Title: Energy efficient aggregate monitoring for sensor fields with time-varying statistics

Authors: Esmaeil Biazar Amlashi; Ahmad Khonsari; Samaneh Heidari; Ronak Amani

Addresses: Department of Electrical and Computer Engineering, University of Tehran, Tehran, Iran ' Department of Electrical and Computer Engineering, University of Tehran, Tehran, Iran; School of Computer Science, Institute for Research in Fundamental Sciences, Tehran, Iran ' Department of Electrical and Computer Engineering, University of Tehran, Tehran, Iran ' Institute for Research in Fundamental Science, IPM, Tehran, Iran

Abstract: Monitoring in wireless sensor networks (WSNs) has been considered an issue of important value in research community. Reactive monitoring mechanisms are important in WSNs because of their low energy consumption. The energy efficiency of such mechanisms is relying on setting proper thresholds that act as the parameters of distributed local filters. In this paper, we address how to appropriately design local filters for reactive aggregate monitoring scenarios in random sensor fields with dynamic statistics. Since previous optimal local filter designs are proposed for stationary fields, they cannot work efficiently in the presence of statistical variations in the sensor field. We propose dynamic sigmoid approximated threshold assignment (D-SATA) algorithm, which is a distributed algorithm that adapts the optimal local filter design to the variation of statistics of sensor field. Simulation experiments confirm that the offered monitoring mechanism of D-SATA is efficient in terms of overhead burden to the network.

Keywords: WSNs; wireless sensor networks; sensor fields; distributed systems; reactive monitoring; consensus averaging; energy efficiency; aggregate monitoring; time-varying statistics; energy consumption; filter design; simulation.

DOI: 10.1504/IJSNET.2016.079324

International Journal of Sensor Networks, 2016 Vol.22 No.1, pp.37 - 46

Received: 19 May 2014
Accepted: 07 Apr 2015
Published online: 27 Sep 2016 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article