Description:
The IoT-Based Load Monitoring System Using ESP32 is an advanced project designed to monitor and control electrical devices in real-time through an Internet of Things (IoT) framework. Leveraging the capabilities of the ESP32 microcontroller, this system integrates relays, current sensors, and a mobile application to manage and oversee the power consumption of connected devices. The primary goal is to ensure efficient power management, prevent overloading, and provide users with remote control capabilities. The system's design includes real-time monitoring of current loads, automated control features, and user notifications, all managed via an intuitive mobile app interface.

Objectives
Real-Time Monitoring: To continuously measure and display the current consumption of up to nine connected devices using a current sensor.
Remote Control: To enable users to turn devices on or off remotely through a mobile application, utilizing MQTT (Message Queuing Telemetry Transport) protocol for seamless IoT communication.
Overload Protection: To set and monitor threshold values for current consumption, providing warnings when thresholds are exceeded and automatically turning off devices to prevent damage or safety hazards.
User Interface: To design a user-friendly mobile application that allows for easy control and monitoring of devices, and provides real-time feedback on current consumption.
Data Display: To present real-time data and status updates on a 20x4 LCD screen integrated with the system.

Key Features
Nine Relay Control: Ability to control up to nine electrical devices independently through relays, each of which can be switched on or off via the mobile app.
Current Sensing and Monitoring: Utilization of a current sensor to measure the power consumption of each device and display this information in real-time.
Threshold Alerts: Configurable current load thresholds that trigger warnings and automatic device shutdowns to prevent overloading.
Mobile App Integration: A custom mobile application developed for both Android and iOS platforms, offering users control and monitoring capabilities via MQTT protocol.
Real-Time LCD Display: A 20x4 LCD screen to provide immediate visual feedback on the status of devices and current consumption.
Automated Safety Mechanism: Automatic disconnection of all devices if the current load exceeds the set threshold for a specified duration.

Application Areas
Home Automation: Enhancing home automation systems by adding load monitoring and control capabilities to household appliances and devices.
Industrial Monitoring: Implementing load monitoring in industrial settings to manage and control machinery, ensuring safe operation and preventing overloads.
Energy Management: Assisting in energy management and efficiency by providing insights into power consumption and enabling remote control of devices.
Smart Buildings: Integrating with smart building systems to manage electrical loads and enhance overall building automation.
Remote Facilities: Monitoring and controlling electrical devices in remote or hard-to-access locations where direct supervision is not feasible.

Detailed Working of IoT-Based Load Monitoring System Using ESP32

Device Control and Relay Operation:

The ESP32 microcontroller interfaces with nine relays, each connected to a separate electrical device.
The mobile application sends commands to the ESP32 via MQTT protocol to switch relays on or off, thereby controlling the connected devices.

Current Measurement:

A current sensor is integrated into the system to measure the electrical current flowing through each device.
The ESP32 processes this data to calculate and display the current consumption of each device in real-time.

Threshold Configuration and Alerts:

Users can set a threshold current value through the mobile app.
If the current consumption of any device exceeds this threshold, the system triggers a warning.
If the overload condition persists, the system automatically turns off all devices to prevent damage or safety risks.

Data Display:

Current measurements and device status are displayed on a 20x4 LCD screen for immediate visual feedback.
The mobile app also reflects real-time data and device status, providing users with a comprehensive view of the system.

System Integration:

The ESP32 microcontroller acts as the central hub, coordinating between the relays, current sensors, LCD display, and mobile app.
The MQTT protocol ensures reliable communication between the mobile app and the ESP32, enabling real-time control and monitoring.

Modules Used to Make IoT-Based Load Monitoring System Using ESP32

ESP32 Microcontroller Module: Serves as the main control unit for processing data and managing device operations.

Relay Module: Used to control the on/off state of up to nine electrical devices.

Current Sensor Module: Measures the current consumption of connected devices.

20x4 LCD Display: Provides real-time visual feedback on the current status and measurements.

