Enabling Backscatter Communication among Commodity WiFi Radios

Zhang, Pengyu; Bharadia, Dinesh; Joshi, Kiran

doi:10.1145/2934872.2959072

Cited by 26 publications

(53 citation statements)

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“…Backscatter technology is the most energy-efficient wireless communication link [5,15,[35][36][37]. A typical backscatter system consists of two parts: a reader and one or multiple backscatter tags.…”

Section: Background 21 Backscatter Communicationmentioning

confidence: 99%

Millimeter Wave Backscatter

Mazaheri

Chen

Abari

2020

Proceedings of the 19th ACM Workshop on Hot Topics in Networks

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Backscatter networks (such as RFID, and WiFi backscatter) are very attractive for IoT applications due to their ultra-low energy consumption. In fact, their required energy to operate is low enough that it can be harvested from the environment without having a battery. However, existing backscatter networks offer very limited data-rates (i.e. at most one Mbps). Hence, despite their energy benefit, their applications are very limited. This paper presents the design of mmTag, a backscatter network which can achieve Gbps data-rates. mmTag achieves this by developing a backscatter technology operating in the mmWave spectrum band. mmWave promises to enable high throughput wireless links by offering massive chunks of high-frequency spectrum. However, to use mmWave frequencies in backscatter networks, we need to address a fundamental challenge: beam alignment. mmWave devices require highly directional antennas with very narrow beams, and communication is possible only when the transmitter's beam is aligned with the receiver's beam. However, existing beam searching techniques require power hungry components, and most importantly require the node to transmit a signal which is not possible for a backscatter device. mmTag solves this problem by building a mmWave backscatter tag which performs beam alignment without using any active component. Finally, we implement mmTag and empirically demonstrate some results.

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Section: Background 21 Backscatter Communicationmentioning

confidence: 99%

Millimeter Wave Backscatter

Mazaheri

Chen

Abari

2020

Proceedings of the 19th ACM Workshop on Hot Topics in Networks

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“…To enhance the performance under interference-rich environments, our future implementations of P2PLocate can integrates some recent advances in WiFi interference cancellation technologies [29]. In addition, the backscatter interference may degrade the performance of P2PLocate which can be alleviated by employing the MAC layer protocol proposed in [56].…”

Section: Wifi and Backscatter Interferencementioning

confidence: 99%

Peer-to-Peer Localization for Single-Antenna Devices

Zhang

Wang

Xiao

et al. 2020

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Some important indoor localization applications, such as localizing a lost kid in a shopping mall, call for a new peer-to-peer localization technique that can localize an individual's smartphone or wearables by directly using another's on-body devices in unknown indoor environments. However, current localization solutions either require pre-deployed infrastructures or multiple antennas in both transceivers, impending their wide-scale application. In this paper, we present P2PLocate, a peer-to-peer localization system that enables a single-antenna device co-located with a batteryless backscatter tag to localize another single-antenna device with decimeter-level accuracy. P2PLocate leverages the multipath variations intentionally created by an on-body backscatter tag, coupled with spatial information offered by user movements, to accomplish this objective without relying on any pre-deployed infrastructures or pre-training. P2PLocate incorporates novel algorithms to address two major challenges: (i) interference with strong direct-path signal while extracting multipath variations, and (ii) lack of direction information while using single-antenna transceivers. We implement P2PLocate on commercial off-the-shelf Google Nexus 6p, Intel 5300 WiFi card, and Raspberry Pi B4. Real-world experiments reveal that P2PLocate can localize both static and mobile targets with a median accuracy of 0.88 m. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing.

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“…HitchHike [7] performs codeword translation of a received Wi-Fi signal at a tag and generates a signal decodable by a standard 802.11b receiver. FreeRider [19] extends HitchHike for communication with multiple commodity radios such as 802.11g/n Wi-Fi, ZigBee and Bluetooth with the support of multiple tags. Our algorithm requires more information (I/Q samples) from a Wi-Fi receiver chip, but does not rely on a specific version of 802.11.…”

Section: Related Workmentioning

confidence: 99%

Pattern-Based Decoding for Wi-Fi Backscatter Communication of Passive Sensors

Hwang

Lim

Yun

et al. 2019

Sensors

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Ambient backscatter communication enables passive sensors to convey sensing data on ambient RF signals in the air at ultralow power consumption. To extract data bits from such signals, threshold-based decoding has generally been considered, but suffers against Wi-Fi signals due to severe fluctuation of OFDM signals. In this paper, we propose a pattern-matching-based decoding algorithm for Wi-Fi backscatter communications. The key idea is the identification of unique patterns of signal samples that arise from the inevitable smoothing of Wi-Fi signals to filter out noisy fluctuation. We provide the mathematical basis of obtaining the pattern of smoothed signal samples as the slope of a line expressed in a closed-form equation. Then, the new decoding algorithm was designed to identify the pattern of received signal samples as a slope rather than classifying their amplitude levels. Thus, it is more robust against signal fluctuation and does not need tricky threshold configuration. Moreover, for even higher reliability, the pattern was identified for a pair of adjacent bits, and the algorithm decodes a bit pair at a time rather than a single bit. We demonstrate via testbed experiments that the proposed algorithm significantly outperforms conventional threshold-based decoding variants in terms of bit error rate for various distances and data rates.

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Enabling Backscatter Communication among Commodity WiFi Radios

Cited by 26 publications

References 0 publications

Millimeter Wave Backscatter

Millimeter Wave Backscatter

Peer-to-Peer Localization for Single-Antenna Devices

Pattern-Based Decoding for Wi-Fi Backscatter Communication of Passive Sensors

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