Middleware technologies for cloud of things: a survey

The next wave of communication and applications will rely on new services provided by the Internet of Things which is becoming an important aspect in human and machines future. IoT services are a key solution for providing smart environments in homes, buildings, and cities. In the era of massive number of connected things and objects with high growth rate, several challenges have been raised, such as management, aggregation, and storage for big produced data. To address some of these issues, cloud computing emerged to the IoT as Cloud of Things (CoT), which provides virtually unlimited cloud services to enhance the large-scale IoT platforms. There are several factors to be considered in the design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying a suitable “middleware” which sits between things and applications as a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next, we study different architecture styles and service domains. Then, we present several middlewares that are suitable for CoT-based platforms and finally, a list of current challenges and issues in the design of CoT-based middlewares is discussed.     

可信系统信任链研究综述

信任链是实施可信系统的关键技术之一,本文从信任链理论和应用系统出发,介绍了研究信任链理论的典型模型及近年来的研究进展,包括基于无干扰理论的信任链传递模型和基于组合安全理论的信任链模型,详细阐述了这两种信任链理论模型的优势和不足.介绍了基于静态信任根和动态信任根的信任链应用系统的研究状况,介绍了信任链远程证明技术,介绍了云计算环境下的信任链应用系统,对信任链应用系统存在的安全缺陷以及一致性和安全性测评方法进行了分析论述,并展望了该领域未来的发展趋势.     

基于上下文语义联合 YOLOv7 的分心驾驶检测算法

针对分心驾驶检测方法存在实时性差、精度低、可部署性差的问题,提出了一种基于上下文语义增强联合 YOLOv7 的 分心驾驶检测算法。首先将模型 backbone 和 head 部分的 ELAN 模块替换成语义上下文增强模块(contextual transformer, CoT), 提高上下文语义信息的捕获能力。其次,将语义关联增强机制(triplet attention)融入卷积块中,插入 backbone 和 head 的连接头之间以及融合 MP2 模块,强化目标间的关联关系以及提升目标特征提取能力。最后,将自注意力双向 Transformer 模块(Biformer) 模块融合 SPPCSPC 模块,提升模型对分心驾驶中的复杂场景和遮挡目标的处理能力。改进的 YOLOv7 算法 在分心驾驶数据集下平均精度均值(mean average precision,mAP)达到了87.3%,比原算法提高了4.3%,模型参数量减少了 4.7%,每秒传输帧数达到了90 fps,具有较好的检测精度与速度。