Wi-Fi is the de facto standard for providing wireless access to the Internet using the 2.4GHz ISM (Industrial Scientific Medical) band. LoRa (Long Range) is specially designed for Low-Power, Wide-Area Networks (LPWANs) and has a broad range of applications in Internet of Things. Tens of billions of 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 challenge, we propose a method that enables LoRa devices to establish connections and engage in communication with Wi-Fi networks. A key observation of this study is that when a LoRa frame collides with an ongoing Wi-Fi transmission, the Wi-Fi receiver captures and retains the LoRa data. By analyzing the decoded Wi-Fi payload, we can retrieve the LoRa data, and this method remains fully compatible with existing commodity Wi-Fi hardware. Moreover, evaluations with Universal Software Radio Peripheral (USRP) and commodity devices demonstrate reliable wireless communication from LoRa to Wi-Fi networks with a high reliability in frame reception and low frame error rates across various indoor and outdoor environments.

Real-time semantic recognition is crucial for virtual reality (VR) applications, but the efficient fusion of multi-modal data poses significant challenges under resource-constrained VR scenarios. While integrating millimeter-wave (mmWave) radar point clouds with vision data offers a promising solution, existing methods often suffer from excessive data overhead and degraded accuracy due to redundant and noisy information. To address this limitation, this paper presents VR-PCT, a multi-modal transformer for edge-client collaborative VR semantic recognition that fuses mmWave radar point cloud and vision data for VR applications. VR-PCT introduces a novel collaborative design where VR clients perform lightweight semantic region detection while VR edge processes multi-modal VR semantic recognition. Through efficient edge-client collaboration, VR-PCT optimizes the transmission of mmWave point cloud and vision data by transmitting only the VR semantic region of vision data instead of the entire video. Additionally, it incorporates adaptive cross-modal data selection and fusion strategies to achieve real-time semantic recognition while significantly reducing data redundancy. Across 22 participants engaged in four experimental scenes utilizing VR devices from three different manufacturers, our evaluation demonstrates that VR-PCT achieves 97.6% recognition accuracy while reducing transmission overhead by 81.5% compared to existing approaches. These results highlight the effectiveness of VR-PCT in enabling efficient and accurate multi-modal VR semantic recognition for VR applications. The code and data of VR-PCT are released onhttps://github.com/luoyumei1-a/VR-PCT.

Pulse modulation based communication techniques have enabled various Internet of Things (IoT) applications, such as smart meters and automotive systems. However, the existing pulse modulators rely on platform-specific hardware components, leading to limited extensibility and hardware dependency when supporting diverse variants such as Pulse Position Modulation (PPM) and Pulse Amplitude Modulation (PAM). This paper introduces NN-Pulse, an innovative neural networkdefined pulse modulator designed to enhance extensibility and flexibility, ensuring compatibility with multiple pulse modulation schemes. Specifically, NN-Pulse realizes the pulse modulation process using fundamental neural network modules with carefully tailored weights, via the proposed spike neural network (SNN)-based position selection module and transposed convolutional layers for phase modulation. Evaluations show that NNPulse generates PPM and PAM signals with bit error ratios of 0.6% and 0.2%, respectively. Moreover, the time consumption of modulating a pulse symbol via NN-Pulse is only$1.4 \mu \mathrm{s}$, outperforming traditional methods by 47 times.

Recent advancements in 3D object detection and novel category detection have made significant progress, yet research on learning generalized 3D objectness remains insufficient. In this paper, we delve into learning open-world 3D objectness, which focuses on detecting all objects in a 3D scene, including novel objects unseen during training. Traditional closed-set 3D detectors struggle to generalize to open-world scenarios, while directly incorporating 3D open-vocabulary models for open-world ability struggles with vocabulary expansion and semantic overlap. To achieve generalized 3D object discovery, We propose OP3Det, a class-agnostic Open-World Prompt-free 3D Detector to detect any objects within 3D scenes without relying on hand-crafted text prompts. We introduce the strong generalization and zero-shot capabilities of 2D foundation models, utilizing both 2D semantic priors and 3D geometric priors for class-agnostic proposals to broaden 3D object discovery. Then, by integrating complementary information from point cloud and RGB image in the cross-modal mixture of experts, OP3Det dynamically routes uni-modal and multi-modal features to learn generalized 3D objectness. Extensive experiments demonstrate the extraordinary performance of OP3Det, which significantly surpasses existing open-world 3D detectors by up to 16.0% in AR and achieves a 13.5% improvement compared to closed-world 3D detectors.

