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Global sensor management: Real-time ...

North Carolina State University.

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Global sensor management: Real-time reallocation of military assets among competing tasks and functions.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Global sensor management: Real-time reallocation of military assets among competing tasks and functions./
Author:	Dulin, Johnathon Louis.
Description:	249 p.
Notes:	Adviser: Thom J. Hodgson.
Contained By:	Dissertation Abstracts International70-01B.
Subject:	Engineering, Industrial. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3345360
ISBN:	9781109005516

Global sensor management: Real-time reallocation of military assets among competing tasks and functions.
Dulin, Johnathon Louis.

Global sensor management: Real-time reallocation of military assets among competing tasks and functions. - 249 p.

Adviser: Thom J. Hodgson.

Thesis (Ph.D.)--North Carolina State University, 2009.

The United States military maintains a network of sensor assets for the multiple purposes of detecting threats, collecting intelligence, monitoring space, and other objectives. Because of its nature, the network must achieve high probabilities of successfully completing all of its varying missions. There is a requirement to assign these sensors to tasks and functions in such a manner as to maximize the capability to meet each of the objectives, remaining flexible enough to be changed in response to the dynamic nature of the environment in which it is employed.

ISBN: 9781109005516Subjects--Topical Terms:

626639
Engineering, Industrial.

Global sensor management: Real-time reallocation of military assets among competing tasks and functions.
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100 1 $a Dulin, Johnathon Louis. $3 1064637
245 1 0 $a Global sensor management: Real-time reallocation of military assets among competing tasks and functions.
300 $a 249 p.
500 $a Adviser: Thom J. Hodgson.
500 $a Source: Dissertation Abstracts International, Volume: 70-01, Section: B, page: 0601.
502 $a Thesis (Ph.D.)--North Carolina State University, 2009.
520 $a The United States military maintains a network of sensor assets for the multiple purposes of detecting threats, collecting intelligence, monitoring space, and other objectives. Because of its nature, the network must achieve high probabilities of successfully completing all of its varying missions. There is a requirement to assign these sensors to tasks and functions in such a manner as to maximize the capability to meet each of the objectives, remaining flexible enough to be changed in response to the dynamic nature of the environment in which it is employed.
520 $a Due to the environment, it is imperative to quickly obtain a good solution that defines an allocation of sensors to tasks. While it is possible to accurately determine the best network allocation based on a total enumeration of potential sensor assignments, this becomes intractable for large problems. Further, once an initial allocation scheme is determined, some sensors may need to be reassigned in response to certain types of events or sensors may simply fail.
520 $a This research addresses these issues through the development of a heuristic approach that finds optimal or nearly optimal solutions to representative sensor networks in only a fraction of the time required to guarantee optimality. The approach is also expanded to respond to changes in the network due to the loss of an assigned sensor. Evaluation of the heuristic's performance using several methods demonstrates its utility in achieving the objectives of determining the best possible allocation of sensors given the time limitations and its ability to respond to the dynamic nature of the environment in which it is intended to operate.
590 $a School code: 0155.
650 4 $a Engineering, Industrial. $3 626639
650 4 $a Military Studies. $3 1017606
650 4 $a Operations Research. $3 626629
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710 2 $a North Carolina State University. $3 1018772
773 0 $t Dissertation Abstracts International $g 70-01B.
790 $a 0155
790 1 0 $a Hodgson, Thom J., $e advisor
791 $a Ph.D.
792 $a 2009
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