Interval Data Fusion with Preference Aggregation for Balancing Measurement Accuracy and Energy Consumption in WSN

Research output: Contribution to journalArticlepeer-review

Abstract

An effective way to conserve energy in wireless sensor networks is reducing the amount of data transmissions. However, this can affect the accuracy and reliability of the sensed data considerably. To provide energy-accuracy trade-off, data fusion technique can be applied exploiting temporal and spatial correlation of sensed data. In this paper, we propose a novel approach for balancing energy consumption and measurement accuracy in wireless sensor networks. The approach is a combination of accuracy enhancement algorithm SensAcc and active node selection algorithm ActiveNode, which are based on the robust interval fusion with preference aggregation (IF&PA) method. The approach is aimed at selecting minimum number of nodes that can provide data of sufficient volume and quality to maintain required accuracy. The performance of the proposed algorithms has been evaluated by both simulation and real data processing. Simulation results show that the proposed approach significantly enhances the network lifetime while providing highly accurate measurement outcomes. Results of real data processing demonstrate noticeable decrease of measurement uncertainty even for small number of sensor nodes.

Original languageEnglish
JournalWireless Personal Communications
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Cluster topology
  • Energy-accuracy trade-off
  • Interval fusion
  • Preference aggregation
  • Wireless sensor network

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Interval Data Fusion with Preference Aggregation for Balancing Measurement Accuracy and Energy Consumption in WSN'. Together they form a unique fingerprint.

Cite this