An Efficient and Robust Computational Framework for Studying Lifetime and Information Capacity in Sensor Networks
In this paper we investigate the expected lifetime and information capacity, defined as the maximum amount of data (bits) transferred before the first sensor node death due to energy depletion, of a data-gathering wireless sensor network. We develop a fluid-flow based computational framework that ex...
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sg-smu-ink.sis_research-16602017-11-01T06:59:16Z An Efficient and Robust Computational Framework for Studying Lifetime and Information Capacity in Sensor Networks DUARTE-MELO, Enrique J. LIU, Mingyan MISRA, Archan In this paper we investigate the expected lifetime and information capacity, defined as the maximum amount of data (bits) transferred before the first sensor node death due to energy depletion, of a data-gathering wireless sensor network. We develop a fluid-flow based computational framework that extends the existing approach, which requires precise knowledge of the layout/deployment of the network, i.e., exact sensor positions. Our method, on the other hand, views a specific network deployment as a particular instance (sample path) from an underlying distribution of sensor node layouts and sensor data rates. To compute the expected information capacity under this distribution-based viewpoint, we model parameters such as the node density, the energy density and the sensed data rate as continuous spatial functions. This continuous-space flow model is then discretized into grids and solved using a linear programming approach. Numerical studies show that this model produces very accurate results, compared to averaging over results from random instances of deployment, with significantly less computation. Moreover, we develop a robust version of the linear program, which generates robust solutions that apply not just to a specific deployment, but also to topologies that are appropriately perturbed versions. This is especially important for a network designer studying the fundamental lifetime limit of a family of network layouts, since the lifetime of specific network deployment instances may differ appreciably. As an example of this model's use, we determine the optimal node distribution for a linear network and study the properties of optimal routing that maximizes the lifetime of the network. 2005-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/661 info:doi/10.1007/s11036-005-4440-x https://ink.library.smu.edu.sg/context/sis_research/article/1660/viewcontent/EfficientRobust_ComputationalFramework_2005.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University mathematical programming linear program optimization system design wireless sensor networks lifetime capacity sensor deployment node distribution optimal routing fluid flow model robustness stability Software Engineering |
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mathematical programming linear program optimization system design wireless sensor networks lifetime capacity sensor deployment node distribution optimal routing fluid flow model robustness stability Software Engineering DUARTE-MELO, Enrique J. LIU, Mingyan MISRA, Archan An Efficient and Robust Computational Framework for Studying Lifetime and Information Capacity in Sensor Networks |
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In this paper we investigate the expected lifetime and information capacity, defined as the maximum amount of data (bits) transferred before the first sensor node death due to energy depletion, of a data-gathering wireless sensor network. We develop a fluid-flow based computational framework that extends the existing approach, which requires precise knowledge of the layout/deployment of the network, i.e., exact sensor positions. Our method, on the other hand, views a specific network deployment as a particular instance (sample path) from an underlying distribution of sensor node layouts and sensor data rates. To compute the expected information capacity under this distribution-based viewpoint, we model parameters such as the node density, the energy density and the sensed data rate as continuous spatial functions. This continuous-space flow model is then discretized into grids and solved using a linear programming approach. Numerical studies show that this model produces very accurate results, compared to averaging over results from random instances of deployment, with significantly less computation. Moreover, we develop a robust version of the linear program, which generates robust solutions that apply not just to a specific deployment, but also to topologies that are appropriately perturbed versions. This is especially important for a network designer studying the fundamental lifetime limit of a family of network layouts, since the lifetime of specific network deployment instances may differ appreciably. As an example of this model's use, we determine the optimal node distribution for a linear network and study the properties of optimal routing that maximizes the lifetime of the network. |
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text |
author |
DUARTE-MELO, Enrique J. LIU, Mingyan MISRA, Archan |
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DUARTE-MELO, Enrique J. LIU, Mingyan MISRA, Archan |
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DUARTE-MELO, Enrique J. |
title |
An Efficient and Robust Computational Framework for Studying Lifetime and Information Capacity in Sensor Networks |
title_short |
An Efficient and Robust Computational Framework for Studying Lifetime and Information Capacity in Sensor Networks |
title_full |
An Efficient and Robust Computational Framework for Studying Lifetime and Information Capacity in Sensor Networks |
title_fullStr |
An Efficient and Robust Computational Framework for Studying Lifetime and Information Capacity in Sensor Networks |
title_full_unstemmed |
An Efficient and Robust Computational Framework for Studying Lifetime and Information Capacity in Sensor Networks |
title_sort |
efficient and robust computational framework for studying lifetime and information capacity in sensor networks |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2005 |
url |
https://ink.library.smu.edu.sg/sis_research/661 https://ink.library.smu.edu.sg/context/sis_research/article/1660/viewcontent/EfficientRobust_ComputationalFramework_2005.pdf |
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1770570654433673216 |