MQTT Protocol: Facilitates communication between the ESP32 and the mobile application for remote control and monitoring.

Components Used in IoT-Based Load Monitoring System Using ESP32

ESP32 Development Board
9 Channel Relay Module
Current Sensor (e.g., ACS712)
20x4 LCD Display Module
Power Supply (for ESP32 and peripherals)
Connecting Wires and Breadboard
Mobile Application (custom-developed)
MQTT Broker (server)
Enclosure (for housing the electronics)

Other Possible Projects Using This Project Kit

Smart Energy Meter: Create an energy meter system that tracks and analyzes energy consumption across multiple devices.

Home Security System: Integrate load monitoring with a security system to alert users about unusual power usage or tampering with devices.

Industrial Equipment Monitoring: Expand the system for industrial use to monitor and control machinery, with additional sensors for temperature, humidity, etc.

Smart Agriculture: Adapt the system for agricultural settings to control and monitor irrigation systems and other electrical equipment.

Remote Site Management: Utilize the system in remote or off-grid locations for managing and monitoring electrical loads with minimal manual intervention.

Library Seat Management System

Description:

The "Library Seat Management System" is an innovative project aimed at optimizing the use of seating in libraries. The system uses a combination of load cells (HX711) and ultrasonic sensors to monitor and manage the occupancy status of library seats. Each seat is equipped with both a load cell and an ultrasonic sensor to provide accurate and real-time information about seat usage. A seat is considered "booked" only when two conditions are met simultaneously: the weight detected by the load cell exceeds a predefined threshold, and the ultrasonic sensor registers a distance below a certain threshold, indicating the presence of a person. If either condition is not met, the seat is marked as "vacant." The system's status is displayed on a 20x4 LCD, providing clear and immediate feedback on seat availability, helping library staff and visitors to quickly find vacant seats, and ensuring efficient seat utilization.

Objectives:

1. Enhance Seat Utilization: To ensure optimal use of library seating by accurately detecting and displaying seat occupancy in real time.
2. Improve User Experience: Provide library users with clear information on seat availability, reducing time spent searching for available seats.
3. Facilitate Efficient Library Management: Assist library staff in monitoring seating arrangements, reducing manual effort, and improving the overall management of library resources.
4. Promote Order and Convenience: Maintain a quiet and organized environment by minimizing disruptions caused by users searching for seats.
5. Real-Time Monitoring: Ensure up-to-date status monitoring of seats to handle peak times efficiently.

Key Features:

• Dual-Sensor Detection: Combines load cell data and ultrasonic sensor readings to accurately detect seat occupancy.
• Real-Time Status Display: Shows seat status on a 20x4 LCD, allowing users and staff to see current occupancy at a glance.
• Threshold-Based Booking: Uses predefined thresholds for load cells and ultrasonic sensors to ensure reliable detection of seat occupancy.
• Automated Monitoring: Continuously monitors seat status without the need for manual intervention, improving operational efficiency.
• User-Friendly Interface: Provides an easy-to-read display for both library staff and visitors to quickly check seat availability.
• Low Power Consumption: Efficiently designed to operate with minimal power, making it cost-effective for long-term use.

Application Areas:

• Libraries and Study Rooms: Monitor and manage seating to ensure efficient use of resources and enhance the user experience.
• Educational Institutions: Use in classrooms, study halls, or lecture rooms to track attendance and seat utilization.
• Co-Working Spaces: Helps manage and display seat availability in shared work environments.
• Public Waiting Areas: Can be adapted for use in airports, bus stations, and hospitals to indicate available seating.

Detailed Working of the Library Seat Management System:

1. Initialization: The system is initialized by powering on the microcontroller, which activates all connected components, including load cells, ultrasonic sensors, and the LCD display.
2. Seat Monitoring: Each seat is equipped with one HX711 load cell and one ultrasonic sensor. The load cell measures the weight on the seat, while the ultrasonic sensor measures the distance to the nearest object (typically the user).
3. Data Processing: The system continuously reads data from both sensors. If the load cell value exceeds a predefined threshold and the ultrasonic sensor detects a distance shorter than its set threshold, the system determines that the seat is occupied.
4. Seat Status Update: When both conditions are met, the system marks the seat as "booked." If either condition is not met, the seat is marked as "vacant."
5. Display Output: The 20x4 LCD display shows the real-time status of each seat, updating dynamically as the occupancy changes.
6. Continuous Monitoring: The system operates continuously, ensuring that any changes in seat occupancy are immediately detected and displayed.

