Cognitive smart cities: Challenges and trending solutions

摘要

A smart city implies the realization of sustainable city growth enabled by technology-based intelligent solutions to give a good quality of life to its citizens. Information and communication technologies play a crucial role as the nerve centre of the smart city for collecting and analysing data from various sources, like mobile, social media, and sensors. Internet of things (IoT) and big data (BD) also play a critical role in the smart city infrastructures, changing the way we analyse patterns and trends in human behaviour. Smart cities generate a huge amount of data, and therefore need many flexible ways to implement data and processing gateways. Recently, cognitive analytics have attracted the attention of researchers and practitioners worldwide as a technology-based smart solution. It is a novel approach to information discovery and decision making which uses multiple intelligent technologies such as statistical machine learning, deep learning, distributed artificial intelligence, natural language processing and visual pattern recognition to understand data and generate insights. A cognitive smart city refers to the convergence of emerging IoT and smart city technologies to realize cyber-physical social systems, their generated BD from sensing to communication and computing, and artificial intelligence techniques for all aspects of collaborative computing in sensors, actuators and human-machine interfaces. A cognitive city is one that learns and adapts its behaviour based on the past experiences and can sense, understand and respond to changes of a smart environment with many human and robotic elements. In cognitive cities, data flows not only from the citizens to city management systems (e.g., intelligent transportation systems and healthcare centres), but also from citizen to citizen. Citizens act as human sensors, and intelligence-enabled frameworks build a cyber-physical social system. Thus, consistent citizen engagement, ubiquitous data collection and sophisticated analytics are required to produce the best kind of cognitive city. The implementation of cognitive smart city is highly context-dependent. Initiatives may range from incremental to disruptive and the deployment is shaped by many factors such as governance, economic, technology, social, environmental, legal and ethical issues. As smart cities projects become more pervasive across geography, technology and applications, it is imperative to identify key learnings to foster a deeper understanding of the technology evolution landscape and provide tangible benefits to smart city planners and key decision-makers. Viable intersection between technology solutions and digital urbanization design principles (people-centred and inclusive infrastructure, resilience and sustainability, interoperability and flexibility, managing risks and ensuring safety) need to be evaluated in order to provide balanced and replicable solutions. In the research community, several works propose cognitive solutions that fit the needs of sustainable urban development. Academic literature, government consultation documents and policy papers articulate numerous challenges and research directions for incorporating cognition to realize new smart city services. Both qualitative and quantitative studies carefully consider BD analytics, semantic derivation and knowledge discovery, intelligent decision-making and on-demand service provision for a large number of smart city applications. This special issue aims to stimulate discussion on the design, use and evaluation of self-correction and human cognition for continuous learning as key knowledge discovery drivers within socially connected urban ecosystems. The issue is focused on articles describing cognitive models for cyber–physical social systems with urban BD to leverage deeper insights from the vast amount of generated data with near real-time intelligence. We received a very good response to our special issue call for articles. During the rev