Asymptotic Performance of a Censoring Sensor Network
We consider the problem of decentralized binary detection in a sensor network where the sensors have access to side information that affects the statistics of their measurements, or reflects the quality of the available channel to a fusion center. Sensors can decide whether or not to make a measurem...
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| Veröffentlicht in: | IEEE transactions on information theory 2007-11, Vol.53 (11), p.4191-4209 |
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| container_title | IEEE transactions on information theory |
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| creator | Wee Peng Tay Tsitsiklis, J.N. Win, M.Z. |
| description | We consider the problem of decentralized binary detection in a sensor network where the sensors have access to side information that affects the statistics of their measurements, or reflects the quality of the available channel to a fusion center. Sensors 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 mapping from measurements to messages. We consider the case of a large number of sensors, and an asymptotic criterion involving error exponents. We study both a Neyman-Pearson and a , Bayesian formulation, characterize the optimal error exponent, and derive asymptotically optimal strategies for the case where sensor decisions are only allowed to depend on locally available information. Furthermore, we show that for the Neyman-Pearson case, global sharing of side information ("sensor cooperation") does not improve asymptotic performance, when the Type I error is constrained to be small. |
| doi_str_mv | 10.1109/TIT.2007.907441 |
| format | Article |
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Sensors 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 mapping from measurements to messages. We consider the case of a large number of sensors, and an asymptotic criterion involving error exponents. We study both a Neyman-Pearson and a , Bayesian formulation, characterize the optimal error exponent, and derive asymptotically optimal strategies for the case where sensor decisions are only allowed to depend on locally available information. 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Sensors 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 mapping from measurements to messages. We consider the case of a large number of sensors, and an asymptotic criterion involving error exponents. We study both a Neyman-Pearson and a , Bayesian formulation, characterize the optimal error exponent, and derive asymptotically optimal strategies for the case where sensor decisions are only allowed to depend on locally available information. Furthermore, we show that for the Neyman-Pearson case, global sharing of side information ("sensor cooperation") does not improve asymptotic performance, when the Type I error is constrained to be small.</description><subject>Applied sciences</subject><subject>Bayesian analysis</subject><subject>Bayesian methods</subject><subject>Censoring</subject><subject>cooperation</subject><subject>Costs</subject><subject>decentralized detection</subject><subject>Energy efficiency</subject><subject>error exponent</subject><subject>Errors</subject><subject>Exact sciences and technology</subject><subject>Face detection</subject><subject>Information theory</subject><subject>Information, signal and communications theory</subject><subject>Large-scale systems</subject><subject>Monitoring</subject><subject>Sensor fusion</subject><subject>sensor networks</subject><subject>Sensor phenomena and characterization</subject><subject>Sensors</subject><subject>Services and terminals of telecommunications</subject><subject>Statistics</subject><subject>Systems, networks and services of telecommunications</subject><subject>Telecommunication network reliability</subject><subject>Telecommunications</subject><subject>Telecommunications and information theory</subject><subject>Telemetry. 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Sensors 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 mapping from measurements to messages. We consider the case of a large number of sensors, and an asymptotic criterion involving error exponents. We study both a Neyman-Pearson and a , Bayesian formulation, characterize the optimal error exponent, and derive asymptotically optimal strategies for the case where sensor decisions are only allowed to depend on locally available information. Furthermore, we show that for the Neyman-Pearson case, global sharing of side information ("sensor cooperation") does not improve asymptotic performance, when the Type I error is constrained to be small.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TIT.2007.907441</doi><tpages>19</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Applied sciences Bayesian analysis Bayesian methods Censoring cooperation Costs decentralized detection Energy efficiency error exponent Errors Exact sciences and technology Face detection Information theory Information, signal and communications theory Large-scale systems Monitoring Sensor fusion sensor networks Sensor phenomena and characterization Sensors Services and terminals of telecommunications Statistics Systems, networks and services of telecommunications Telecommunication network reliability Telecommunications Telecommunications and information theory Telemetry. Remote supervision. Telewarning. Remote control |
| title | Asymptotic Performance of a Censoring Sensor Network |
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