Energy Consumption Final Year Projects with Source Code

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

Energy Consumption Final Year Projects

  1. Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Industrial IoT in MEC Federation System
    This study focuses on improving smart industrial systems that use IoT devices. These devices have limited power and memory, so the project uses mobile edge computing to help them process data more efficiently. The researchers developed a smart method using deep learning to decide how tasks are shared and resources are used across devices. Their approach reduces energy use and processing delays in real-world scenarios.
  2. 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.
  3. 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.
  4. TMaLB A Tolerable Many-Objective Load Balancing Technique for IoT Workflows Allocation
    This project studies how to balance heavy and uneven data loads in Internet of Things systems. It looks at what factors matter most for good service, such as cost, speed, and energy use. The method uses an intelligent search algorithm to choose the best way to share work across devices. It improves both performance and the number of tasks the system can handle.
  5. DeepMist Toward Deep Learning Assisted Mist Computing Framework for Managing Healthcare Big Data
    This project focuses on predicting heart disease using advanced computing techniques. It proposes a system called DeepMist that uses deep learning on a Mist computing setup to analyze healthcare data quickly and efficiently. The model predicts heart disease with high accuracy while reducing energy use and processing delay. Testing shows it performs better than other popular machine learning methods.
  6. Research on ATO Control Method for Urban Rail Based on Deep Reinforcement Learning
    This project develops an intelligent system to control urban trains automatically. It uses a deep learning algorithm to adjust the train’s speed and stops in real time. The system improves punctuality, parking accuracy, and saves energy. Tests on a Beijing subway line show it works better than traditional control methods.
  7. Adaptive Sampling Approach Exploiting Spatio-Temporal Correlation and Residual Energy in Periodic Wireless Sensor Networks
    This project focuses on saving energy in wireless sensor networks, which often collect a lot of repetitive data. It uses smart sampling that adjusts how often sensors collect data based on the energy left in the nodes and patterns in the data. Missing data is later estimated using a simple method called linear regression. The approach reduces energy use while keeping the data accurate and can be applied in areas like water resource management.
  8. An Efficient Authenticated Elliptic Curve Cryptography Scheme for Multicore Wireless Sensor Networks
    This project focuses on improving the lifespan and security of Wireless Sensor Networks. It groups sensors together and uses multicore sensors to reduce power consumption. The system applies advanced cryptography to keep data secure and verify devices. Tests show it is energy-efficient, fast, scalable, and resistant to attacks.
  9. An Energy-Efficient Hybrid Clustering Technique EEHCT for IoT-Based Multilevel Heterogeneous Wireless Sensor Networks
    This project focuses on improving energy efficiency in IoT-based wireless sensor networks. It introduces a new clustering method called EEHCT, which reduces energy use when forming clusters and balances the network load. The approach combines dynamic and static clustering to extend the network’s lifetime. Simulations show it performs better than existing methods in stability, throughput, and overall network longevity.
  10. An Energy-Efficient MAC Protocol for Three-Dimensional Underwater Acoustic Sensor Networks With Time Synchronization and Power Control
    This project focuses on improving underwater sensor networks that monitor oceans and lakes. It introduces a new communication method that organizes sensors in layers and groups to save energy. The system carefully controls timing and power to reduce data collisions and energy use. Simulations show it sends data efficiently while using less power.
  11. CDEIR Intelligent Routing for Efficient Wireless Sensor Networks Using BUG Algorithm
    This project focuses on improving data transmission in Wireless Sensor Networks. It proposes a new routing method that avoids congested nodes, reduces delays, and saves energy. The system finds better paths for data to travel to the main Base Station. Simulations show that this approach works efficiently and quickly.
  12. Distributed Energy-Efficient Clustering and Routing for Wearable IoT Enabled Wireless Body Area Networks
    This project focuses on improving wearable health monitoring networks. It designs a smart method to group devices and send data efficiently while saving energy. Each device considers nearby nodes to form clusters and select leaders using an optimization algorithm. The approach ensures reliable data delivery and performs better than existing methods in tests.
  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. Efficient Cluster Heads Selection Based on Index-Modulation in Wireless Sensor Networks
    This project improves wireless sensor networks by making data collection more efficient and fair. It introduces a new method called index-shift, which balances the work among sensor nodes. This reduces energy waste and extends the network’s lifetime by up to 50%. The method also improves decision accuracy compared to older approaches.
  15. Energy Cooperation Among Sustainable Base Stations in Multi-Operator Cellular Networks
    This project focuses on making cellular networks more energy-efficient and environmentally friendly. It helps base stations share harvested energy in an optimal way, reducing energy loss and costs. The system predicts future energy availability using a smart learning method called Deep Q-Learning. Simulations show that this approach works better than current methods in saving energy and cutting costs.
  16. Energy Saving Multi-Relay Technique for Wireless Sensor Networks Based on Hw Sw MPSoC System
    This project focuses on making wireless sensor networks more energy-efficient. The researchers designed a system where sensors send data through special relay nodes to reduce power use while keeping communication reliable. They tested this method using a combination of software and hardware, which showed it saves energy and reduces workload compared to regular software solutions.
  17. GS-MAC A Scalable and Energy Efficient MAC Protocol for Greenhouse Monitoring and Control Using Wireless Sensor Networks
    This project focuses on improving wireless sensor networks in agricultural greenhouses. The goal is to save energy and extend network lifetime since sensor nodes rely on batteries. The proposed system, GS-MAC, lets nodes sleep efficiently without frequent synchronization, reducing wasted power. Simulations show it uses much less energy and lasts longer than previous methods, though communication can be slightly slower.
  18. Low-Latency Low-Energy Adaptive Clustering Hierarchy Protocols for Underwater Acoustic Networks
    This project improves communication in underwater sensor networks by reducing the extra information added to each data packet. It introduces a new protocol that gives shorter, locally assigned IDs to the nodes, which saves time and energy. A second version further reduces energy use by embedding IDs more efficiently. Overall, the system makes underwater networks faster, more efficient, and better for large deployments.
  19. 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.
  20. 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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