Owing to a paradigm shift toward Internet of Things (IoT), researches into IoT services have been conducted in a wide range of fields. As a major application field of IoT, waste management has become one such issue. The absence of efficient waste management has caused serious environmental problems and cost issues. Therefore, in this paper, an IoT-based smart garbage system (SGS) is proposed to reduce the amount of food waste. In an SGS, battery-based smart garbage bins (SGBs) exchange information with each other using wireless mesh networks, and a router and server collect and analyze the information for service provisioning. Furthermore, the SGS includes various IoT techniques considering user convenience and increases the battery lifetime through two types of energy-efficient operations of the SGBs: stand-alone operation and cooperation-based operation. The proposed SGS had been operated as a pilot project in Gangnam district, Seoul, Republic of Korea, for a one-year period. The experiment showed that the average amount of food waste could be reduced by 33%.

Recently, fire accidents in buildings have become bigger around the world, and it has become necessary to build an efficient building disaster management system suitable for fires in a Smart City. As building fires increase the number of casualties and property damage, it is necessary to take appropriate action accordingly. There has been an increasing effort to develop such disaster management systems worldwide by applying information communication technology (ICT), and many studies have been conducted in practice. In this paper, an augmented reality (AR)-based Smart Building and Town Disaster Management System is suggested in order to acquire visibility and to grasp occupants in case of fire disasters in buildings. This system provides visualization information and optimal guide for quick initial response by utilizing smart element AR-based disaster management service through linkage of physical virtual domain in the building. Additionally, we show a scenario flow chart of the fire extinguishment process according to the time from the ignition stage to the extinguishment stage in the building. Finally, we introduce the related sensors, the actuators, and a small test-bed for AR-based disaster management service. This test-bed was designed for interlocking and interoperability test of the system between the sensors and the actuators. It is expected that the proposed system can provide a quick and safe rescue guideline to the occupants and rescuers in the building where fire is generated and in regions of poor visibility.

To build sustainable smart energy cities (SECs) around the world, many countries are now combining customized services and businesses within their energy infrastructure and urban environments. Such changes could then promote the development of platforms that ultimately provide benefits for citizens such as convenience, safety, and cost savings. Currently, the development of technologies for SECs focuses on independent products and unit technology. However, this is problematic, as it may not be possible to develop sustainable cities if there is a lack of connectivity between various elements within the SEC. To solve such problems, this paper presents an AI-based physical and virtual platform using a 5-layer architecture to develop a sustainable smart energy city (SSEC). The architecture employs both a top-down and bottom-up approach and the links between each energy element in the SSEC can readily be analyzed. The economic analysis based on return on investment (ROI) is carried out by comparing the economic benefits before and after the application of this system. Deploying the proposed platform will enable the speedy development and application of new services for SSECs and will provide SSECs with measures to ensure sustainable development, such as rapid urban development, and cost reductions.

Currently, many intelligent building energy management systems (BEMSs) are emerging for saving energy in new and existing buildings and realizing a sustainable society worldwide. However, installing an intelligent BEMS in existing buildings does not realize an innovative and advanced society because it only involves simple equipment replacement (i.e., replacement of old equipment or LED (Light Emitting Diode) lamps) and energy savings based on a stand-alone system. Therefore, artificial intelligence (AI) is applied to a BEMS to implement intelligent energy optimization based on the latest ICT (Information and Communications Technologies) technology. AI can analyze energy usage data, predict future energy requirements, and establish an appropriate energy saving policy. In this paper, we present a dynamic heating, ventilation, and air conditioning (HVAC) scheduling method that collects, analyzes, and infers energy usage data to intelligently save energy in buildings based on reinforcement learning (RL). In this regard, a hotel is used as the testbed in this study. The proposed method collects, analyzes, and infers IoT data from a building to provide an energy saving policy to realize a futuristic HVAC (heating system) system based on RL. Through this process, a purpose-oriented energy saving methodology to achieve energy saving goals is proposed.

The scale of human accidents and the resultant damage has increased due to recent large-scale urban (building) fires, meaning there is a need to devise an effective strategy for urban disasters. In the event of a fire, it is difficult to evacuate in the early stages due to the loss of detection function, difficulty in securing visibility, and confusion over evacuation routes. Accordingly, for rapid evacuation and rescue, it is necessary to build a city-level fire safety service and digital system based on smart technology. In addition, both forest and building fires emit a large amount of carbon dioxide, which is the main cause of global warming. Therefore, we need to prepare both energy and fire management to achieve carbon neutrality by 2030. In this study, we developed an AI-based smart fire safety system for efficient urban integrated management using a city-based fire safety architecture. In addition, we designed a fire management infrastructure and an energy management system for buildings. The proposal was demonstrated by building a test bed in the A building, and the AR-based mobile/web application was tested for optimized evacuation management. Furthermore, AI-based fire detection and the optimal evacuation of occupants were implemented through deep learning-based fire information data analysis. As a result, this paper presents four points for safety and energy management, and we demonstrate that the optimization of occupant evacuation ability and energy saving can be achieved. We also analyze the efficiency of the data transfer rate to prevent data communication delays by using Virtual Edge Gateway (VEG) management. In the future, we expect that the appearance of future fire and energy management buildings through this research will produce more accurate data prediction technology and the development of cutting-edge smart technology in smart city infrastructures.

A smart grid (SG) has attracted great attention due to recent environmental problems. SG technologies enable users, such as energy system operators and consumers, to reduce energy consumption and the emission of greenhouse gases, by changing energy infrastructure more efficiently. As a part of the SG, home energy management system (HEMS) has become increasingly important, because energy consumption of a residential sector accounts for a significant amount of total energy consumption. However, a conventional HEMS has some architectural limitations on scalability, reusability, and interoperability. Furthermore, the cost of implementation of a HEMS is very expensive, which leads to the disturbance of the spread of a HEMS. Therefore, this paper proposes an Internet of Things- (IoT-) based HEMS with lightweight photovoltaic (PV) system over dynamic home area networks (DHANs), which enables the construction of a HEMS to be more scalable, reusable, and interoperable. We suggest the techniques for reducing the cost of the HEMS with various perspectives on system, network, and middleware architecture. We designed and implemented the proposed HEMS and conducted a experiment to verify the performance of the proposed system.