Research Breakthrough Possible @S-Logix pro@slogix.in

Office Address

Social List

Final Year EdgeSim Projects in Edge Computing

final-year-edgesim-projects-in-edge-computing.jpg

EdgeSim Projects in Edge Computing for Final Year Computer Science

  • Edge computing represents a paradigm shift in data processing, where computational resources are moved closer to the data source”typically on the "edge" of the network”such as IoT devices, routers, and gateways. This reduces latency, decreases bandwidth usage, and enables real-time decision-making, making it a significant advancement in distributed computing and an integral part of modern IT architectures.

    Edge computing is particularly beneficial in applications where real-time processing is crucial, such as autonomous vehicles, smart grids, industrial IoT (IIoT), and healthcare systems. As organizations collect and analyze massive amounts of data from distributed devices, edge computing alleviates the pressure on centralized cloud servers by offloading some of the computational work to edge devices.

    Edge computing is revolutionizing the way data is processed and analyzed in distributed networks, offering faster decision-making, improved efficiency, and enhanced data privacy. By bringing computation closer to where data is generated, edge computing allows for scalable, real-time applications across a range of industries, including healthcare, transportation, and agriculture. In final year projects, edge computing offers students the opportunity to explore cutting-edge technology, enabling them to develop solutions that respond instantly to the challenges of modern IoT environments.

Software Tools and Technologies

  • • Operating System: Ubuntu 20.04 LTS 64bit / Windows 10
  • • Development Tools: Apache NetBeans IDE 22b / EdgecloudSim 1.0 / CloudSim 4.0.0
  • • Language Version: JAVA SDK 21.0.2

List Of Final Year EdgeSim Projects in Edge Computing

  • • Mobile Cloud Offloading Based Energy-Efficient Decision Making Problem.
  • • A Sustainable Platform Based Green Cloudlet Networks in Mobile Cloud Computing.
  • • Enhancing Cloud Service Reliability with Proactive Fault-Tolerance Strategies in Mobile Cloud.
  • • Market Equilibrium-Based Pricing Strategies for Resource Allocation in Edge Computing.
  • • A Wireless Metropolitan Area Networks(WMAN) Based Optimal Cloudlet Placement and User to Cloulet Allocation.
  • • Optimizing Joint Scheduling and Cloud Offloading Techniques for Mobile Applications.
  • • Resource Optimization for Efficient Mobile Edge Computing by using Geo-Clustering Strategies.
  • • Optimized Task Scheduling with Deadline-Aware in Mobile Edge Computing.
  • • Energy Efficiency based on the Joint Computation and Communication Co-Operation for Mobile Edge Computing
  • • Efficient Application Offloading based Distributed Multi-dimensional Pricing in Mobile Cloud Computing.
  • • Optimizing Cloudlet Sharing with Auction-Based Resource Allocation in Mobile Cloud Computing.
  • • Task Allocation with Distributed Truthful Auction Mechanisms in Mobile Cloud Computing.
  • • Geo-Distributed Mobile Cloud Computing based Decentralized and Optimal Resource Cooperation.
  • • Optimizing Energy Efficient Edge Scheduling with Double Deep Q-Learning in Edge Computing.
  • • Multi-Tiered Services for Resource Provisioning in the Edge for IOT Applications.
  • • Green Mobile Edge Cloud Computing for Multi-User Multi-Task Computation Offloading.
  • • Maximum Processing Capacity with Power Constraints Edge Computing for IoT Networks.
  • • Joint Task Offloading and Resource Allocation Strategies for Multi-Server Mobile-Edge Networks.
  • • IoT Using Mobile Edge Computing for Privacy Preserving Data Aggregation Scheme.
  • • Optimizing the Framework for Edge Node Resource Management in Mobile-Edge Computing.
  • • Optimizing Cost Effective Provisioning of Healthcare Data by using Edge-of-Things Computing Framework.
  • • Edge Server Placement Solutions for Mobile Edge Computing Environments.
  • • Optimizing Task Prediction and Computation Offloading in Mobile-Edge Cloud Computing.
  • • Improving User Experience Based on Joint Optimization of Data Placement and Scheduling in Edge Computing.
  • • Hybrid Cloud and Edge Environment Based Secure Management.
  • • Optimizing Response Time for Cloudlets in Mobile Edge Computing.
  • • Mobility-Aware and Caching-Enhanced Task Scheduling Strategies for Mobile Edge Computing.
  • • Optimizing Task Scheduling in Mobile Edge Computing by using User Mobility Awareness.
  • • Smart Edge Caching Solutions for Mobile Multimedia Content in Information-Centric Networks.
  • • Resource Augmentation based Economic and Energy Consideration in Mobile Cloud Environments.

Related Papers