Category: Bluetooth LE

The new paper Passive Indoor People Counting by Bluetooth Signal Deformation Analysis with Deep Learning, proposes a method for counting people in indoor spaces using Bluetooth Low Energy (BLE) signals and deep learning techniques. The goal is to offer a privacy-preserving, device-free, and non-intrusive solution for occupancy monitoring in environments where camera use is inappropriate, such as hospitals and laboratories.

The method relies on analysing how human presence distorts BLE signals, particularly their Received Signal Strength Indicator (RSSI). Unlike traditional camera-based or wearable solutions, this approach does not require people to carry any devices. BLE beacons emit signals that, when passing through or reflecting off human bodies, become altered in predictable ways. These signal deformations are then analysed using deep neural networks to estimate the number of occupants.

Five deep learning models were evaluated: Dense Neural Network (DenseNN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), a hybrid CNN+LSTM model, and a Transformer-based model. Both classification and regression approaches were tested. The hybrid CNN+LSTM model consistently outperformed the others in terms of accuracy and mean absolute error.

A key strength of the method is its flexibility and efficiency in new environments. The model is pre-trained on a large, varied dataset, and only requires a brief fine-tuning session with a small sample of data from the new location. In some cases, the model could even interpolate occupancy values it was not explicitly trained on. This means that with minimal setup time, the system can be deployed effectively in a range of environments, achieving accuracies of over 96%, and in some configurations even exceeding 99%.

The authors also developed a comprehensive data preprocessing and filtering strategy to account for signal noise and variability caused by human movement and the BLE protocol’s frequency hopping. They configured BLE beacons to transmit on fixed channels to maintain consistency in RSSI measurements.

In conclusion, the proposed BLE-based passive people counting system demonstrates high adaptability, accuracy, and practicality for real-time occupancy monitoring, with notable advantages over existing BLE and even some WiFi-based solutions. However, it still requires some calibration in each new environment due to limitations in generalising across different room geometries. Future work aims to develop a model that can generalise without this fine-tuning step.

There’s an interesting new paper titled Modeling the Trade-off between Throughput and Reliability in a Bluetooth Low Energy Connection that provides a comprehensive analysis of the performance of Bluetooth Low Energy (BLE) communication in terms of throughput and reliability under various interference conditions.

The primary objective of the study was to develop and validate mathematical models that predict the throughput and reliability of BLE connections under interference.

Two models were developed, a Throughput Model using a Markov chain approach to predict the throughput of BLE connections under interference, and a Reliability Model that quantified the reliability of BLE connections by considering various transmission parameters and interference levels.

The throughput model was validated through extensive practical experiments under different interference scenarios. The experiments involved varying parameters such as packet length, number of packets, and connection intervals. The results showed a close match between the theoretical predictions and the experimental data, highlighting the accuracy of the models.

As might be expected, the study found that the interference level in the environment significantly affects both throughput and reliability. Higher interference levels (higher BER) reduce both metrics.

There is a non-linear relationship between payload size and throughput. While larger payload sizes can increase throughput in low-interference environments, they significantly reduce reliability and throughput in high-interference conditions.

Increasing the connection interval improves energy efficiency but reduces throughput without affecting reliability. This suggests that connection interval adjustments can optimise energy usage without compromising communication reliability.

Bluetooth devices should be configured based on the specific interference environment they will operate in. For instance, smaller payload sizes are preferable in high-interference environments to maintain reliability.