In mmWave UAV-to-UAV communication, the most critical aspect is that the flight trajectory through the ultra-high-speed communication spot (ultra-spot) must be precomputed as soon as the position and orientation of the ultra-spot are detected. The challenge is that the UAV must maintain its flight trajectory (including position and altitude) as well as a stable antenna angle while passing through the ultra-spot. Any deviation from the planned trajectory can lead to communication failure or prevent achieving the maximum throughput at the ultra-spot. This study introduces a method for detecting flight trajectory deviations and an adaptive altitude correction mechanism that adjusts the flight path accordingly, based on space-time synchronization using Wireless Two-way Interferometry (Wi-Wi) devices. The results obtained from UAV experiments show that the proposed algorithm successfully detects and corrects altitude deviations with a low latency of 50 ms, maintaining altitude stability during communication at the ultra-spot with a mean error of 19.6 cm. Additionally, we verify experimentally the capability of high-speed data transmission when the UAV flies through the ultra-spot, achieving an ultra-spot utilization rate (USUR) of up to 24.18%.

Convolutional neural networks (CNNs) have emerged as one of the most successful deep learning approaches to image recognition and classification. A recent line of research, which includes zkCNN (ACM CCS ’21), vCNN (Cryptology ePrint Archive), and ZEN (Cryptology ePrint Archive), aims at protecting the privacy of CNN models by developing publicly verifiable proofs of correct classification which do not leak any information about the underlying CNN models themselves. A shared feature of these schemes is that they require the entity constructing the proof to have access to both the model and the input in the clear. In other words, a client holding a potentially sensitive input is required to reveal this input to the entity holding the CNN model, thereby sacrificing his privacy, to be able to obtain a verifiable proof of correct classification. This is in contrast to the security guarantees provided by secure classification considered in privacy-preserving machine learning, which does not require the client to reveal his input to obtain a (non-verifiable) classification. In this paper, we propose a privacy-preserving verifiable CNN scheme that overcomes this limitation of the previous schemes by allowing the client to obtain a classification proof without having to reveal his input. The obtained proof allows the client to selectively reveal properties of the obtained classification and his input, which will be verifiable to any third-party verifier. Our scheme is based on the recent notion of collaborative zk-SNARKs by Ozdemir and Boneh (USENIX ’22). Specifically, we construct a new collaborative zk-SNARK based on Bulletproofs achieving an efficient maliciously secure proof generation protocol. Based on this, we then present an optimized approach to CNN evaluation. Finally, we demonstrate the feasibility of our approach by measuring the performance of our scheme on a CNN for classifying the MNIST dataset.

This paper proposes a novel spot detection method employing Wireless Two-way Interferometry (Wi-Wi) technology, which helps estimate when drones will enter and exit mmWave or Terahertz communication spots. To overcome issues like RSSI degradation, unstable phase data, and packet loss caused by obstructions from the drone’s body, we propose using multiple Wi-Wi modules with antennas placed at different locations. We conducted two field experiments to validate our methods: the first in an anechoic chamber to assess the impact of UAV body and wings on Wi-Wi signals, and the second experiment took place outdoors to validate the spot detection algorithm’s performance when two UAVs rapidly passed each other. Experimental results on mmWave spot detection reveal millisecond-level accuracy, surpassing the time accuracy when using GNSS-based positioning technology and approaching that of the Real-Time Kinematic (RTK) technique. Additionally, spatial diversity when using multiple Wi-Wi modules assists in mitigating packet loss issues encountered due to obstacles and multipath fading, thereby enhancing the stability of spot detection.

We propose equiphase distance following, which is a method to keep a constant distance between mobilities by seeking the position at a certain phase of a carrier wave by using wireless two-way interferometry (Wi-Wi). Wi-Wi offers a high-precision phase sensing capability and leads on to ranging on the order of millimeters. Exploiting this advantage, a millimeter-precision automatic following technique is promising in spite of the used bandwidth of 200 kHz in the 920-MHz band. For stable following behavior, the influence of wireless environment upon the phase sensing capability of Wi-Wi is considerable issue to be examined. In this paper, we investigated the effect of an AWGN channel and co-channel interferences on the error and dispersion of phase detection. The root mean squared error of the detected phase was independent of the magnitude of CNR/CIR, and was kept at the original performance of 1-mm accuracy. The dispersion also stayed almost constant at 4.5 mm as long as CNR/CIR is greater than 10 dB, and otherwise increased rapidly as the channel degraded. Finally, we discuss about a smoothing filter for suppressing this dispersion, and have demonstrated constant-distance following with the accuracy of 5 mm.

The wiretap channel is a setting where one aims to obtain information-theoretic security of communicated data under the sole assumption that the channel from a sender Alice to an eavesdropper Eve is “noisier” than that from Alice to a receiver Bob. However, in practice, the difference between the two channels in terms of noise may be much smaller because of the wide spread application of high-performance communication technologies. Thus, establishing a wiretap channel over real channels is a challenging task. In this paper, we report a wiretap channel in the form of a “protocol” (a so-called virtual wiretap channel) realized by using a highly precise wireless clock-synchronization technology called the wireless two-way interferometry (abbreviated as Wi-Wi). Wi-Wi technology has already been implemented in wireless communication devices, and it measures the clock time difference and signal propagation time in a situation where the precisions of both values are mutually affected. That is, Alice or Bob can induce a noise on Eve's clock time difference measurement by physically introducing noise in the signal propagation time between Alice and Bob. We analyzed the proposed wiretap channel referred to as the Wi-Wi wiretap channel experimentally, and estimated its parameters (e.g., the error probabilities). We confirmed that the Wi-Wi wiretap channel works well as a virtual wiretap channel by simulating and experimentally demonstrating a well-known, provably secure, optimal wiretap code over it. The results show that Wi-Wi wiretap channel can serve as a physical layer security, useful for synchronized Internet of Things (IoT) devises.