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Data Gathering Algorithms and Sink Mobility Models for Wireless Sensor Networks

Data Gathering Algorithms and Sink Mobility Models for Wireless Sensor Networks

Natarajan Meghanathan
Copyright: © 2014 |Pages: 29
ISBN13: 9781466647152|ISBN10: 1466647159|EISBN13: 9781466647169
DOI: 10.4018/978-1-4666-4715-2.ch008
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MLA

Meghanathan, Natarajan. "Data Gathering Algorithms and Sink Mobility Models for Wireless Sensor Networks." Multidisciplinary Perspectives on Telecommunications, Wireless Systems, and Mobile Computing, edited by Wen-Chen Hu, IGI Global, 2014, pp. 123-151. https://doi.org/10.4018/978-1-4666-4715-2.ch008

APA

Meghanathan, N. (2014). Data Gathering Algorithms and Sink Mobility Models for Wireless Sensor Networks. In W. Hu (Ed.), Multidisciplinary Perspectives on Telecommunications, Wireless Systems, and Mobile Computing (pp. 123-151). IGI Global. https://doi.org/10.4018/978-1-4666-4715-2.ch008

Chicago

Meghanathan, Natarajan. "Data Gathering Algorithms and Sink Mobility Models for Wireless Sensor Networks." In Multidisciplinary Perspectives on Telecommunications, Wireless Systems, and Mobile Computing, edited by Wen-Chen Hu, 123-151. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-4715-2.ch008

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Abstract

In the first half of the chapter, the authors provide a comprehensive description of two broad categories of data gathering algorithms for wireless sensor networks: the classical energy-unaware algorithms and the modern energy-aware algorithms, as well as presented an exhaustive performance comparison of representative algorithms from both these categories. While the first half of the chapter focuses on static sink (that is located outside on the network boundary), the second half of the chapter explores the use of mobile sinks that gather data by stopping at the vicinity of the sensor nodes. As a first step, the authors investigate the performance of three different strategies to develop sink mobility models for delay and energy-efficient data gathering in static wireless sensor networks. The three strategies differ on the approach to take to determine the next stop for data gathering: randomly choosing a sensor node that is yet to be covered (Random), choose the sensor node that has the maximum number of uncovered neighbor nodes (Max-Density), and choose the sensor node that has the largest value for the product of the maximum number of uncovered neighbor nodes and the residual energy (Max-Density-Energy). Based on the simulation results, the authors recommend incorporating the random node selection-based strategy to be a better strategy for sink mobility models (with minimal deployment overhead) rather than keeping track of the number of uncovered neighbor nodes per node and the residual energy available at the nodes.

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