A Hybrid WiFi/Bluetooth RSS Dataset with Application to Multilateration-Based Localization

Given the growing importance of Location-Based Services (LBS) in the broader Internet of Things (IoT) context, efficient and optimized location algorithms are essential. To address this, a hybrid WiFi/Bluetooth (BLT) localization algorithm is experimentally investigated in this paper. This approach uses Received Signal Strength (RSS) information to estimate target-anchors' distances, which are then fed at the input of a Least Squares (LS)-based localization algorithm to finally estimate the target position. The study relies on a dataset created by the authors with the goal of developing and evaluating RSS-based localization algorithms that incorporate the fusion of data from different technologies. The experimental results presented in this paper confirm that such an approach improves the accuracy, resilience, and robustness of location estimation and optimizes IoT services based on contextual information with respect to schemes based on a single technology.