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dc.contributor.authorWang, Honggang
dc.contributor.authorFang, Hua
dc.contributor.authorEspy, Kimberly Andrews
dc.contributor.authorPeng, Dongming
dc.contributor.authorSharif, Hamid
dc.date2022-08-11T08:10:43.000
dc.date.accessioned2022-08-23T17:17:56Z
dc.date.available2022-08-23T17:17:56Z
dc.date.issued2007-01-01
dc.date.submitted2010-11-29
dc.identifier.citationWang, H., Fang, H., Espy, K. A., Peng, D, Sharif, H. (2007). A Bayesian Multilevel Modeling Approach for Data Query in Wireless Sensor Networks. Lecture Notes in Computer Science (LNCS),Y. Shi et al. (Eds.): Part III, LNCS vol. 4489, pp. 859–866. DOI: 10.1007/978-3-540-72588-6_137
dc.identifier.doi10.1007/978-3-540-72588-6_137
dc.identifier.urihttp://hdl.handle.net/20.500.14038/47744
dc.description.abstractIn power-limited Wireless Sensor Network (WSN), it is important to reduce the communication load in order to achieve energy savings. This paper applies a novel statistic method to estimate the parameters based on the real-time data measured by local sensors. Instead of transmitting large real-time data, we proposed to transmit the small amount of dynamic parameters by exploiting both temporal and spatial correlation within and between sensor clusters. The temporal correlation is built on the level-1 Bayesian model at each sensor to predict local readings. Each local sensor transmits their local parameters learned from historical measurement data to their cluster heads which account for the spatial correlation and summarize the regional parameters based on level-2 Bayesian model. Finally, the cluster heads transmit the regional parameters to the sink node. By utilizing this statistical method, the sink node can predict the sensor measurements within a specified period without directly communicating with local sensors. We show that this approach can dramatically reduce the amount of communication load in data query applications and achieve significant energy savings.
dc.language.isoen_US
dc.relation.urlhttp://dx.doi.org/10.1007/978-3-540-72588-6_137
dc.subjectBayes Theorem
dc.subjectModels, Statistical
dc.subjectComputer Communication Networks
dc.subjectBioinformatics
dc.subjectBiostatistics
dc.subjectEpidemiology
dc.subjectHealth Services Research
dc.titleA Bayesian Multilevel Modeling Approach for Data Query in Wireless Sensor Networks
dc.typeJournal Article
dc.source.journaltitleLecture Notes in Computer Science
dc.source.volume4489
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/qhs_pp/865
dc.identifier.contextkey1663590
html.description.abstract<p>In power-limited Wireless Sensor Network (WSN), it is important to reduce the communication load in order to achieve energy savings. This paper applies a novel statistic method to estimate the parameters based on the real-time data measured by local sensors. Instead of transmitting large real-time data, we proposed to transmit the small amount of dynamic parameters by exploiting both temporal and spatial correlation within and between sensor clusters. The temporal correlation is built on the level-1 Bayesian model at each sensor to predict local readings. Each local sensor transmits their local parameters learned from historical measurement data to their cluster heads which account for the spatial correlation and summarize the regional parameters based on level-2 Bayesian model. Finally, the cluster heads transmit the regional parameters to the sink node. By utilizing this statistical method, the sink node can predict the sensor measurements within a specified period without directly communicating with local sensors. We show that this approach can dramatically reduce the amount of communication load in data query applications and achieve significant energy savings.</p>
dc.identifier.submissionpathqhs_pp/865
dc.contributor.departmentDepartment of Quantitative Health Sciences
dc.source.pages859-866


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