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Improving reliability of wireless se...

Auburn University.

Improving reliability of wireless sensor networks for target tracking using wireless acoustic sensors.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Improving reliability of wireless sensor networks for target tracking using wireless acoustic sensors.
作者:	Neelisetti, Raghu Kisore.
面頁冊數:	159 p.
附註:	Source: Dissertation Abstracts International, Volume: 71-02, Section: B, page: 1117.
附註:	Adviser: Alvin S. Lim.
Contained By:	Dissertation Abstracts International71-02B.
標題:	Engineering, Computer.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3394633
ISBN:	9781109626544

Improving reliability of wireless sensor networks for target tracking using wireless acoustic sensors.
Neelisetti, Raghu Kisore.

Improving reliability of wireless sensor networks for target tracking using wireless acoustic sensors. - 159 p.

Source: Dissertation Abstracts International, Volume: 71-02, Section: B, page: 1117.

Thesis (Ph.D.)--Auburn University, 2009.

The advancement of MEMS technologies has made it possible to produce tiny wireless sensor devices. These tiny sensors hold the promise of revolutionizing sensing in a wide range of application domains because of their flexibility and low cost. One such application is target localization and tracking using acoustic signal of the target. The capabilities of these tiny devices are limited by their battery power, storage capacity, computational power and communication bandwidth. These limited capabilities make the decisions made by each sensor error prone. Hence most target detection and tracking algorithms require the sensors to work in groups in order to improve the reliability of target tracking algorithms. This makes it necessary for deployed sensors to discover and group together so that their coverage can be maximized. In addition, with the advent of video sensor networks it has become possible to record a video of the target once it is detected and later be relayed to an external agent. In this paper, we propose a clustering algorithm that tries to produce the optimal number of possible clusters for any sensor deployment scenario. The proposed clustering algorithm is distributed in nature and has the ability to reconfigure in the event of node failure. The algorithm is localized in nature and hence does not need flooding across the entire network. Since the algorithm allows for more than one cluster to track the same region the system reliability is greatly improved. The algorithm achieves 97% coverage for all the node deployment scenarios evaluated. In each case the average probability of detection achieved is 92% of the theoretical best possible. The other metrics evaluated are support weight and breach weight. The clustering algorithm achieves 89% and 91% of the theoretical best possible. In each of these cases the algorithm is able to form clusters in about 5 seconds of the simulation time.

ISBN: 9781109626544Subjects--Topical Terms:

384375
Engineering, Computer.

Improving reliability of wireless sensor networks for target tracking using wireless acoustic sensors.
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500 $a Adviser: Alvin S. Lim.
502 $a Thesis (Ph.D.)--Auburn University, 2009.
520 $a The advancement of MEMS technologies has made it possible to produce tiny wireless sensor devices. These tiny sensors hold the promise of revolutionizing sensing in a wide range of application domains because of their flexibility and low cost. One such application is target localization and tracking using acoustic signal of the target. The capabilities of these tiny devices are limited by their battery power, storage capacity, computational power and communication bandwidth. These limited capabilities make the decisions made by each sensor error prone. Hence most target detection and tracking algorithms require the sensors to work in groups in order to improve the reliability of target tracking algorithms. This makes it necessary for deployed sensors to discover and group together so that their coverage can be maximized. In addition, with the advent of video sensor networks it has become possible to record a video of the target once it is detected and later be relayed to an external agent. In this paper, we propose a clustering algorithm that tries to produce the optimal number of possible clusters for any sensor deployment scenario. The proposed clustering algorithm is distributed in nature and has the ability to reconfigure in the event of node failure. The algorithm is localized in nature and hence does not need flooding across the entire network. Since the algorithm allows for more than one cluster to track the same region the system reliability is greatly improved. The algorithm achieves 97% coverage for all the node deployment scenarios evaluated. In each case the average probability of detection achieved is 92% of the theoretical best possible. The other metrics evaluated are support weight and breach weight. The clustering algorithm achieves 89% and 91% of the theoretical best possible. In each of these cases the algorithm is able to form clusters in about 5 seconds of the simulation time.
520 $a On successful detection of a target, the video information needs to be relayed to an external agent which could be several hops away from the point of detection. The lossy nature of wireless links makes the end-to-end delivery ratio decrease exponentially. We address the problem of end-to-end reliability by proposing reliable directed diffusion (RDD) that uses a localized route repair algorithm that does not require a global re-flooding. A route repair algorithm is important to directed diffusion (DD) as the path selected by the protocol is not based on any historical data of link quality and hence prone to packet losses. The node density and power constraints of sensor networks coupled with the ever changing link quality makes it difficult for a node to keep track of its links and hence choose the best possible path. We present the design and implementation of the reliable directed diffusion. RDD repairs the established paths locally by using backup nodes, i.e. nodes that can overhear the positive reinforcement and the corresponding data packets that go in the reverse direction. RDD detects failures at the sender through the MAC layer. Reliable Directed Diffusion provides 30% improvement in the delivery ratio. The end-to-end delay is only 3% more than that of Directed Diffusion. Finally, the average energy consumed is only 5% more than that of Directed Diffusion.
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