Antenna array calibration is essential for mmWave networks to deliver multi-gigabit throughput, especially for emerging AI-driven applications like immersive XR. However, conventional calibration pipelines rely on laboratory-grade over-the-air (OTA) test cells and specialized instruments, which must be deployed per device and used repeatedly over its lifetime. These requirements significantly increase the cost and complexity of large-scale mmWave deployments. In this paper, we present 0cal, a novel technique that operates purely using everyday communication, without laboratory environments, external sensors, or hardware modifications. The core idea of 0cal is to decouple constant relative antenna gains from the variable Angle of Departure (AoD) encoded in routine signals. By leveraging the statistical diversity of in-field traffic, 0cal averages out AoD-dependent sinusoidal terms while preserving gain-dependent components. It further incorporates a codebook-based selection algorithm to synthesize an approximately uniform effective AoD subset from accumulated communication instances, ensuring robust performance even with skewed AoD distributions. We implement 0cal on an open-source mmWave platform; experimental results show that after approximately 700 instances, 0cal achieves SNR performance comparable to laboratory-based methods, demonstrating its practicality for large-scale deployments.

Recent advances in cross-technology communication can support direct communication among heterogeneous IoT devices (i.e., WiFi, ZigBee, and BLE) without requiring any modifications to the hardware, which has significantly improved the communication efficiency and shown great advantages for supporting smart applications. However, until now a key establishment protocol to support secure and reliable asymmetric cross- technology communication (CTC) is missing, which introduces severe privacy and security issues. Existing solutions are not designed for CTC, since they mainly focus on the symmetric communication among homogeneous IoT devices. In this work, we present a Key Establishment Protocol (KEP), which explores and lever- ages the unique feature of CTC - Possibility PN Sequence Reception (PSR) to not only perform key establishment between heterogeneous IoT devices with different physical layers (i.e., WiFi and ZigBee) but also improve the communication reliability at the same time. Our extensive real-world experiments show that KEP can finish the key establishment in seconds and effectively defend against multiple types of attacks. Furthermore, KEP doubles the packet reception ratio compared to the state-of-the-art solutions.

To maximize Signal-to-Noise Ratio (SNR), it is necessary to move beyond selecting beams from a codebook. While the state-of-the-art approaches can significantly improve SNR compared to codebook-based beam selection by exploiting the globally-optimal beam, they incur significant beam-training overhead, which limits the applicability to large-scale antenna arrays and the scalability for multiple users. In this paper, we propose FTP, a highly-scalable beam-training solution that can find the globally-optimal beam with minimal beam-training overhead. FTP works by estimating per-path direction along with its complex gain and synthesizes the globally-optimal beam from these parameters. Our design significantly reduces the search space for finding such path parameters, which enables FTP to scale to large-scale antenna arrays. We implemented and evaluated FTP on a mmWave experimental platform with 32 antenna elements. Our results demonstrate that FTP achieves optimal SNR performance comparable with the state-of-the-art while reducing the beam-training overhead by 3 orders of magnitude. Under simulated settings, we demonstrate that the gain of FTP can be even more significant for larger antenna arrays with up to 1024 elements.

Orthogonal frequency-division multiplexing (OFDM) has been widely used in WiFi, LTE, and adopted in 5G. Recently, researchers have proposed multiple OFDM-based WiFi backscatter systems that use the same underlying design principle (i.e., codeword translation) at the OFDM symbol-level to transmit the tag data. However, since the phase error correction in WiFi receivers can eliminate the phase offset created by a tag, the codeword translation requires specific WiFi receivers that can disable the phase error correction. As a result, phase error is introduced into the decoding procedure of the codeword translation, which significantly increases the tag data decoding error. To address this issue, we designed a novel OFDM backscatter called TScatter, which uses high-granularity sample-level modulation to avoid the phase offset created by a tag being eliminated by phase error correction. Moreover, by taking advantage of the phase error correction, our system is able to work in more dynamic environments. Our design also has two advantages: much lower bit error rate (BER) and higher throughput. We conducted extensive evaluations under different scenarios. The experimental results show that TScatter has i) three to four orders of magnitude lower BER when its throughput is similar to the latest OFDM backscatter system MOXcatter; or ii) more than 212 times higher throughput when its BER is similar to MOXcatter. Our design is generic and has the potential to be applied to backscatter other OFDM signals (e.g., LTE and 5G).

