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在上一期前沿文献中推荐中，介绍了5G/6G通信器件材料的最新进展，包括：100 GHZ的氧化锌肖特基二极管、超薄单晶LiNbO3 薄膜体声波谐振器，以及超表面辅助反向散射无线通信系统，此外，也选取了用于5G和B5G通信的低轨道（LEO）小卫星组网系统的最新文献。本期讨论中着眼于5G/6G的网络安全方面，选取的文献涵盖了硬件和软件网络安全技术：基于喷墨打印金属氧化物晶体管的混合低压物理不可克隆特性，以及智能城市应用的密钥交换模型。此外，对于5G网络中的物联网应用，电池生命周期对于应用体验会产生重要影响，本期推荐最新的研究文献：IOT网络中电池生命周期的预测模型。最后，介绍一篇关于少模光纤快速模式恢复算法的文献，基于该方法可以制造低成本的相位恢复接收器，对于少模光纤在通信中的应用研究具有参考价值。以上为本期推送内容，供相关领域的科研人员参考。领域一基于喷墨打印金属氧化物晶体管的混合低压物理不可克隆特性 Hybrid low-voltage physical unclonable function based on inkjet-printed metal-oxide transistors Alexander Scholz, etc. Nature Communications, 2020 Modern society is striving for digital connectivity that demands information security. As an emerging technology, printed electronics is a key enabler for novel device types with free form factors, customizability, and the potential for large-area fabrication while being seamlessly integrated into our everyday environment. At present, information security is mainly based on software algorithms that use pseudo random numbers. In this regard, hardware-intrinsic security primitives, such as physical unclonable functions, are very promising to provide inherent security features comparable to biometrical data. Device-specific, random intrinsic variations are exploited to generate unique secure identifiers. Here, we introduce a hybrid physical unclonable function, combining silicon and printed electronics technologies, based on metal oxide thin film devices. Our system exploits the inherent randomness of printed materials due to surface roughness, film morphology and the resulting electrical characteristics. The security primitive provides high intrinsic variation, is non-volatile, scalable and exhibits nearly ideal uniqueness. 领域二智能城市应用的密钥交换模型 Cryptographic keys exchange model for smart city applications Muzafer Sara?evi?, etc. IET Intelligent Transport Systems ,2020, 14(11):1456 - 1464 This study presents a secret key sharing protocol that establishes cryptographically secured communication between two entities. A new symmetric key exchange scenario for smart city applications is presented in this research. The protocol is based on the specific properties of the Fuss-Catalan numbers and the Lattice Path combinatorics. The proposed scenario consists of three phases: generating a Fuss-Catalan object based on the grid dimension, defining the movement in the Lattice Path Grid and defining the key equalisation rules. In the experimental part, the authors present the security analysis of the protocol as well as its test. Also, they examine the equivalence of the proposed with Maurer's satellite scenario and suggest a new scenario that implements an information-theoretical protocol for the public key distribution. Additionally, a comparison with related studies and methods is provided, as well as a comparison with satellite scenario, which proves the advantages of solution presented by the authors. Finally, they propose further research directions regarding key management in smart city applications. 领域三 IOT网络中电池生命周期的预测模型 Predictive model for battery life in IoT networks Praveen Kumar Reddy Maddikunta, etc IET Intelligent Transport Systems, 2020, 14(11): 1388 - 1395 The internet of things (IoT) is prominently used in the present world. Although it has vast potential in several applications, it has several challenges in the real-world. One of the most important challenges is conservation of battery life in devices used throughout IoT networks. Since many IoT devices are not rechargeable, several steps to conserve the battery life of an IoT network can be taken using the early prediction of battery life. In this study, a machine learning based model implementing a random forest regression algorithm is used to predict the battery life of IoT devices. The proposed model is experimented on ‘Beach Water Quality – Automated Sensors’ data set generated from sensors in an IoT network from the city of Chicago, USA. Several pre-processing techniques like normalisation, transformation and dimensionality reduction are used in this model. The proposed model achieved a 97% predictive accuracy. The results obtained proved that the proposed model performs better than other state-of-art regression algorithms in preserving the battery life of IoT devices. 领域四少模光纤的快速模式恢复算法 Fast mode decomposition in few-mode fibers Egor S. Manuylovich, etc. Nature Communications, 2020 Retrieval of the optical phase information from measurement of intensity is of a high interest because this would facilitate simple and cost-efficient techniques and devices. In scientific and industrial applications that exploit multi-mode fibers, a prior knowledge of spatial mode structure of the fiber, in principle, makes it possible to recover phases using measured intensity distribution. However, current mode decomposition algorithms based on the analysis of the intensity distribution at the output of a few-mode fiber, such as optimization methods or neural networks, still have high computational costs and high latency that is a serious impediment for applications, such as telecommunications. Speed of signal processing is one of the key challenges in this approach. We present a high-performance mode decomposition algorithm with a processing time of tens of microseconds. The proposed mathematical algorithm that does not use any machine learning techniques, is several orders of magnitude faster than the state-of-the-art deep-learning-based methods. We anticipate that our results can stimulate further research on algorithms beyond popular machine learning methods and they can lead to the development of low-cost phase retrieval receivers for various applications of few-mode fibers ranging from imaging to telecommunications.

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