Internet Of Things Devices Final Year Projects with Source Code

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Internet Of Things Devices Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Internet Of Things Devices projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Internet Of Things Devices Final Year Projects

  1. A Novel Resource-Saving and Traceable Tea Production and Supply Chain Based on Blockchain and IoT
    This project focuses on improving the tea production and supply chain using blockchain and IoT technologies. It creates a system called DeTea that tracks tea products, prevents counterfeiting, and automates plantation management. The system encourages honest behavior among participants and monitors the environment to optimize resources like water and equipment use. Overall, it makes tea production safer, more efficient, and fully traceable from planting to sale.
  2. An Integrated Scalable Framework for Cloud and IoT Based Green Healthcare System
    This project focuses on creating a smart healthcare system using IoT and cloud technology. Patients can send their health data from wearable devices, and doctors can view it in real-time. The system uses advanced algorithms to analyze the data and provides an easy-to-use interactive interface. It also emphasizes efficiency, scalability, and making healthcare more environmentally friendly.
  3. An Intelligent Approach to Improving the Performance of Threat Detection in IoT
    This project focuses on making Internet of Things (IoT) systems more secure. It uses machine learning and data analysis techniques to detect attacks that try to overwhelm the system, known as DDoS attacks. The researchers tested their approach using real datasets and measured how well the system could detect attacks and how fast it could learn. Overall, their method improved both detection accuracy and training speed.
  4. Anonymous Broadcast Authentication With One-to-Many Transmission to Control IoT Devices
    This project focuses on safely controlling multiple IoT devices from a distance. It ensures that only the intended devices can recognize and act on commands sent by a system manager. The system can also detect and block any malicious attempts to manipulate commands. The researchers developed a secure method called anonymous broadcast authentication that works quickly and reliably over regular wireless networks.
  5. Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoT
    This project focuses on making Internet of Things (IoT) devices more reliable and secure. It uses blockchain technology to protect sensitive data and a smart machine learning model to detect faults in IoT networks. The system also optimizes the model for better accuracy. Experiments show that this approach can detect faults with up to 99.6% accuracy, making IoT systems safer and more trustworthy.
  6. Boosted Barnacles Algorithm Optimizer Comprehensive Analysis for Social IoT Applications
    This project focuses on improving the Social Internet of Things (SIoT), where smart devices share data for health monitoring, emergency alerts, and learning systems. It introduces a new method using the Barnacles Mating Optimizer to make data transfer faster and more accurate. The method was tested on real datasets and showed better performance than existing approaches. Overall, it helps smart devices work together more efficiently.
  7. Distributed Split Computing System in Cooperative Internet of Things IoT
    This project focuses on improving how IoT devices work together to process data. Instead of sending all tasks to the cloud, nearby IoT devices share the computing work. The system decides which devices should help based on energy use and speed. This approach reduces energy consumption by over 20% while ensuring tasks finish on time.
  8. EGCrypto A Low-Complexity Elliptic Galois Cryptography Model for Secure Data Transmission in IoT
    This project focuses on keeping data in Internet of Things (IoT) networks safe while it is being sent over public networks. The authors created a system called EGCrypto that encrypts IoT data using a low-complexity cryptography method and hides it inside images for secure transfer. They also use optimization techniques to make the system more efficient and accurate. Experiments show that EGCrypto is better than existing methods in security, data quality, and transmission efficiency.
  9. Enabling IoT Service Classification A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT Services
    This project studies how different Internet of Things (IoT) services interact and how they can be organized efficiently. It classifies IoT services into five groups based on their key characteristics. Machine learning methods like decision trees, SVM, and voting classifiers are used to make this classification. The results show that decision trees provide accurate and reliable predictions, helping improve IoT resource management.
