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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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publications
LTE2B: Time-Domain Cross-Technology Emulation under LTE Constraints.
Published in , 1900
Abstract
Conventional gateway solutions are limited in satisfying the demand for ubiquitous connections among heterogeneous wireless devices, e.g., wide-area and personal-area network devices, due to the deployment complexity, high cost, and the incurred extra traffic. Recent advances propose the physical layer cross-technology communication to address these issues. However, existing CTC techniques commonly emulate the target waveform in the frequency domain (FDE). Despite their success, these FDE based techniques inherently suffer from high quantization errors and are insufficient for IoT applications that require high communication reliability.
To improve the emulation accuracy, we are the first to introduce the time-domain emulation (TDE) that significantly outperforms FDE techniques in reducing quantization errors and offers reliable emulation even with limited sources, e.g., low modulation schemes. To validate our idea, we propose LTE2B, the first TDE based CTC work that enables LTE devices (e.g., smartphones) to transmit data frames demodulatable by ZigBee and Bluetooth low energy (BLE) devices.
We implement the LTE2B on commodity devices (Nexus 5X smartphone and CC2530/CC1350 ZigBee/BLE SoC) with only payload embedding by penetrating the extremely complicated LTE stack. Our extensive evaluation demonstrates that TDE outperforms FDE, while LTE2B can achieve a robust ( > 99% accuracy), long distance ( > 400 m ) CTC performance under a full range of wireless configurations including indoor/outdoor, mobility, and duty-cycle settings.
Recommended citation: Ruofeng Liu, Zhimeng Yin, Wenchao Jiang, Tian He. 17th ACM Conference on Embedded Networked Sensor Systems . (ACM Sensys 2019).
WiBeacon: Expanding BLE Location-based Services via WiFi..
Published in , 1900
Abstract
Despite the popularity of Bluetooth low energy (BLE) location-based services (LBS) in Internet of things applications, large-scale BLE LBS are extremely challenging due to the expenses of deploying and maintaining BLE beacons. To alleviate this issue, this work presents WiBeacon, which repurposes ubiquitously deployed WiFi access points (AP) into virtual BLE beacons via only moderate software upgrades. Specifically, a WiBeacon-enabled AP can broadcast elaborately designed WiFi packets that could be recognized as iBeacon-compatible location identifiers by unmodified mobile BLE devices. This offers fast deployment of BLE LBS with zero additional hardware costs and low maintenance burdens. WiBeacon is carefully integrated with native WiFi services, retaining transparency to WiFi clients. We implement WiBeacon on commodity WiFi APs (with various chipsets such as Qualcomm, Broadcom, and MediaTek) and extensively evaluate it across various scenarios, including a real commercial application for courier check-ins. During the two-week pilot study, WiBeacon provides reliable services, i.e., as robust as conventional BLE beacons, for 697 users with 150 types of smartphones.
Recommended citation: Ruofeng Liu, Zhimeng Yin, Wenchao Jiang, Tian He. The 27th Annual International Conference On Mobile Computing And Networking . (ACM MobiCom 2021).
XFi: Cross-technology IoT Data Collection via Commodity WiFi.
Published in , 1900
Abstract
Wireless technologies are increasingly diversified to serve various Internet-of-things applications. Yet, our mobile devices (e.g., smartphones) are manufactured with limited types of wireless radio, making it challenging to access the data in the heterogeneous IoT devices. To address this fundamental problem, this work proposes XFi, which enables mobile devices to use commodity WiFi radio to directly and simultaneously collect data from diverse heterogeneous IoT devices. Our critical insight is that when an IoT frame collides with an ongoing WiFi transmission, its IoT data is captured by WiFi receiver and retained even after the demodulation procedures in WiFi hardware. Motivated by this observation, XFi proposes a general approach to obtain IoT data by analyzing the decoded WiFi payload. The method is fully compatible with existing commodity WiFi hardware and generally applicable to various IoT protocols.
We implement XFi on commodity devices (e.g., RTL8812au, CC2650, and SX1280). Our comprehensive evaluation demonstrates that XFi can collect data from 8 IoT devices in parallel with over 97% accuracy, offering reliable cross-technology data collection.
Recommended citation: Ruofeng Liu, Zhimeng Yin, Wenchao Jiang, Tian He. The 28th IEEE International Conference on Network Protocols . (ACM ICNP 2020).