Decentralized detection in resource-limited sensor network architectures
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2008
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| Online Access: | http://hdl.handle.net/1721.1/42910 |
| _version_ | 1811073696537772032 |
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| author | Tay, Wee Peng |
| author2 | John N. Tsitsiklis and Moe Z. Win. |
| author_facet | John N. Tsitsiklis and Moe Z. Win. Tay, Wee Peng |
| author_sort | Tay, Wee Peng |
| collection | MIT |
| description | Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008. |
| first_indexed | 2024-09-23T09:37:07Z |
| format | Thesis |
| id | mit-1721.1/42910 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T09:37:07Z |
| publishDate | 2008 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/429102019-04-12T08:54:20Z Decentralized detection in resource-limited sensor network architectures Tay, Wee Peng John N. Tsitsiklis and Moe Z. Win. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Includes bibliographical references (leaves 201-207). We consider the problem of decentralized binary detection in a network consisting of a large number of nodes arranged as a tree of bounded height. We show that the error probability decays exponentially fast with the number of nodes under both a Neyman-Pearson criterion and a Bayesian criterion, and provide bounds for the optimal error exponent. Furthermore, we show that under the Neyman-Pearson criterion, the optimal error exponent is often the same as that corresponding to a parallel configuration, implying that a large network can be designed to operate efficiently without significantly affecting the detection performance. We provide sufficient, as well as necessary, conditions for this to happen. For those networks satisfying the sufficient conditions, we propose a simple strategy that nearly achieves the optimal error exponent, and in which all non-leaf nodes need only send 1-bit messages. We also investigate the impact of node failures and unreliable communications on the detection performance. Node failures are modeled by a Galton-Watson branching process, and binary symmetric channels are assumed for the case of unreliable communications. We characterize the asymptotically optimal detection performance, develop simple strategies that nearly achieve the optimal performance, and compare the performance of the two types of networks. Our results suggest that in a large scale sensor network, it is more important to ensure that nodes can communicate reliably with each other(e.g.,by boosting the transmission power) than to ensure that nodes are robust to failures. In the case of networks with unbounded height, we establish the validity of a long-standing conjecture regarding the sub-exponential decay of Bayesian detection error probabilities in a tandem network. We also provide bounds for the error probability, and show that under the additional assumption of bounded Kullback-Leibler divergences, the error probability is (e cnd ), for all d> 1/2, with c c(logn)d being a positive constant. Furthermore, the bound (e), for all d> 1, holds under an additional mild condition on the distributions. This latter bound is shown to be tight. Moreover, for the Neyman-Pearson case, we establish that if the sensors act myopically, the Type II error probabilities also decay at a sub-exponential rate. (cont.) Finally, we consider the problem of decentralized detection when sensors have access to side-information that affects the statistics of their measurements, and the network has an overall cost constraint. Nodes can decide whether or not to make a measurement and transmit a message to the fusion center("censoring"), and also have a choice of the transmission function. We study the tradeoff in the detection performance with the cost constraint, and also the impact of sensor cooperation and global sharing of side-information. In particular, we show that if the Type I error probability is constrained to be small, then sensor cooperation is not necessary to achieve the optimal Type II error exponent. by Wee Peng Tay. Ph.D. 2008-11-07T14:09:27Z 2008-11-07T14:09:27Z 2008 2008 Thesis http://hdl.handle.net/1721.1/42910 243864746 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 207 leaves application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Tay, Wee Peng Decentralized detection in resource-limited sensor network architectures |
| title | Decentralized detection in resource-limited sensor network architectures |
| title_full | Decentralized detection in resource-limited sensor network architectures |
| title_fullStr | Decentralized detection in resource-limited sensor network architectures |
| title_full_unstemmed | Decentralized detection in resource-limited sensor network architectures |
| title_short | Decentralized detection in resource-limited sensor network architectures |
| title_sort | decentralized detection in resource limited sensor network architectures |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/42910 |
| work_keys_str_mv | AT tayweepeng decentralizeddetectioninresourcelimitedsensornetworkarchitectures |