AIOT Projects
1. Smart Agriculture System
Overview
This project implements a smart irrigation system that uses IoT sensors to monitor soil moisture
and weather conditions, optimizing water usage in agriculture.
Components Required
• Microcontroller (Arduino or Raspberry Pi): $35 - $50
• Soil Moisture Sensor: $5 - $10
• DHT11 Sensor: $3 - $5
• Relay Module: $2 - $5
• Water Pump: $10 - $20
• Wi-Fi Module (ESP8266): $3 - $8
• Power Supply: $10 - $20
Total cost: Approximately $70 - $120
Role of AI
AI analyzes historical weather and soil data using machine learning algorithms to predict optimal
irrigation schedules.
Future Applications and Problem Solving
Addresses water scarcity and improves crop yield.
Novelty
Integration of AI-Powered Pest Detection: This system incorporates image recognition to
identify pests and diseases in crops using a camera module, allowing for targeted pesticide use,
thus reducing chemical usage and increasing sustainability.
2. IoT-based Health Monitoring System
Overview
This project focuses on creating a health monitoring system that tracks vital signs such as heart
rate and temperature, sending alerts if values exceed normal ranges.
Components Required
• Microcontroller (Arduino): $35 - $50
• Heart Rate Sensor: $10 - $20
• Temperature Sensor (LM35): $1 - $3
• Wi-Fi Module (ESP8266): $3 - $8
• Buzzer or LED: $1 - $3
• Power Supply: $10 - $20
Total cost: Approximately $60 - $100
Role of AI
AI algorithms analyze health data to identify trends and predict potential health issues.
Future Applications and Problem Solving
Improves patient outcomes through early detection.
Novelty
Predictive Health Alerts Using AI: The system employs predictive analytics to forecast
potential health crises based on real-time data trends, enabling proactive medical interventions
before symptoms arise.
3. Smart Vehicle Parking System
Overview
This project develops a smart parking system that uses IoT sensors to detect available parking
spots and provides real-time information to users via a mobile app.
Components Required
• Microcontroller (Raspberry Pi): $35 - $50
• Ultrasonic Sensors: $5 - $10 each
• Wi-Fi Module (ESP8266): $3 - $8
• Mobile App: Cost varies based on development
• Power Supply: $10 - $20
Total cost: Approximately $60 - $100 (excluding mobile app development)
Role of AI
AI optimizes parking space utilization by predicting peak parking times.
Future Applications and Problem Solving
Alleviates urban congestion and reduces search time for parking.
Novelty
Dynamic Pricing Model Based on Demand Forecasting: This system integrates a dynamic
pricing model that adjusts parking fees based on demand forecasts, encouraging off-peak parking
and maximizing revenue for parking facilities.
4. IoT-based Drone Surveillance System
Overview
This project creates a drone surveillance system that monitors specific areas and sends live video
feeds to a control center using IoT technology.
Components Required
• Drone Kit: $200 - $500
• Camera Module (Raspberry Pi Camera): $20 - $50
• Microcontroller (Raspberry Pi): $35 - $50
• Wi-Fi Module: $3 - $8
• Battery Pack: $20 - $50
Total cost: Approximately $280 - $660
Role of AI
AI enables image recognition and anomaly detection.
Future Applications and Problem Solving
Enhances security and monitoring capabilities.
Novelty
Autonomous Flight Path Optimization: This drone uses AI to autonomously adjust its flight
path in real-time based on detected threats or unusual activities, improving response times and
operational efficiency in surveillance.
5. Smart Home Automation System
Overview
This project automates home appliances using IoT, allowing users to control lights, fans, and
other devices remotely via a mobile app.
Components Required
• Microcontroller (Arduino or Raspberry Pi): $35 - $50
• Relay Modules: $2 - $5 each
• Wi-Fi Module (ESP8266): $3 - $8
• Mobile App: Cost varies based on development
• Power Supply: $10 - $20
Total cost: Approximately $50 - $90 (excluding mobile app development)
Role of AI
AI learns user preferences for automation.
Future Applications and Problem Solving
Enhances convenience and energy efficiency.
