Project Overview

This project aims to develop an asset-tracking system suitable for both indoor and outdoor environments. A Raspberry Pi mounted on the asset periodically broadcasts Bluetooth Low Energy (BLE) signals. Two stationary Raspberry Pis, placed at fixed and known locations, detect these signals and measure their Received Signal Strength Indicator (RSSI) values. The collected RSSI data is then transmitted to a centralized server, which estimates the asset’s position using RSSI-based distance estimation and trilateration techniques.

To support outdoor tracking, a GPS module is also interfaced with the mobile Raspberry Pi, providing accurate location coordinates when satellite connectivity is available. This hybrid approach enables seamless switching between indoor RSSI-based localization and outdoor GPS tracking. The system ensures real-time monitoring of asset location with minimal infrastructure, making it ideal for various industrial and research applications.

Introduction

Asset tracking is a critical requirement across various industries, including logistics, manufacturing, healthcare, and warehousing, where real-time location of equipment or inventory is essential for operational efficiency. Traditional tracking systems often rely on expensive infrastructure and specialized wireless networks, which may not be practical for indoor environments or budget-constrained deployments.

This project introduces a cost-effective, Raspberry Pi- based asset-tracking system designed to operate seamlessly in both indoor and outdoor settings. By leveraging Bluetooth Low Energy (BLE) and the signal strength of BLE advertisements (RSSI), the system estimates the location of an asset using trilateration.

A mobile Raspberry Pi unit attached to the asset emits periodic BLE beacons, which are picked up by two or more stationary Raspberry Pis strategically placed at fixed, known positions. These receivers record the RSSI values and forward the data to a central server, where distance estimation algorithms compute the asset's position.

For outdoor tracking, a GPS module is interfaced with the mobile Raspberry Pi to provide precise latitude and longitude coordinates when satellite connectivity is available. This hybrid approach allows the system to switch seamlessly between indoor RSSI-based localization and outdoor GPS tracking, ensuring continuous and accurate asset monitoring under varied conditions.

Literature Survey

Asset-tracking technologies have evolved significantly to support real-time monitoring of items across sectors like logistics, healthcare, and manufacturing. Several positioning methods have been explored in literature, including GPS, RFID, Wi-Fi, and Bluetooth Low Energy (BLE).

GPS is highly accurate for outdoor environments but suffers from poor indoor performance and high power consumption, making it unsuitable for indoor tracking scenarios (Zafari et al., 2019). RFID systems offer low-cost tracking but require dense reader infrastructure, especially for real-time applications (Want, 2006). Wi-Fi-based localization is commonly used due to infrastructure availability, but its accuracy is limited by RSSI fluctuations caused by multipath interference (Youssef & Agrawala, 2005).

BLE has emerged as a viable alternative due to its low power usage, ease of deployment, and compatibility with mobile devices. BLE-based localization techniques rely on the Received Signal Strength Indicator (RSSI) to estimate the distance between the asset and fixed receivers. Although RSSI is inherently noisy, filtering techniques such as moving averages or Kalman filters can improve consistency (Faragher & Harle, 2015).

WiFi SLAM: Research by Stanford explores indoor localization through Wi-fi based simultaneous localization and mapping.

Position estimation often uses trilateration, which involves calculating distances from at least three known points based on signal strength to estimate a target’s position. In contrast, triangulation uses angles of arrival from multiple reference points. While triangulation typically requires angle sensors or antenna arrays, trilateration is more suitable for BLE systems since it works with scalar RSSI values and simple hardware.

Several studies have demonstrated the use of Raspberry Pi as a cost-effective platform for BLE scanning and data aggregation. For instance, Patil et al. (2021) implemented a BLE-based indoor localization system using Raspberry Pi and achieved room-level accuracy. However, many existing solutions require extensive calibration or proprietary software.

This project builds upon such efforts by proposing a low-cost, Raspberry Pi-based asset-tracking system that uses BLE beacons and RSSI-based trilateration. By leveraging a centralized server and cloud tunneling (via ngrok), the system enables real-time tracking with minimal infrastructure, making it ideal for rapid prototyping and educational use.

