Neural enhancement through super-resolution (SR) deep neural networks (DNNs) opens up new possibilities for ultra-high-definition (UHD) live streaming. Yet, the heavy SR DNN inference overhead leads to severe deployment challenges. To reduce the overhead, existing systems propose to apply DNN-based SR only on carefully selected anchor frames while upscaling non-anchor frames via the lightweight reusing-based SR approach. However, frame-level scheduling is coarse-grained and fails to deliver optimal efficiency. In this work, we propose Palantír, the first neural-enhanced UHD live streaming system with fine-grained patch-level scheduling.

On low-power, low-cost Internet of Things (IoT) edges, coarse-grained entropy source-based physical-layer key generation (PKG) is often used, which results in a very low bit generation rate (BGR). In this paper, a novel PKG scheme, Hinge, designed to adapt to varying environmental conditions is introduced to optimize the trade-off between the bit mismatch rate (BMR) and BGR using fine-grained entropy sources on IoT devices. Hinge predicts channel reciprocity levels from one side and dynamically adjusts the quantization strategy, maintaining a low BMR while maximizing BGR. Compared with existing PKG solutions on Bluetooth devices, Hinge yields significant improvements in BGR, with a comparable BMR. Through extensive experiments, Hinge showcases its potential for providing a secure and efficient key generation mechanism for IoT devices in complex real-world scenarios.

With the increasing popularity of industrial networks, driven by the development of the Internet of Things, cloud computing, and big data, there are still security threats when it comes to using wireless communication technologies, including BLE, in these networks. This is primarily due to the heterogeneity and resource limitations of the devices. To address the issues of device cloning and enhance BLE device access authentication, a device authentication mechanism based on physical features can be employed. By leveraging the uniqueness and nonreplicability of physical attributes, such as fingerprints, this mechanism effectively mitigates attacks. Therefore, this paper proposes a BLE device authentication scheme called FingerBLE, which relies on the physical fingerprints of devices at the physical-layer. In terms of system design, this article also introduces a fingerprint database authentication mechanism that utilizes the aforementioned fingerprints for node recognition and legitimacy authentication. Experimental results demonstrate that FingerBLE is capable of successfully extracting corresponding device fingerprints and accurately identifying nodes across a wide range of tests.