Novelty
Context-Aware Automation: The system utilizes AI to learn and adapt to users' daily routines
and preferences, automatically adjusting settings based on context (e.g., time of day, occupancy,
or user location).
6. Internet of Battlefield Things (IoBT) - Smart Soldier Monitoring System
Overview
This project focuses on monitoring soldiers' health and location in real-time using IoT devices.
Components Required
• Wearable Health Sensors: $20 - $50 each
• GPS Module: $5 - $10
• Microcontroller (Arduino): $35 - $50
• Wi-Fi Module (ESP8266): $3 - $8
• Mobile App: Cost varies based on development
Total cost: Approximately $70 - $120 (excluding mobile app development)
Role of AI
AI analyzes health data for emergency predictions.
Future Applications and Problem Solving
Enhances soldier safety and operational effectiveness.
Novelty
Real-Time Stress and Fatigue Monitoring: The system incorporates AI to assess soldiers'
stress and fatigue levels through biometric data analysis, providing commanders with crucial
insights to manage troop readiness and welfare.
7. Internet of Underwater Things (IoUT) - Smart Aquaculture System
Overview
This project develops an underwater monitoring system for aquaculture.
Components Required
• Underwater Sensors: $20 - $50 each
• Microcontroller (Raspberry Pi): $35 - $50
• Buoy with Wi-Fi Module: $50 - $100
• Power Supply: $10 - $20
Total cost: Approximately $120 - $220
Role of AI
AI analyzes water quality data for optimal feeding schedules.
Future Applications and Problem Solving
Improves sustainability in aquaculture.
Novelty
AI-Driven Fish Behavior Analysis: The system employs AI to analyze fish behavior patterns
using underwater cameras, allowing farmers to optimize feeding times and quantities based on
real-time activity levels, reducing waste and improving growth rates.
8. Internet of Space (IoSpace) - Satellite Health Monitoring System
Overview
This project focuses on monitoring the health of satellites using IoT sensors.
Components Required
• Sensors: $20 - $50 each
• Microcontroller (Raspberry Pi): $35 - $50
• Communication Module: $10 - $20
• Power Supply: $10 - $20
Total cost: Approximately $75 - $140
Role of AI
AI analyzes telemetry data for failure predictions.
Future Applications and Problem Solving
Extends the operational lifespan of satellites.
Novelty
Self-Healing Algorithms for Satellite Systems: The system integrates AI algorithms capable of
diagnosing issues and autonomously initiating corrective actions, enhancing satellite resilience
and reducing the need for ground intervention.
9. Internet of People (IoP) - Smart Attendance System
Overview
This project implements a smart attendance system using facial recognition.
Components Required
• Camera Module: $20 - $50
• Microcontroller (Raspberry Pi): $35 - $50
• Facial Recognition Software: Cost varies based on development
• Wi-Fi Module: $3 - $8
• Power Supply: $10 - $20
Total cost: Approximately $70 - $130 (excluding facial recognition software development)
Role of AI
AI automates attendance tracking.
Future Applications and Problem Solving
Streamlines administrative processes.
Novelty
Emotion Recognition for Engagement Assessment: In addition to attendance, the system
incorporates emotion recognition to assess student engagement during classes, providing
educators with insights to improve teaching strategies.
10. Internet of Nano Things (IoNT) - Smart Drug Delivery System
Overview
This project develops a smart drug delivery system that monitors patient adherence to medication
schedules.
Components Required
• Wearable Device: $20 - $50
• Microcontroller (Arduino): $35 - $50
• Wi-Fi Module (ESP8266): $3 - $8
• Mobile App: Cost varies based on development
Total cost: Approximately $60 - $110 (excluding mobile app development)
Role of AI
AI analyzes adherence data for personalized reminders.
Future Applications and Problem Solving
Improves patient compliance and health outcomes.
Novelty
Adaptive Drug Delivery Based on Real-Time Health Monitoring: The system can adjust
medication dosages in real-time based on continuous monitoring of vital signs, ensuring that
patients receive the most effective treatment tailored to their current health status.These projects
demonstrate the potential of IoT and AI technologies to solve real-world problems while
remaining budget-friendly. The prices provided are estimates and may vary depending on factors
such as quantity, location, and market conditions.