Technologies Used:

Raspberry Pi Zero 2 W

Neo 7M

Flask

ngrock

Methodology

Indoor Tracking

Components:

● RPi-A and RPi-B → send BLE RSSI values

● Laptop server → receives and prints them via Flask

● ngrok → makes the server reachable over the internet

Step 1: Central Server (Laptop)

1.1 Install Flask

pip install flask

1.2 Create rssi_server.py

from flask import Flask, request

app = Flask(__name__)

@app.route("/rssi", methods=["POST"])
def receive_rssi():
    data = request.json
    rpi_id = data.get("rpi")
    rssi = data.get("rssi")
    print(f"Received from {rpi_id}: RSSI = {rssi} dBm")
    return "OK", 200

if __name__ == "__main__":
    app.run(port=5000)

1.3 Run the Server

python rssi_server.py

Step 2: Expose Server Using ngrok

2.1 Download ngrok

Visit: https://ngrok.com/download

2.2 Run ngrok

ngrok http 5000

Public URL like this is obtained:
Forwarding: https://1234abcd.ngrok.io → http://localhost:5000

Copy the URL — needed on both RPis.

Step 3: RPi-A and RPi-B Setup

Install Dependencies

pip install bluepy requests

3.1 Create rssi_sender.py on Each RPi
Changed RPi_ID to "RPi-A" or "RPi-B" as needed.

import requests
import time
from bluepy.btle import Scanner

NGROK_URL = "https://1234abcd.ngrok.io/rssi"

RPi_ID = "RPi-A"  # Change to "RPi-B" on the second Raspberry Pi
TARGET_DEVICE_MAC = "XX:XX:XX:XX:XX:XX"  # mobile BLE MAC address

def scan_for_device(mac_addr):
    scanner = Scanner()
    devices = scanner.scan(3.0)  # 3-second BLE scan
    for dev in devices:
        if dev.addr.lower() == mac_addr.lower():
            return dev.rssi
    return None

while True:
    rssi = scan_for_device(TARGET_DEVICE_MAC)
    if rssi is not None:
        data = {"rpi": RPi_ID, "rssi": rssi}
        try:
            res = requests.post(NGROK_URL, json=data)
            print(f"Sent {rssi} dBm from {RPi_ID}")
        except Exception as e:
            print(f"Error sending data: {e}")
    else:
        print(f"{RPi_ID} couldn't find target device.")
    time.sleep(2)

Outdoor Tracking

To ensure accurate asset positioning in outdoor environments, the system integrates a NEO-7M GPS module with the mobile Raspberry Pi. The NEO-7M is known for its high sensitivity and reliable performance, making it an ideal choice for outdoor tracking.

Check this out!

Results

The asset-tracking system was tested in both indoor and outdoor environments. Indoors, RSSI values were successfully received from both RPi-A and RPi-B, and approximate position estimates were derived using trilateration. Outdoors, the GPS module interfaced with the mobile Raspberry Pi provided accurate latitude and longitude coordinates, enabling location tracking even in the absence of BLE receivers.

Key Observations:

• Real-time RSSI Logging: The central server received RSSI values from both RPis at ~2-second intervals. This confirmed stable communication between the RPis and the server.

• BLE Detection Range: RSSI values showed a consistent inverse relationship with distance—stronger (less negative) RSSI values were observed when the asset was closer to the receiver.

• RSSI Fluctuations: Some fluctuation in RSSI values was observed due to environmental factors (e.g., obstacles, reflections). Smoothing techniques (e.g., moving average) can be applied to enhance accuracy.

• Position Estimation: Although the current setup uses only two receivers (which gives approximate location via rough distance estimation), trilateration logic can be extended by adding a third fixed node to improve localization precision.

• Outdoor Tracking Observations: The GPS module provided coordinates with an average accuracy of 3–5 meters in open sky conditions.

References

• Faragher, R., & Harle, R. (2015). Location Fingerprinting With Bluetooth Low Energy Beacons. IEEE Journal on Selected Areas in Communications, 33(11), 2418–2428.

• Zafari, F., Gkelias, A., & Leung, K. K. (2019). A Survey of Indoor Localization Systems and Technologies. IEEE Communications Surveys & Tutorials, 21(3), 2568–2599.

• Groves, P. D. (2013). Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems. Artech House.

• Misra, P., & Enge, P. (2006). Global Positioning System: Signals, Measurements, and Performance. Ganga-Jamuna Press.

• Patil, A., Desai, A., & Jadhav, R. (2021). BLE-based Indoor Positioning System Using Raspberry Pi. International Journal of Engineering Research & Technology (IJERT), 10(4).