| dc.description.abstract | Location-based service is one of the
primary services with high demand on the
Internet of Things (IoT) applications. However,
indoor position estimation is challenging due to
interference and the inability to use GPS in indoor
environments. Among few feasible solutions for
this problem are Received Signal Strength
Indicator (RSSI)-based indoor position
estimation, one of the emerging best contenders.
This research conducts a comparative study on
trilateration techniques versus supervised
learning models for estimating the position of a
mobile node in an indoor environment. For the
experiment, an existing dataset available publicly
is used. The experiment testbed consists of three
beacon sensor nodes designed using Bluetooth
Low Energy (BLE) wireless technology and one
mobile node. The RSSI readings at the mobile
node from three stationary beacon wireless
access nodes are used. Three popular regression
models, namely, Decision Tree Regression (DTR),
Random Forest Regression (RFR), and Support
Vector Regression (SVR) algorithms were trained
using the dataset. Also, trilateration techniques
were performed to obtain the estimated location.
The Mean Square Error (MSE) was utilized to
analyse the model performance. Out of the three
regression models and Trilateration tested, RFR
showed better position estimation in indoor
environments. | en_US |
