With the evolution of the Internet of things and smart cities, a new trend of the Internet of simulation has emerged to utilise the technologies of cloud, edge, fog computing, and high-performance computing for design and analysis of complex cyber-physical systems using simulation. These technologies although being applied to the domains of big data and deep learning are not adequate to cope with the scale and complexity of emerging connected, smart, and autonomous systems. This study explores the existing state-of-the-art in automating, augmenting, and integrating systems across the domains of smart cities, autonomous vehicles, energy efficiency, smart manufacturing in Industry 4.0, and healthcare. This is expanded to look at existing computational infrastructure and how it can be used to support these applications. A detailed review is presented of advances in approaches providing and supporting intelligence as a service. Finally, some of the remaining challenges due to the explosion of data streams; issues of safety and security; and others related to big data, a model of reality, augmentation of systems, and computation are examined.

Sparse representation has been widely applied to multi-focus image fusion in recent years. As a key step, the construction of an informative dictionary directly decides the performance of sparsity-based image fusion. To obtain sufficient bases for dictionary learning, different geometric information of source images is extracted and analysed. The classified image bases are used to build corresponding subdictionaries by principle component analysis. All built subdictionaries are merged into one informative dictionary. Based on constructed dictionary, compressive sampling matched pursuit algorithm is used to extract corresponding sparse coefficients for the representation of source images. The obtained sparse coefficients are fused by Max-L1 fusion rule first, and then inverted to form the final fused image. Multiple comparative experiments demonstrate that the proposed method is competitive with other the state-of-the-art fusion methods.

Hashtags of microblogs can provide valuable information for many natural language processing tasks. How to recommend reliable hashtags automatically has attracted considerable attention. However, existing studies assumed that all the training corpus crawled from social networks are labelled correctly, while large sample statistics on real social media shows that there are 8.9% of microblogs with hashtags having wrong labels. The notable influence of noisy data to the classifier is ignored before. Meanwhile, recency also plays an important role in microblog hashtag, but the information is not used in the existing studies. Some temporal hashtags such as World Cup will ignite at a particular time, but at other times, the number of people talking about it will sharply decrease. To address the twofold shortcomings above, the authors propose an long short-term memory-based model, which uses temporal enhanced selective sentence-level attention to reduce the influence of wrong labelled microblogs to the classifier. Experimental results using a dataset of 1.7 million microblogs collected from SINA Weibo microblogs demonstrated that the proposed method could achieve significantly better performance than the state-of-the-art methods.

Sensor data pre-processing is an essential phase of crowd sensing application. Existing studies do not effectively solve the problem, and there still exist various sensor data pre-processing optimisation problems at the acquisition end in crowd-sensing process. This study presents an improved sliding average method to achieve data compression and reduce the time complexity by using a dynamic window with improved processing time. Through adopting locally sorting and gradient change of the filter window, an improved extremum median filtering method is proposed to relieve the time-consuming problem when denoising high pixel images. A transmission strategy for optimisation is also proposed, in which only the demarcation points of each group of data and the data points with large differences when comparing with the demarcation points are recorded. This strategy reduces the storage pressure and the amount of data transmission of mobile terminal and improves the efficiency of data transmission. The experimental results show that their methods have higher speed and lower cost, and thus they can run better in crowd-sensing environment.

Healthcare is a big application scenario of blockchain, and blockchains used in healthcare are called health blockchain. In general, blockchain blocks are open and the transactions in them are public. If some privacy data are involved in these transactions, they will be leaked. Owing to healthcare system involving a great deal of privacy data, certain security mechanisms must be built to protect these privacy data in health blockchain. Furthermore, because the core of security mechanisms is the key management schemes, the appropriate key management schemes should be designed before blockchains can be used in healthcare system. Here, according to the features of health blockchain, the authors use a body sensor network to design a lightweight backup and efficient recovery scheme for keys of health blockchain. The authors’ analyses show that the scheme has high security and performance, and it can be used to protect privacy messages on health blockchain effectively and to promote the application of health blockchain.

This study presents the authors’ recent research and application of a new visual programming language and its development environment: VIPLE (Visual IoT/Robotics Programming Language Environment) at Arizona State University (ASU). ASU VIPLE supports a variety of IoT devices and robots based on an open architecture. Based on computational thinking, VIPLE supports the integration of engineering design process, workflow, fundamental programming concepts, control flow, parallel computing, event-driven programming, and service-oriented computing seamlessly into a wide range of curricula, such as introduction to computing, introduction to engineering, service-oriented computing, and software integration. It is actively used at ASU in several sections of FSE 100: Introduction to Engineering and in CSE 446: Software Integration and Engineering, as well as in several other universities worldwide.