  10. Enhancing DDoS Attack Detection Using Snake Optimizer With Ensemble Learning on Internet of Things Environment
    This project focuses on protecting Internet of Things (IoT) devices from DDoS cyber-attacks that can overload them with traffic. It uses machine learning to detect attacks by selecting the most important data features. The proposed method combines a “snake optimizer” for feature selection with three deep learning models to improve detection. Tests show that this approach works better than existing methods in identifying attacks accurately.
  11. Enhancing Intrusion Detection in IoT Communications Through ML Model Generalization With a New Dataset IDSAI
    This project focuses on improving computer security in networks of connected devices, like IoT systems. The researchers created a new dataset of real attacks to train and test machine learning models. They found that certain AI models can accurately detect both simple and multiple types of attacks, reaching over 90% accuracy. This work helps make network security smarter and more reliable.
  12. Holochain An Agent-Centric Distributed Hash Table Security in Smart IoT Applications
    This project studies a new technology called Holochain as an alternative to blockchain for securing Internet of Things (IoT) networks. It focuses on smart agriculture, where land records and data need protection from unauthorized access and corruption. Holochain allows peer-to-peer transactions with better scalability and local data storage compared to blockchain. The research explains its architecture, challenges, and how it can enable secure, distributed applications.
  13. IoT Network Cybersecurity Assessment With the Associated Random Neural Network
    This project develops a smart system to detect hacked devices in an IoT network. It uses a special type of neural network that looks at all devices together instead of checking them one by one. The system learns from real attack data to decide whether each device is safe or compromised. Tests show it works better than older methods.
  14. IoT Underlying Cellular Uplink Through D2D Communication Principle
    This project explores a way for Internet of Things (IoT) devices to communicate directly with each other, instead of always using the main cellular network. The idea is to reduce network congestion by letting nearby devices share data efficiently. The researchers propose a method to manage communication channels and device power. Their results show that IoT traffic can be handled well without affecting the main cellular network.
  15. MAC Protocol Based IoT Network Intrusion Detection Using Improved Efficient Shuffle Bidirectional COOT Channel Attention Network
    This project focuses on protecting IoT networks from cyberattacks. It uses a smart system to detect intrusions while keeping data secure and reducing energy use and delays. The approach balances and processes IoT data, selects important features, and then classifies attacks using an advanced AI model. Overall, it improves accuracy and performance compared to existing methods.
  16. Modeling of Blockchain Assisted Intrusion Detection on IoT Healthcare System Using Ant Lion Optimizer With Hybrid Deep Learning
    This project focuses on making healthcare IoT systems more secure. It uses blockchain to protect sensitive medical data and detects unauthorized access using smart algorithms. The system combines advanced deep learning methods with optimization techniques to improve accuracy in spotting intrusions. Tests show it works better than existing methods for keeping IoT healthcare systems safe.
  17. Quantifying IoT Security Parameters An Assessment Framework
    This project studies many different ways researchers measure security in IoT systems. It collects forty-six metrics from past studies and groups them into clear categories. The work shows which metrics are used most often and which areas are still missing good measures. It helps future researchers choose better methods to evaluate IoT security.
  18. Securing IoT With Deep Federated Learning A Trust-Based Malicious Node Identification Approach
    This project builds a system that helps smart devices identify which devices in a network can be trusted. It uses learning from many devices together without sharing their data. The system can detect unusual or harmful behavior in the network. This helps improve safety and privacy in Internet of Things environments.
  19. Security-Aware Provenance for Transparency in IoT Data Propagation
    This project studies how to make data in an Internet of Things system more transparent and trustworthy. It adds extra security information to track every step of data movement. The system is tested with different cyber-attack situations to see how well it detects problems. This helps users understand risks and make better decisions without slowing down the system.
  20. Toward Secured IoT-Based Smart Systems Using Machine Learning
    This project studies how smart systems like smart cities and early warning systems use sensors and devices to collect data. Machine learning is applied to this data to make predictions and improve decision-making. The research also examines security methods to keep these systems safe. Two case studies on smart campuses and earthquake warning systems show how this works in practice.
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