Modules Used to Make the Library Seat Management System:

1. Sensor Module: Includes the HX711 load cells and ultrasonic sensors to detect seat occupancy based on weight and distance.
2. Data Processing Module: A microcontroller (such as an Arduino or Raspberry Pi) processes the sensor data and determines seat status.
3. Display Module: The 20x4 LCD display shows the real-time status of each seat.
4. Power Management Module: Manages the power supply to all components, ensuring efficient energy consumption.
5. Threshold Control Module: Sets and manages the thresholds for both the load cells and ultrasonic sensors to accurately detect seat occupancy.

Components Used in the Library Seat Management System:

• HX711 Load Cells (x4): Detect the weight on each seat to determine if it is occupied.
• Ultrasonic Sensors (x4): Measure the distance to the nearest object (the user) to confirm seat occupancy.
• Microcontroller (e.g., Arduino or Raspberry Pi): Central unit for processing data from sensors and controlling the display.
• LCD Display (20x4): Provides a visual representation of seat status for library users and staff.
• Connecting Wires and Breadboards: For circuit connections and sensor interfacing.
• Power Supply: Supplies necessary power to the microcontroller, sensors, and LCD display.

Other Possible Projects Using this Project Kit:

1. Classroom Attendance System: Adapt the system to track student attendance based on seat occupancy in classrooms.
2. Smart Office Desk Management: Use the sensors to monitor desk usage in co-working spaces or offices, optimizing space allocation.
3. Public Transport Seat Monitoring: Implement the system in buses or trains to indicate available seating.
4. Smart Theater Seat Booking: Use the system in theaters or auditoriums to automatically update seat occupancy and booking status.

Real-time Parking Slot Monitoring with AI & Deep Learning

The "Real-time Parking Slot Monitoring with AI & Deep Learning" project is designed to streamline the process of monitoring parking spaces using advanced AI and deep learning techniques. This system allows users to create and manage parking slots interactively, detect vehicle presence in real-time, and provide valuable information on parking availability. By utilizing image processing and computer vision, the system enhances parking management efficiency and user experience.

Objectives

The primary objectives of the project are as follows:

1. Interactive Slot Creation and Management:

   • To enable users to define, customize, and manage parking slots in a flexible manner. This is done through an intuitive graphical user interface (GUI) where users can draw and delete slots with mouse clicks.
   • The goal is to provide a system that adapts to different parking layouts and configurations, making it suitable for various parking environments.

2. Real-Time Vehicle Detection:

   • To implement a robust mechanism for detecting the presence of vehicles in each parking slot using AI and image processing techniques. This ensures that the system provides accurate, real-time updates on slot occupancy.
   • The detection process must be efficient enough to handle live video feeds, enabling continuous monitoring without significant delays.

3. Visual Feedback and User Interaction:

   • To deliver instant visual feedback on the status of each parking slot. Occupied slots are highlighted in red, while vacant slots are shown in green. This color-coding helps users quickly assess parking availability.
   • The system also provides additional information, such as the total number of available parking spaces and the location of the nearest available slot relative to the entry point.

4. Optimization of Parking Management:

   • To assist parking facility managers in optimizing the use of parking spaces. By providing real-time data on slot occupancy, the system helps in better space management and reduces the time spent by drivers searching for parking.

