Title: Collaboration adaptive filtering model for data reduction in wireless sensor networks

Authors: Walaa M. Elsayed; Hazem M. El-Bakry; Salah M. El-Sayed

Addresses: Faculty of Computer and Information Sciences, Mansoura University, Egypt ' Faculty of Computer and Information Sciences, Mansoura University, Egypt ' Faculty of Computer and Information Sciences, Benha University, Egypt

Abstract: Wireless sensor networks (WSNs) are collecting data periodically by randomly dispersed sensors (motes) that typically exploit high energy in monitoring a specified application. Furthermore, dissemination mode in WSN is resulting noisy or missing information that affects the behaviour of WSN. So, data prediction-based filtering is an important approach to reduce redundant data transmissions, conserve node energy and overcome the defects resulted from data dissemination. Therefore, in this article, a distributed data-reduction model (DDRM) is proposed to prolong the network lifetime by decreasing the energy consumption of sensor nodes. It is built upon a distributive clustering model for predicting diffusion-faults in WSN. The proposed model is developed using the RLS adaptive filter integrated with a FIR filter for minimising the amount of transmitted data and provide high convergence of the signals. A dataset of atmospheric changes was handled. The results clarify that DDRM reduced the rate of data transmission to ~20%. Also, it depressed the energy consumption to ~95% throughout the dataset sample. DDRM effectively upgraded the performance of the sensory network by about 19.5%, and hence extend its lifetime.

Keywords: wireless sensor networks; WSN; cluster head; data dissemination; adaptive RLS filter; data prediction; value failure.

DOI: 10.1504/IJHI.2019.10028273

International Journal of Hybrid Intelligence, 2019 Vol.1 No.4, pp.284 - 307

Received: 29 Sep 2018
Accepted: 01 Dec 2018

Published online: 20 Apr 2020 *

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