TA : Social Distancing Detector menggunakan Bluetooth Berbasis Website

Seluruh dunia dikejutkan dengan wabah virus COVID-19, korban dari virus ini tidak sedikit yang ada diseluruh dunia. Salah satu cara untuk mencegah tertularnya virus COVID-19 adalah dengan menjaga jarak minimal dua meter dengan orang lain. Namun banyak orang yang bisa mengetahui jarak antar orang lain dengan perkiraan. Tujuan dari penelitian ini adalah menentukan jarak antar orang dengan menerapkan Kalman filter dan dimonitoring dari website. Dengan memanfaatkan teknologi Bluetooth yang merupakan salah satu teknologi yang dapat memperkirakan jarak dengan Received Signal Strength Indicator (RSSI). RSSI dikonversi menjadi jarak dengan menggunakan model log distance path loss. RSSI akan melewati Kalman filter untuk memberikan hasil yang stabil. Bluetooth sebagai End node akan mengirimkan data ke webserver menggunakan komunikasi LoRa. Kemudian website akan mengambil data dari database untuk monitoring. Berdasarkan hasil pengujian menunjukkan bahwa semakin jauh jarak, nilai RSSI akan semakin kecil. Penerapan Kalman filter dapat memperkecil kesalahan konversi jarak sebesar 42,649%. Penerapan kalman filter pada komunikasi 3 end node memiliki error terkecil 2,75%. Pengujian transmisi data dilakukan selama 19 menit dengan tingkat keberhasilan 100%

Real-time localisation system for GPS denied open areas using smart street furniture

Real-time measurement of crowd dynamics has been attracting significant interest, as it has many applications including real-time monitoring of emergencies and evacuation plans. To effectively measure crowd behaviour, an accurate estimate for pedestrians’ locations is required. However, estimating pedestrians’ locations is a great challenge especially for open areas with poor Global Positioning System (GPS) signal reception and/or lack of infrastructure to install expensive solutions such as video-based systems. Street furniture assets such as rubbish bins have become smart, as they have been equipped with low-power sensors. Currently, their role is limited to certain applications such as waste management. We believe that the role of street furniture can be extended to include building real-time localisation systems as street furniture provides excellent coverage across different areas such as parks, streets, homes, universities. In this thesis, we propose a novel wireless sensor network architecture designed for smart street furniture. We extend the functionality of sensor nodes to act as soft Access Point (AP), sensing Wifi signals received from surrounding Wifi-enabled devices. Our proposed architecture includes a real-time and low-power design for sensor nodes. We attached sensor nodes to rubbish bins located in a busy GPS denied open area at Murdoch University (Perth, Western Australia), known as Bush Court. This enabled us to introduce two unique Wifi-based localisation datasets: the first is the Fingerprint dataset called MurdochBushCourtLoC-FP (MBCLFP) in which four users generated Wifi fingerprints for all available cells in the gridded Bush Court, called Reference Points (RPs), using their smartphones, and the second is the APs dataset called MurdochBushCourtLoC-AP (MBCLAP) that includes auto-generated records received from over 1000 users’ devices. Finally, we developed a real-time localisation approach based on the two datasets using a four-layer deep learning classifier. The approach includes a light-weight algorithm to label the MBCLAP dataset using the MBCLFP dataset and convert the MBCLAP dataset to be synchronous. With the use of our proposed approach, up to 19% improvement in location prediction is achieved