Hybrid work and temporary employment often lead to chaotic and decentralized attendance management. AIRA's Facial Recognition Solution simplifies personnel management. Without external hosting equipment, the airaTablet Plus integrates access control, attendance reports, and video streaming into a single device. It features the facial recognition system, airaFace, ensuring precise identification of faces within 1 second. Users can customize clock-in periods and export automatic attendance reports. The built-in multi-mode dashboard caters to various scenarios, such as attendance, occupancy and welcom dashboard. Moreover, the system seamlessly integrates with the central management system, airaManager, which can cnetralize attendance data from each office. The encrypted transmission enhances security, ensuring a comprehensive and secure solution for pesonnel management

The application of digital twin technology in building smart cities is becoming increasingly clear, serving as a crucial engine for driving urban transformation. In the development of smart cities in recent years, citizens can view the growth and development of the entire city, including population growth, new construction, and other major activities, in a 3D format, gradually emphasizing the concept of a virtual city. Digital twin technology can simulate disaster scenarios, including weather events and traffic accidents, assisting disaster response personnel in rapidly understanding the situation and optimizing the efficiency of disaster response. In recent years, with the advancement of aerial photography, simulation technology, and computing resources, The National Science and Technology Center for Disaster Reduction has leveraged advanced technology to create a mountainous flood inundation model system. This system enhances disaster warning and impact analysis, providing a comprehensive view of vulnerable settlements. It utilizes dynamic models and virtual-real integration, presenting alert information and disaster impacts through visual elements, contributing to informed decision-making in disaster situations.

The application of digital twin technology in building smart cities is becoming increasingly clear, serving as a crucial engine for driving urban transformation. In the development of smart cities in recent years, citizens can view the growth and development of the entire city, including population growth, new construction, and other major activities, in a 3D format, gradually emphasizing the concept of a virtual city. Digital twin technology can simulate disaster scenarios, including weather events and traffic accidents, assisting disaster response personnel in rapidly understanding the situation and optimizing the efficiency of disaster response. In recent years, with the advancement of aerial photography, simulation technology, and computing resources, The National Science and Technology Center for Disaster Reduction has leveraged advanced technology to create a mountainous flood inundation model system. This system enhances disaster warning and impact analysis, providing a comprehensive view of vulnerable settlements. It utilizes dynamic models and virtual-real integration, presenting alert information and disaster impacts through visual elements, contributing to informed decision-making in disaster situations.

Self-developed wireless low-power high-capacity communication protocol, suitable for medical IoT devices that pursue high battery life and low latency, and can connect 2,000 devices

Vehicle Classification enables transportation authorities to automatically identify and classify vehicles according to their visual attributes. This technology finds widespread application in traffic management systems, toll collection, parking facilities, and security setups. FETCi leverages AI algorithms and machine learning methodologies to analyze video data collected through LiDAR technology. With an extensive database and repository, FETCi's AI AVC proficiently sorts vehicles into distinct categories, including cars, trucks, motorcycles, buses, and more. By integrating LiDAR technology with AI-powered image recognition, FETCi establishes a vehicle recognition system equipped with self-enhancement and deep learning capabilities.

The Smart Dust Sensor technology is an innovative solution for integrating IoT networks into air quality monitoring. Developed collaboratively by the National Science and The National Science and Technology Council (NSTC), in collaboration with Taiwan Instrument Research Institute (TIRI) and Taiwan Semiconductor Research Institute (TSRI) of NARLabs, and leading academic teams in Taiwan, this technology has successfully miniaturized gas sensors while achieving low power consumption and high sensitivity. Its core features include exceptional sensitivity and stability, enabling real-time monitoring of multiple gases. Moreover, the fully domestically developed technology reduces dependence on imports and offers cost-effective sensor modules, driving the widespread adoption of air quality monitoring devices. The Smart Dust Sensor has undergone extensive field testing and third-party certifications across various applications. These include environmental monitoring stations (e.g., Taichung Dali Station), public recreational areas (e.g., odor monitoring in public restrooms), and industrial settings (e.g., semiconductor facilities and wastewater treatment plants). With its proven accuracy, reliability, and traceable data, the technology plays a key role in advancing air quality monitoring solutions, facilitating the development of smart cities, and contributing to improved environmental management.

