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A simulation-based approach for effi...

Bland, William Steven.

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A simulation-based approach for efficient sensor management of distributed sensor networks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A simulation-based approach for efficient sensor management of distributed sensor networks./
作者:	Bland, William Steven.
面頁冊數:	165 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-05, Section: B, page: 2367.
Contained By:	Dissertation Abstracts International64-05B.
標題:	Engineering, System Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3091180

A simulation-based approach for efficient sensor management of distributed sensor networks.
Bland, William Steven.

A simulation-based approach for efficient sensor management of distributed sensor networks. - 165 p.

Source: Dissertation Abstracts International, Volume: 64-05, Section: B, page: 2367.

Thesis (Ph.D.)--University of Virginia, 2003.

Distributed Sensor Networks (DSNs) are sensor fields consisting of intelligent, disparate sensors that are distributed spatially and geographically. DSNs typically incorporate many remote, unattended sensors, creating new capabilities but also introducing new constraints on power, computation, and communications resources. These constraints have created a renewed interest in DSN management strategy, with an increased emphasis on sensor management and efficiency of DSN operations.Subjects--Topical Terms:

1018128
Engineering, System Science.

A simulation-based approach for efficient sensor management of distributed sensor networks.
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245 1 0 $a A simulation-based approach for efficient sensor management of distributed sensor networks.
300 $a 165 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-05, Section: B, page: 2367.
500 $a Adviser: Stephen D. Patek.
502 $a Thesis (Ph.D.)--University of Virginia, 2003.
520 $a Distributed Sensor Networks (DSNs) are sensor fields consisting of intelligent, disparate sensors that are distributed spatially and geographically. DSNs typically incorporate many remote, unattended sensors, creating new capabilities but also introducing new constraints on power, computation, and communications resources. These constraints have created a renewed interest in DSN management strategy, with an increased emphasis on sensor management and efficiency of DSN operations.
520 $a Sensor management deals with the inherent tradeoff between DSN performance and resource consumption. In our context, we view it as a sequential activation of sensing assets in such a way as to reach an acceptably certain representation of the search region while minimizing operating costs. In this dissertation, we hypothesize that the use of dynamic control policies based on the underlying conditional probability distribution associated with the current search region representation will lead to more efficient DSN operations, thereby extending the operating lifetime of the DSN.
520 $a In order to evaluate the performance of several heuristic dynamic control policies, we developed a simulation model of a military surveillance DSN. We use the simulation model to compare the performance of the dynamic control policies to a Base Policy and a Benchmark Policy. The Base Policy consists of activating all sensors during each sensing iteration. The Benchmark Policy consists of an approximate dynamic programming (ADP) solution to the sensor management problem that uses a simulation-based policy iteration process to minimize DSN operating costs while reaching acceptable levels of certainty about the validity of our search region representation.
520 $a This research demonstrates that simple dynamic control policies can be more efficient than the Base Policy and some can even perform comparably with the Benchmark Policy. In addition, the DSN simulation model we developed provides a re search base to (1) investigate the fusion of observations from different sensor types, (2) demonstrate the use of non-imaging sensors to provide adequate situational awareness where “precision” emplacement of more capable sensors is not possible, (3) develop operational concepts to integrate DSN operations with user needs, and (4) experiment to find additional, more-efficient dynamic control policies.
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