Design and Implementation of an IOT-Based Anti-Theft Mat Using Arduino Nano, ESP32 Camera, and Piezo Sensors

Abstract

As urbanization and crime rates surge, conventional anti-theft security systems face vulnerabilities. This project introduces an innovative solution – the IoT-based Anti-theft Flooring System utilizing ESP32 camera and Arduino Nano. By monitoring floor movement, the system ensures comprehensive security. Activated when occupants leave, the system detects intruders through a secure flooring tile, triggering an alert via IoT. The ESP32 & Arduino Nano powered setup includes a Piezo sensor. Upon detecting an intruder, the system captures and transmits images. This novel approach enhances home security by countering evasion tactics and providing real-time images to homeowners.

Introduction

I. INTRODUCTION

With the rapid advancement of IoT technologies, the concept of interconnected devices has permeated various domains, including security systems. Traditional security measures, although effective to some extent, often lack real-time monitoring and proactive response capabilities. In response to this challenge, we introduce an IoT-based anti-theft mat designed to bolster security measures by employing a combination of hardware components and intelligent software algorithms. The anti-theft mat serves as a vigilant guardian, detecting unauthorized access attempts and promptly notifying the user, thereby enhancing overall security and peace of mind.

II. SYSTEM ARCHITECTURE

The proposed anti-theft mat consists of three primary components: Arduino Nano microcontroller, ESP32 camera module, and piezo sensors. The Arduino Nano serves as the central processing unit, responsible for sensor data acquisition, threshold-based decision making, and signaling to the ESP32 camera module. The ESP32 camera module, equipped with a camera, captures visual evidence upon triggering and communicates with the user via Telegram messaging service. Piezo sensors embedded within the mat detect pressure changes, indicative of potential intrusion attempts.

III. KEY COMPONENTS

A. Hardware Components

1) Arduino Nano

The Arduino Nano stands out as a closely packed  and flexible micro controller board centered around the ATmega328P micro controller chip. It is a vita part of the Arduino family of development boards, renowned for its ease of use and wide-ranging applications in electronics prototyping and projects.

The Arduino Nano is ideal for projects where space is limited and its small form factor allows for flawless amalgamation into various devices and applications,  making it suitable for projects with space constraints or those requiring portability. Despite its compact size, the Nano retains most of the features found in its larger counterparts, such as the Arduino Uno, including digital and analog input/output pins, PWM (Pulse Width Modulation) o results, UART (Universal Asynchronous Receiver-Transmitter) communication, andI2C(Inter-Integrated Circuit)and SPI (Serial Peripheral Interface) interfaces. Furthermore, the Arduino Nano can be programmed effortlessly using the Arduino IDE (Integrated Development Environment), allowing users to write, compile, and upload code easily to the board. Its affordability, versatility, and ease of programming make the Arduino Nano a popular choice among hobbyists, students, and professionals for a wide range of electronic projects, from robotics and automation to IoT (Internet of Things) applications and beyond.

The Arduino Nano serves as a vital component in the anti-theft flooring mat system, contributing to data processing and control functions. As the central processing unit, the Arduino Nano processes signals received from the piezo sensors and coordinates the activation of the ESP32 Camera module. Its role encompasses interpreting sensor data, executing predefined algorithms for intrusion detection, and triggering alerts when unauthorized activity is detected. Additionally, the Arduino Nano facilitates communication between the piezo sensors, ESP32 Camera module.

The ESP32 Camera module is widely used in projects ranging from security systems and surveillance cameras to smart home devices and industrial monitoring applications.

The ESP32 Camera module serves as the primary sensor for detecting and recording unauthorized movement on the mat's surface. Its functionality includes capturing high-resolution images upon activation by the pressure sensors embedded within the mat and sending the images to the House owner via Telegram. These images provide visual evidence of potential intrusions and facilitate immediate assessment and response by the homeowner.

3) Piezo Sensors

Piezo sensors, short for piezoelectric sensors, are devices that produce  an electrical charge in feedback to mechanical stress or pressure. They utilize the piezoelectric effect, which is the  characteristics of certain materials to produce an electric potential when subjected to mechanical distortion. Piezo sensors consist of a piezoelectric material sandwiched between two conductive electrodes.

When pressure or force is applied to the piezo sensor, it causes the piezoelectric material to deform, resulting in a change in electrical voltage or current across the electrodes. This change in voltage or current can be measured and one can use to discern  the timing  and magnitude of the applied force.Piezo sensors are commonly used in various applications, including touch-sensitive input devices, vibration detection, acoustic sensing, pressure monitoring, and impact detection. Due to their high sensitivity, fast response time, and rugged construction, piezo sensors are used for applications where precise and reliable sensing of mechanical stimuli is required.

Conclusion

In conclusion, the proposed IoT-based anti-theft mat offers an effective solution for enhancing security measures in residential and commercial premises. By leveraging the capabilities of Arduino Nano, ESP32 camera module, and piezo sensors, the system provides real-time intrusion detection and timely notifications to the user, enabling proactive response to potential security threats. Future enhancements may include integration with cloud-based surveillance platforms, machine learning algorithms for anomaly detection, and scalability to accommodate larger deployment environments.

References

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Copyright

Copyright © 2024 Dr. Krishna Samalla, Lowkya Chandaka, Linga Ushaswini, Malyala Likhitha , Maddela Jahnavi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.