Criminal and street violence cases will affect and cause panic to citizens. In order to diminish street violence cases, reducing the time to solve these cases, protecting citizens’ lives and properties, and allow citizens to live without fear, the Kaohsiung City Police Department (KCPD) built the “3D Street Violence Early Warning, AI Intelligence Collection and Integration Platform” (hereinafter called “the Platform”). The platform utilizes the Internet of Things (IoT) to combine AI image recognition and sound sensing technologies for cross-domain big data analyses. KCPD established cross-platform data interfacing and integration, mainly through “data integration” and “network analyses” to collect with the municipal government’s transportation bureaus and social bureaus; and import existing data from the KCPD data platforms. The Platform will, through web service API, (a) retrieve data built by information systems, (b) conduct big data analysis, aggregate applicable intelligence data and present it in the form of dynamic information, (c) Integrate all information and display it as maps and topologies, improve case handling efficiency. According to KCPD employees’ query, they can adjust factor weights and relationships. Analyze the results based on rational mathematical models and professional case-handling concepts, so that intelligence analysis goes beyond the limitations of platform developers and allows criminal police to maximize their capabilities.

The Civil IoT data service platform collected and stored environmental sensor data in four key areas: earthquake, water resources, air quality, and disaster prevention, and provides real-time data service using the OGC SensorThings API that conforms to the international data modeling standards for value-added services through a unified data modeling framework. In order to solve the problem of insufficient communication in remote areas, Band20 frequency band is utilized to build a backbone network core service for IoT application, which provides remote areas with the use of Band20 frequency band as a network communication method for IoT data transmission, and expands the scope of IoT deployment in remote areas.

The Ministry of Economic Affairs, Industrial Technology Bureau, has commissioned the Smart Sensing and System Technology Center at the Industrial Technology Research Institute (ITRI) to develop a "Composite Water Quality Sensing System." This system enables simultaneous measurement and detection of parameters such as Chemical Oxygen Demand (COD), Suspended Solid Concentration (SS), Copper Ion (Cu2+) concentration, conductivity, pH value, temperature, and more. Employing UV light sterilization, it inhibits the attachment and growth of biological films on the sensing interface, preventing interference with sensor accuracy and extending the sensor cleaning cycle. With a design incorporating water sampling, optimized power management technology, and reduced reliance on frequent manual maintenance, the system achieves long-term operational efficiency. It has been successfully applied in various settings, including industrial zones, aquaculture, chemical plating lines, metal electroplating industries, and landfill sites. The system provides intelligent wastewater discharge management and real-time monitoring of processes, meeting the demands of low-carbon management and waste reduction.

Billion Electric Group , in collaboration with Taiwanese partners, has successfully deployed 495 kWp of solar PV and 1,997 kWh of battery energy storage systems (BESS) in Palau, Tuvalu, and the Marshall Islands. These modular microgrids reduce reliance on imported fossil fuels, ensuring stable green energy supply for agriculture, aquaculture, and local communities. The project is expected to cut carbon emissions by 800 metric tons annually, with over 50% renewable energy self-sufficiency at aquaculture centers and demonstration farms. Meanwhile, livestock farms, offices, and dormitories now operate on 100% green energy, significantly lowering both energy costs and environmental impact. Billion Electric Group provides end-to-end energy solutions, from R&D and manufacturing to system integration and deployment. The project features MIT ( Made In Taiwan) renewable energy technologies, including Billion’s Fusio series BESS, Giga series PV inverters, and an AI-driven EMS (Energy Management System). The system enables real-time power optimization, bidirectional energy transmission, and cloud-based monitoring platform, significantly enhancing grid stability and energy efficiency.

WeatherRisk provides tailored energy forecasting solutions for the energy industry, including: 1. High-resolution, bankable historical data (metocean, solar irradiation) 2. Weather forecasting systems for O&M (offshore wind farms, solar assets) 3. Solar power forecasting (solar assets and grids) 4. Wind power forecasting (wind farms and grids) 5. Load forecasting Energy forecasting has a wide range of applications in the energy industry: • For FTM (front-of-the-meter) power dispatch, ancillary services, asset evaluation, and PV-Storage. • For BTM (behind-the-meter) virtual power plants, microgrids, renewable energy trading, as well as strategic support for energy market operations.

Civil IoT Taiwan is part of the government’s Forward-looking Infrastructure Development Program under the Digital Infrastructure category. With the collective effort of the National Science and Technology Council (NSTC), the Ministry of Transportation and Communications (MOTC), the Ministry of Economic Affairs (MOEA), the Ministry of the Interior (MOI), the Ministry of Environment (MOENV), the Ministry of Agriculture (MOA), the Ministry of Digital Affairs (MODA) and the Academia Sinica, the project aims to address the core needs of the public by prioritizing four key mission areas, namely air quality, earthquake, water resources, and disaster prevention and response. By leveraging AI and IoT technologies, the project strives to deliver well-rounded, smart living services to facilitate the government and the people to respond to the challenges in an ever-changing environment.