Analyzing the Scalability of Bi-static Backscatter Networks for Large Scale Applications
Kartik Patel, Junbo Zhang, John Kimionis, Lefteris Kampianakis, Michael S. Eggleston, Jinfeng Du

TL;DR
This paper evaluates the scalability of bi-static backscatter networks for large-scale IoT deployments, introducing a theoretical framework and experimental validation to support thousands of tags with high reliability.
Contribution
It develops a theoretical model for network reliability, refines it with experimental data, and proposes a methodology for designing large-scale bi-static backscatter IoT networks.
Findings
Supports 1000 tags with 99.9% reliability at BER 0.2
Provides a systematic network tuning methodology
Validates model with experimental prototypes
Abstract
Backscatter radio is a promising technology for low-cost and low-power Internet-of-Things (IoT) networks. The conventional monostatic backscatter radio is constrained by its limited communication range, which restricts its utility in wide-area applications. An alternative bi-static backscatter radio architecture, characterized by a dis-aggregated illuminator and receiver, can provide enhanced coverage and, thus, can support wide-area applications. In this paper, we analyze the scalability of the bi-static backscatter radio for large-scale wide-area IoT networks consisting of a large number of unsynchronized, receiver-less tags. We introduce the Tag Drop Rate (TDR) as a measure of reliability and develop a theoretical framework to estimate TDR in terms of the network parameters. We show that under certain approximations, a small-scale prototype can emulate a large-scale network. We then…
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