Browsing by Subject "WiFi"

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    IoT Networking via Cross-technology Communication
    (2022-06) Liu, Ruofeng
    The prevalence of Internet of Things (IoT) brings various heterogeneous wireless techniques, such as Wi-Fi, LTE, ZigBee,Bluetooth, and LoRa. Due to the scarcity of spectrum resources, these wireless technologies commonly share the unlicensed industrial, scientific and medical (ISM) radio band. The coexistence scenario motivates the studies of IoT networking among heterogeneous wireless devices, which breaks the boundary between wireless protocols and paves way for a lot of novel applications (e.g., cross-technology data dissemination, data collection, location services, etc.). This dissertation focuses on the key enabler of IoT networking among heterogeneity - cross-technology communication (CTC) which allows heterogeneous wireless devices to directly exchange data without modifying their hardware. To address the critical roadblock of CTC - incompatibility in their physical (PHY) layers, we propose two approaches, demonstrating the feasibility of CTC both from high-speed to low-speed radios and from low-speed to high-speed ones. First, we present LTE2B which enables CTC from high-speed radios (e.g., LTE) to low-speed radios (e.g., ZigBee and Bluetooth). The key technical contribution is a time-domain signal emulation (TDE) approach which allows a LTE transmitter to produce emulated ZigBee or Bluetooth signal by approximating their time-domain waveform. In addition, it addresses other practical constraints (e.g., turbo coding constraint) to achieve transparent CTC with full compatibility with LTE standards. Our experiment result shows that LTE smartphones can directly disseminate messages to ZigBee devices within a 400-meter range. Second, we introduce XFi which shows the feasibility of CTC in the reverse direction, i.e., from low-speed radios (e.g., ZigBee and LoRa) to high-speed Wi-Fi. To address the fundamental limitation of bandwidth disparity, XFi adopts signal hitchhiking - low-speed IoT packets from ZigBee and LoRa devices can hitchhike on the high-speed Wi-Fi traffic and be captured by Wi-Fi radios. The unique discovery is that Wi-Fi devices can obtain the hitchhiking IoT data from the errors in decoded Wi-Fi payloads that are available in the software. The key insight enables CTC with zero modification to Wi-Fi hardware. Our evaluation demonstrates that Wi-Fi can collect data from 8 IoT devices in parallel with an overall throughput of 1.8 Mbps. Finally, we adopt CTC technique in a commercial Bluetooth location service to study and address the challenges of applying cross-technology design in real-world scenarios. We propose WiBeacon which repurposes ubiquitously deployed WiFi access points (AP) into virtual BLE beacons via only moderate software upgrades. 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.
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    IoT Networking: From Isolation to Collaboration
    (2020-09) Yin, Zhimeng
    Emerging Internet of Things (IoT) dramatically enriches every aspect of our daily life through pervasive IoT objectives. To satisfy the specific requirements of disparate IoT applications, researchers introduce various wireless protocols, such as WiFi, ZigBee, Bluetooth, and LTE. Designed independently, heterogeneous IoT protocols have incompatible Physical/MAC layers and lack direct communication. As a result, their spectrum competition leads to wireless interference that downgrades many important aspects of IoT performance such as delay, throughput, and energy consumption. This dissertation aims at enabling secure collaboration among the isolated IoT protocols for achieving synergic performance. First, it introduces cross-technology communication (CTC) for connecting heterogeneous IoT devices through C-Morse, a technique that slightly perturbs the transmission timing of existing WiFi packets for constructing dedicated and unique energy patterns. The energy patterns are detected through energy sensing, which is generally available across different types of IoT. To further boost the CTC throughput, it leverages all WiFi traffic (such as data packets and control frames) while being transparent for upper-layer applications without causing a significant delay. We evaluate C-Morse on testbeds such as WARP and MicaZ. Experiments demonstrate that C-Morse achieves a free side channel, with a throughput of 936bps and negligible delay for applications and end-users. Based on the direct connectivity offered by CTC, this dissertation presents ECC - a collaborative coexistence technique that uniquely enables explicit channel coordination among heterogeneities for improving spectrum efficiency. Specifically, ECC generates the guaranteed white space using WiFi CTS, which is then explicitly notified to ZigBee through CTC for immediate use. ECC's technical highlight lies in protecting low-power ZigBee communication from wireless interference while maintaining transparency to WiFi applications. This is achieved through dynamic adjustment of CTS duration with respect to traffic amount and spectrum availability. Our evaluation on commercial platforms shows that ECC achieves a 1.8x ZigBee packet reception ratio through collaborative coexistence. Despite the success of CTC, it also leads to potential security issues across heterogeneous systems. To ensure secure IoT applications, this dissertation further proposes CTCMon, a general detection framework for identifying CTC attacks based on the received payload at commodity devices. Since CTC techniques need to approximate the target waveform with heterogeneous devices, the transmitted waveform inevitably contains distinctive signal patterns. By examining these unique signal patterns like fingerprints, CTCMon reliably distinguishes CTC from regular wireless transmissions. Extensive experiments on multiple testbeds demonstrate the general applicability and reliability of CTCMon.