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Rapid urbanisation has brought about great challenges to our daily lives, such as traffic congestion, environmental pollution, energy consumption, public safety and so on. Research on smart cities aims to address these issues with various technologies developed for the Internet of Things. Very recently, the research focus has shifted towards processing of massive amount of data continuously generated within a city environment, e.g., physical and participatory sensing data on traffic flow, air quality, and healthcare. Techniques from computational intelligence have been applied to process and analyse such data, and to extract useful knowledge that helps citizens better understand their surroundings and informs city authorities to provide better and more efficient public services. Deep learning, as a relatively new paradigm in computational intelligence, has attracted substantial attention of the research community and demonstrated greater potential over traditional techniques. This paper provides a survey of the latest research on the convergence of deep learning and smart city from two perspectives: while the technique-oriented review pays attention to the popular and extended deep learning models, the application-oriented review emphasises the representative application domains in smart cities. Our study showed that there are still many challenges ahead for this emerging area owing to the complex nature of deep learning and wide coverage of smart city applications. We pointed out a number of future directions related to deep learning efficiency, emergent deep learning paradigms, knowledge fusion and privacy preservation, and hope these would move the relevant research one step further in creating truly distributed intelligence for smart cities.

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The 5th generation wireless system (5G) will support Internet of Things (IoT) by increasing the interconnectivity of electronic devices to support a variety of new and promising networked applications such as the home of the future, environmental monitoring networks, and infrastructure management systems. The potential benefits of the IoT are as profound as they are diverse. However, the benefits of the IoT come with some significant challenges. Not the least of these is that the increased interconnectivity integral to an IoT network increases its vulnerability to malevolent attacks. There is still no proven methodology for the design of security frameworks with device authentication and access control. This paper attempts to address this problem through the development of a prototype security framework with robust and transparent security protection. This includes an investigation into the security requirements of three different characteristic IoT scenarios (concretely, body IoT, home IoT, and hotel IoT), a design of new authentication mechanisms, and an access control subsystem with fine‐grained roles and risk indicators. Our prototype security framework gives us an insight into some of the major difficulties of IoT security as well as providing some feasible solutions. Copyright © 2015 John Wiley & Sons, Ltd.

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This paper is concerned with reliable control problems of cyber-physical systems against a class of frequency-constrained sensor and actuator attacks. We consider a continuous-time linear physical system equipped with an observer-based abnormal detector, and it is assumed that control signals and partial sensor outputs transmitted via network layers are vulnerable to cyber attacks. With the use of detection mechanisms of the abnormal monitor, an upper bound of the worst stealthy attacks is obtained, which is composed of the information of the detector's threshold, the attack's structure, and frequency characteristic. By exploiting the bound information, a novel attack compensator, which can stabilize the system with a nearly desired system performance, is proposed for a situation where an attack may occur without triggering an alarm. Finally, the effectiveness of the proposed control policy is verified by a numerical example.