Inference from Randomized Transmissions by Many Backscatter Sensors

TL;DR

This paper introduces a novel backscatter sensing framework that uses random encoding and statistical inference to accurately detect sensing-values from many sensors with simple transmissions, enabling scalable smart city monitoring.

Contribution

It proposes a new backscatter sensing approach combining random encoding at sensors and Bayesian inference at readers, addressing the challenge of limited processing capabilities.

Findings

Effective inference algorithms for sensing-values from randomized transmissions.

Framework supports large-scale sensor deployment with simple communication schemes.

Improved accuracy in sensing-value detection under minimal hardware assumptions.

Abstract

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 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.