Key Features

• Interactive Slot Management:
  • Users can define parking slots by simply drawing them on the interface with a left-click. If any adjustments are needed, slots can be removed or redefined using a right-click. This feature allows for easy customization of parking layouts according to the specific needs of the facility.
• Real-Time Occupancy Detection:
  • The system constantly monitors the defined slots by analyzing the video feed. Each slot is processed individually to determine whether it is occupied or vacant. This detection is performed using a combination of image processing techniques, ensuring that updates are provided in real-time.
• Color-Coded Slot Status:
  • The occupancy status of each slot is visually represented on the interface. Occupied slots are marked in red, signaling that they are unavailable, while vacant slots are marked in green, indicating that they are free. This color-coding system makes it easy for users to quickly understand the parking situation.
• Additional Information Display:
  • Beyond just showing the occupancy status, the system also provides helpful information such as the total number of parking slots, the number of available slots, and the nearest available slot to the entry point. This helps drivers and parking managers make informed decisions.
• Support for Multiple Parking Areas:
  • The system is designed to manage multiple parking areas, each with up to 10 slots. Each slot is uniquely identified by an ID, allowing for precise tracking and management. This feature is particularly useful for large facilities with multiple parking zones.

Application Areas

This AI-powered parking monitoring system is versatile and can be applied in a wide range of environments, including:

• Commercial Parking Lots:

  • Ideal for shopping malls, office complexes, airports, and other commercial facilities where efficient parking management is crucial for customer satisfaction. The system helps reduce the time drivers spend searching for parking, thereby improving the overall experience.

• Residential Complexes:

  • Useful in residential areas to manage parking spaces for both residents and visitors. By providing real-time updates on parking availability, the system can help prevent disputes and optimize space utilization.

• Public Parking Facilities:

  • Applicable in public parking garages and lots, particularly in urban areas where parking demand is high. The system can help reduce congestion and improve traffic flow by directing drivers to available spaces quickly.

• Event Venues:

  • Beneficial for managing parking during large events such as concerts, sports games, or festivals. The system ensures that attendees can find parking efficiently, reducing the likelihood of traffic jams and enhancing the event experience.

Detailed Working of Real-time Parking Slot Monitoring with AI & Deep Learning

The system's operation can be broken down into the following key stages:

1. Slot Creation:

   • User Interaction: The user starts by defining the parking slots on the system's graphical interface. This is done by left-clicking on the interface to draw the boundaries of each slot. Each slot is then assigned a unique ID for tracking purposes.
   • Customization: If adjustments are needed, such as removing or resizing a slot, the user can right-click to delete the slot and redraw it as necessary. This flexibility ensures that the system can adapt to different parking layouts and configurations.

2. Slot Monitoring:

   • Video Feed Processing: The system continuously captures and processes the video feed from the parking area. Each frame of the video is analyzed to monitor the defined slots.
   • Frame Analysis: The system isolates the area within each defined slot and applies image processing techniques to detect the presence of a vehicle. This involves background subtraction, edge detection, and other algorithms to differentiate between an occupied and a vacant slot.

3. Vehicle Detection:

   • Algorithm Application: The system uses a combination of computer vision algorithms to detect vehicles. For example, edge detection might be used to identify the outline of a car, while background subtraction could be employed to differentiate between stationary objects and vehicles.
   • Status Update: Once a vehicle is detected within a slot, the system updates the status of the slot to "occupied" and changes its color to red on the interface. If no vehicle is detected, the slot remains marked as "vacant" and is colored green.

4. Information Display:

   • Real-Time Updates: The system continuously updates the display to show the current status of all parking slots. It also provides additional information such as the total number of parking spaces, the number of available slots, and the nearest vacant slot to the entry point.
   • User Guidance: This information helps both drivers and parking managers make quick, informed decisions about where to park and how to manage the parking facility.

Modules Used in Real-time Parking Slot Monitoring with AI & Deep Learning

• OpenCV:

  • The core library used for real-time video processing and image analysis. OpenCV handles tasks such as capturing video frames, processing images to detect vehicles, and updating the status of each parking slot.
  • It provides tools for background subtraction, edge detection, and other image processing functions that are critical for accurate vehicle detection.

