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Compressive Data Gathering in Wireless Sensor Networks

Title:

Compressive Data Gathering in Wireless Sensor Networks

Ebrahimi, Dariush (2016) Compressive Data Gathering in Wireless Sensor Networks. PhD thesis, Concordia University.

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Abstract

The thesis focuses on collecting data from wireless sensors which are deployed randomly in a region. These sensors are widely used in applications ranging from tracking to the monitoring of environment, traffic and health among others. These energy constrained sensors, once deployed may receive little or no maintenance. Hence gathering data in the most energy efficient manner becomes critical for the longevity of wireless sensor networks (WSNs).

Recently, Compressive data gathering (CDG) has emerged as a useful method for collecting sensory data in WSN; this technique is able to reduce global scale communication cost without introducing intensive computation, and is capable of extending the lifetime of the entire sensor network by balancing the forwarding load across the network. This is particularly true due to the benefits obtained from in-network data compression. With CDG, the central unit, instead of receiving data from all sensors in the network, it may receive very few compressed or weighted sums from sensors, and eventually recovers the original data.

To prolong the lifetime of WSN, in this thesis, we present data gathering methods based on CDG. More specifically, we propose data gathering schemes using CDG by building up data aggregation trees from sensor nodes to a central unit (sink). Our problem aims at minimizing the number of links in the forwarding trees to minimize the number of overall transmissions. First, we mathematically formulate the problem and solve it using optimization program. Owing to its complexity, we present real-time algorithmic (centralized and decentralized) methods to efficiently solve the problem. We also explore the benefits one may obtain when jointly applying compressive data gathering with network coding in a wireless sensor network. Finally, and in the context of compressive data gathering, we study the problem of joint forwarding tree construction and scheduling under a realistic interference model, and propose some efficient distributed methods for solving it. We also present a primal dual decomposition method, using the theory of column generation, to solve this complex problem.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (PhD)
Authors:Ebrahimi, Dariush
Institution:Concordia University
Degree Name:Ph. D.
Program:Computer Science
Date:12 February 2016
Thesis Supervisor(s):Assi, Chadi
ID Code:980879
Deposited By: DARIUSH EBRAHIMI
Deposited On:16 Jun 2016 15:41
Last Modified:18 Jan 2018 17:52

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