Inference From Randomized Transmissions by Many Backscatter Sensors

Attaining the vision of Smart Cities requires the deployment of an enormous number of sensors for monitoring various conditions of the environment. Backscatter sensors have emerged to be a promising solution due to the uninterruptible energy supply and relative simple hardwares. On the other hand, backscatter sensors with limited signal processing capabilities are unable to support conventional algorithms for multiple access and channel training. Thus, the key challenge in designing backscatter sensor networks is to enable readers to accurately detect sensing values given simple 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) featuring random encoding at sensors and statistical inference at readers. Specifically, assuming the on/off keying for backscatter transmissions, the practical random encoding scheme 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, namely 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, which grows with increasing numbers of received symbols and reader antennas.