Fog-Enabled Smart Security System

A real-time, low-latency intrusion detection system using Fog/Edge computing principles on an ESP32. This project avoids the latency and reliability issues of cloud-only systems by processing sensor data locally.

The Problem with Cloud-Only IoT Security

Traditional smart security systems rely on sending all sensor data to the cloud for processing. This approach has critical flaws:

• High Latency: The round-trip (Sensor -> Cloud -> User Alert) can take several seconds, which is too long for a security event.
• Poor Reliability: If your internet connection fails, the entire security system is blind.
• Bandwidth Waste: Constantly streaming data 24/7 (even "no motion detected") wastes bandwidth and energy.

Our Solution: Fog Computing at the Edge

This system moves the "intelligence" to the edge (the "Fog"), right where the sensors are.

1. The Fog Node (ESP32): The ESP32 processes data from the PIR and LDR sensors instantly. It makes decisions locally, in milliseconds.
2. The Cloud (Blynk): The cloud is only used for what it's best at: receiving critical alerts (e.g., "Intrusion Detected!") and displaying the system status on a global dashboard.

This hybrid model gives you the best of both worlds: the instant response of a local system and the global accessibility of a cloud app.

Key Features

• Low-Latency Alerts: Local processing provides near-instantaneous alerts (~5-10ms) vs. seconds for cloud-only systems.
• High Efficiency: Reduces network traffic by over 99% by only sending critical alerts, not constant data streams.
• Sensor Fusion: Combines a PIR (motion) and LDR (light) sensor. An alert is only triggered if motion is detected and it's dark, reducing false positives.
• Offline Functionality: The core security logic works even if the internet is down.
• Global Dashboard: Uses the Blynk platform to send mobile notifications and display real-time sensor status from anywhere.

Hardware & Software

Hardware

• ESP32 Microcontroller
• HC-SR501 PIR Motion Sensor
• LDR (Light Dependent Resistor)
• 10kΩ Resistor
• Breadboard and Jumper Wires

Software

• Wokwi (for simulation)
• Blynk (for mobile dashboard and notifications)
• Arduino IDE or PlatformIO

How to Use

1. Clone this Repository:

   git clone [https://github.com/YOUR_USERNAME/YOUR_REPO_NAME.git](https://github.com/YOUR_USERNAME/YOUR_REPO_NAME.git)

2. Set up Hardware:

   • Wire the components according to the circuit diagram (see diagram.json or the project report).
   • PIR Sensor: VCC -> 3V3, GND -> GND, OUT -> GPIO 27
   • LDR Sensor: VCC -> 3V3, GND -> GND, AO (with 10k pulldown) -> GPIO 34

3. Configure Software:

   • Open sketch.ino in the Arduino IDE.
   • Install the ESP32 board definitions and the BlynkSimpleEsp32 library.
   • Set up a new project in the Blynk app to get your Auth Token.
   • Update the sketch.ino file with your credentials:

     #define BLYNK_AUTH_TOKEN "YOUR_BLYNK_TOKEN"
     char ssid[] = "YOUR_WIFI_SSID";
     char pass[] = "YOUR_WIFI_PASSWORD";

4. Deploy:

   • Connect your ESP32 to your computer.
   • Select the correct board and port in the Arduino IDE.
   • Click Upload.
   • Open the Serial Monitor to watch it connect, and use the Blynk app to monitor the system.

Project Files

• /sketch.ino: The main Arduino code for the ESP32.
• /diagram.json: The circuit diagram for the Wokwi simulator.
• /project_report.pdf: The full academic report for this project.
• /README.md: This file.

Future Work

• Integrate a camera (like an ESP32-CAM) for local image verification.
• Implement a TinyML model to distinguish between a person and a pet.
• Add a deep-sleep mode to optimize for battery power.