2019
DOI: 10.1109/access.2019.2947433
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Statistical Inference-Based Distributed Blind Estimation in Wireless Sensor Networks

Abstract: To realize the Internet of Things, one of the essential elements is wireless sensor networks which can sense the physical conditions of the environment. The ubiquitous sensing is achieved by a large number of spatially dispersed sensors and distributed estimation technology. However, the low-cost sensors are insufficient to support conventional distributed estimation schemes. Since most conventional schemes include channel training process, the resource consumption of which is enormous. Thus, one key challenge… Show more

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Cited by 2 publications
(1 citation statement)
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“…Therefore, two constellation learning-based signal detection methods are proposed which are derived from the EM algorithm. A distributed blind estimation scheme is presented in [124] where random transmission approach converts sensors' sensing values are utilized to monitor their states and statistical inference methodology extracts the sensor values with the help of EM and Gaussian mixture model. In [125], EM is used to estimate the parameters of Gaussian mixture model in channel multipath clustering applications.…”
Section: ) Expectation Maximization (Em)mentioning
confidence: 99%
“…Therefore, two constellation learning-based signal detection methods are proposed which are derived from the EM algorithm. A distributed blind estimation scheme is presented in [124] where random transmission approach converts sensors' sensing values are utilized to monitor their states and statistical inference methodology extracts the sensor values with the help of EM and Gaussian mixture model. In [125], EM is used to estimate the parameters of Gaussian mixture model in channel multipath clustering applications.…”
Section: ) Expectation Maximization (Em)mentioning
confidence: 99%