Cross-Technology Communication (CTC) is a cornerstone for seamless interoperability in heterogeneous wireless environments, enabling diverse devices to coexist and cooperate effectively. In this paper, we present Wi-Fi-to-X, designed to leverage deep learning techniques to generate waveforms that are compatible with multiple communication protocols, allowing seamless data transmission between Wi-Fi and other wireless technologies such as ZigBee, LoRa, and Bluetooth. This approach enables devices operating under different wireless standards to communicate effectively without requiring hardware modifications or protocol standardization. By training a specialized neural network on simulations of Orthogonal Frequency Division Multiplexing (OFDM) and Quadrature Amplitude Modulation (QAM), we have improved the efficiency and reliability of signal processing in CTC, Wi-Fi-to-X achieves robust signal modulation and demodulation across disparate technologies, enabling communication from Wi-Fi to other IoT devices, including ZigBee, LoRa, and Bluetooth. We evaluated both USRP and commodity devices, demonstrated that Wi-Fi-to-X can achieve concurrent wireless communication from Wi-Fi to other IoT devices.

Wi-Fi is the de facto standard for providing wireless access to the Internet in the 2.4 GHz ISM band. Tens of billions of Wi-Fi devices (e.g., smartphones) have been shipped worldwide with limited types of wireless radios operating only when Wi-Fi connectivity is available, making it challenging to access data in heterogeneous IoT devices. However, the direct connection between Wireless Personal Area Network (WPAN) technologies, such as Bluetooth, and Wi-Fi presents challenges due to the inherent distinct physical layer. In our work, a novel communication method called BlueWi has been introduced, which serves as a cross technology communication method that enables BLE devices to establish connections and engage in communication with Wi-Fi based WPAN networks. We let BLE signals hitchhike on ongoing Wi-Fi signals, enabling Wi-Fi to recognize specific BLE signal waveforms in the frequency domain. By analyzing the decoded Wi-Fi payload, BlueWi can retrieve the BLE data, ensuring this method remains fully compatible with existing commodity Wi-Fi hardware. The direct sequence spread spectrum scheme is appended to handle general BLE frames and can be considered as “COPY” operation, which allows for better correlation and detection of the signal at the receiver. Evaluations conducted using both USRP and commodity devices have demonstrated that BlueWi can achieve concurrent wireless communication from BLE commercial chips to Wi-Fi networks with a frame reception rate exceeding 96%.

Illegal parking prediction is a crucial problem to help stakeholders with better urban planning and management. Existing works advance the field by capturing complex traffic correlations from spatial and temporal perspectives using deep learning models, and achieve state-of-the-art performance. However, current works do not consider the unique perspective from the illegal parking data collection process carried out by patrol officers, which can reflect a wealth of knowledge gained from each officer's on-the-ground experiences for more effective patrol. In this paper, we propose a novel behavior-aware hypergraph convolutional network named BHIPP for city-wide illegal parking prediction. To better represent the correlations of illegal parking events from patrol officers' perspective, we construct a new patrol hypergraph integrating patrol officers' experience alongsie multi-source contextual information. Additionally, we design a behavior-aware hypergraph convolutional network, which captures the complex and high-order illegal parking event correlations with officers' patrol behaviors explicitly considered. Further, we introduce a spatial-temporal illegal parking approximation module to estimate parking violations in under-patrolled regions using both historical and multi-source contextual data. Extensive experiments on real-world datasets demonstrate the superiority of our proposed BHIPP compared with a broad range of state-of-the-art baseline models across varying spatial-temporal granularities, from both regression and ranking aspects.

Millimeter-wave (mmWave) radar is emerging as a promising sensor for various human sensing tasks. Deep learning is frequently applied in radar-based applications, which typically require extensive data collection and labeling. In this demo, we present a low-cost hardware setup and a cross-platform software pipeline that automatically captures radar data of human activities, labels ground truth, and tests inference models in real time. The effectiveness of the testbed is demonstrated through real-time human pose estimation.

