The number of disaster occurrences around the world based on the climate changes due to the global warming has been indicating an increase. To prevent and cope with such disaster, a number of researches have been actively conducted to combine the user location service as well as the sensor network technology into the expanded IoT to detect the disaster at early stages. However, due to the appearance of the new technologies, the scope of the security threat to the pre-existing system has been expanding. In this thesis, the D-SASS using the beacon to provide the notification service to the disaster-involved region and the safe service to the users is proposed. The LEA Algorithm is applied to the proposed system to design the beacon protocol collected from the smartphone to safely receive the notification information as well as to provide the confidentiality during the data transfer between smartphone and notification server.
Background/Objectives: With the development of IT, accidents of industrial secret leakage have occurred more than before. Such acci-dents are mostly caused by insiders. Methods/Statistical analysis: An existing access control system uses RFID and NFC tag. The system saves only the final location in-formation in DB. For the reason, it is hard to track a user’s location data in real time. However, a beacon-based access control system saves a user’s location information in DB in real time. By analyzing the location information of DB, it is possible to track a user. Findings: Beacons are used for determining a user’s location. The determined location information is converted into location data which is saved into DB. The location data is converted into coordinates. The converted coordinate data is analyzed for understanding a user’s behavior pattern. In the pattern analysis, if a user takes an abnormal behavior, policy-based response is performed. The user behavior pattern analysis system proposed in this study is able to respond to an accident in real time. Therefore, it is expected to contribute to reducing the number of industrial secret leakage accidents caused by insiders. Improvements/Applications: This study designs a model that analyzes behavior pattern by using the indoor location data of a user based on beacon.
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