Fog Computing Security Research Topics

Fog Computing Security Research Topics is used to secure the data on the computer network. It is widely used in many regions and applications. Here we utilize this to propose this research and below are the details that are related to this fog computing technology.

  1. Define Fog Computing

At the first stage we first see the definition for Fog Computing technique. It is also referred to as fogging or fog networking and is a localized computing technique which lengthens the capacity of cloud computing (CC) to the network’s edge. It intends to overcome several restrictions of traditional cloud computing by carrying data processing and computation nearer to the end-user device or data source, instead of depending uniquely on the focused data centers.

  1. What is Fog Computing?

Subsequent to the definition we recognize the in-depth interpretation for Fog Computing technology. It is also referred to as “edge computing”, and is a dispersed computer technique that expands the ability of CC to the edge of a network. In this technique the storing and data processing are relocated nearer to the end-users or source of data, instead of depending individually on concentrated cloud centers.

  1. Where Fog Computing used?

Afterwards the in-depth interpretation we converse where to use this technology. It is employed in a different kind of industry and applications where actual-time processing, distributed computing abilities and low latency are important. Some of the general fields of use and regions where the fog computing technologies used are as follows: Industrial Automation, Internet of Things (IoT), Smart Cities, Energy Management, Retail, Autonomous Vehicles, Agriculture and Healthcare.

  1. Why Fog Computing technology proposed? , Previous technology issues

The Fog Computing Technology is proposed to overcome some existing technology issues and difficulties that are linked with the traditional cloud computing framework, particularly in strategies where actual-time processing, effective data handling and low latency were important. Several major issues that affect the improvement of fog computing are Scalability, Real-time Decision-Making, Privacy and Security, Latency, Reliability, Bandwidth Constraints and Limited Cloud Resources. With response to these difficulties, fog computing was presented as a corresponding computing model which lengthens cloud abilities to the edge of the network. By taking data storage and computation nearer to the traditional cloud computing and offer an effective and responsive solution for different industries and applications.

  1. Algorithms / Protocols

Here the fog computing technique is proposed in this research and it overcomes several previous technology issues by using these novel methods. The method that we utilized for this research is Adaptive Multi-keyed Fully Homomorphic Encryption (AMFHE), Hash-based message code authentication – Time-based One-Time password (HMAC-TOTP) and Key Distribution Center (KDL).

  1. Comparative Study / Analysis

We propose a fog computing technology that faces some challenges in the existing technologies to overcome that. The methods that we compared are as follows:

  • A key Distribution Center (KDC) is one of the portions of cryptosystem which has the purpose to decrease the threats essential in interchanging keys.
  • To validate the messages in a safe way HMAC is extensively used among different communication protocols. TOTP offers an appliance for switching on to a service or network by utilizing a distinct secret code which will be utilized only one time.
  • Fully Homomorphic Encryption is employed to protect the data in the cloud. It also assists to enhance protection and transparency in elections and other systems where the important data is being transferred.
  1. Simulation Results / Parameters

In this research we propose a Fog Computing technology and this can be compared with some of the existing technology methods to get the accurate results. The performance metrics that we compared are Degree of privacy preservation with the Different sensitivity scores (with AMFHE) and the Time of Encryption (s), Time of Decryption (s) and Time of key generation (s) with the Number of attributes and the data rate with Message length and the Ciphertext storage with the different number of attributes and the Communication overhead (bytes) with the Number of hybrid IoT device.

  1. Dataset LINKS / Important URL

Succeeding the methods to be compared with various performance metrics, now we see the links that are provided to go through the process, applications or other relevant details related to this proposed research.

  1. Fog Computing Applications

Now we see the applications that are utilized for this research. It is an expansion of cloud computing which carries computing sources nearer to the edge of the network, frequently at or close to the place of data creation. This method helps to decrease latency, improve the whole performance of applications and enhance response particularly in strategies where the low latency and actual-time processing are important. Some of the general applications for the fog computing technology are Video Surveillance, Environmental Monitoring, IoT, Industrial automation, Retail, Healthcare, Autonomous vehicles and smart grids.

  1. Topology for Fog Computing

Let’s note the topology to be employed for this research. Its topology defines the settlement or framework of structure of computing resources in a fog computing environment. The topology will differ on the basis of particular requirements and the situations, but we give some general fog computing topologies like Distributed edge Topology, Ring Topology, Star Topology, Hierarchical Topology, Mesh Topology and End-to-cloud Topology.

  1. Environment in Fog Computing

Our proposed technology environment is a dispersed structure on computing which expands the capability in the network’s edge over the CC, nearer to the end-users and data sources. It generates a network environment that allows actual-time data processing, services and analytics while decreasing latency and minimizes the requirement to send all data to remote data centers. Some primary components and features of fog computing environments are Hybrid cloud integration, Edge devices, Fog nodes, Redundancy and reliability, Resource Management, Security and Authentication, Real-time Communication, Connectivity, Data Processing and Analytics, Fog Computing Middleware, Scalability and Edge AI and Machine Learning.

  1. Simulation Tools

The software requirements that are required for this research are as follows. The tools here we utilized to implement the work are Netbeans-12.3 or above and JDK 1.8 or above and IFOGSIM 2. Then the programming language here we utilized to execute this research is JAVA. The operating system used for this research is Windows 10-[64-bits].

