Abstract:
We present multiscatter, a novel battery-free backscatter design that can simultaneously work with multiple excitation signals for personal IoT sensors. Specifically, we show for the first time that the backscatter tag can identify various excitation signals in an ultra-low-power way, including WiFi, Bluetooth, and ZigBee. Further, we employ a new modulation approach, overlay modulation, that can leverage those excitation signals to convey tag data on top of productive data, which makes decoding both data possible with only a single personal radio. Moreover, we introduce a low-power listening scheme to improve energy efficiency. Since 2.4 GHz signals and personal radios are everywhere, multiscatter is readily deployable in our everyday IoT applications.
We prototype multiscatter using an FPGA and various commodity radios. Extensive experiments show that for mixed 802.11b&n, Bluetooth and ZigBee signals, the average identification accuracy of four protocols is more than 93%. The maximal aggregate throughput of both productive and tag data is 278.4 kbps with a single Bluetooth radio. When the transmitter-to-tag distance is increased from 0.2 to 1.8 m, the maximal communication for BLE drops from 71 m to 29 m. And it can leverage excitation diversity to provide uninterrupted communication and greater throughput gains, whereas the single-protocol tag being idle when carrier signals are unavailable. With indoor office light as harvesting sources, the low-power listening scheme can support backscatter rate at 12 pkts/s.
Authors:
Longzhi Yuan, Qiwei Wang, Jia Zhao, Wei Gong.
Publication:
IEEE/ACM Transactions on Networking
Keywords:
Design, Internet of Things, Sensor networks
