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Conference Paper: Cooperative sensing and compression in vehicular sensor networks for urban monitoring

TitleCooperative sensing and compression in vehicular sensor networks for urban monitoring
Authors
KeywordsCommunication cost
Compression approach
Computational capacity
Cooperative sensing
Data sensing
Issue Date2010
PublisherIEEE. The Journal's web site is located at http://www.ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000104
Citation
The IEEE International Conference on Communications (ICC) 2010, Cape Town, South Africa, 23-27 May 2010. In Proceedings of the IEEE ICC, 2010, p. 1-5 How to Cite?
AbstractA Vehicular Sensor Network (VSN) may be used for urban environment surveillance utilizing vehicle-based sensors to provide an affordable yet good coverage for the urban area. The sensors in VSN enjoy the vehicle's steady power supply and strong computational capacity not available in traditional Wireless Sensor Network (WSN). However, the mobility of the vehicles results in highly dynamic and unpredictable network topology, leading to packet losses and distorted surveillance results. To resolve these problems, we propose a cooperative data sensing and compression approach with zero inter-sensor collaboration overhead based on sparse random projections. The algorithm provides excellent reconstruction accuracy for the sensed field, and by taking advantage of the spatial correlation of the data, enjoys much smaller communication traffic load compared to traditional sampling algorithms in wireless sensor networks. Real urban environment data sets are used in the experiments to test the reconstruction accuracy and energy efficiency under different vehicular mobility models. The results show that our approach is superior to the conventional sampling and interpolation strategy which propagates data in an uncompressed form, with 4-5dB gain in reconstruction quality and 21-55% savings in communication cost for the same sampling times. ©2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/126187
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorYu, Xen_HK
dc.contributor.authorZhao, Hen_HK
dc.contributor.authorZhang, Len_HK
dc.contributor.authorWu, Sen_HK
dc.contributor.authorKrishnamachari, Ben_HK
dc.contributor.authorLi, VOKen_HK
dc.date.accessioned2010-10-31T12:14:31Z-
dc.date.available2010-10-31T12:14:31Z-
dc.date.issued2010en_HK
dc.identifier.citationThe IEEE International Conference on Communications (ICC) 2010, Cape Town, South Africa, 23-27 May 2010. In Proceedings of the IEEE ICC, 2010, p. 1-5en_HK
dc.identifier.issn0536-1486en_HK
dc.identifier.urihttp://hdl.handle.net/10722/126187-
dc.description.abstractA Vehicular Sensor Network (VSN) may be used for urban environment surveillance utilizing vehicle-based sensors to provide an affordable yet good coverage for the urban area. The sensors in VSN enjoy the vehicle's steady power supply and strong computational capacity not available in traditional Wireless Sensor Network (WSN). However, the mobility of the vehicles results in highly dynamic and unpredictable network topology, leading to packet losses and distorted surveillance results. To resolve these problems, we propose a cooperative data sensing and compression approach with zero inter-sensor collaboration overhead based on sparse random projections. The algorithm provides excellent reconstruction accuracy for the sensed field, and by taking advantage of the spatial correlation of the data, enjoys much smaller communication traffic load compared to traditional sampling algorithms in wireless sensor networks. Real urban environment data sets are used in the experiments to test the reconstruction accuracy and energy efficiency under different vehicular mobility models. The results show that our approach is superior to the conventional sampling and interpolation strategy which propagates data in an uncompressed form, with 4-5dB gain in reconstruction quality and 21-55% savings in communication cost for the same sampling times. ©2010 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE. The Journal's web site is located at http://www.ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000104-
dc.relation.ispartofProceedings of the IEEE International Conference on Communicationsen_HK
dc.rightsIEEE International Conference on Communications. Copyright © IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectCommunication cost-
dc.subjectCompression approach-
dc.subjectComputational capacity-
dc.subjectCooperative sensing-
dc.subjectData sensing-
dc.titleCooperative sensing and compression in vehicular sensor networks for urban monitoringen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1550-3607&volume=&spage=&epage=&date=2010&atitle=Cooperative+sensing+and+compression+in+vehicular+sensor+networks+for+urban+monitoring-
dc.identifier.emailLi, VOK:vli@eee.hku.hken_HK
dc.identifier.authorityLi, VOK=rp00150en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICC.2010.5502562en_HK
dc.identifier.scopuseid_2-s2.0-77955402725en_HK
dc.identifier.hkuros181410en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77955402725&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage1-
dc.identifier.epage5-
dc.description.otherThe IEEE International Conference on Communications (ICC) 2010, Cape Town, South Africa, 23-27 May 2010. In Proceedings of the IEEE ICC, 2010, p. 1-5-
dc.identifier.scopusauthoridYu, X=55230988100en_HK
dc.identifier.scopusauthoridZhao, H=36444648700en_HK
dc.identifier.scopusauthoridZhang, L=11040255900en_HK
dc.identifier.scopusauthoridWu, S=35319386300en_HK
dc.identifier.scopusauthoridKrishnamachari, B=7004879601en_HK
dc.identifier.scopusauthoridLi, VOK=7202621685en_HK

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