Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks
Target detection is a widely used application for area surveillance, elder care, and fire alarms; its purpose is to find a particular object or event in a region of interest. Usually, fixed observing stations or static sensor nodes are arranged uniformly in the field. However, each part of the field...
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doaj-b730f1b60c354594aff14391b32ed9932020-11-24T21:17:59ZengMDPI AGSensors1424-82202017-05-01175102810.3390/s17051028s17051028Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor NetworksTengyue Zou0Zhenjia Li1Shuyuan Li2Shouying Lin3College of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaTarget detection is a widely used application for area surveillance, elder care, and fire alarms; its purpose is to find a particular object or event in a region of interest. Usually, fixed observing stations or static sensor nodes are arranged uniformly in the field. However, each part of the field has a different probability of being intruded upon; if an object suddenly enters an area with few guardian devices, a loss of detection will occur, and the stations in the safe areas will waste their energy for a long time without any discovery. Thus, mobile wireless sensor networks may benefit from adaptation and pertinence in detection. Sensor nodes equipped with wheels are able to move towards the risk area via an adaptive learning procedure based on Bayesian networks. Furthermore, a clustering algorithm based on k-means++ and an energy control mechanism is used to reduce the energy consumption of nodes. The extended Kalman filter and a voting data fusion method are employed to raise the localization accuracy of the target. The simulation and experimental results indicate that this new system with adaptive energy-efficient methods is able to achieve better performance than the traditional ones.http://www.mdpi.com/1424-8220/17/5/1028wireless sensor networkalarmintrusive detectionenergy controldata fusion |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tengyue Zou Zhenjia Li Shuyuan Li Shouying Lin |
spellingShingle |
Tengyue Zou Zhenjia Li Shuyuan Li Shouying Lin Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks Sensors wireless sensor network alarm intrusive detection energy control data fusion |
author_facet |
Tengyue Zou Zhenjia Li Shuyuan Li Shouying Lin |
author_sort |
Tengyue Zou |
title |
Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks |
title_short |
Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks |
title_full |
Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks |
title_fullStr |
Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks |
title_full_unstemmed |
Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks |
title_sort |
adaptive energy-efficient target detection based on mobile wireless sensor networks |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2017-05-01 |
description |
Target detection is a widely used application for area surveillance, elder care, and fire alarms; its purpose is to find a particular object or event in a region of interest. Usually, fixed observing stations or static sensor nodes are arranged uniformly in the field. However, each part of the field has a different probability of being intruded upon; if an object suddenly enters an area with few guardian devices, a loss of detection will occur, and the stations in the safe areas will waste their energy for a long time without any discovery. Thus, mobile wireless sensor networks may benefit from adaptation and pertinence in detection. Sensor nodes equipped with wheels are able to move towards the risk area via an adaptive learning procedure based on Bayesian networks. Furthermore, a clustering algorithm based on k-means++ and an energy control mechanism is used to reduce the energy consumption of nodes. The extended Kalman filter and a voting data fusion method are employed to raise the localization accuracy of the target. The simulation and experimental results indicate that this new system with adaptive energy-efficient methods is able to achieve better performance than the traditional ones. |
topic |
wireless sensor network alarm intrusive detection energy control data fusion |
url |
http://www.mdpi.com/1424-8220/17/5/1028 |
work_keys_str_mv |
AT tengyuezou adaptiveenergyefficienttargetdetectionbasedonmobilewirelesssensornetworks AT zhenjiali adaptiveenergyefficienttargetdetectionbasedonmobilewirelesssensornetworks AT shuyuanli adaptiveenergyefficienttargetdetectionbasedonmobilewirelesssensornetworks AT shouyinglin adaptiveenergyefficienttargetdetectionbasedonmobilewirelesssensornetworks |
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1726011027626131456 |