KAIST

<(From Left) Prof. Dong-Soo Han, Dr. Kyuho Son, Dr. Byeongcheol Moon, Dr. Sumin Ahn, Ph.D candidate Seungwoo Chae>

A Korean research team has developed a technology that enables precise indoor positioning using only a smartphone. Developed over eight years by KAIST researchers, this technology is expected to help secure critical time in missing-person searches and is being recognized as a “location sovereignty” solution that could reshape the current location service ecosystem dominated by global big tech companies such as Google and Apple.

KAIST (President Kwang Hyung Lee) announced on the 2nd pf April that a research team led by Professor Dongsoo Han of the School of Computing has developed a core technology that can build a nationwide high-precision positioning infrastructure in a short time and at low cost by combining smartphone Wi-Fi signals with real-world address data. This achievement is the result of eight years of research, during which the team filed around ten patents to enhance the technology’s completeness.

The key feature of this technology is its use of Wi-Fi signals collected by smartphones in everyday life. It can provide precise location information anywhere in the country without requiring large-scale equipment or additional infrastructure. It also maintains high accuracy in environments where GPS is weak, such as indoors, underground, or in dense high-rise areas.

In particular, this research is seen as a challenge to the location service ecosystem currently led by global platform companies. Today, most location data worldwide is accumulated and managed by a small number of big tech firms, and Korea also relies heavily on these platforms.

Most importantly, this research establishes a foundation for independently building and managing location data generated domestically. Amid ongoing debates over exporting high-resolution national maps (1:5,000 scale spatial data detailing buildings and roads), the importance of data sovereignty is growing. This technology is drawing attention as an alternative that could reduce dependence on global big tech and realize “location sovereignty.”

The research team proposed a method that automatically combines Wi-Fi signals collected during smartphone app usage with the actual address of the location. This allows the construction of a unique “signal pattern map” (signal fingerprint) for each place, with accuracy improving as more data accumulates.

In a real-world demonstration in Daejeon, using a gas meter reading app, an average of about 30 Wi-Fi signals were detected per household in apartment complexes. This confirmed that city-scale location data can be rapidly built using this approach.

<Status of Radio Map Construction in Daejeon Using a Gas Meter Reader App>

<Address-Based Automation of Wireless Signal Collection and AI-Based Location Labeling Techniques for Collected Wireless Signals>

This technology is expected to significantly reduce location errors—previously up to hundreds of meters—in emergency situations such as missing-person searches, helping secure critical response time. It can also be applied to “location-based authentication,” allowing payments only at specific locations, thereby helping prevent financial crimes such as identity theft or unauthorized remote transactions.

Furthermore, precise location data is a key infrastructure for future AI industries, including autonomous driving, robotics, and logistics. This achievement is expected to enhance competitiveness across these sectors.

<Research Use Image (AI-Generated Image)>

Professor Dongsoo Han stated, “Positioning infrastructure is not just a convenience technology but a core asset directly linked to national data sovereignty,” adding, “It is time for the government, telecom companies, and platform providers to collaborate in building an independent national positioning infrastructure.”

This research was supported by the Ministry of Science and ICT, the National Research Foundation of Korea, the National Fire Agency, and the Korea Evaluation Institute of Industrial Technology (KEIT) (Grant No. RS-2025-02313957).