Wireless Networks Final Year Projects with Source Code

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

  1. A QoS-Aware Data Aggregation Strategy for Resource Constrained IoT-Enabled AMI Network in Smart Grid
    This project focuses on making smart electricity networks more efficient and cost-effective. It uses smart meters to collect and send data to the control center without relying on expensive devices. The system combines data from multiple meters in a smart way to reduce workload and make better use of limited resources. Simulations show that this approach improves performance while keeping the network reliable.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. A Survey of Resource Management in D2D Communication for B5G Networks
    This project studies a new type of wireless communication called Device-to-Device (D2D) for future networks beyond 5G. D2D allows nearby devices to connect directly without relying on a base station. The goal is to increase data speed, network coverage, and energy efficiency while reducing delays. The study reviews current challenges, like interference and security, and suggests ways to improve D2D communication in upcoming networks.
  7. An Anonymous Authentication With Received Signal Strength Based Pseudonymous Identities Generation for VANETs
    This project focuses on allowing mobile users to securely verify their identity without revealing personal information. It creates temporary pseudonymous identities that are unique and can be revoked if needed. The system generates shared secret keys using signal strength and ensures fast and reliable authentication, even when users move at different speeds or network traffic varies. Tests show the method is accurate, efficient, and practical for real-world mobile networks.
  8. An Online Learning Approach to Shortest Path and Backpressure Routing in Wireless Networks
    This project focuses on improving data transmission in wireless networks where the quality of connections is uncertain and changes over time. It develops a smart algorithm called OLSB that learns which paths are best for sending data while keeping costs low. The algorithm adapts automatically, improving network efficiency without knowing all connection details in advance. Simulations show that OLSB performs very well and closely matches the best possible routing strategy.
  9. Autonomous Decentralized Spectral Clustering for Hierarchical Routing of Multi-Hop Wireless Networks
    This project focuses on organizing multi-hop wireless networks into clusters automatically, without a central controller. It develops a method that allows devices to form groups using only local information, even when the network changes or nodes move. The approach uses mathematical equations to create a structure that guides clustering. It works for both fixed and dynamic networks, making network management easier and more scalable.
  10. Cluster-Based Protocol for Prioritized Message Communication in VANET
    This project focuses on improving communication in Vehicular Ad Hoc Networks (VANETs), which connect vehicles wirelessly. Traditional methods are often too slow for moving vehicles, especially for road safety alerts. The proposed approach uses a clustering method to send emergency messages faster. Non-urgent information is stored temporarily to ensure urgent messages reach vehicles without delay.
  11. Detecting and Preventing False Nodes and Messages in Vehicular Ad-Hoc Networking VANET
    This project focuses on making vehicle communication networks safer. It detects and prevents fake vehicles and false messages on the road network. The system checks each vehicle’s profile and only accepts messages that meet certain rules. Simulations show that the approach successfully identifies and blocks fake nodes and messages in real time.
  12. Dispersal Foraging Strategy With Cuckoo Search Optimization Based Path Planning in Unmanned Aerial Vehicle Networks
    This project focuses on improving how drones (UAVs) move and communicate in complex environments like oceans. It proposes a new method that helps drones find the best paths while avoiding obstacles and efficiently sharing data. The approach also manages resources better and avoids getting stuck in poor solutions. Simulations show that this method performs better than existing techniques.
  13. Dual-Tier Cluster-Based Routing in Mobile Wireless Sensor Network for IoT Application
    This project focuses on improving mobile wireless sensor networks, which connect many devices to monitor real-world environments. It introduces a new routing method called Dual Tier Cluster-Based Routing (DTC-BR) that organizes sensors into virtual zones with smart cluster heads. The method reduces energy use, extends network lifetime, and works well even for large networks. Simulations show it performs better than existing routing protocols.
  14. Link Characterization and Edge-Centric Predictive Modeling in an Ocean Network
    This project focuses on improving internet communication for fishing vessels at sea. It studies how weather, waves, and antenna movement affect wireless signals. The researchers use a combination of past and real-time data to predict signal strength using a smart learning system. This helps make sea communication more reliable and efficient.
  15. Machine Learning Empowered Emerging Wireless Networks in 6G Recent Advancements Challenges and Future Trends
    This project explains how future 6G networks will use machine learning to make communication faster, smarter, and more reliable. It shows how ML helps the network manage resources on its own and solve problems like energy use, delays, and smooth connections. The paper reviews different ML methods and how they improve new types of networks, such as device-to-device and vehicular systems. It also discusses open challenges and future research directions for building intelligent 6G networks.
  16. MD-MARS Maintainability Framework Based on Data Flow Prediction Using Multivariate Adaptive Regression Splines Algorithm in Wireless Sensor Network
    This project aims to make wireless sensor networks work smoothly for a long time. It studies how sensors communicate and how to keep the network reliable. The researchers use simulations and machine learning to improve data flow and reduce congestion. Their method predicts network behavior accurately and shows that the system can be repaired quickly when problems occur.
  17. Optimized MAC Protocol Using Fuzzy-Based Framework for Cognitive Radio AdHoc Networks
    This project focuses on improving cognitive radio ad hoc networks, which help share limited radio spectrum efficiently. It uses a fuzzy-based system to adjust network settings like packet length and contention window. By doing this, the network can send data faster and reduce delays. The results show significant improvement in both speed and response time compared to traditional methods.
  18. Pizzza A Joint Sector Shape and Minimum Spanning Tree-Based Clustering Scheme for Energy Efficient Routing in Wireless Sensor Networks
    This project focuses on improving wireless sensor networks, which often run out of energy quickly. It introduces a new method called Pizzza, which organizes sensors into clusters and carefully chooses leaders to manage data. This approach reduces unnecessary energy use, balances workload among sensors, and prevents wasted data transmission. As a result, the network lasts longer and uses energy more efficiently compared to existing methods.
  19. Reinforcement Learning for Delay Tolerance and Energy Saving in Mobile Wireless Sensor Networks
    This project uses a type of machine learning called Q-learning to improve wireless sensor networks. It focuses on choosing special nodes, called cluster heads, to collect and send data efficiently. The method reduces energy use and shortens the travel path of a mobile base station. Simulations show it performs better than traditional approaches in saving energy and extending network life.
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