Integrated data reduction model in wireless sensor networks

Walaa M. Elsayed

doi:10.1016/j.aci.2019.03.003

Integrated data reduction model in wireless sensor networks

2020-07-29

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DOI：10.1016/j.aci.2019.03.003

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Wireless sensor networks (WSNs) are periodically collecting data through randomly dispersed sensors (motes), which typically consume high energy in radio communication that mainly leans on data transmission within the network. Furthermore, dissemination mode in WSN usually produces noisy values, incorrect measurements or missing information that affect the behaviour of WSN. In this article, a Distributed Data Predictive Model (DDPM) was proposed to extend the network lifetime by decreasing the consumption in the energy of sensor nodes. It was built upon a distributive clustering model for predicting dissemination-faults in WSN. The proposed model was developed using Recursive least squares (RLS) adaptive filter integrated with a Finite Impulse Response (FIR) filter, for removing unwanted reflections and noise accompanying of the transferred signals among the sensors, aiming to minimize the size of transferred data for providing energy efficient. The experimental results demonstrated that DDPM reduced the rate of data transmission to similar to 20%. Also, it decreased the energy consumption to 95% throughout the dataset sample and upgraded the performance of the sensory network by about 19.5%. Thus, it prolonged the lifetime of the network.

Keyword:

WSN

Cluster head

Data dissemination

FIR adaptive filter

RLS adaptive filter

Data prediction

Value failure

Integrated data reduction model in wireless sensor networks

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