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Semantic communications have emerged as a promising solution to address the challenge of efficient communication in rapidly evolving and increasingly complex Internet of Things (IoT) networks. However, protecting the security of semantic communication systems within the distributed and heterogeneous IoT networks is critical issues that need to be addressed. We develop a secure and efficient distributed semantic communication system in IoT scenarios, focusing on three aspects: secure system maintenance, efficient system update, and privacy-preserving system usage. Firstly, we propose a blockchain-based interaction framework that ensures the integrity, authentication, and availability of interactions among IoT devices to securely maintain system. This framework includes a novel digital signature verification mechanism designed for semantic communications, enabling secure and efficient interactions with semantic communications. Secondly, to improve the efficiency of interactions, we develop a flexible semantic communication scheme that leverages compressed semantic knowledge bases. This scheme reduces the data exchange required for system update and is adapt to dynamic task requirements and the diversity of device capabilities. Thirdly, we exploit the integration of differential privacy into semantic communications. We analyze the implementation of differential privacy taking into account the lossy nature of semantic communications and wireless channel distortions. An joint model-channel noise mechanism is introduced to achieve differential privacy preservation in semantic communications without compromising the system's functionality. Experiments show that the system is able to achieve integrity, availability, efficiency and the preservation of privacy.

Neural networks often encounter various stringent resource constraints while deploying on edge devices. To tackle these problems with less human efforts, automated machine learning becomes popular in finding various neural architectures that fit diverse Artificial Intelligence of Things (AIoT) scenarios. Recently, to prevent the leakage of private information while enable automated machine intelligence, there is an emerging trend to integrate federated learning and neural architecture search (NAS). Although promising as it may seem, the coupling of difficulties from both tenets makes the algorithm development quite challenging. In particular, how to efficiently search the optimal neural architecture directly from massive non-independent and identically distributed (non-IID) data among AIoT devices in a federated manner is a hard nut to crack. In this paper, to tackle this challenge, by leveraging the advances in ProxylessNAS, we propose a Federated Direct Neural Architecture Search (FDNAS) framework that allows for hardware-friendly NAS from non- IID data across devices. To further adapt to both various data distributions and different types of devices with heterogeneous embedded hardware platforms, inspired by meta-learning, a Cluster Federated Direct Neural Architecture Search (CFDNAS) framework is proposed to achieve device-aware NAS, in the sense that each device can learn a tailored deep learning model for its particular data distribution and hardware constraint. Extensive experiments on non-IID datasets have shown the state-of-the-art accuracy-efficiency trade-offs achieved by the proposed solution in the presence of both data and device heterogeneity., Comment: arXiv admin note: text overlap with arXiv:2011.03372

Combining high-speed photography with laser profilometry, we study the dynamics and the morphology of liquid-drop impact cratering in wet granular media---a ubiquitous phenomenon relevant to many important geological, agricultural, and industrial processes. By systematically investigating important variables such as impact energy, the size of impinging drops and the degree of liquid saturation in granular beds, we uncover a novel scaling for the size of impact craters. We show that this scaling can be explained by considering the balance between the inertia of impinging drops and the strength of impacted surface. Such a theoretical understanding confirms that the unique energy partition originally proposed for liquid-drop impact cratering in dry granular media also applies for impact cratering in wet granular media. Moreover, we demonstrate that compressive stresses, instead of shear stresses, control granular impact cratering. Our study enriches the picture of generic granular impact cratering and sheds light on the familiar phenomena of raindrop impacts in granular media., Comment: 8 pages, 8 figures

When a granular material is impacted by a sphere, its surface deforms like a liquid yet it preserves a circular crater like a solid. Although the mechanism of granular impact cratering by solid spheres is well explored, our knowledge on granular impact cratering by liquid drops is still very limited. Here, by combining high-speed photography with high-precision laser profilometry, we investigate liquid-drop impact dynamics on granular surface and monitor the morphology of resulting impact craters. Surprisingly, we find that, despite the enormous energy and length difference, granular impact cratering by liquid drops follows the same energy scaling and reproduces the same crater morphology as that of asteroid impact craters. Inspired by this similarity, we integrate the physical insight from planetary sciences, the liquid marble model from fluid mechanics and the concept of jamming transition from granular physics into a simple theoretical framework that quantitatively describes all the main features of liquid-drop imprints in granular media. Our study sheds light on the mechanisms governing raindrop impacts on granular surfaces and reveals a remarkable analogy between familiar phenomena of raining and catastrophic asteroid strikes., Comment: 6 pages, 6 figures, Proceedings of the National Academy of Sciences (2015)