Wireless Sensor Networks Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Wireless Sensor 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.

Wireless Sensor Networks Final Year Projects

Attack Detection for Medical Cyber-Physical SystemsA Systematic Literature Review

This project looks at cyber attacks in hospitals, focusing on medical cyber-physical systems, which include devices connected to hospital networks. The researchers reviewed existing studies to understand how intrusions are detected, what datasets are used, and what gaps exist. They found that most work focuses on detecting unusual activity at the network level, often targeting insider threats. The study suggests creating specialized hospital datasets, improving standards, and developing methods that use medical context to better prevent cyber attacks and protect patients.
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.
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.
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.
High-Efficiency Resource Allocation Scheme Introducing the Concept of Resource Sharing Paths in Industrial IoT

This project focuses on improving industrial wireless networks used in smart factories. It addresses the problem of resource wastage when many devices send data along overlapping routes. The researchers propose a method that shares communication resources on common paths and considers network conditions like congestion. Their simulations show this approach uses resources more efficiently and handles more devices than traditional methods.
Location Centric Energy Harvesting Aware Routing Protocol for IoT in Smart Cities

This project focuses on making IoT-based wireless sensor networks last longer by using energy harvesting. It introduces a simple way to route data between sensor nodes that uses less energy. The method chooses the best path based on the closest direction to the target node. Experiments show it works well and helps the network run efficiently for a longer time.
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.
Quantum Artificial Hummingbird Algorithm for Feature Selection of Social IoT

This project creates a smart method to pick only the most useful information from large Internet of Things data. It uses an improved search technique inspired by hummingbird behavior and quantum ideas. The method helps computers work faster and make better decisions with fewer inputs. Tests on many datasets show that it improves accuracy while reducing unnecessary data.
Event Detection Through Differential Pattern Mining in Cyber-Physical Systems

This project focuses on finding important events in sensor networks, like detecting damage in buildings or vehicles. It creates a system called DPminer that analyzes sensor data efficiently. The system looks for meaningful patterns across sensors while using less communication and computation. Tests show it detects events better than traditional methods.
Blockchain Based Dynamic Spectrum Access of Non Real Time Data in Cyber Physical Social Systems

This project focuses on sharing wireless spectrum efficiently in systems where devices, humans, and computers interact. It uses blockchain to manage access to unused spectrum channels, making the process fair and secure. Users can earn access rights through mining and can trade them if not needed. The system also supports faster data processing at the network edge for non-urgent data.
Artificial Intelligence and Internet of Things for Sustainable Farming and Smart Agriculture

This project focuses on using AI and IoT to make farming smarter and more sustainable. It looks at how these technologies can monitor crops and manage farming systems efficiently. The work reviews existing tools and proposes a framework to help farmers use AI and IoT effectively. The goal is to improve crop quality and handle large amounts of farming data more easily.
Artificial Intelligence Technology in the Agricultural Sector A Systematic Literature Review

This project explores how artificial intelligence is changing farming. It looks at tools like smart sensors, robots, and data analysis to monitor crops, soil, and water use. The study shows how AI can help farmers grow better crops, use resources efficiently, and increase profits. It also examines the benefits, challenges, and different AI methods used in modern agriculture.
A Blockchain System for TDMA-Based Tactical Wireless Networks With Constrained Resources

This project develops a blockchain system for wireless networks that have limited communication resources. It ensures all devices can share the same data reliably, even when the network is unstable. The system uses an efficient method to decide which device can send data at a given time, saving energy and avoiding unnecessary competition. The design was tested using simulations and numerical analysis to show it works in different situations.
A Blockchain-Based Deep-Learning-Driven Architecture for Quality Routing in Wireless Sensor Networks

This project improves the security and efficiency of wireless sensor networks (WSNs), which are used in areas like healthcare and military services. It detects and removes malicious nodes using deep learning and a blockchain-based validation system. The network is designed to prevent failures by decentralizing data handling and registering legitimate nodes securely. The results show higher accuracy, better throughput, and lower delay compared to traditional routing methods.
A Cross-Layer Solution for Contention Control to Enhance TCP Performance in Wireless Ad-Hoc Networks

This project focuses on improving data transmission in wireless ad-hoc networks, which are used in IoT, vehicle networks, and sensor networks. It introduces a method called CSCC that helps control network congestion by adjusting how many packets are sent. The system ensures fair sharing of the network among users and reduces packet loss. Simulations show that CSCC performs better than traditional TCP methods in speed, fairness, and reliability.
A Lightweight Authentication Framework for Fault-Tolerant Distributed WSN

The project builds a secure way for small wireless devices to share data safely. It uses special math-based methods to protect messages while keeping the process fast and light. The system is tested on different network setups and uses parallel processing to make communication quicker. The results show that the method saves time and memory, making it suitable for reliable sensor networks.
A Lightweight User Authentication Scheme for Multi-Gateway Based Wireless Sensor Networks Using Rabin Cryptosystem

This project improves how wireless sensor networks verify users and devices. It replaces the old single-gateway method with a faster and safer multi-gateway approach. The system uses a light form of encryption so small sensors can work efficiently. Tests show that the new method makes the network more secure while using fewer resources.
A Multi-Hop QoS-Aware and Predicting Link Quality Estimation PLQE Routing Protocol for Reliable WBSN

This project uses small sensors placed on the body to collect important health data in real time. These sensors often lose connection because the body moves, which can delay or drop critical signals like ECG or EEG. The proposed method predicts the best communication path so the data reaches safely and on time. It improves reliability and reduces delays compared to existing methods.
A New Low-Noise I-Squared Buck Converter Suitable for Wireless Sensor Networks With Dual-Current-Acceleration Techniques

This project presents a new type of efficient power converter designed for wireless sensor networks. It reduces noise and responds very quickly to changes in load. The design uses special techniques to speed up current adjustments, making the system up to 45% faster. The converter is small, highly efficient, and performs well under different current conditions.
A New Unsupervised Validation Index Model Suitable for Energy-Efficient Clustering Techniques in VANET

This project focuses on improving how clusters in data are evaluated. It introduces a new method called M2I that measures both how similar nodes are within a cluster and how separate clusters are from each other. This method works better than existing techniques, especially in dynamic networks like VANETs. Tests show that M2I gives very accurate results in both simulated and real-world data.

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Wireless Sensor Networks Final Year Projects with Source Code