Tracking the human arm is essential for a variety of applications, including medical rehabilitation, sports analysis, and human-computer interaction. Current vision-based and wearable sensor-based approaches either struggle with occlusion, poor lighting conditions, and privacy concerns or result in intrusive user experiences. This paper introduces mmHAT, a novel 3D arm trajectory tracking method using mmWave radar. mmHAT proposes an end-to-end neural network design to address two major challenges: the lack of arm semantic information and dynamic variations in the mmWave point clouds. Firstly, mmHAT incorporates a multi-task joint learning framework, where the primary task is 3D arm tracking and the auxiliary task is gesture recognition. This aims to leverage the auxiliary task to guide the network in developing a deeper understanding of the user's arm movements. Secondly, for dynamic mmWave point clouds, mmHAT incorporates a new spatial-temporal feature encoder that aggregates the features of the arms point cloud from a global perspective. We collect ~320K frames of daily arm activity data for experimental validation. The results show that mmHAT achieves an average joint location error of 1.67 cm and angle estimation error of 4.23° for arm joints (i.e., elbow, wrist), while delivering excellent performance with only 2.67 ms latency.

This paper presents Mission, the first-of-this-kind cross-modal reidentification (ReID) design for mmWave Radar and RGB cameras. Given a person of interest detected by Radar in camera-restricted scenarios, Mission can identify the image of the person from cameras that are ubiquitously deployed in camera-allowed areas. We envision that cross Vison-RF ReID can significantly enrich mmWave human sensing with a wide spectrum of applications in security surveillance, tracking, and personalized services. Technically, we introduce a novel method for cross-modal similarity estimation that exploits inherent synergies between fine-grained 2D images and coarse-grained 3D Radar point clouds to effectively overcome their modal discrepancy. Through extensive experiments, we demonstrated that our proposed system can achieve 85% top-1 accuracy and 90% top-5 accuracy among 58 volunteers.

A physical-layer modulator is crucial for IoT gateways, but current solutions face issues like limited extensibility and platform-specificity due to soldered chipsets for specific technologies or diverse software toolkits for software radios. With the rapid expansion of the Internet of Things (IoT), such limitations are hard to ignore as the demand for versatile wireless technologies has increased. This paper introduces a novel approach using neural networks as an abstraction layer for these modulators in IoT gateways, termed NN-defined modulators. This method overcomes the challenges of extensibility and portability across different hardware platforms. The NN-defined modulator employs a model-driven approach based on mathematical principles, resulting in a lightweight, hardware-acceleration-friendly structure. These modulators are containerized with necessary runtime, facilitating agile deployment on varied platforms. We tested NN-defined modulators on platforms like Nvidia Jetson Nano and Raspberry Pi, showing they perform comparably to traditional modulators while offering efficiency improvements. The implementation is memory-efficient and adds minimal latency. Additionally, we demonstrate real-world applications of our NN-defined modulators in generating ZigBee and WiFi packets, compatible with standard TI CC2650 (ZigBee) and Intel AX201 (WiFi NIC) devices.

The successful operation of autonomous vehicles hinges on their ability to accurately identify objects in their vicinity, particularly living targets such as bikers and pedestrians. However, visual interference inherent in real-world environments, such as omnipresent billboards, poses substantial challenges to extant vision-based detection technologies. These visual interference exhibit similar visual attributes to living targets, leading to erroneous identification. We address this problem by harnessing the capabilities of mmWave radar, a vital sensor in autonomous vehicles, in combination with vision technology, thereby contributing a unique solution for liveness target detection. We propose a methodology that extracts features from the mmWave radar signal to achieve end-to-end liveness target detection by integrating the mmWave radar and vision technology. This proposed methodology is implemented and evaluated on the commodity mmWave radar IWR6843ISK-ODS and vision sensor Logitech camera. Our extensive evaluation reveals that the proposed method accomplishes liveness target detection with a mean average precision of 98.1%, surpassing the performance of existing studies.

A digital modulator plays a crucial role in converting symbols into signals in an IoT gateway. However, the ever-increasing modulation schemes pose practical challenges, such as flexibility for different schemes and portability with different hardware platforms. To address these challenges, we propose a new approach that employs a neural network as an abstraction layer for physical layer modulators, called the NN-defined modulator. We will demonstrate that the NN-defined modulator functions like traditional modulators and offers high portability and efficiency with example communication to ZigBee and WiFi.