Recent advances in Cross-Technology Communication (CTC) have opened a new door for cooperation among heterogeneous IoT devices to support ubiquitous applications, such as smart homes and smart offices. However, existing work mainly focuses on physical layer performance improvements. In this paper, we explore how to leverage the latest CTC techniques for network layer performance improvements. Specifically, we introduce Waves, which leverages WiFi to ZigBee CTC and WiFi access point’s adaptive transmit power control techniques for reliable and fast data dissemination in low-duty-cycle ZigBee networks. We extensively evaluate our design under various settings. Evaluation results show that Waves can provide reliable data dissemination and is 33.5 times faster than the state-of-the-art protocol in terms of dissemination time.

The camera is one of the most important sensors for an autonomous vehicle (AV) to perform Environment Perception and Simultaneous Localization and Mapping (SLAM). To secure the camera, current autonomous vehicles not only utilize the data gathered from multiple sensors (e.g., Camera, Ultrasonic Sensor, Radar, or LiDAR) for environment perception and SLAM but also require the human driver to always realize the driving situation, which can effectively defend against previous attack approaches (i.e., creating visible fake objects or introducing perturbations to the camera by using advanced deep learning techniques). Different from their work, in this paper, we in-depth investigate the features of Infrared light and introduce a new security challenge called I-Can-See-the-Light- Attack (ICSL Attack) that can alter environment perception results and introduce SLAM errors to the AV. Specifically, we found that the invisible infrared lights (IR light) can successfully trigger the image sensor while human eyes cannot perceive IR lights. Moreover, the IR light appears magenta color in the camera, which triggers different pixels from the ambient visible light and can be selected as key points during the AV's SLAM process. By leveraging these features, we explore to i) generate invisible traffic lights, ii) create fake invisible objects, iii) ruin the in-car user experience, and iv) introduce SLAM errors to the AV. We implement the ICSL Attack by using off-the-shelf IR light sources and conduct an extensive evaluation on Tesla Model 3 and an enterprise-level autonomous driving platform under various environments and settings. We demonstrate the effectiveness of the ICSL Attack and prove that current autonomous vehicle companies have not yet considered the ICSL Attack, which introduces severe security issues. To secure the AV, by exploring unique features of the IR light, we propose a software-based detection module to defend against the ICSL Attack.

Recent advances in Cross-Technology Communication (CTC) have opened a new door for cooperation among heterogeneous IoT devices to support ubiquitous applications, such as smart homes and smart offices. However, existing work mainly focuses on physical layer performance improvements. In this paper, we explore how to leverage the latest CTC techniques for network layer performance improvements. Specifically, we introduce Waves, which leverages WiFi to ZigBee CTC and WiFi access point’s adaptive transmit power control techniques for reliable and fast data dissemination in low-duty-cycle ZigBee networks. We extensively evaluate our design under various settings. Evaluation results show that Waves can provide reliable data dissemination and is 33.5 times faster than the state-of-the-art protocol in terms of dissemination time.

Routing and flooding are important functions in wireless networks. However, until now routing and flooding protocols are investigated separately within the same network (i.e., a WiFi network or a ZigBee network). Moreover, further performance improvement has been hampered by the assumption of the harmful cross technology interference. In this paper, we present coexistent routing and flooding (CRF), which leverages the unique feature of physical layer cross-technology communication technique for concurrently conducting routing within the WiFi network and flooding among ZigBee nodes using a single stream of WiFi packets. We extensively evaluate our design under different network settings and scenarios. The evaluation results show that CRF i) improves the throughput of WiFi network by 1.12 times than the state-of-the-art routing protocols; and ii) significantly reduces the flooding delay in ZigBee network (i.e., 31 times faster than the state-of-the-art flooding protocol).

To support ubiquitous computing for various applications (such as smart health, smart homes, and smart cities), the communication system requires to be ubiquitously available, ultra-low-power, high throughput, and low-latency. A passive communication system such as backscatter is desirable. However, existing backscatter systems cannot achieve all of the above requirements. In this paper, we present the first LTE backscatter (LScatter) system that leverages the continuous LTE ambient traffic for ubiquitous, high throughput and low latency backscatter communication. Our design is motivated by our observation that LTE ambient traffic is continuous (v.s. bursty and intermittent WiFi/LoRa traffic), which makes LTE ambient traffic a perfect signal source of a backscatter system. Our design addresses practical issues such as time synchronization, phase modulation, as well as phase offset elimination. We extensively evaluated our design using a testbed of backscatter hardware and USRPs in multiple real-world scenarios. Results show that our LScatter's performance is consistently orders of magnitude better than WiFi backscatter in all the above scenarios. For example, LScatter's throughput is 13.63Mbps, which is 368 times higher than the latest ambient WiFi backscatter system [54]. We also demonstrate the effectiveness of our system using two real-world applications.

