Inference From Randomized Transmissions by Many Backscatter Sensors

Abstract

Attaining the vision of Smart Cities requires the deployment of an enormous number of sensors for monitoring various conditions of the environment ranging from air quality to traffic. Backscatter sensors have emerged to be a promising solution for two reasons. First, transmissions by backscattering allow sensors to be powered wirelessly by radio-frequency (RF) waves, overcoming the difficulty in battery recharging for billions of sensors. Second, the simple backscatter hardware leads to low-cost sensors suitable for large-scale deployment. On the other hand, backscatter sensors with limited signalprocessing 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 and statistics. We tackle this challenge by proposing the novel framework of backscatter sensing (BackSense) featuring random encoding at backscatter sensors and statistical inference at readers. Specifically, assuming the widely used on/off keying for backscatter transmissions, the practical random-encoding scheme causes the on/off transmission of a sensor to be randomized and 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 backscatter sensors. The specific design procedure involves the construction of Bayesian networks, namely deriving conditional distributions for relating unknown parameters and variables (including sensing values, noise power, sensing measurements, number of active sensors) to signals observed by the reader. Then based on the Bayesian networks and the well-known expectationmaximization (EM) 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.

Wirelessly Powered Data Aggregation for IoT via Over-the-Air Function Computation: Beamforming and Power Control

“…To this end, a collaborative energy-beamforming scheme is proposed in [20] for efficiently powering a sensor network. For large-scale wirelessly powered sensor networks, a novel framework of backscatter sensing was recently proposed in [21], where low-cost passive sensors upload their sensing data to a drone mounted reader by concurrently reflecting the beamed power signal from a power beacon in a designed probabilistic manner. Then statistical inference algorithms can be devised for sensing value recovery without the knowledge of channel state information.…”

Sum Throughput Maximization in Multi-Tag Backscattering to Multiantenna Reader

“…Whereas, a frequencymodulated continuous-wave BSC system with monostatic reader, whose one antenna was dedicated for transmission and remaining for the reception of backscattered signals, was studied in [14] to precisely determine the number and position of active tags. On similar lines, considering a multiantenna power beacon assisted bi-static BSC model, robust inference algorithms, not requiring any channel state or statistics information, were proposed in [15] to detect the sensing values of multiple single antenna backscatter sensors at a multiantenna reader by constructing Bayesian networks and using expectation maximization principle. Pairwise error probability and diversity order achieved by the orthogonal space time block codes over the dyadic backscatter channel (i.e., monostatic BSC system with multiple-antennas at the reader for transmission and reception from a multiantenna tag) were derived in [9] and [16].…”

Multi-Tag Backscattering to MIMO Reader: Channel Estimation and Throughput Fairness

“…Authors in [22] investigated blind CE algorithms for obtaining the estimates for :(a) absolute values of the channel coefficients for RF source to tag link, and (b) the scaled product of forward and backward links in BSC. Alternatively, robust inference algorithms not requiring any CSI were proposed in [23] for detecting the backscattered information from multiple singleantenna sensors at a multiantenna reader. Very recently, a least-squares (LS) based channel estimator for the forward and backward channels between a multiantenna reader and single-antenna tag was proposed in [24] to come up with an optimal energy allocation scheme maximizing underlying BSC performance.…”