Self-driving platooning trucks are becoming increasingly common due to the economic gains for companies that utilize them. However, high throughput, long range communication between truck platoons can be very difficult if they are not in areas with pre-built cellular infrastructures. To combat this problem, we propose PlaCoB (Platoon Collaborative Beamforming), which enables multiple platooning trucks to collaboratively beamform to maintain communication over long distances. We conduct extensive simulations under various settings to evaluate PlaCoB’s performance compared to single-truck methods. By leveraging multi-truck platoons, collaborative beamforming, and frequency shifting, (1) PlaCoB can transmit 1.23x more data compared to single vehicle transmission methods, and (2) PlaCoB can transmit data 2.44x further than single vehicle transmission methods. These results demonstrate the effectiveness of our proposed PlaCoB’s approach.

With the proliferation of Internet-of-Things and the advancements in deep learning and Artificial Intelligence, there is an increased need for task-specific mobile devices that can efficiently utilize the wireless spectrum while operating in a wide variety of environments and channel conditions. This work proposes a novel end-to-end Visual-Inertial Odometry architecture that utilizes Semantic Communication-enhanced modulation constellation design for pose estimation of mobile devices. Our proposed model occupies less wireless spectrum bandwidth and does not require complex forward error-correction mechanisms. Yet, it is able to infer the pose information of a remote mobile device with comparable accuracy to a standard wireless Visual-Inertial Odometry system. We propose utilizing a Vision Transformer-based Image-IMU encoder for a network-constrained remote mobile device that transmits wireless encoded data to an Edge receiver. The receiver automatically detects the modulation scheme and decodes the received data for pose estimation. We propose a novel modulation constellation coding design scheme that can transmit data in multiple Modulation and Coding Schemes (MCS) within the same burst, without the need to embed an MCS symbol within the burst. Our proposed receiver can automatically detect the MCS as well. We illustrate how our proposed architecture can transmit encoded Image-IMU data over a wide range of distances and Signal-to-Noise Ratio (SNR) channel conditions and have a decoded pose accuracy comparable to a standard wireless Visual-Inertial Odometry system, with significantly less wireless bandwidth consumption and computational complexity.

A secure fingerprint recognition system must contain both a presentation attack (i.e., spoof) detection and recognition module in order to protect users against unwanted access by malicious users. Traditionally, these tasks would be carried out by two independent systems; however, recent studies have demonstrated the potential to have one unified system architecture in order to reduce the computational burdens on the system, while maintaining high accuracy. In this work, we leverage a vision transformer architecture for joint spoof detection and matching and report competitive results with state-of-the-art (SOTA) models for both a sequential system (two ViT models operating independently) and a unified architecture (a single ViT model for both tasks). ViT models are particularly well suited for this task as the ViT’s global embedding encodes features useful for recognition, whereas the individual, local embeddings are useful for spoof detection. We demonstrate the capability of our unified model to achieve an average integrated matching (IM) accuracy of 98.87% across LivDet 2013 and 2015 CrossMatch sensors. This is comparable to IM accuracy of 98.95% of our sequential dual-ViT system, but with $\sim 50\%$ of the parameters and $\sim 58\%$ of the latency.

Drones are gaining more interest thanks to their advantages and great potential for applications. However, present swarming drones’ stand-alone centralized radio frequency control mode from a base station has non-trivial drawbacks such as severe interference, latency caused localization error, etc. Differently, optical camera communication (OCC) is promising as an alternative for integrated communication and sensing for swarming drones. We propose PoseFly, the first 4-in-1 OCC approach for swarming drones. With exploited rolling shutter effect and the already installed camera and LED nodes, PoseFly provides (1) massive drone indication and identification, (2) multilevel on-site localization, (3) quick-link channel among drones, and (4) basic lighting. The design methodology of PoseFly gives a valuable example for the low-cost integrated sensing and communication for swarming drones.

Much of the success of fingerprint recognition is attributed to minutiae-based fingerprint representation. It was believed that minutiae templates could not be inverted to obtain a high fidelity fingerprint image, but this assumption has been shown to be false. The success of deep learning has resulted in alternative fingerprint representations (embeddings), in the hope that they might offer better recognition accuracy as well as non-invertibility of deep network-based templates. We evaluate whether deep fingerprint templates suffer from the same reconstruction attacks as the minutiae templates. We show that while a deep template can be inverted to produce a fingerprint image that could be matched to its source image, deep templates are more resistant to reconstruction attacks than minutiae templates. In particular, reconstructed fingerprint images from minutiae templates yield a TAR of about 100.0% (98.3%) @ FAR of 0.01% for type-I (type-II) attacks using a state-of-the-art commercial fingerprint matcher, when tested on NIST SD4. The corresponding attack performance for reconstructed fingerprint images from deep templates using the same commercial matcher yields a TAR of less than 1% for both type-I and type-II attacks; however, when the reconstructed images are matched using the same deep network, they achieve a TAR of 85.95% (68.10%) for type-I (type-II) attacks. Furthermore, what is missing from previous fingerprint template inversion studies is an evaluation of the black-box attack performance, which we perform using 3 different state-of-the-art fingerprint matchers. We conclude that fingerprint images generated by inverting minutiae templates are highly susceptible to both white-box and black-box attack evaluations, while fingerprint images generated by deep templates are resistant to black-box evaluations and comparatively less susceptible to white-box evaluations.

In this paper, we propose a blockchain inspired Internet-of-Things architecture for creating a transparent food supply chain. The architecture uses a proof-of-object-based authentication protocol, which is analogous to the cryptocurrency's proof-of-work protocol. The complete architecture was realized by integrating a radio frequency identification (RFID)-based sensor at the physical layer and blockchain at the cyber layer. The RFID provides a unique identity of the product and the sensor data, which helps in real time quality monitoring. For this purpose, a small feature size 900-MHz RFID coupled sensor was fabricated and demonstrated for real time sensor data acquisition. The blockchain architecture aids in creating a tamper-proof digital database of the food packages at each instance. A detailed security analysis was performed to investigate the vulnerability of the proposed architecture under different types of cyber attacks.