• With the rapid growth of the Internet of Things (IoT), smart devices, and the ever-increasing amount of data being generated at the networks edge, traditional cloud computing faces several challenges in terms of latency, bandwidth, and real-time processing. Fog computing (or fog networking) was introduced as a complementary approach to cloud computing to address these issues. Fog computing extends cloud capabilities closer to the edge of the network by providing computation, storage, and networking services locally, rather than relying on distant data centers. This helps in minimizing latency, enhancing security, and improving real-time decision-making in edge-based applications.
  
  Fog computing plays a significant role in IoT environments where data needs to be processed closer to where it is generated, reducing delays and improving system efficiency. It allows devices like sensors, routers, gateways, and other edge nodes to process data locally before sending it to the cloud, offering a more distributed and decentralized approach. The increasing reliance on real-time data processing in areas like smart cities, healthcare, autonomous vehicles, and industrial automation makes fog computing an essential technology to explore in student projects.

Software Tools and Technologies

• • Operating System: Ubuntu 20.04 LTS 64bit / Windows 10
• • Development Tools: Apache NetBeans IDE 22 / iFogSim 4.0 / CloudSim SDN 3.0.0
• • Language Version: JAVA SDK 21.0.2

List Of Final Year IFogSim Projects in Fog Computing

• • Energy Consumption Optimization With a Delay Threshold in Cloud-Fog Cooperation Computing.
• • IOT Health Care Applications with Improved African Buffalo Optimization Algorithm in Fog Computing.
• • Minimising Delay and Energy with Computational Offloading in Online Dynamic Fog System.
• • Quality of Experience (QoE)-Aware Placement of Applications in Fog Computing.
• • Efficient Scientific Workflow Scheduling for Deadline-Constrained Parallel Tasks in Cloud Computing Environments.
• • Augmenting Resource Utilization with Scheduling-Based Fog Computing Framework.
• • Machine Learning based Minimizing Latency by using Fog Computing Approach.
• • Optimizing Task Scheduling Algorithm by using Improved-List in Fog Computing Environment.
• • Deep Reinforcement Learning for Autonomous Computation Offloading and Auto-Scaling in Mobile Fog Computing.
• • Scientific Workflow Application based Fog Computing Architecture of Load Balancing.
• • Developing Offload and Migration Enabled Smart Gateway for Cloud of Things in Cognitive Fog Framework.
• • The Cloud-Fog Environments for Resource-Aware Cost-Effiecient Scheduler.
• • Fog Computing Environment Based on the Technique for Resource Allocation and Management.
• • Hidden Markov Model - Based Approach for Latency-Aware and Energy-Efficient Computation Offloading in Mobile Fog Computing.
• • Optimized Resource Provisioning by using Learning-Based Techniques in Fog Computing.
• • Hybrid Meta-Heuristic Approaches for Energy-Aware Task Scheduling in Fog Computing.
• • Energy-Aware Resource Management in Fog-Based IoT Using a Hybrid Algorithm.
• • Optimizing Cost-Aware Task Scheduling in Fog-Cloud Environments.
• • IFogSim-Based Offloading Techniques in Cloud and Fog Hybrid Infrastructures.
• • Improved Firework Algorithm Based Task Scheduling Algorithm in Fog Computing .
• • Optimizing Delay and Performance for Job Scheduling Algorithm in Fog Computing.
• • Optimizing Delay and Performance for Job Scheduling Algorithm in Fog Computing.
• • Ciphertext-Policy Attribute-Based Encryption for Compulsory Traceable Against Privilege Abuse in Fog Computing.
• • Delay-Aware Task Scheduling and Offloading in Fog Networks.
• • FOLO: Vehicular Fog Computing based Latency and Quality Optimized Task Allocation.
• • Improved NSGA-II Based Multi-Objective Optimization for Efficient Fog Computing Resource Scheduling.
• • Fog Computing Network based on the Planning and Design Problem.
• • Application Placement Strategies in Fog Computing for Enhanced Quality of Experience(QoE).
• • Optimized Task Offloading and Data offloading Techniques in Mobile Fog-Based Collaborative Networks.
• • Large-Scale Fog Computing based Self-Similarity-Based Load Balancing(SSLB)
• • Efficient Resource Scheduling in Fog Computing through Extended Particle Swarm Optimization.