A Continuous Object Boundary Detection and Tracking Scheme forFailure-Prone Sensor Networks

Collection with item attached
2017
Item details URL
http://open-repository.kisti.re.kr/cube/handle/open_repository/473536.do
DOI
10.3390/s17020361
Title
A Continuous Object Boundary Detection and Tracking Scheme forFailure-Prone Sensor Networks
Description
This research was supported by Basic Science Research Program throughthe National Research Foundation of Korea (NRF) funded by the Ministryof Education (NRF-2015R1D1A1A01059049).
abstract
In wireless sensor networks, detection and tracking of continuous natured objects is more challenging owing to their unique characteristics such as uneven expansion and contraction. A continuous object is usually spread over a large area, and, therefore, a substantial number of sensor nodes are needed to detect the object. Nodes communicate with each other as well as with the sink to exchange control messages and report their detection status. The sink performs computations on the received data to estimate the object boundary. For accurate boundary estimation, nodes at the phenomenon boundary need to be carefully selected. Failure of one or multiple boundary nodes (BNs) can significantly affect the object detection and boundary estimation accuracy at the sink. We develop an efficient failure-prone object detection approach that not only detects and recovers from BN failures but also reduces the number and size of transmissions without compromising the boundary estimation accuracy. The proposed approach utilizes the spatial and temporal features of sensor nodes to detect object BNs. A Voronoi diagram-based network clustering, and failure detection and recovery scheme is used to increase boundary estimation accuracy. Simulation results show the significance of our approach in terms of energy efficiency, communication overhead, and boundary accuracy.
provenance
Made available in Cube on 2018-09-28T10:22:16Z (GMT). No. of bitstreams: 0
language
English
author
Imran, Sajida
Ko, Young-Bae
accessioned
2018-09-28T10:22:16Z
available
2018-09-28T10:22:16Z
issued
2017
citation
SENSORS(17): 2
issn
1424-8220
uri
http://open-repository.kisti.re.kr/cube/handle/open_repository/473536.do
Funder
교육부
Funding Program
BK21플러스사업(0.5)
Project ID
1345274189
Jurisdiction
Rep.of Korea
Project Name
Research Team on Autonomous Network Software for Smart Collaboration among Heterogeneous Devices
rights
openAccess
subject
continuous object detection and tracking
node failure
wireless sensornetwork
type
article


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