Internet-of-things (IoT) devices are sharing the radio frequency band (e.g., 2.4 GHz ISM band). The exponentially increasing number of IoT devices introduces potential security issues at the gateway in IoT networks. In this paper, we introduce a set of new attacks through concealed jamming - an adversary pretends to be (or compromises) a legitimate WiFi device, then sends out WiFi packets to prevent ZigBee devices' communication or collide with ZigBee's packets. By doing this, concealed jamming has the potential to severely delay the reception of ZigBee packets that may contain important information (e.g., critical health data from wearables, fire alarms, and intrusion alarms). To defend against these attacks, we designed a novel ZigBee data extraction technique that can recover ZigBee data from the ZigBee packets that were collided with WiFi packets. We extensively evaluated our design in different real-world settings. The results show that ZigBee devices (protected by our proposed methods) achieve similar performance as those that are not under the concealed jamming attack. Moreover, compared with unprotected devices, their throughput is more than 15 times higher than the unprotected one that is under concealed jamming attacks.

Routing and flooding are important functions in wireless networks. However, until now routing and flooding protocols are investigated separately within the same network (i.e., a WiFi network or a ZigBee network). Moreover, further performance improvement has been hampered by the assumption of the harmful cross technology interference. In this paper, we present coexistent routing and flooding (CRF), which leverages the unique feature of physical layer cross-technology communication technique for concurrently conducting routing within the WiFi network and flooding among ZigBee nodes using a single stream of WiFi packets. We extensively evaluate our design under different network settings and scenarios. The evaluation results show that CRF i) improves the throughput of WiFi networks by 1.2 times than the state-of-the-art routing protocols; and ii) significantly reduces the flooding delay in ZigBee networks (i.e., 31 times faster than the state-of-the-art flooding protocol).

WiFi and Bluetooth Low Energy (BLE) are widely used in Internet of Things (IoT) devices. Since WiFi and BLE work within the overlapped ISM 2.4 GHz band, they will interfere with each other. Existing approaches have demonstrated their effectiveness in mitigating the interference. However, further performance improvement has been hampered by the design goal of exclusive communication of WiFi or BLE, which only allows one WiFi or BLE device to transmit packets at any specific time slot on the overlapped channel within the communication range. In this paper, we explore a new communication method, called Parallel Inclusive Communication (PIC), which leverages the unique modulation schemes of WiFi and BLE for parallel inclusive bi-directional transmission of both WiFi and BLE data at the same time within the overlapped channel. In this communication system, the PIC gateway is designed upon the IEEE 802.11g and 802.15.1 frameworks while the WiFi and BLE clients are commercial off-the-shelf devices. PIC achieves similar data rates for these parallel WiFi and BLE communications as if WiFi and BLE are communicating separately. PIC's system architecture naturally fits at the edge of the Internet, which is an optimal site for concurrently collecting (or disseminating) data from (or to) an exponentially increasing number of IoT devices that are using WiFi or BLE. We conducted extensive evaluations under four real-world scenarios. Results show that compared with existing approaches, PIC can significantly i) increase the packet reception ratios by 183%; ii) reduce the round-trip delay time by 590 times and energy consumption by 50.5 times; and iii) improve the throughput under WiFi and BLE coexistence scenarios.

Recent advances in cross-technology communication have significantly improved the spectrum efficiency in the same Industrial, Scientific, and Medical band among heterogeneous wireless devices (e.g., WiFi and ZigBee). However, further performance improvement in the whole network is hampered because the cross-technology network layer is missing. As the first cross-technology network layer design, our work, named ECT , opens a promising direction for significantly reducing the packet delivery delay via collaborative and concurrent cross-technology communication between WiFi and ZigBee devices. Specifically, ECT can dynamically change the nodes’ priorities and reduce the delivery delay from high-priority nodes under unreliable links. The key idea of ECT is to leverage the concurrent transmission of important data and raw data from ZigBee nodes to the WiFi access point. We extensively evaluate ECT under different network settings, and results show that our ECT’s packet delivery delay is more than 29 times lower than the current state-of-the-art solution.

Recent advances in cross-technology communication have significantly improved the spectrum efficiency in the same ISM band among heterogeneous wireless devices (e.g., WiFi and ZigBee). However, further performance improvement in the whole network is hampered because the cross-technology network layer is missing. As the first cross-technology network layer design, our work, named ECT, opens a promising direction for significantly reducing the packet delivery delay via collaborative and concurrent cross-technology communication between WiFi and ZigBee devices. Specifically, ECT can dynamically change the nodes' priorities and reduce the delivery delay from high priority nodes under unreliable links. The key idea of ECT is to leverage the concurrent transmission of important data and raw data from ZigBee nodes to the WiFi AP. We extensively evaluate ECT under different network settings and results show that our ECT's packet delivery delay is more than 29 times lower than the current state-of-the-art solution.

