Energy-accuracy aware active node selection in wireless sensor networks

Bibliographic Details
Parent link:Control and Communications (SIBCON): Proceedings of the XII International Siberian Conference, Moscow, May 12-14, 2016. [5 p.].— , 2016
Main Author: Khudonogova L. I. Ludmila Igorevna
Corporate Author: Национальный исследовательский Томский политехнический университет (ТПУ) Институт кибернетики (ИК) Кафедра информатики и проектирования систем (ИПС)
Other Authors: Muravyov (Murav’ev) S. V. Sergey Vasilyevich
Summary:Title screen
One of the main issues in wireless sensor networks (WSNs) is energy conservation due to limited power of sensor node battery. To save energy, WSNs operate in duty-cycling work mode, when the sensor nodes alternate between active and dormant states. In this paper, we propose an active node set selection algorithm (ANSS), based on preference aggregation. The aim of the algorithm is to reduce the number of active sensor nodes, thus decreasing energy consumption while keeping required measurement accuracy level. Nodes, included in the active node set, are selected on the base of such parameters as energy consumption, distance to a cluster head, and multisensor general accuracy.
Language:English
Published: 2016
Subjects:
Online Access:https://doi.org/10.1109/SIBCON.2016.7491835
Format: Electronic Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=653729
Description
Summary:Title screen
One of the main issues in wireless sensor networks (WSNs) is energy conservation due to limited power of sensor node battery. To save energy, WSNs operate in duty-cycling work mode, when the sensor nodes alternate between active and dormant states. In this paper, we propose an active node set selection algorithm (ANSS), based on preference aggregation. The aim of the algorithm is to reduce the number of active sensor nodes, thus decreasing energy consumption while keeping required measurement accuracy level. Nodes, included in the active node set, are selected on the base of such parameters as energy consumption, distance to a cluster head, and multisensor general accuracy.
DOI:10.1109/SIBCON.2016.7491835