LRIT Associated Unit to the CNRST-URAC N29, Faculty of Sciences, University Mohammed V, Morocco
Driss Aboutajdine
; LRIT Associated Unit to the CNRST-URAC N29, Faculty of Sciences, University Mohammed V, Morocco
Abstract
Wireless Sensor Networks (WSNs) consist of a large number of small interconnected devices. The aim of such networks is the monitoring of some types of area. This work is done by collaboration between these devices. All of them must sense and send information to the sink. These devices are characterized by limited memory, limited computing resource and they are usually powered by an irreplaceable battery, which limits their lifetime. Therefore it is essential to design an effective and energy aware protocols in order to extend the network lifetime by reducing the energy consumption. In this article, a new communication mechanism for IEEE 802.15.4 based WSNs called "Kalman based MAC (K-MAC) protocol" is proposed. K-MAC is designed to maximize the efficiency of the energy consumption. Therefore, the network nodes lifetime will extend through a predicting filter. The objectif of this filter is to optimize the sleep interval time of nodes between consecutive wake-ups.The network node be awake only if it have to receive or to send data. In other words, there will be an adaptation between the activation of the transceivers and the node traffic load. The simulation results show that K-MAC obtains better performance in terms of energy efficiency, Packet Delivery Ratio (PDR), the whole without affecting negatively the latency.
Keywords
WSN; Mac protocol; IEEE 802.15.4 standard; Energy consumption; Kalman filter.
Hrčak ID:
215766
URI
Publication date:
1.3.2019.
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