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.

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.

With the exponentially increasing number of Internet of Things (IoT) devices and the huge volume of data generated by these devices, there is a pressing need to investigate a more efficient communication method in both frequency and time domains at the edge of IoT networks. In this paper, we present Amphista, a novel cross-layer design for IoT communication and data forwarding that can more efficiently utilize the ever increasingly crowded 2.4 GHz spectrum near the gateway. Specifically, to enable the communication from ZigBee to WiFi, we leverage WiFi's fine-grained channel state information to extract the concurrently transmitted ZigBee-to-WiFi message from time overlapped ZigBee and WiFi packet. We further leverage this unique feature and design a novel forwarding protocol that can simultaneously forward uplink (e.g., collecting sensing data) and downlink (e.g., disseminating control messages) data by using a single ZigBee data stream. Our extensive experimental results show that Amphista significantly improves throughput (by up to 400x) and reduces the latency.

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.

With the exponentially increasing number of Internet of Things (IoT) devices and the huge volume of data generated by these devices, there is a pressing need to investigate a more efficient communication method in both frequency and time domains at the edge of the IoT networks. In this paper, we present Amphista, a novel cross-layer design for IoT communication and data forwarding that can more efficiently utilize the ever increasingly crowded 2.4 GHz spectrum near the gateway. Specifically, by using a single ZigBee data stream, Amphista enables a ZigBee device to send out two different pieces of information to both the WiFi gateway and another ZigBee device. We further leverage this unique feature and design a novel forwarding protocol that can simultaneously forward uplink (e.g., collecting sensing data) and downlink (e.g., disseminating software updates) data by using a single ZigBee data stream. Our extensive experimental results show that Amphista significantly improves throughput (by up to 400×) and reduces the latency.

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.

The exponentially increasing number of Internet of Things (IoT) devices and the data generated by these devices introduces the spectrum crisis at the already crowded ISM 2.4-GHz band. To address this issue and enable more flexible and concurrent communications among IoT devices, we propose B2W2, a novel communication framework that enables N-way concurrent communication among Wi-Fi and Bluetooth low energy (BLE) devices. Specifically, we demonstrate that it is possible to enable the BLE to Wi-Fi cross-technology communication while supporting the concurrent BLE to BLE and Wi-Fi to Wi-Fi communications. We conducted extensive experiments under different real-world settings, and results show that its throughput is more than 85X times higher than that of the most recently reported cross-technology communication system, which only supports one-way communication (i.e., broadcasting) at any specific time.

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.

Whereas a lot of efforts have been put on energy conservation in wireless sensor networks (WSNs), the limited lifetime of these systems still hampers their practical deployments. This situation is further exacerbated indoors, as conventional energy harvesting (e.g., solar) may not always work. To enable long-lived indoor sensing, we report in this article a self-sustaining sensing system that draws energy from indoor environments, adapts its duty-cycle to the harvested energy, and pays back the environment by enhancing the awareness of the indoor microclimate through an “energy-free” sensing. First of all, given the pervasive operation of heating, ventilation, and air conditioning (HVAC) systems indoors, our system harvests energy from airflow introduced by the HVAC systems to power each sensor node. Secondly, as the harvested power is tiny, an extremely low but synchronous duty-cycle has to be applied whereas the system gets no energy surplus to support existing synchronization schemes. So, we design two complementary synchronization schemes that cost virtually no energy. Finally, we exploit the feature of our harvester to sense the airflow speed in an energy-free manner. To our knowledge, this is the first indoor wireless sensing system that encapsulates energy harvesting, network operating, and sensing all together.

The exponentially increasing number of Internet of things (IoT) devices introduces spectrum crisis to the widely used industrial, scientific, and medical (ISM) frequency band. Since IoT devices use heterogeneous radios with different bandwidths (e.g., 20 MHz for WiFi and 2 MHz for ZigBee), traditional interference avoidance methods, such as time-division multiple access (TDMA) and carrier-sense multiple access (CSMA), have very low spectrum utilization. This is because TDMA and CSMA allocate the packets at time domain, without considering the bandwidth difference of different IoT radios. To address this issue, we propose PMC, a novel communication system that enables parallel multi-protocol communication to heterogeneous IoT radios (i.e., WiFi and ZigBee) within a single WiFi channel. Our extensive evaluations show that PMC achieves the throughput of up to 121.02 kbit/s and 319.76 Mbit/s for parallel communication to ZigBee and WiFi, respectively. Compared with TDMA and CSMA, the spectrum utilization of PMC is increased by 2.3 and 1.8 times, respectively.

