View Proposal

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Proposer

Zi Hau Chin

Title

Robust Proximity Zone Classification using Adaptive Filtering and Machine Learning for BLE Devices

Goal

To develop a robust and adaptive system for precise BLE device proximity classification by integrating real-time RSSI smoothing with machine learning.

Description

This project develops a smart sensing system that accurately classifies the proximity of Bluetooth Low Energy (BLE) devices into defined zones. It leverages an Adaptive Kalman Filter to dynamically denoise noisy Received Signal Strength Indicator (RSSI) data and employs machine learning techniques to establish robust proximity zones resilient to environmental variations. The core work involves: 1. Ingest Raw RSSI: Set up the IanMercer pi-sniffer to collect raw RSSI data from BLE devices and stream it (e.g., via MQTT) to a Python script. 2. Apply Adaptive Kalman Filter (AKF): In Python, implement an AKF for each unique device to smooth the noisy raw RSSI in real-time, dynamically adjusting filter parameters. 3. Train ML Classifier (Offline): Collect labeled training data using the smoothed RSSI from your AKF in various environments, then use scikit-learn to train a machine learning model to classify proximity zones. 4. Integrate & Deploy: Load the trained ML model into your real-time Python script, so that smoothed RSSI values are immediately classified into proximity zones. 5. Evaluation!

Resources

Raspberry Pi 4b

Background

C Programming, Python, Mathematics, IoT

Url

Difficulty Level

High

Ethical Approval

None

Number Of Students

1

Supervisor

Zi Hau Chin

Keywords

bluetooth low energy (ble), rssi, proximity detection, machine learning, iot

Degrees

Bachelor of Science in Computing Science