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Stochastic modelling and optimizatio...

Ge, Qi.

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Stochastic modelling and optimization of resource constrained sensor networks.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Stochastic modelling and optimization of resource constrained sensor networks./
作者:	Ge, Qi.
面頁冊數:	62 p.
附註:	Adviser: R. Chandramouli.
Contained By:	Dissertation Abstracts International68-06B.
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3269201
ISBN:	9780549085225

Stochastic modelling and optimization of resource constrained sensor networks.
Ge, Qi.

Stochastic modelling and optimization of resource constrained sensor networks. - 62 p.

Adviser: R. Chandramouli.

Thesis (Ph.D.)--Stevens Institute of Technology, 2007.

This thesis addresses some key problems in the field of sensor processing using stochastic process modeling and data fusion techniques. In the first part of this thesis we study few battery consumption issues in sensor processing. Sensors in most practical case are non-rechargeable once they are deployed over the field. Therefore, battery energy efficiency becomes a critical issue. We use a discrete-time Markov chain model for the randomly varying battery energy consumption. Specifically, we investigate stochastic models that match pulsed discharging behavior of electrochemical cells and compute the expected battery life time. By modeling cell discharging behavior as a random walking process several statistics of the battery discharge behavior are computed.

ISBN: 9780549085225Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Stochastic modelling and optimization of resource constrained sensor networks.
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245 1 0 $a Stochastic modelling and optimization of resource constrained sensor networks.
300 $a 62 p.
500 $a Adviser: R. Chandramouli.
500 $a Source: Dissertation Abstracts International, Volume: 68-06, Section: B, page: 4009.
502 $a Thesis (Ph.D.)--Stevens Institute of Technology, 2007.
520 $a This thesis addresses some key problems in the field of sensor processing using stochastic process modeling and data fusion techniques. In the first part of this thesis we study few battery consumption issues in sensor processing. Sensors in most practical case are non-rechargeable once they are deployed over the field. Therefore, battery energy efficiency becomes a critical issue. We use a discrete-time Markov chain model for the randomly varying battery energy consumption. Specifically, we investigate stochastic models that match pulsed discharging behavior of electrochemical cells and compute the expected battery life time. By modeling cell discharging behavior as a random walking process several statistics of the battery discharge behavior are computed.
520 $a Sensor search theory is the topic of the second part of this thesis. In sensor search theory the main goal is to devise an optimal search plan under resource constraints, to identify a target of interest. Side information such as the probability distribution of the target's spatial location may or may not be available a priori. Conventional search theory deals with one sensor and one target. In this paper we generalize some of the conventional results to a multiple sensor problem with data fusion. We do not assume any communication between the sensors. The optimal search plan is computed by the data fusion center. Our theoretical and simulation results show that an improvement in the overall system performance is achieved under some circumstances.
590 $a School code: 0733.
650 4 $a Engineering, Electronics and Electrical. $3 626636
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710 2 $a Stevens Institute of Technology. $3 1019501
773 0 $t Dissertation Abstracts International $g 68-06B.
790 $a 0733
790 1 0 $a Chandramouli, R., $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3269201