Smart cities employ technology and data to increase efficiencies, economic development, sustainability, and life quality for citizens in urban areas. Inevitably, clean technologies promote smart cities development including for energy, transportation and health. The smart city concept is ambitious and is being refined with standards. Standards are used to help with regulating how smart cities function and contributing to define a smart city. Smart cities must be officially recognized by national and international authorities and organizations in order to promote societal advancement. There are many research and review articles on smart cities. However, technical standards are seldom discussed in the current literature. This review firstly presents the study of smart city definitions and domain. The well-known smart city standards will be presented to better recognize the smart city concept. Well-defined standards allow meaningful comparisons among smart cities implementation. How smart city initiatives make a city smarter and improve the quality of life will be discussed for various countries. This review highlights that technical standards are important for smart cities implementation. This paper serves as a guide to the most recent developments of smart cities standards.

Mental health problems are an increasingly common social issue severely affecting health and well-being. Multimedia processing technologies via facial expression show appealing prospects in the consumer field for mental health monitoring, while still suffer from intensive computation and low energy efficiency. This paper proposes an energy-efficiency memristive sequencer network (EMSN) for human emotion classification, which offers an environmentally friendly approach for consumers with low cost and easily deployable hardware. Firstly, two-dimensional (2D) materials are employed to construct an eco-friendly memristor, the efficacy and reliability of which are confirmed through performance testing. Then, a sequencer block is proposed using memristive circuits. Notably, it is a core component of the EMSN, consisting of a bidirectional long short-term memory circuit, normalisation circuit module, and multi-layer perception module. After combining some necessary function modules, the EMSN can be achieved. Furthermore, the proposed EMSN is applied for human emotion classification. The experimental results demonstrate that the proposed EMSN has advantages in computational efficiency and classification accuracy compared to existing mainstream methods, indicating an advancement in consumer health monitoring.

To enable smart home applications, embedding home monitoring systems with different sensors can be a feasible remedy to capture the multimodal sensory information from daily life. However, the previously developed monitoring systems have high implementation cost, large power consumption, and complicated device configuration, which are not conducive to achieving carbon neutrality and carbon peak goals. Here, we demonstrate a multimodal neuromorphic sensory-processing system with memristor circuits for smart homes, offering a more environmentally friendly approach with low cost and easily deployable hardware. We used three components to facilitate the multimodal neuromorphic sensory-processing system design. First, a memristor crossbar array is fabricated using low-cost, reliable and eco-friendly two-dimensional (2D) materials; enabling a parallel connection to reduce the system complexity and the deep-learning computational cost. Second, a neuromorphic multimodal sensory-processing module is designed for collecting and processing multiple sensory cues efficiently; to establish accurate depiction of the environment and realize timely response. Third, the biomimetic hierarchical learning module is realized for smart home monitoring and relative applications (e.g., indoor human behavior recognition). The developed multimodal neuromorphic sensory-processing system effectively combines the nanotechnology, intelligent sensing, and deep learning; indicating an advancement in smart home applications.