PROJECT TITLE :
Inference From Randomized Transmissions by Many Backscatter Sensors - 2018
Attaining the vision of Good Cities needs the deployment of an monumental variety of sensors for monitoring varied conditions of the surroundings. Backscatter sensors have emerged to be a promising solution thanks to the uninterruptible energy provide and relative straightforward hardwares. On the opposite hand, backscatter sensors with restricted Signal Processing capabilities are unable to support conventional algorithms for multiple access and channel coaching. Therefore, the key challenge in coming up with backscatter sensor networks is to enable readers to accurately detect sensing values given straightforward ALOHA random access, primitive transmission schemes, and no knowledge of channel states. We tackle this challenge by proposing the novel framework of backscatter sensing (BackSense) that includes random encoding at sensors and statistical inference at readers. Specifically, assuming the on/off keying for backscatter transmissions, the sensible random encoding theme causes the on/off transmission of a sensor to follow a distribution parameterized by the sensing values. Facilitated by the scheme, statistical inference algorithms are designed to enable a reader to infer sensing values from randomized transmissions by multiple sensors. The specific design procedure involves the construction of Bayesian networks, particularly deriving conditional distributions for relating unknown parameters and variables to signals observed by the reader. Then based on the Bayesian networks and the well-known expectation-maximization principle, inference algorithms are derived to recover sensing values. Simulation of the BackSense system demonstrates high accuracy in reader inference despite the mentioned limitations of backscatter sensors, that grows with increasing numbers of received symbols and reader antennas.
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