Internet Of Things Networks Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Internet Of Things Networks 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 Networks Final Year Projects

Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree

This project focuses on improving computer network security by detecting cyber-attacks more accurately. It reviews different intrusion detection methods, datasets, and challenges faced by researchers. Machine learning and deep learning are used to identify threats and reduce false alarms. The study proposes using a decision tree model to create an efficient system for spotting unusual activity in networks.
A Novel Energy-Efficient Scheme for RPL Attacker Identification in IoT Networks Using Discrete Event Modeling

This project focuses on making IoT networks more secure. The researchers created a system that can detect hidden attacks in IoT devices that are hard to spot. Their method uses smart checking and a special model to tell normal activity from attacks. Tests show it works accurately, uses little energy, and can find the harmful devices without complex setup.
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.
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.
Opportunistic Backscatter Communication Protocol Underlying Energy Harvesting IoT Networks

This project focuses on improving communication in Internet of Things (IoT) networks where devices can send data without batteries using backscatter technology. It introduces a new method that helps these devices share wireless signals more smoothly and efficiently while also harvesting energy from the network. The method reduces data collisions and increases overall network speed and energy use. Compared to older approaches, it makes IoT networks faster and more energy-efficient.
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.
Cooperative Beamforming With Artificial Noise Injection for Physical-Layer Security

This project focuses on improving security in future 6G wireless networks with many connected IoT devices. It proposes a method that combines cooperative beamforming and artificial noise to protect data from nearby eavesdroppers. The method works in a fully distributed way, reduces network overhead, and can significantly increase the secrecy of communications. Simulations show it can double the security performance compared to existing techniques, especially in high-risk scenarios.

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Internet Of Things Networks Project Synopsis & Presentation

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Internet Of Things Networks Final Year Projects with Source Code