Sub-6GHz Assisted MAC for Millimeter Wave Vehicular Communications

Sub-6GHz vehicular communications (using DSRC, ITS-G5 or C-V2X) have been developed to support active safety applications. Future connected and automated driving applications can require larger bandwidth and higher data rates than currently supported by sub-6GHz V2X technologies. This has triggered the interest in developing mmWave vehicular communications. However, solutions are necessary to solve the challenges resulting from the use of high-frequency bands and the high mobility of vehicles. This paper contributes to this active research area by proposing a sub-6GHz assisted mmWave MAC that decouples the mmWave data and control planes. The proposal offloads mmWave MAC control functions (beam alignment, neighbor identification and scheduling) to a sub-6GHz V2X technology, and reserves the mmWave channel for the data plane. This approach improves the operation of the MAC as the control functions benefit from the longer range, and the broadcast and omnidirectional transmissions of sub-6GHz V2X technologies. This simulation study demonstrates that the proposed sub-6GHz assisted mmWave MAC reduces the control overhead and delay, and increases the spatial sharing compared to a mmWave-only configuration (IEEE 802.11ad tailored to vehicular networks). The proposed MAC is here evaluated for V2V communications using 802.11p for the control plane and 802.11ad for the data plane. However, the proposal is not restricted to these technologies, and can be adapted to other technologies such as C-V2X and 5G NR.Comment: 8 pages, 5 figure

CardioCam: Leveraging Camera on Mobile Devices to Verify Users While Their Heart is Pumping

With the increasing prevalence of mobile and IoT devices (e.g., smartphones, tablets, smart-home appliances), massive private and sensitive information are stored on these devices. To prevent unauthorized access on these devices, existing user verification solutions either rely on the complexity of user-defined secrets (e.g., password) or resort to specialized biometric sensors (e.g., fingerprint reader), but the users may still suffer from various attacks, such as password theft, shoulder surfing, smudge, and forged biometrics attacks. In this paper, we propose, CardioCam, a low-cost, general, hard-to-forge user verification system leveraging the unique cardiac biometrics extracted from the readily available built-in cameras in mobile and IoT devices. We demonstrate that the unique cardiac features can be extracted from the cardiac motion patterns in fingertips, by pressing on the built-in camera. To mitigate the impacts of various ambient lighting conditions and human movements under practical scenarios, CardioCam develops a gradient-based technique to optimize the camera configuration, and dynamically selects the most sensitive pixels in a camera frame to extract reliable cardiac motion patterns. Furthermore, the morphological characteristic analysis is deployed to derive user-specific cardiac features, and a feature transformation scheme grounded on Principle Component Analysis (PCA) is developed to enhance the robustness of cardiac biometrics for effective user verification. With the prototyped system, extensive experiments involving 25 subjects are conducted to demonstrate that CardioCam can achieve effective and reliable user verification with over 99% average true positive rate (TPR) while maintaining the false positive rate (FPR) as low as 4%

Optimal Radiometric Calibration for Camera-Display Communication

We present a novel method for communicating between a camera and display by embedding and recovering hidden and dynamic information within a displayed image. A handheld camera pointed at the display can receive not only the display image, but also the underlying message. These active scenes are fundamentally different from traditional passive scenes like QR codes because image formation is based on display emittance, not surface reflectance. Detecting and decoding the message requires careful photometric modeling for computational message recovery. Unlike standard watermarking and steganography methods that lie outside the domain of computer vision, our message recovery algorithm uses illumination to optically communicate hidden messages in real world scenes. The key innovation of our approach is an algorithm that performs simultaneous radiometric calibration and message recovery in one convex optimization problem. By modeling the photometry of the system using a camera-display transfer function (CDTF), we derive a physics-based kernel function for support vector machine classification. We demonstrate that our method of optimal online radiometric calibration (OORC) leads to an efficient and robust algorithm for computational messaging between nine commercial cameras and displays.Comment: 10 pages, Submitted to CVPR 201

Localization to Enhance Security and Services in Wi-Fi Networks under Privacy Constraints

Developments of seamless mobile services are faced with two broad challenges, systems security and user privacy - access to wireless systems is highly insecure due to the lack of physical boundaries and, secondly, location based services (LBS) could be used to extract highly sensitive user information. In this paper, we describe our work on developing systems which exploit location information to enhance security and services under privacy constraints. We describe two complimentary methods which we have developed to track node location information within production University Campus Networks comprising of large numbers of users. The location data is used to enhance security and services. Specifically, we describe a method for creating geographic firewalls which allows us to restrict and enhance services to individual users within a specific containment area regardless of physical association. We also report our work on LBS development to provide visualization of spatio-temporal node distribution under privacy considerations

Evaluation of IEEE 802.11ad for mmWave V2V Communications

Autonomous vehicles can construct a more accurate perception of their surrounding environment by exchanging rich sensor data with nearby vehicles. Such exchange can require larger bandwidths than currently provided by ITS-G5/DSRC and Cellular V2X. Millimeter wave (mmWave) communications can provide higher bandwidth and could complement current V2X standards. Recent studies have started investigating the potential of IEEE 802.11ad to support high bandwidth vehicular communications. This paper introduces the first performance evaluation of the IEEE 802.11ad MAC (Medium Access Control) and beamforming mechanism for mmWave V2V communications. The study highlights existing opportunities and shortcomings that should guide the development of mmWave communications for V2V communications.Comment: 6 pages, 5 figures, 1 tabl

ViFi-Loc: Multi-modal Pedestrian Localization using GAN with Camera-Phone Correspondences

In Smart City and Vehicle-to-Everything (V2X) systems, acquiring pedestrians' accurate locations is crucial to traffic safety. Current systems adopt cameras and wireless sensors to detect and estimate people's locations via sensor fusion. Standard fusion algorithms, however, become inapplicable when multi-modal data is not associated. For example, pedestrians are out of the camera field of view, or data from camera modality is missing. To address this challenge and produce more accurate location estimations for pedestrians, we propose a Generative Adversarial Network (GAN) architecture. During training, it learns the underlying linkage between pedestrians' camera-phone data correspondences. During inference, it generates refined position estimations based only on pedestrians' phone data that consists of GPS, IMU and FTM. Results show that our GAN produces 3D coordinates at 1 to 2 meter localization error across 5 different outdoor scenes. We further show that the proposed model supports self-learning. The generated coordinates can be associated with pedestrian's bounding box coordinates to obtain additional camera-phone data correspondences. This allows automatic data collection during inference. After fine-tuning on the expanded dataset, localization accuracy is improved by up to 26%