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In this short paper, we prove that smartphones connected to Wi-Fi can be detected (scanned) easily with a Raspberry Pi help. According to the observation, the smartphone eventually broadcasts some packets of data containing MAC layers data. The period of broadcasting data depends on the smartphone’s state (active scanning/sleep). Besides MAC layers data, we also detect/capture other parameters, i.e., wireless signature data transmitted by smartphone (RSSI) and Time-stamp. The RSSI value measured in this test has a range from –30 dBm to –80 dBm. The result proves that different smartphones give different RSSI values (each smartphone emits different power strength). The RSSI value has more significant changes in a short-range (in this test result, 1 to 10 meters) and less significant change in a long-distance (above 20 meters). MAC address, time-stamp, and RSSI scanned/captured successfully through Raspberry Pi from the smartphones can be used as a reference for various purposes/applications in future work, such as Wi-Fi scanning/tracking system.

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In a modern meeting room, a smart system to make attendance quickly is mandatory. Most of the existing systems perform manual attendance, such as registration and fingerprint. Despite the fingerprint method can reject the Unknown person and give the grant access to the Known person, it will take time to register first a person one-by-one. Moreover, it is possible to create long queues for fingerprint checking before entering the meeting room. Machine learning, along with the Internet of Things (IoT) technology is the best solution; it offers many advantages when applied in the meeting rooms. Generally, the method used is to create a presence by detecting faces. In this paper, we present a facial recognition authentication based on machine learning technology for connection to the meeting rooms. Furthermore, specific website to display the detection result and data storage design testing is developed. The method uses 1) the Dlib library for deep learning purposes, 2) OpenCV for video camera processing, and 3) Face Recognition for Dlib processing. The proposed system allows placing the multiple cameras in a meeting room as needed. However, in this work, we only used one camera as the main system. Tests conducted include identification of one Known person, identification of one Unknown person, identification of two people, and three people. The parameter to be focused is the required time in detecting the number of faces recorded by the camera. The results reveal that the face can be recognized or not recognized, then it will be displayed on the website.