• Numpy:

  • Used for handling arrays and performing numerical operations on the image data. Numpy is essential for manipulating the pixel data extracted from the video feed, enabling efficient image processing.

• Pandas:

  • This library is used for data manipulation and analysis, particularly if the system is extended to log parking slot usage statistics over time. It helps in managing and analyzing the data generated by the system, such as the number of cars parked, slot occupancy rates, and more.

• Tkinter (or similar GUI library):

  • A Python library used to create the graphical user interface (GUI) that allows users to interactively draw and manage parking slots. Tkinter provides the tools needed to build an intuitive, user-friendly interface that makes the system easy to use.

Components Used in Real-time Parking Slot Monitoring with AI & Deep Learning

• Camera:

  • A high-resolution camera captures the live video feed from the parking area. The camera is strategically placed to cover the entire parking area, ensuring that all slots are within the frame. The quality and placement of the camera are crucial for accurate vehicle detection.

• Computer/Server:

  • The processing unit that runs the Python-based application. It handles the real-time video processing, slot management, and user interface. The computer must have sufficient processing power to handle the image processing tasks required for real-time operation.

• Python Software Environment:

  • The system relies on Python and its libraries (OpenCV, Numpy, Pandas, Tkinter) for coding, image processing, and GUI development. Python provides the flexibility and tools needed to implement the various features of the system.

Other Possible Projects Using this Project Kit

The methods and technologies used in this project can be adapted for various other applications, such as:

1. Automated Toll Booth Monitoring:

   • The system can be adapted to monitor vehicles passing through toll booths, capturing license plates, and ensuring accurate fee collection based on vehicle occupancy and type.

2. Traffic Flow Monitoring:

   • Modify the system to monitor traffic flow in real-time, detecting traffic congestion and providing data that can be used to optimize traffic light timings and improve overall traffic management.

3. Smart Parking Guidance System:

   • Expand the project by developing a mobile app or web dashboard that guides drivers to the nearest available parking slot based on real-time data from the monitoring system.

4. Warehouse Slot Monitoring:

   • Apply the same principles to monitor storage slots in a warehouse. The system could track which slots are occupied, manage inventory, and optimize space utilization within the warehouse.

AI-Powered Real-Time Surveillance: Detecting Violence, Theft, and Sending Alerts

The "AI-Powered Real-Time Surveillance" project is a Python-based system that uses deep learning and computer vision to automatically detect violence, the presence of weapons, and suspicious activities such as face covering in real-time. Upon detection, the system records the footage and sends an alert via email with the recorded video. This solution enhances security by providing automated, real-time monitoring for various settings.

Objectives

The primary goal of this project is to create an intelligent surveillance system that enhances security by automatically detecting suspicious or dangerous activities in real-time. The objectives include:

1. Violence Detection: To develop a model that can identify violent actions, such as fighting, in video footage and respond immediately.

2. Weapon Detection: To detect the presence of weapons, particularly guns, in the video feed and highlight them for attention.

3. Theft Detection: To recognize when a person is attempting to conceal their identity by covering their face, potentially indicating intent to commit theft.

4. Automated Alert System: To integrate an alert mechanism that records the footage of detected activities and sends a notification via email, including the recorded video, to the relevant authorities or security personnel.

5. Real-Time Processing: To ensure the system operates efficiently and can analyze video feeds in real-time, providing instant detection and response to potential threats.

Key Features

• Real-Time Detection: The system is designed to analyze live video feeds and detect specific actions or objects (violence, weapons, face covering) instantaneously.

• Multi-Category Classification: The system classifies detected activities into three main categories: Violence, Weapon Detection, and Theft (Face Covering).

• Custom Deep Learning Models: The project uses custom deep learning models built with TensorFlow to accurately identify violent behavior and face covering.

• Weapon Detection Using OpenCV: OpenCV is used to detect weapons, focusing on identifying firearms in the video footage.

• Automated Incident Recording: When an anomaly is detected, the system records the footage of the event for further review.

• Email Alert System: The system sends an automated email alert with the recorded video footage to pre-configured recipients whenever suspicious activity is detected.