3D human pose plays a critical role in human behavior understanding and has many applications (e.g., VR/AR). Conventional pose estimations deploy sensors as fixed infrastructure, which significantly restrains the mobility of the user. Inspired by the emerging head-mounted devices (e.g., VR/AR glasses) and the recent advance in low-cost mmWave radar, we present mmEgo, the first egocentric human pose estimation design using a head-mounted mmWave radar, which offers ubiquitous pose tracking with high mobility, robustness to complex environments, and privacy preservation. To tackle the unique challenges of radar sensing from the egocentric perspective (e.g., random radar motion and the scarcity of information on the lower body), we propose several technical designs, including root-relative radar motion tracking for radar motion decoupling and a two-stage pose estimator that incorporates human kinematics priors. Extensive experiments and case studies show that our method can reduce the joint localization error by 44.2% and potentially enable a wide spectrum of applications.

loT devices have evolved from providing remote connection to being an essential component of the Metaverse. The integration of loT and vision technologies has been incubating emerging applications such as vision-enhanced device tracking and remote education/medicine/maintenance. Despite the exciting vision, practical challenges include coordinate transformation, angle estimation, target mapping, and personal error. Instead of proposing yet-another localization approach, we propose a novel vision-enhanced device tracking system, called VisBLE. VisBLE takes advantage of the new localization capability introduced in BLE 5.1 and advances in vision technologies for high accuracy, robust, and intuitive BLE device tracking. There are two novel technical mechanisms: i) a rotation-based wireless localization mechanism that accurately and robustly locates the BLE transmitter in the camera coordinate and ii) a homography-based matching mechanism that identifies target BLE devices with high accuracy on the camera screen. We prototype VisBLE and deploy it on the smartphone (i.e., Nexus 5X) and development board (i.e., CC26X2 + BOOSTXL-AOA). Our results show that VisBLE outperforms the state of the art in both angular accuracy and position accuracy.

Autonomous driving requires vehicles to achieve fine detection of objects in the surrounding environment, especially living pedestrians. Nevertheless, in real world road environments there are living pedestrians and roadside portrait billboards. Existing vision-based object detection technologies fail to ac-curately distinguish living pedestrians from human figures. As an important sensor of autonomous driving system, mmWave radar has extra help to detect living pedestrians. In this paper, we extract the radar cross section (RCS) of the object from the low-cost mmWave radar signal as a distinguishing feature between living pedestrian and portrait billboard. Based on this observation, we propose a feature fusion network of mmWave radar and computer vision based on attention mechanism, and detect living pedestrians from fusion features. We implement the design with commodity mmWave radar IWR6843ISK-ODS and RGB camera Logitech Pro C920. The evaluation results show that our method effectively detects living pedestrians with an mAP of 97.7% and outperforms existing studies.

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.

In the paper, we demonstrate an application of the wireless earbud - an acoustic ruler. Approaches are proposed to improve the robustness of the design in the low signal-to-noise ratio environment. We also share our solution to several engineering challenges, which aims at facilitating the transformation of earbuds to into acoustic sensing research platforms without any hardware modification.

The large-scale Bluetooth low energy (BLE) location-based services (LBS) are challenging due to the requirement of additional Bluetooth beacons, which inevitably incur tremendous hardware and maintenance cost. To alleviate this issue, this work presents WiBeacon which repurposes ubiquitously deployed WiFi access points into virtual beacons via cross-technology communication (CTC). WiBeacon only requires moderate software updates in APs, thus enabling fast deployment with zero additional hardware and also low maintenance cost via the remote Internet access.

Overcrowded wireless devices in unlicensed bands compete for spectrum access, generating excessive cross-technology interference (CTI), which has become a major source of performance degradation especially for low-power IoT (e.g., ZigBee) networks. This paper presents a new forward error correction (FEC) mechanism to alleviate CTI, named SafetyNet. Designed for ZigBee, SafetyNet is inspired by the observation that ZigBee is overly robust for environment noises, but insufficiently protected from high-power CTI. By effectively embedding correction code bits into the PHY layer SafetyNet significantly enhances CTI robustness without compromising noise resilience. SafetyNet additionally offers a set of unique features including (i) transparency, making it compatible with millions of readily-deployed ZigBee devices and (ii) zero additional cost on energy and spectrum, as it does not increase the frame length. Such features not only differentiate SafetyNet from known FEC techniques (e.g., Hamming and Reed-Solomon), but also uniquely position it to be critically beneficial for today's crowded wireless environment. Our extensive evaluation on physical testbeds shows that SafetyNet significantly improves ZigBee's CTI robustness under a wide range of networking settings, where it corrects 55% of the corrupted packets.

