Title: Exploiting spatial-temporal correlations to improve energy-efficiency in data collection applications in WSN

Authors: Oualid Demigha; Slimane Bedda; Mossab Chabane

Addresses: Ecole Militaire Polytechnique, P.O. Box 17, Bordj El-Bahri, 16111 Algiers, Algeria ' Ecole Militaire Polytechnique, P.O. Box 17, Bordj El-Bahri, 16111 Algiers, Algeria ' Ecole Militaire Polytechnique, P.O. Box 17, Bordj El-Bahri, 16111 Algiers, Algeria

Abstract: Energy-efficiency is a fundamental constraint that must be resolved to enable wireless sensor network (WSN) applications such as monitoring and zone surveillance where long-term operation mode is required. In this paper, we propose a data-driven approach to optimise energy-efficiency in data collection applications by taking profit from nodes' data correlations without deteriorating data quality specified by the end-user. We present simple but effective spatial and temporal correlation models based on the intrinsic characteristics of randomly deployed WSN. We integrate the correlation distance and the temporal series of the monitored phenomenon into protocol low energy adaptive clustering hierarchy (LEACH) taken as a case study to show the gain of such an approach in terms of two opposite metrics: energy-efficiency and data quality. We implement, test and compare our proposed solutions with protocol LEACH and some other variants. The simulation results confirm our hypotheses and show a clear improvement in terms of network lifetime, network coverage and residual energy of the nodes. As for data quality, our proposed solutions maintain an acceptable level compared to LEACH and its variants.

Keywords: wireless sensor networks; WSN; spatial-temporal correlation; clustering; low energy adaptive clustering hierarchy; LEACH; data quality; data collection; energy-efficiency.

DOI: 10.1504/IJCNDS.2019.097652

International Journal of Communication Networks and Distributed Systems, 2019 Vol.22 No.2, pp.123 - 149

Received: 09 Dec 2017
Accepted: 18 Feb 2018
Published online: 04 Feb 2019 *

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