The exponentially increasing number of IoT devices makes the unlicensed industrial, scientific, and medical (ISM) radio bands (e.g., 2.4 GHz) extremely crowded. Currently, there is no efficient solution to coordinate the large amount heterogeneous IoT devices that have different communication technologies (e.g., WiFi and ZigBee). To fill this gap, in this paper, we introduce embedded multiple flows (EMF) communication method, which (i) embeds different pieces of information in existing traffic and (ii)concurrently sends out these information from one IoT sender to multiple IoT receivers that have a different communication technology from the sender. By doing this, our EMF method (i) enables cross-technology communication among heterogeneous IoT devices, (ii) does not introduce any extra control traffic, and (iii) is transparent to the higher layer applications. Our approach is implemented on USRPs and commercial off-the-shelf (COTS) ZigBee devices. We also conducted extensive experiments to evaluate our approach in real-world settings. The evaluation results show that EMF's throughput is more than 14 times higher than the latest cross-technology communication technique (i.e. FreeBee[1]).

The emerging smart health and smart home applications require pervasive and non-intrusive human activity recognition and monitoring. Traditional technologies (e.g., using cameras or accelerometers and gyroscopes) may introduce privacy issues or require people to wear sensors. To address these issues, recent approaches exploit fine-grained wireless signals for activity recognition. However, these approaches require devices that are costly or need to provide unique wireless features (e.g., Doppler shifts or phase information). With the increasingly available Internet of Things (IoT) devices, in this paper, we propose Harmony, a human activity recognition and monitoring middleware which can utilize the coarse-grained (but pervasively available) received signal strength (RSS) measurements from the radios of IoT devices. We implement a complete evaluation platform (from data collection to data analysis) of the middleware on top of low cost ZigBee compliant MICAz nodes and a laptop. We also conducted extensive experiments. Our results show that our design can achieve similar accuracy as fine-grained WiFi channel state information (CSI) measurement-based approaches. Specifically, our overall human activities recognition accuracy is up to 74% and 90% for RSS readings from a single pair and 3 pairs of IoT devices, respectively.

The exponentially increasing number of internet of things (IoT) devices and the data generated by these devices introduces the spectrum crisis at the already crowded ISM 2.4 GHz band. To address this issue and enable more flexible and concurrent communications among IoT devices, we propose B2W2, a novel communication framework that enables N-way concurrent communication among WiFi and Bluetooth Low Energy (BLE) devices. Specifically, we demonstrate that it is possible to enable the BLE to WiFi cross-technology communication while supporting the concurrent BLE to BLE and WiFi to WiFi communications. We conducted extensive experiments under different real-world settings and results show that its throughput is more than 85X times higher than the most recently reported cross-technology communication system [22], which only supports one-way communication (i.e., broadcasting) at any specific time.

For indoor Wireless Sensor Networks (WSNs), as the conventional energy harvesting (e.g., solar) ceases to work in an indoor environment, the limited lifetime is still a threaten for practical deployment. We report in this demo a self-sustaining indoor sensing system. First of all, given the pervasive operation of heating, ventilation and air conditioning (HVAC) systems indoors, our system harvests energy from airflow introduced by the HVAC systems to power each sensor node. Secondly, as the harvested power is tiny (only of hundreds of μW) such that the exiting sensor products cannot be afforded due to their high energy consumption, we exploit the feature of our harvester to sense the airflow speed in an energy-free manner, which can pay back the environment by enhancing the awareness of the indoor microclimate. We also present two complementary algorithms to synchronize the duty-cycles of the sensor nodes to adapt to the energy harvesting. To our knowledge, this is the first indoor wireless sensing system that encapsulates energy harvesting, network operating, and sensing all together.

Whereas a lot of efforts have been put on energy conservation in wireless sensor networks, the limited lifetime of these systems still hampers their practical deployments. This situation is further exacerbated indoors, as conventional energy harvesting (e.g., solar) ceases to work. To enable long-lived indoor sensing, we report in this paper a self-sustaining sensing system that draws energy from indoor environments, adapts its duty-cycle to the harvested energy, and pays back the environment by enhancing the awareness of the indoor microclimate through an "energy-free" sensing.