@techreport{oai:ipsj.ixsq.nii.ac.jp:00222062,
 author = {Yuqiao, Wang and Takuya, Maekawa and Yuqiao, Wang and Takuya, Maekawa},
 issue = {16},
 month = {Nov},
 note = {Estimating the physical distance between mobile devices such as smartphones with their Wi-Fi modules in an indoor environment has many potential real-world applications such as enhancing indoor navigation, analyzing and discovering communities, Wi-Fi geo-fencing, etc. Such distance estimation tasks have been conducted using Received Signal Strength Indication (RSSI), which leverages the strengths of signals from nearby Wi-Fi Access Points (APs). However, the imprecision of RSSI measurements has limited the performance of the RSSI-based methods. Recently, IEEE 802.11mc introduced Wi-Fi Round Trip Time (RTT) protocol, which enables distance estimation between devices and nearby APs by calculating the time-of-flight of signals, and has greatly improved the accuracy of indoor ranging. Therefore, this study presents a novel method for distance estimation between devices using Wi-Fi RTT, leveraging a graph neural network (GNN) to fully capture the geometric information among smartphones and nearby APs., Estimating the physical distance between mobile devices such as smartphones with their Wi-Fi modules in an indoor environment has many potential real-world applications such as enhancing indoor navigation, analyzing and discovering communities, Wi-Fi geo-fencing, etc. Such distance estimation tasks have been conducted using Received Signal Strength Indication (RSSI), which leverages the strengths of signals from nearby Wi-Fi Access Points (APs). However, the imprecision of RSSI measurements has limited the performance of the RSSI-based methods. Recently, IEEE 802.11mc introduced Wi-Fi Round Trip Time (RTT) protocol, which enables distance estimation between devices and nearby APs by calculating the time-of-flight of signals, and has greatly improved the accuracy of indoor ranging. Therefore, this study presents a novel method for distance estimation between devices using Wi-Fi RTT, leveraging a graph neural network (GNN) to fully capture the geometric information among smartphones and nearby APs.},
 title = {Preliminary Investigation of Distance Estimation between Smartphones via Wi-Fi Round Trip Time},
 year = {2022}
}