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Title Empirical Strategies based on the Histogram Information for Decentralized Detection in Wireless Sensor Networks
Degree MS
Author Seungho Bae
Advisor Kiseon Kim
Graduation Date 2006.02.24 File
    Date 2013-09-11 20:55
Recent advances in wireless communication technologies, signal processing, digital
electronics and micro-electro-mechanical systems (MEMS) have lead to the emergency
of wireless sensor networks (WSNs). In WSNs, geographically dispersed low-power and
low-cost sensors, in which sensing, processing, and communication capabilities are all
integrated, receive information about the state of a source over a sensing ?eld and are
required to transmit a summary of their observations to a fusion center. Based on
the received data, the fusion center makes an estimate of the source, that is, performs
decentralized-detection. As one of the key methods to construct human-centered en-
vironment, WSNs has been employed in a wide variety of applications, starting from
military surveillance to civilian and environmental monitoring.
Because of the wide WSN applications, it is important that sensors e±ciently pro-
cess their observations with simple complexity and low-transmission rate to overcome
uncertainty (channel noise and measurement noise of distributed sensors), and con-
straints (power consumption, channel requirement, reliability) of WSNs.
In addition, the performance of a decentralized-detection scheme based on informa-
tion from sensors generally depends on the distribution of observations (probabilistic
characteristics of a source, channels etc.). However, deriving closed-form expressions
of these statistics and the network-wide updating or beforehand-setting sensors with
them are very di±cult tasks under the limitations of WSNs.Recent advances in wireless communication technologies, signal processing, digital
electronics and micro-electro-mechanical systems (MEMS) have lead to the emergency
of wireless sensor networks (WSNs). In WSNs, geographically dispersed low-power and
low-cost sensors, in which sensing, processing, and communication capabilities are all
integrated, receive information about the state of a source over a sensing ?eld and are
required to transmit a summary of their observations to a fusion center. Based on
the received data, the fusion center makes an estimate of the source, that is, performs
decentralized-detection. As one of the key methods to construct human-centered en-
vironment, WSNs has been employed in a wide variety of applications, starting from
military surveillance to civilian and environmental monitoring.
Because of the wide WSN applications, it is important that sensors e±ciently pro-
cess their observations with simple complexity and low-transmission rate to overcome
uncertainty (channel noise and measurement noise of distributed sensors), and con-
straints (power consumption, channel requirement, reliability) of WSNs.
In addition, the performance of a decentralized-detection scheme based on informa-
tion from sensors generally depends on the distribution of observations (probabilistic
characteristics of a source, channels etc.). However, deriving closed-form expressions
of these statistics and the network-wide updating or beforehand-setting sensors with
them are very di±cult tasks under the limitations of WSNs.
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광주과학기술원 한·러 MT-IT 융합기술연구센터 광주과학기술원정보통신공학부