Semiconductor ALD (Atomic Layer Deposition) is a precision-critical process involving sequential stages and large-scale time-series data from recipe settings and sensors. This paper proposes an edge AI architecture combining lightweight models on edge devices with centralized model training. The system enables early predictions from recipe data, real-time adjustments via sensor inputs, and continuous refinement using post-process outcomes. Only extracted features and result data (film thickness and uniformity) are transmitted to reduce communication overhead and protect sensitive data. The architecture supports performance monitoring and seamless model redeployment, adapting to changing equipment and environments. This approach improves product quality, reduces defect rates, and enhances manufacturing adaptability.

This paper presents the design and implementation of a system for processing and analyzing large-scale time-series data generated in semiconductor deposition processes. By adopting a real-time data collection and analysis architecture divided into Edge and Server layers, the system enables continuous retraining and updating of machine learning models based on real-time data streams. The evaluation of the model's performance demonstrates that additional training data significantly improves the model's accuracy in predicting process outcomes. Our approach not only provides a practical solution for real-time decision-making support in semiconductor manufacturing but also offers a scalable and adaptable framework applicable to various industrial sectors requiring real-time data analysis and processing. The results highlight the potential of integrating big data and artificial intelligence technologies to drive industrial innovation and optimize manufacturing processes.

The system is environment monitoring service based on Wireless Sensor Networks (WSN). Users can know temperature, humidity, light, and CO2 level in real time. Excessive electricity consumption by lighting in the office can be saved and the quality of the office environment can become better by controlling lighting and CO2 level.

Designing a new sensor board is costly, especially for a production in a small quantity. By modularizing common functionalities, a large portion of the sensor board can be reused. In this work, we propose an Extension Board, a sensor board which is modularized into 3 parts. Power module, and MCU and RF module are shared, and only sensing module is redesigned for each sensor board. Diverse sensing modules are produced. The process was simple and inexpensive.

PEAKSAVE system is an energy monitoring service based on Wireless Sensor Networks (WSN). A smartphone is an important point of a system. Users can understand the energy consumption of each electric device and lighting in real time. Responsive energy monitoring service can help in reducing the waste of energy, especially in shaving electric load in a peak time.

Vehicle tracking system is built for a container terminal. In the system, reference nodes are fixed at known locations. They provide reference locations to a mobile node, which is installed in a vehicle. Received Signal Strength Indicator (RSSI) is measured, and the measured data is gathered in a backend server. The backend server analyzes the data, and estimates the location of the mobile node. Filters are added to handle transient fluctuation in RF environment. SonnoOne mote is made to be used as a mobile node. It contains MG2455 chip from RadioPulse, which combines 8051 MCU and IEEE 802.15.4 radio. We expect the cost will be below $20, and the low cost will enable a large number of mobile nodes to be deployed in a container terminal.

Micro Energy Efficiency System (MEES) provides energy saving while enabling each individual office in a large building to control heating, cooling, and electricity with its own policy. The usage of a decentralized independent control of each office, rather than a centralized one, is common in Korea. MEES provides measuring and controlling points at multiple granularities. An installation became easy and efficient using QR code, and the user configuration through an energy web portal further enhances the energy efficiency.

U-City is a city where diverse public information is provided through IT technology. In the past, IT infrastructure for public information was not considered in city planning. However, in recent construction of new cities, this kind of infrastructure is becoming necessary. Safety, transportation, and weather information are gathered and provided to residents through the Internet, mobile devices, etc [1]. To provide this kind of information, local governments operates U-City control center, and there is an issue of increased operating cost of the city. To solve this problem, U-City business platform is designed which can incorporate diverse commercial services. Different from public information platform, a private sector can participate and provide services. This work suggests Business Service Platform (BSP) system structure where USN-based services can be provided in U-City business platform. Then, USN-based service applications are introduced on U-City platform. Business Service Platform (BSP) is a service platform of U-City for services in a private sector, and provides overall functionalities required for the creation, distribution, and billing. A service provider for U-City can develop a new application using BSP. It can also register, distribute, and handle billing using functionalities of BSP. BSP started with a target advertisement service related to public transportation information as its initial service, however USN technology in diverse areas are expected to be applied to BSP in the future. To apply such diverse USN to BSP, a general framework should be provided to integrate USN, and a system is needed that each service provider can control. In this demo, we will explain U-City service platform, which will be deployed at CHEONGRA zone of Korea, and how it is integrated to USN to provide healthcare, smart grid services, and finally major system components.

HONS (Hybrid Open Networking Stack) is a system which can service diverse types of sensor nodes as a single network. By defining open packet format of IEEE 802.15.4 standard, it can form low-power multi-hop network of diverse sensors. HONS system is composed of routers and wireless sensor nodes. Routers are motes with wired power and form the basic backbone of the multi-hop network. Wireless sensor nodes are motes powered by battery, and operate in a lower-power mode, and are attached to the multi-hop network formed by routers.

We demonstrate a working system that utilizes an eventual consistent reliability model for wireless light control application. Initial results using only resource limited nodes, such as the popular mote platform, are promising; we achieve great end-to-end reliability for light control, with latency well within human-time scale.