![]() ![]() We further test the performance of the feature extraction from RSSI values. We present here the results of this preliminary approach of the early and late fusion of RSSI and accelerometer features in room-level localization. Motivated by a current project, where there is the need to locate a missing child in crowded spaces, we intend to test the added value of using an accelerometer along with RSSI for room-level localization and assess the performance of ensemble learning methods. Localization tasks with the goal to locate the room are actually classification problems. A common service offered by IoT systems is the estimation of a person’s position in indoor spaces, which is quite often achieved with the exploitation of the Received Signal Strength Indication (RSSI). Internet-of-Things (IoT) systems are used to provide remote solutions in different domains, like healthcare and security. The continuing advancements in technology have resulted in an explosion in the use of interconnected devices and sensors. ![]()
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