Title: An adaptive scheme for data collection and aggregation in periodic sensor networks

Authors: Abdallah Makhoul; David Laiymani; Hassan Harb; Jacques M. Bahi

Addresses: FEMTO-ST Laboratory, DISC Departement, University of Franche-Comté, 17 Av. Maréchal Juin, 90000 Belfort, France ' FEMTO-ST Laboratory, DISC Departement, University of Franche-Comté, 17 Av. Maréchal Juin, 90000 Belfort, France ' FEMTO-ST Laboratory, DISC Departement, University of Franche-Comté, 17 Av. Maréchal Juin, 90000 Belfort, France ' FEMTO-ST Laboratory, DISC Departement, University of Franche-Comté, 17 Av. Maréchal Juin, 90000 Belfort, France

Abstract: Low data delivery efficiency and high energy consumption are inherent problems in sensor networks. Finding accurate data is difficult and leads to expensive sensor readings. Adaptive data collection and aggregation techniques provide opportunities for reducing the energy consumption of continuous sensor data collection. In this paper, we propose an adaptive data collection and aggregation scheme for periodic sensor networks. Our approach is two-fold: firstly, we present an efficient adaptive sampling approach based on the dependence of conditional variance on measurements varies over time. Then, over-sampling can be minimised and power efficiency can be improved. Furthermore, we propose a multiple levels activity model that uses behaviour functions modelled by modified Bezier curves to define application classes. Secondly, a new data aggregation method is applied on the set of data collected from all sensor nodes. We investigate the problem of finding all pairs of nodes generating similar datasets. Our proposed models are validated via simulation on real sensor data.

Keywords: periodic sensor networks; adaptive sampling rate; ANOVA; modelling; application criticality; data aggregation; prefix frequency filtering; data collection; energy consumption; simulation.

DOI: 10.1504/IJSNET.2015.069872

International Journal of Sensor Networks, 2015 Vol.18 No.1/2, pp.62 - 74

Received: 04 Nov 2013
Accepted: 24 Jun 2014
Published online: 15 Jun 2015 *

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