11. Improvement of “Optimizing Thyroid Cancer Diagnosis Through Deep Learning”
Here are a few ways IoT can be used to optimize thyroid cancer diagnosis through deep learning:
Automated Data Collection and Integration
• IoT devices like ultrasound probes can automatically capture and transmit thyroid
ultrasound images to a central deep learning system
• This streamlines data collection, reduces manual effort, and ensures data is available for
real-time analysis
• IoT sensors can also collect other relevant data like patient vitals, medication intake, etc.
to provide a more holistic view
Distributed Deep Learning
• IoT devices can participate in a federated learning framework to collectively train a deep
learning model for thyroid cancer diagnosis
• Each device trains on its local data and shares model updates, allowing the central model
to learn from a large distributed dataset without compromising privacy
• This enables scaling up the deep learning system across multiple hospitals and clinics
Intelligent Diagnosis Support
• IoT-connected ultrasound probes can use the trained deep learning model to provide real-
time analysis and classification of thyroid nodules
• The model can highlight suspicious regions and provide a malignancy risk score to aid
radiologists in their diagnosis
• Grad-CAM visualizations can explain the model's predictions, making the diagnostic
process more transparent
Remote Monitoring and Telemedicine
• IoT devices can enable remote monitoring of thyroid cancer patients, with data like
ultrasound images and vitals transmitted to their doctors
• This facilitates telemedicine and allows patients to be monitored from the comfort of
their homes
• It also enables early detection of changes that may require medical intervention
Personalized Treatment
• By integrating data from various IoT devices, a comprehensive patient profile can be
built to guide personalized treatment plans
• For example, the deep learning model's diagnosis can be combined with genomic data,
medication history, and lifestyle factors to optimize treatment for each patient
In summary, IoT can greatly enhance thyroid cancer diagnosis by automating data collection,
enabling distributed deep learning, providing intelligent diagnosis support, facilitating remote
monitoring, and enabling personalized treatment. The combination of IoT and deep learning has
immense potential to improve early detection and treatment outcomes for thyroid cancer patients.
To implement an IoT and deep learning-based system for optimizing thyroid cancer diagnosis
within a budget of $500, the following components can be utilized:
Hardware Components
Raspberry Pi 4 Model B
Cost: $55
Description: Acts as the main processing unit for data collection and initial image processing.
Ultrasound Imaging Device
Cost: $300 (entry-level portable ultrasound)
Description: Captures ultrasound images of the thyroid. While high-end models are expensive,
entry-level devices can be sufficient for initial testing.
Camera Module for Raspberry Pi
Cost: $25
Description: If a separate ultrasound device is not used, this can capture images for processing.
MicroSD Card (32GB)
Cost: $10
Description: For storing the operating system and collected data.
Power Supply for Raspberry Pi
Cost: $10
Description: Powers the Raspberry Pi.
Wi-Fi Module (if not built-in)
Cost: $10
Description: Enables IoT connectivity for data transmission.
Software Components
OpenCV (Open Source Computer Vision Library)
Cost: Free
Description: Used for image processing and analysis.
TensorFlow or PyTorch
Cost: Free
Description: Deep learning frameworks for building and training models on ultrasound images.
Raspberry Pi OS
Cost: Free
Description: Operating system for the Raspberry Pi.
Grad-CAM Implementation
Cost: Free
Description: Visualization tool to interpret model predictions.
Estimated Total Cost
Component Estimated Cost (USD)
Raspberry Pi 4 Model B $55
Ultrasound Imaging Device $300
Camera Module for Raspberry Pi $25
MicroSD Card (32GB) $10
Power Supply for Raspberry Pi $10
Wi-Fi Module $10
Total $410
This configuration provides a basic yet functional setup for capturing ultrasound images,
processing them with deep learning algorithms, and utilizing IoT capabilities for data
transmission and remote monitoring, all within the budget of $500.
12. Improvement of “Automated Bone Fracture Detection Through Deep Learning”
13. Improvement of “Automatic Tourism Facilitation System Through Machine Learning”
14. Improvement of “Automated Judiciary system Trough Deep Learning”
