Biblio

Passive radio frequency identification (RFID) tags are ubiquitous today due to their low cost (a few cents), relatively long communication range (\$$\backslash$sim\$7-11\textasciitildem), ease of deployment, lack of battery, and small form factor. Hence, they are an attractive foundation for environmental sensing. Although RFID-based sensors have been studied in the research literature and are also available commercially, manufacturing them has been a technically-challenging task that is typically undertaken only by experienced researchers. In this paper, we show how even hobbyists can transform commodity RFID tags into sensors by physically altering (`hacking') them using COTS sensors, a pair of scissors, and clear adhesive tape. Importantly, this requires no change to commercial RFID readers. We also propose a new legacy-compatible tag reading protocol called Differential Minimum Response Threshold (DMRT) that is robust to the changes in an RF environment. To validate our vision, we develop RFID-based sensors for illuminance, temperature, touch, and gestures. We believe that our approach has the potential to open up the field of batteryless backscatter-based RFID sensing to the research community, making it an exciting area for future work.

Device-free localization of people and objects indoors not equipped with radios is playing a critical role in many emerging applications. This paper presents a novel channel state information (CSI) pre-processing scheme that enables accurate device-free localization indoors. The basic idea is simple: CSI is sensitive to a target's location and by modelling the CSI measurements of multiple wireless links as a set of power fading based equations, the target location can be determined. However, due to rich multipaths in indoor environment, the received signal strength (RSS) or even the fine-grained CSI can not be easily modelled. We observe that even in a rich multipath environment, not all subcarriers are equally affected by multipath reflections. Our preprocessing scheme tries to identify the subcarriers not affected by multipath. Thus, CSIs on the "clean" subcarriers can be modelled and utilized for accurate localization. Extensive experiments demonstrate the effectiveness of the proposed pre-processing scheme.

Device-free localization of people and objects indoors not equipped with radios is playing a critical role in many emerging applications. This paper presents an accurate model-based device-free localization system LiFS, implemented on cheap commercial off-the-shelf (COTS) Wi-Fi devices. Unlike previous COTS device-based work, LiFS is able to localize a target accurately without offline training. The basic idea is simple: channel state information (CSI) is sensitive to a target's location and by modelling the CSI measurements of multiple wireless links as a set of power fading based equations, the target location can be determined. However, due to rich multipath propagation indoors, the received signal strength (RSS) or even the fine-grained CSI can not be easily modelled. We observe that even in a rich multipath environment, not all subcarriers are affected equally by multipath reflections. Our pre-processing scheme tries to identify the subcarriers not affected by multipath. Thus, CSIs on the "clean" subcarriers can be utilized for accurate localization. We design, implement and evaluate LiFS with extensive experiments in three different environments. Without knowing the majority transceivers' locations, LiFS achieves a median accuracy of 0.5 m and 1.1 m in line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios respectively, outperforming the state-of-the-art systems. Besides single target localization, LiFS is able to differentiate two sparsely-located targets and localize each of them at a high accuracy.