Smart buildings are supposed to be able to send alerts and localize threats. Despite traditional smart security devices such as fire alarms, entrance guards, and cameras, modern smart buildings also need to identify and track hostiles who hide potentially harmful metal objects under their clothes. We introduce Magneto, the first metal-sensing and biometric-based tracking system that makes use of existing power cables and WiFi infrastructures. Magneto tracks and identifies individuals' gait signatures while simultaneously sensing and discriminating metal objects. By leveraging existing power line infrastructure and WiFi networks, Magneto turns normal buildings into automated secure smart buildings. We built a prototype that fused both magnetic and RF sensing networks and evaluated it with individuals carrying 10 different metal objects. To show the robustness of our system, the volunteers have also sat in a metal wheelchair in the process of evaluation. Our extensive evaluation in a real-life environment shows that Magneto achieves a metal detection accuracy of 91.4% and a localization accuracy above 97%.

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 on any WiFi frequency bands and at any unknown locations outside of the protected area; ii) has minimum interference to the on-going WiFi communication; and iii) preserves authorized RF sensing inside the private region. Our extensive evaluation shows that when Aegis is activated, the accuracy of legitimate sensing system only decreases by 0.08, while the accuracy of the illegitimate sensing system is as low as 0.04. Moreover, the on-going data communication throughput is even increased by$\text{10}\;\text{MB/s}$on$\text{2.4}\;\;\text{GHz}$WiFi band and$\text{5}\;\text{MB/s}$on$\text{5}\;\;\text{GHz}$WiFi band.

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.

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.

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.

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.

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.

With the advent of the Internet of Things (IoT) and big data, high fidelity localization and tracking systems that employ cameras, RFIDs, and attached sensors intrude on personal privacy. However, the benefit of localization information sharing enables trend forecasting and automation. To address this challenge, we introduce Wobly, an attribute based signature (ABS) that measures gait. Wobly passively receives Wi-Fi beacons and produces human signatures based on the Doppler Effect and multipath signals without attached devices and out of direct line-of-sight. Because signatures are specific to antenna placement and room configuration and do not require sensor attachments, the identities of the individuals can remain anonymous. However, the gait based signatures are still unique, and thus Wobly is able to track individuals in a building or home. Wobly uses the physical layer channel and the unique human gait as a means of encoding a person's identity. We implemented Wobly on a National Instruments Radio Frequency (RF) test bed. Using a simple naive Bayes classifier, the correct identification rate was 87% with line-of-sight (LoS) and 77% with non-line-of-sight (NLoS).

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.