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Control and optimization approaches ...

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Control and optimization approaches for power management in wireless sensor networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Control and optimization approaches for power management in wireless sensor networks./
Author:	Ning, Xu.
Description:	187 p.
Notes:	Adviser: Christos G. Cassandras.
Contained By:	Dissertation Abstracts International70-05B.
Subject:	Engineering, Electronics and Electrical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3357764
ISBN:	9781109152296

Control and optimization approaches for power management in wireless sensor networks.
Ning, Xu.

Control and optimization approaches for power management in wireless sensor networks. - 187 p.

Adviser: Christos G. Cassandras.

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

A Wireless Sensor Network (WSN) is a distributed wireless network consisting of low-cost, battery-powered nodes that have sensing and wireless communication capabilities. Power management is one of the key issues in WSNs because it directly impacts the network lifetime. This dissertation focuses on reducing power consumption from two different aspects: transmission control and topology optimization.

ISBN: 9781109152296Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Control and optimization approaches for power management in wireless sensor networks.
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100 1 $a Ning, Xu. $3 1035518
245 1 0 $a Control and optimization approaches for power management in wireless sensor networks.
300 $a 187 p.
500 $a Adviser: Christos G. Cassandras.
500 $a Source: Dissertation Abstracts International, Volume: 70-05, Section: B, page: 3091.
502 $a Thesis (Ph.D.)--Boston University, 2009.
520 $a A Wireless Sensor Network (WSN) is a distributed wireless network consisting of low-cost, battery-powered nodes that have sensing and wireless communication capabilities. Power management is one of the key issues in WSNs because it directly impacts the network lifetime. This dissertation focuses on reducing power consumption from two different aspects: transmission control and topology optimization.
520 $a Transmission control concerns the delivery of messages across a network link. In this aspect, two approaches are proposed to reduce control packet overhead. The first one is Message Batching from the sender's side, which waits for several messages before sending the batch of messages out in a single transmission. The key problem is how to find out the optimal batching size/time resulting in the best tradeoff in energy vs. performance. Analytical results for Markovian systems are derived. Perturbation Analysis is used to derive unbiased gradient estimators for online optimization. The second approach is Dynamic Sleep Time Control from the receiver's side. While traditional approaches use fixed sleep time (time between consecutive channel pollings in Low-Power Listening), better performance can be achieved by varying sleep time dynamically because more statistical information is used. Two ways to control sleep time are proposed, catering to different constraints and objectives. An online distribution learning algorithm is also devised for practical implementation.
520 $a Topology optimization concerns the problem of how a message is routed from one node to another, and how the network is deployed in order to reduce communication energy consumption. Two approaches are investigated. The first one focuses on routing optimization in a flat-topology network that maximizes network lifetime. Previous results are extended by deriving simpler equivalent formulations. Also incorporated is a more realistic battery dynamical model, based on which a new lifetime maximization problem is formulated and solved. The second one focuses on both deployment and routing on a hierarchical WSN with additional constraints on reliability. While the energy optimization formulation is very difficult to solve, a decomposition algorithm is proposed. This algorithm greatly increases the solving speed and scalability compared to existing commercial solvers. An incremental deployment scheme based on this algorithm is also proposed.
590 $a School code: 0017.
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Engineering, System Science. $3 1018128
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710 2 $a Boston University. $3 1017454
773 0 $t Dissertation Abstracts International $g 70-05B.
790 $a 0017
790 1 0 $a Cassandras, Christos G., $e advisor
791 $a Ph.D.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3357764