Performance Evaluation of an Artificial Neural Network Model for RSSI-Based Indoor Localization of Workplace Assets
Keywords:
Indoor Positioning System, Bluetooth Low Energy, RSSI, Artificial Neural Network, Work Device LocalizationAbstract
The inefficiency of conventional manual methods for tracking work equipment often results in asset record inaccuracies and time-consuming physical inventory processes. This research evaluates the performance of an Artificial Neural Network (ANN) implemented within a Bluetooth Low Energy (BLE)-based Indoor Positioning System (IPS) that leverages Received Signal Strength Indicator (RSSI) data. The proposed system utilizes a Multilayer Perceptron (MLP) with a 3-14-7-3 configuration, featuring Rectified Linear Unit (ReLU) activations in its hidden layers and a Softmax function at the output, optimized using the Adam algorithm. The model was trained and validated on a balanced dataset comprising 600 RSSI samples collected from three distinct rooms, divided into 80% for training and 20% for testing. The experimental results reveal a high-performing model, achieving an accuracy of 90.83%, a precision of 90.88%, a recall of 90.83%, an F1-Score of 90.85%, and a Mean Absolute Error (MAE) of 0.1083. These outcomes confirm the ANN's robust capability to interpret the complex, non-linear signal fluctuations typical of indoor environments for reliable device localization.
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