• Scalable Design: The system can be scaled to monitor multiple cameras or adapted to detect additional behaviors or objects.

Application Areas

This AI-powered surveillance system can be deployed in various environments where security is critical. Some of the key application areas include:

• Public Spaces: Ideal for monitoring public areas like parks, plazas, shopping malls, and transportation hubs to detect and respond to violent incidents or potential threats quickly.

• Business Premises: Useful in retail stores, banks, and offices to enhance security by detecting theft (face covering) and potential armed robbery scenarios.

• Residential Security: Can be used in homes and residential complexes to monitor for suspicious activities, such as individuals covering their faces or trespassing with weapons.

• Educational Institutions: Provides an extra layer of security in schools and universities by monitoring for violence and unauthorized access by armed individuals.

Detailed Working of AI-Powered Real-Time Surveillance

The system operates by continuously analyzing the video feed from a surveillance camera to detect predefined actions or objects. Here’s how it works in detail:

1. Video Feed Capture: The system starts by capturing live video from the surveillance camera. This video stream is continuously fed into the AI model for analysis.

2. Preprocessing: The captured video frames are preprocessed to ensure they are in the correct format for analysis. This includes resizing, normalization, and other image processing techniques.

3. Action Detection:

   • Violence Detection: The deep learning model analyzes the movements and actions within the video frame to detect violent behavior. The model is trained on various datasets that include different types of aggressive actions like fighting, pushing, etc.

   • Weapon Detection: Using OpenCV, the system scans each frame for objects that resemble weapons, particularly guns. This involves object detection techniques that can differentiate between normal objects and weapons.

   • Face Covering Detection: The system uses a classification model to detect if a person’s face is obscured by a mask, scarf, or any other covering, which could indicate an attempt to conceal identity.

4. Incident Recording: Upon detecting any of these activities, the system automatically records a short video clip of the event. This clip is stored locally or in a cloud storage system for review.

5. Alert Generation: The system generates an email alert that includes the recorded video footage. This email is sent to a preconfigured list of recipients, such as security personnel, law enforcement, or designated authorities.

6. Post-Detection: The system returns to continuous monitoring after sending the alert, ready to detect any further incidents.

Modules Used in AI-Powered Real-Time Surveillance

• TensorFlow: This library is used to build and train custom deep learning models for detecting violence and face covering. TensorFlow’s flexibility allows for the development of highly accurate models tailored to the specific tasks of this project.

• OpenCV: OpenCV is essential for real-time video processing and weapon detection. It provides tools for image processing, object detection, and other computer vision tasks.

• Numpy: Used for handling arrays and performing numerical operations during both the preprocessing and model inference stages.

• Pandas: Used for data manipulation and analysis, particularly during the training of the AI models where large datasets are processed.

• smtplib and email.mime: These libraries are used to implement the email alert system. They handle the construction and sending of email notifications with video attachments.

Components Used in AI-Powered Real-Time Surveillance

• Camera: A high-definition camera is used to capture the live video feed. The camera is positioned to monitor the area of interest and is connected to the system for continuous feed input.

• Computer/Server: The core processing unit that runs the Python code and models. It handles the real-time video processing, detection, recording, and alert generation tasks.

• Python Software Environment: The system relies on Python for coding, TensorFlow for model building, OpenCV for video processing, and other necessary libraries to ensure the project functions smoothly.

Other Possible Projects Using this Project Kit

The technology and methodologies used in this project can be adapted to create other innovative security and monitoring systems:

1. Intruder Detection System: The system can be modified to detect unauthorized entry by identifying unusual movements or unauthorized access to restricted areas.

2. Fire Detection System: Integrate smoke or flame detection capabilities to create an early warning system for fire hazards.

3. Traffic Violation Detection: The system can be adapted to monitor traffic and detect violations such as running red lights, illegal turns, or speeding.

4. Smart Home Security System: Expand the project into a comprehensive home security solution that integrates with IoT devices to provide automated surveillance and alerting.