Search for: All records

Internet-of-things (IoT) devices (e.g., micro camera and microphone) are usually small form factor, low-cost, and low-power, which makes them easy to conceal and deploy in the indoor environment to spy on people for human private information such as location and indoor activities. As a result, these IoT devices introduce a great privacy and ethical threat. Therefore, it is important to reveal these concealed IoT devices in the indoor environment for human privacy protection. This paper presents RFScan, a system that can passively detect, fingerprint, and localize diverse concealed IoT devices in the indoor environment by sensing their unintentional electromagnetic emanations. However, sensing these emanations is challenging due to the weak emanation strength and the interference from the ambient wireless communication signals. To this end, we boost the emanation strength through the non-coherent averaging based on the emanation signal's characteristics and design a novel suppression algorithm to mitigate interference from the wireless communication signals. We further profile emanations across frequency and time that act as the emanation source's unique signature and customize a deep neural network architecture to fingerprint the emanation sources. Furthermore, we can localize the emanation source with an angle-of-arrival (AoA) based triangulation approach. Our experimental results demonstrate the efficiency of the IoT devices' detection, fingerprinting, and localization across different indoor environments. 

The future of global connectivity relies on the seamless integration of satellite and terrestrial networks. Recent advancements have enabled terrestrial devices to connect directly to satellites, while high-speed 5G millimeter-wave links offer a promising solution for backhauling ground station data. This paper introduces the concept of joint satellite and terrestrial networks (Jointnets), which necessitates both coexistence and backhaul. In this framework, satellites and ground stations act as relays between terrestrial base stations and devices, removing coverage barriers and providing global connectivity. However, the significant spectrum overlap between 27.5 to 30.0 GHz leads to co-channel interference degrading efficiency or causing complete link failure. Existing approaches only focus on coexistence, resulting in spectrum inefficiency and coverage gaps. We present mmSubArray: Array of Sub-band Phased Arrays, a novel solution utilizing commercial off-the-shelf phased arrays to achieve full-spectrum utilization and enable Jointnets. Through extensive simulations and real-world measurements, we demonstrate the interference challenges and evaluate the efficacy of our approach. Additionally, we have open-sourced our Python simulator and hardware implementation source codes, providing valuable tools for industrial deployment and future research. 

This work presents SPARC (Spatio-Temporal Adaptive Resource Control), a novel approach for multi-site spectrum management in NextG cellular networks. SPARC addresses the challenge of limited licensed spectrum in dynamic environments. We leverage the O-RAN architecture to develop a multi-timescale RAN Intelligent Controller (RIC) framework, featuring an xApp for near-real-time interference detection and localization, and a xApp for real-time intelligent resource allocation. By utilizing base stations as spectrum sensors, SPARC enables efficient and fine-grained dynamic resource allocation across multiple sites, enhancing signal-to-noise ratio (SNR) by up to 7dB, spectral efficiency by up to 15%, and overall system throughput by up to 20%. Comprehensive evaluations, including emulations and over-the-air experiments, demonstrate the significant performance gains achieved through SPARC, showcasing it as a promising solution for optimizing resource efficiency and network performance in NextG cellular networks. 

Mobile devices continuously beacon Bluetooth Low Energy (BLE) advertisement packets. This has created the threat of attackers identifying and tracking a device by sniffing its BLE signals. To mitigate this threat, MAC address randomization has been deployed at the link-layer in most BLE transmitters. However, attackers can bypass MAC address randomization using lower-level physical-layer fingerprints resulting from manufacturing imperfections of radios. In this work, we demonstrate a practical and effective method of obfuscating physical-layer hardware imperfection fingerprints. Through theoretical analysis, simulations, and field evaluations, we design and evaluate our approach to hardware imperfection obfuscation. By analyzing data from thousands of BLE devices, we demonstrate obfuscation significantly reduces the accuracy of identifying a target device. This makes an attack impractical, even if a target is continuously observed for 24 hours. Furthermore, we demonstrate the practicality of this defense by implementing it by making firmware changes to commodity BLE chipsets. 

Connectivity on-the-go has been one of the most impressive technological achievements in the 2010s decade. However, multiple studies show that this has come at an expense of increased carbon footprint, that also rivals the entire aviation sector's carbon footprint. The two major contributors of this increased footprint are (a) smartphone batteries which affect the embodied footprint and (b) base-stations that occupy ever-increasing energy footprint to provide the last mile wireless connectivity to smartphones. The root-cause of both these turn out to be the same, which is communicating over the last-mile lossy wireless medium. We show in this paper, titled DensQuer, how base-station densification, which is to replace a single larger base-station with multiple smaller ones, reduces the effect of the last-mile wireless, and in effect conquers both these adverse sources of increased carbon footprint. Backed by a open-source ray-tracing computation framework (Sionna), we show how a strategic densification strategy can minimize the number of required smaller base-stations to practically achievable numbers, which lead to about 3x power-savings in the base-station network. Also, DensQuer is able to also reduce the required deployment height of base-stations to as low as 15m, that makes the smaller cells easily deployable on trees/street poles instead of requiring a dedicated tower. Further, by utilizing newly introduced hardware power rails in Google Pixel 7a and above phones, we also show that this strategic densified network leads to reduction in mobile transmit power by 10-15 dB, leading to about 3x reduction in total cellular power consumption, and about 50\% increase in smartphone battery life when it communicates data via the cellular network.