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.

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.

The cross-technology communication (CTC) is a promising technique proposed recently to bridge heterogeneous wireless technologies in the ISM bands. Existing solutions use only the coarse-grained packet-level information for CTC modulation, suffering from a low throughput (e.g., 10 b/s). Our approach, called BlueBee, explores the dense PHY-layer information for CTC by emulating legitimate ZigBee frames with the Bluetooth radio. Uniquely, BlueBee achieves dual-standard compliance and transparency for its only modifying the payload of Bluetooth frames, requiring neither hardware nor firmware changes at either the Bluetooth sender or the ZigBee receiver. Our implementation on both USRP and commodity devices shows that BlueBee can achieve standard ZigBee bit rate of 250 kb/s at more than 99% accuracy, which is over 10000 x faster than the state-of-the-art packet-level CTC technologies.

Cross-Technology Communication is an emerging research direction providing a promising solution to the coexistence problem of heterogeneous wireless technologies in the ISM bands. However, existing works use only the coarse-grained packet-level information for cross-technology modulation, suffering from a low throughput (e.g., 10bps). Our approach, called BlueBee, aims at achieving much higher CTC throughput thus extends CTC applications. We pro- poses a new direction by emulating legitimate ZigBee frames using a Bluetooth Low Energy (BLE) radio. Uniquely, BlueBee achieves dual-standard compliance (i.e., BLE and ZigBee) and transparency by selecting only the payload of Bluetooth frames, requiring neither hardware nor firmware changes at the BLE senders and ZigBee receivers. Our implementation on commodity device testbeds shows that BlueBee can achieve a more than 99% accuracy and a through- put 10,000x faster than the state-of-the-art CTC reported so far. In addition, we show a demo of using BlueBee on a smartphone to control several smart light bulbs a ached with ZigBee radio.

The applicability of existing Cross-Technology Communication (CTC) methods, which rely on packet-level modulation, is severely limited due to their very low throughput, e.g., tens of bps. Our work, named as WEBee, opens a promising direction for high throughput CTC via physical-level emulation. Specifically, WEBee synthesizes the time-domain signals by choosing appropriate frequency-domain components fed into the subcarriers of WiFi OFDM. WE-Bee can emulate the desired physical-layer ZigBee signals by manipulating only the data bits in WiFi packet payload, requiring neither hardware nor firmware changes in commodity technologies. Moreover, WEBee enables the parallel CTC, where one WiFi frame emulates two ZigBee frames simultaneously. To evaluate the performance, we implemented WEBee on commodity devices (the Atheros AR2425 WiFi card, BCM 4330 WiFi card and CC2420, CC2530 ZigBee devices). Our comprehensive evaluation reveals that WEBee can achieve the CTC between WiFi and ZigBee with a reliable throughput of 126Kbps in noisy environment, 16,000x faster than current state-of-the-art CTC methods.

Cross-Technology Communication is an emerging research direction providing a promising solution to the wireless coexistence problem in the ISM bands. However, the state-of-the-art CTC designs have intrinsic limitations in the throughput due to their use of coarse-grained packet-level information. In contrast, we propose to exploit the fine-grained signal modulation information via a technique called PHY-layer emulation to boost CTC throughput. We can embed a legitimate packet of a target technology, e.g., ZigBee, within the payload of a source technology, e.g., WiFi or Bluetooth Low Energy (BLE). At the mean time, we require no modification at the hardware or firmware at either sender or receiver. We can achieve 8,000x throughput from WiFi to ZigBee and 10,000x throughput from BLE to ZigBee compared to the state of the art. We also have a demo showcasing how our designs can be implemented on off-the-shelf smartphones for smart light bulbs control.

Cross-Technology Communication is a promising solution proposed recently to the coexistence problem of heterogeneous wireless technologies in the ISM bands. The existing works use only the coarse-grained packet-level information for cross-technology modulation, suffering from a low throughput (e.g., 10bps). Our approach, called BlueBee, proposes a new direction by emulating legitimate ZigBee frames using a Bluetooth radio. Uniquely, BlueBee achieves dual-standard compliance and transparency by selecting only the payload of Bluetooth frames, requiring neither hardware nor firmware changes at the Bluetooth senders and ZigBee receivers. Our implementation on both USRP and commodity devices shows that BlueBee can achieve a more than 99% accuracy and a throughput 10,000x faster than the state-of-the-art CTC reported so far.