Researchers have demonstrated the feasibility of detecting human motion behind the wall with radio frequency (RF) sensing techniques. With these techniques, an eavesdropper can monitor people's behavior from outside of the room without the need to access the room. This introduces a severe privacy-leakage issue. To address this issue, we propose Aegis, an interference-negligible RF sensing shield that i) incapacitates the RF sensing of eavesdroppers that work at any unknown locations outside of the protected area; ii) has minimum interference to the ongoing WiFi communication; and iii) preserves authorized RF sensing inside the private region. Our extensive evaluation shows that when Aegis is activated, it i) has a negligible impact on the legitimate sensing system; ii) effectively prevents the illegitimate sensing system from sensing human motions. Moreover, the ongoing data communication throughput is even increased.

The exponentially increasing number of heterogeneous Internet of Things (IoT) devices motivate us to explore more efficient and higher throughput communication, especially at the bottleneck (i.e., edge) of the IoT networks. Our work, named Chiron, opens a promising direction for Physical (PHY) layer concurrent high throughput communication to heterogeneous IoT devices (e.g., wider-band WiFi and narrower-band ZigBee). Specifically, at the PHY layer, Chiron enables concurrently transmitting (or receiving) 1 stream of WiFi data and up to 4 streams of ZigBee data to (or from) commodity WiFi and ZigBee devices as if there is no interference between these simultaneous connections. We extensively evaluate our system under different real-world settings. Results show that Chiron's concurrent WiFi and ZigBee communication can achieve similar throughput as the sole WiFi or ZigBee communication. Chiron's spectrum utilization is more than 16 times better than the traditional gateway.

The exponentially increasing number of Internet-of-Thing (IoT) devices introduces a spectrum crisis in the shared ISM band. However, it also introduces opportunities for conducting radio frequency (RF) sensing using pervasively available signals generated by heterogeneous IoT devices. In this paper, we explore how to leverage the ambient wireless traffic that i) generated by uncontrollable IoT devices and ii sensed by ambient noise floor measurements (a widely available metric in IoT devices) for human gesture recognition. Specifically, we introduce our system EAR, which can conduct fine-grained human gesture recognition using coarse-grained measurements (i.e., noise floor) of ambient RF signals generated from uncontrollable signal sources. We conducted extensive evaluations in both residential and academic buildings. Experimental results show that although EAR uses coarse-grained noise floor measurements to sense the uncontrollable signal sources, the signal sources can be distinguished with an accuracy up to 99.76%. Moreover, EAR can recognize fine-grained human gestures with high accuracy even under extremely low traffic rate (i.e., 4%) from uncontrollable ambient signal sources.

Within heterogenous IoT sensor networks, users of ZigBee devices expect long-lasting battery usage due to its ultra-low power and duty cycle. In IoT networks, to demonstrate even further ultra-low power consumption, we introduce Passive-ZigBee that demonstrates we can transform an existing productive WiFi signal into a ZigBee packet for a CoTS low-power consumption receiver while consuming 1,440 times lower power compared to traditional ZigBee. Moreover, this low power backscatter radio can bridge between the ZigBee and WiFi devices by relaying data allowing heterogenous radios to communicate with each other. We built a hardware prototype and implement these devices on a commodity ZigBee, WiFi, and an FPGA platform. Our experimental evaluation demonstrates the backscattered WiFi packets can be decoded by CoTS ZigBee receivers over a distance of 55 meters in none-line-of-sight and with human movements. Our Passive-ZigBee can consume only 25μW when transferring sensor data and relay ZigBee and WiFi data compared to traditional ZigBee (36mW). Our FPGA synthesis tool demonstrated the extremely low power consumption.

Energy internet is now an industry hot spot which enables the interconnection and sharing of energy just like the Internet. Inspired by the concept of energy internet, this paper will focus on a designed energy distribution network, using city bus lines running Electric Vehicles (EV) to achieve electric power storage and transmission. This network is made of renewable energy sources providing power, charge stations for power exchange and bus lines as delivery, electric buses serving as the carriers of flowing power in network. This paper will mainly discuss and solve the problem of placing charge stations on city bus map to compose the network framework. Our work includes two optimization algorithms using some ideas of graph theory, simulating with real-world transporting data of different city maps and analyzing the results to evaluate efficiency as well as advantages and disadvantages on algorithms and data sets.