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.

Taiwan's first domestically developed 5G O-RAN system integrates with Oceanic & Meteorological Applications to collect environmental data related to oceans and geology. Through the analysis of this environmental data, the system anticipates the overall conditions of offshore wind power fields, facilitating operations in offshore wind power. This integration aids in minimizing the needs of offshore patrols and contributes to the establishment of data system for Taiwan's marine energy environment, serving as a foundation for sustainable environmental development.

The Central Weather Administration (CWA) is the government agency responsible for earthquake monitoring and reporting in Taiwan. In order to reduce fatalities and property losses caused by earthquakes, a high-density real-time seismic network and the advanced earthquake early warning (EEW) system have been implemented and in operation. Currently, the CWA EEW system can issue the warning message in about 10 seconds after a strong earthquake occurring in or very near Taiwan. Starting from 2021, in order to further enhance the benefit of EEW to the practical earthquake hazard mitigation, the CWA has begun to develop the customized urban EEW system. By means of introducing the on-site earthquake early warning methods, and developing the artificial intelligence models. The systems, particularly for the big urban shallow earthquake, aim to further shorten the time of EEW to 7 seconds. That means it can give warning to the areas about 25 km away from the epicenter, and more people could be alerted before the destructive shaking.

Most cold chain logistics vehicles currently use engine-driven compressors to power refrigeration units. This increases engine wear and fuel consumption, especially during idling when unloading. With global warming and stricter carbon reduction policies, retrofitting fuel-based cold chain vehicles has become urgent to reduce emissions and fuel costs. Our solution provides an independent power system that allows refrigeration/freezing units to operate without engine idling. It uses solar panels for supplemental energy and high-performance, safe LiFePO₄ batteries to ensure stable power. This setup reduces fuel use and lowers carbon emissions during stops. The system also offers emergency power support and features remote monitoring for real-time management and quick troubleshooting. Additionally, it is fully compatible with electric logistics vehicles, requiring no redesign. This makes it a sustainable, green solution that meets future low-carbon logistics needs.

LiftMind is a non-invasive AIoT elevator add-on system that enables rapid upgrades to smart functionality without modifying existing elevator control systems. The system leverages advanced external sensors and AI technology to deliver features such as passenger flow monitoring, emergency incident alerts, user behavior analysis, elevator operation optimization, and enhanced, user-friendly riding experiences. Its open architecture seamlessly integrates with Building Management Systems (BMS) and other IoT devices, significantly improving elevator management efficiency and user satisfaction. LiftMind supports a wide range of elevator brands and models, offering flexible adaptability to various application scenarios, making it the ideal solution for smart building upgrades.

The Ministry of Environment integrates IoT and generative AI for smart environmental governance. For fire emergency response, our AI-powered system provides real-time assist during challenging periods like weekends and midnight hours. Through an AI Q&A system, emergency personnel can instantly analyze air quality data and weather information, enabling quick response and public notification. This has reduced response time by 66% and decreased public complaints from 70 cases to single digits.

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.

Handling abnormal complaints requires extensive experience and expertise. However, the resignation or retirement of senior employees often leads to knowledge loss, impacting organizational efficiency and competitiveness. Through vector models, Morale AI transforms unstructured data (such as emails, PDFs, and Word documents) into structured data and integrates it into databases, enhancing data usability. Multi-modal models combine diverse data types, including text and images, to systematically record and preserve experience. By leveraging Retrieval-Augmented Generation (RAG) technology and similarity search, the model can accurately categorize issues, identify the root causes, and recommend relevant cases, significantly reducing manual processing time and error rates. This systematic approach not only enhances response efficiency and enables real-time analysis and resolution of customer issues but also reduces repetitive work for employees, improving overall productivity and customer satisfaction.

The BSMX series of early warning modules for lithium battery thermal runaway, developed by Taiwan Intelligent Pollutant Sensing Co., Ltd., is a next-generation BMS upgrade solution that requires only simple installation to integrate with existing systems. The module is equipped with high-sensitivity sensors developed specifically for energy storage safety applications, with a rapid response time of as fast as three seconds. The module offers multiple signal output interfaces to choose from and features a proprietary smart algorithm that eliminates the need for calibration after installation. It is particularly suitable for energy storage systems that operate continuously for long periods and are difficult to maintain. E-bike version has a significant improvement in power consumption.

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.

According to the design in a 20-foot container, the noimal output power can be selected by 100/150/200 or 250 kW (optional), and the total capacity of Lithium-ion battery is 272 ~ 408 kWh (optional). The whole system, including battery, PCS and other auxiliary system. This solution can be integrated with Grid, solar PV system, Diesel genset to form a regional power system (microgrid).

Specially for micro parts with a diameter less than 2mm, the CMHT is equipped with integrate automatic feeding system, automatic monitoring and carburizing quenching furnace for real-time adjustment, innovative oil tank, collecting device and intelligent digital services. It provides the heat treatment industry with customized, intelligent production solution. Its advantages are as follows ‧ CMHT has 100% collecting rate (50~60% for traditional equipment), 99% yield rate (50~65% for traditional one). ‧ CMHT reduces heat treatment schedule from 30 days to 2 days only. Based on demand, CMHT can rapidly replace process modules to substitute whole traditional heat treatment equipment and price of CMHT is only half of traditional one. ‧ Introducing intelligent digital service function, CMHT transforms experiences of traditional equipment into data decision, reduces reliance on human experiences, sets up industrial IoT solution and provides heat treatment industry with smart manufacturing via cloud services. ‧ Won the international honor of R&D 100 Awards and Edison Awards.