  1. Results

The research here we proposed is fog computing and it faces several difficulties in the existing technologies to overcome them. The proposed technology metrics are compared with the existing technology methods and verify that our proposed research gives the best outcomes. This proposed research is executed by utilizing the tool Netbeans-12.3 or above.

Fog Computing Research Ideas:

Below we provided are the research topics that are related to the fog computing technology and these topics are useful when we overview the concepts of our proposed technology:

  1. Content Replica Placement Method for Fault Tolerance in Fog Computing Environment
  2. A Collaborative Computation and Offloading for Compute-Intensive and Latency-Sensitive Dependency-Aware Tasks in Dew-Enabled Vehicular Fog Computing: A Federated Deep Q-Learning Approach
  3. Advantages of Fog Computing: A Comparative Analysis with Cloud Computing for Enhanced Edge Computing Capabilities
  4. Performance and Energy Aware Task Scheduling in Fog Computing
  5. Improving Quality of Services of Fog Computing Through Efficient Work Flow Scheduling
  6. CNN-based Smart Waste Management System in Fog Computing Environment
  7. AI-Based Energy-Saving for Fog Computing-Empowered Data Centers
  8. The Impact of Fog computing in the IoT World
  9. TAFS: A Truthful Auction for IoT Application Offloading in Fog Computing Networks
  10. Mutual user Authentication using Inherent Techniques for Cloud and Fog Computing
  11. Cyber Security in Fog Computing Using Blockchain: A Mini Literature Review
  12. A Comprehensive Survey on Role of Fog Computing and Need for Semantic Technology in Modern Computing Platform
  13. IoT-Fog Computing Sustainable System for Smart Cities: A Queueing-based Approach
  14. Computation Offloading and Task Scheduling Based on Improved Integer Particle Swarm Optimization in Fog Computing
  15. An Investigation of the Security and Privacy Implications of fog Computing Systems using IFogSim
  16. A Distributed Deep Reinforcement Learning Technique for Application Placement in Edge and Fog Computing Environments
  17. Bandit Learning-Based Distributed Computation in Fog Computing Networks: A Survey
  18. An Enhancement Algorithm for Mobility over Fog Computing
  19. Middleware for Resource Sharing in Fog Computing with IoT Applications
  20. Fog Computing in Optical Access Networks: An Energy-efficient and Deadline-aware Task Scheduling Mechanism
  21. A Comprehensive Survey on the Cooperation of Fog Computing Paradigm-Based IoT Applications: Layered Architecture, Real-Time Security Issues, and Solutions
  22. OCVC: An Overlapping-Enabled Cooperative Vehicular Fog Computing Protocol
  23. Load Balancing Algorithms in Fog Computing
  24. Wearable Brain Computer Interfaces (BCI) in Fog Computing using Wireless Technology
  25. Security Assessment and Hardening of Fog Computing Systems
  26. Intelligent Service Provisioning in Fog Computing
  27. Security Policy Matching Model between Mobile IoT and Public Fog Computing
  28. An Efficient Machine Learning-Based Resource Allocation Scheme for SDN-Enabled Fog Computing Environment
  29. Directed Search: A New Operator in NSGA-II for Task Scheduling in IoT Based on Cloud-Fog Computing
  30. A Fog Computing based Agriculture-IoT Framework for Detection of Alert Conditions and Effective Crop Protection
  31. Switch Migration Frequency for Load Balancing in Fog Computing Using Machine Learning Algorithm
  32. Container-based Migration Technique for Fog Computing Architecture
  33. Task Offloading Scheduling in Fog Computing Using Hybrid Genetic Algorithm
  34. Iot Enabled Fog Based Computing with Deep Learning Models to Increase The Allocation of Resource
  35. TBOMC: A Task-Block-Based Overlapping Matching-Coalition Scheme for Task Offloading in Vehicular Fog Computing
  36. Context-Aware Routing in Fog Computing Systems
  37. Research on Terminal Security Protection of zero-trust Smart Grid Based on Fog Computing
  38. Smart Vehicle Parking System on Fog Computing for Effective Resource Management
  39. Offloading Mechanisms Based on Reinforcement Learning and Deep Learning Algorithms in the Fog Computing Environment
  40. Devise Road Sign Alert Detection for Vehicular Systems Using Fog Computing
  41. The Task Scheduling Algorithm for Fog Computing in Intelligent Production Lines Based on DQN
  42. An Extensive Study of Scheduling the Task using Load Balance in Fog Computing
  43. Building a Model of a Patient State based on Machine Learning Methods in a Fog Computing Environment
  44. Using Applied Computing on Embedded Computers to Build Digital Twins in a Fog Computing Environment
  45. TEVAC: Trusted Evacuation System based Fog Computing
  46. Task Scheduling Mechanisms for Fog Computing: A Systematic Survey
  47. Handling Node Discovery Problem in Fog Computing using Categorical51 Algorithm With Explainability
  48. Interoperable Fog Computing Model for Data Transmission in Internet of Medical Things
  49. Enhancing Efficiency and Empowering Institutions: Leveraging Wireless Sensor Devices, IoT Edge Computing, and Fog Computing in Educational Systems
  50. Efficient Data as a Service in Fog Computing: An Adaptive Multi-Agent Based Approach