Internet Connected Vehicles Research Topics

Fog with Internet of Connected Vehicles (IoCV) Environment Research Topics is one of the recent popular topics that are widely used in many applications. It joins both principles of Fog Computing (FC) with the concept of connected vehicles. Here we offer the information or details that are related to our proposed technique.

1. Define Fog enabled IoCV Environment

At first we see the definition for Fog enabled IoCV Environment technology. It defines a system that combines the standards of both FC and the concept of connected vehicles, generating a dispersed and flexible framework for data processing created by the vehicles in actual-time.

2. What is Fog enabled IoCV Environment?

After the definition we look over the in-depth understanding of our proposed technique. It utilizes intermediary computing nodes called “fog nodes” over the vehicular network to process and examine the data created by the vehicles. IoCV intends to improve traffic efficiency, road safety and the whole driving knowledge by providing co-operation and data change among vehicles and their environment. The “Fog enabled Internet of Connected Vehicles (IoCV)” defines a technological concept that combines both principles of FC into the linked vehicles over the Internet of Things (IoT) environment.

3. Where Fog enabled IoCV Environment used?

Next to the in-depth understandings we converse over the utilization of this proposed technology. It is used in different situations where the integration of both FC with the connected vehicles technology in IoCV application causes decreased congestion, enhanced road safety, increased driving experience And more effective transportation system.

4. Why Fog enabled IoCV Environment technology proposed? , Previous technology issues

We proposed a FC with IOCV technique as a solution to overcome a few technological difficulties and limitations that were presented in the traditional connected vehicle framework. Several previous technology issues that it intends to overcome are Absence of security, High Car Crashes, and Inefficient driver behavior analysis.

5. Algorithms / Protocols

In this research we overcome previous technology issues by proposing some methods in our research. The methods that we utilized are as follows: Hidden Markov Model (HMM), Long Short-Term Memory algorithm (LSTM), Gaussian Mixture Model (GMM) and Whale Optimization based Restricted Boltzmann Machine Algorithm (WOA-RBM).

6. Comparative Study / Analysis

The proposed FC enabled IoCV environment system compares the following methods to obtain the best outcomes. The methods that we compared are as follows:

• Gaussian Mixture Model (GMM) is a clustering method in Machine Learnig method that is utilized to arrange data by finding common terms and differentiate them with others.
• To manage subsequent data and seizure long-term dependencies, creating them relevant for time series prediction tasks. It also offers enhanced accuracy in classification tests.
•  The HMM method is utilized to forecast the future recordings or categorize sequences on the basis of the basic hiding process which creates the data.

7. Simulation results / Parameters

Here our proposed technology performance metrics is compared with some of the existing technologies to verify that our proposed one gives the best findings. The metrics that we compared are as follows: Latency, Motion Prediction Error, Number of risk maneuvers, security strength and Number of Alerts with the Number of Vehicles and the Behavior Detection Accuracy with the Number of Drivers and the Vehicle motion detection Accuracy with the vehicle speed.

8. Dataset LINKS / Important URL

The FC enabled IoCV is proposed in this research to address some previous technology issues. In this we provide some important links that are useful to us when we have to overview the concept of this proposed research.

• https://www.mdpi.com/1999-5903/14/1/12
• https://www.mdpi.com/2076-3417/11/21/10462
• https://www.mdpi.com/1424-8220/22/7/2686

9. Fog enabled IoCV Environment Applications

Let’s see the applications to be utilized in this proposed research are as follows. The applications that we utilize are Automated Parking, Actual -Time Traffic Management, V2X Communication, Driver Assistance System, Collision Avoidance and Improved Navigation.

10. Topology for Fog enabled IoCV Environment

Now we see the topology that was used for this proposed research. The topologies that we employed are Data Analysis and Decision-Making, Monitoring and Management, Connected Vehicles, Fog Layer, Communication Infrastructure and Central Cloud or Data Center.

11. Environment in Fog enabled IoCV Environment

The environment that we utilized for this proposed FC enabled IoCV environments are as follows. The environment that we utilize is Testing and Validation, Physical Infrastructure, Data Processing and Storage, Security and Privacy, Communication Network, Software Architecture and Connected Vehicles.

12. Simulation Tools

For this research we use the software requirements as follows. The development tool that we utilized for this research is Omnet ++ 4.6 and SUMO 0.19.0. Then the operating system here we employed to execute the research is Windows 10 [64 – bit].

13. Results

The FC enabled IoCV environment is proposed to address several previous technology issues and that can be overcome by using some novel methods or algorithms. Then we also compare some parameters or the performance metrics with the existing techniques to identify our proposed system gives the best results when compared to others. Then the work is implemented by using the operating system Windows 10 [64-bit].

Internet Connected Vehicles Ideas:

Below we offer the research topics based on our proposed research FC enabled IoCV environment. These topics are helpful to us when we have any doubts related to this research.

1. Fog computing based Distributed Denial of Service Attack Detection Method for Large-Scale Internet of Things
2. A Systematic Review of Task Offloading & Load Balancing Methods in a Fog Computing Environment: Major Highlights & Research Areas
3. Real-Time Task Scheduling and Resource Scheduling in Fog Computing using Deep Learning Techniques
4. A Multifactor Combined Data Sharing Scheme for Vehicular Fog Computing Using Blockchain
5. Fog Computing in Smart Cities: A Systematic Review
6. A Blockchain-Based Lightweight Secure Authentication and Trust Assessment Framework for IoT Devices in Fog Computing
7. Energy Consumption Scheduling as a Fog Computing Service in Smart Grid
8. A Load Balancing Algorithm for Equalising Latency Across Fog or Edge Computing Nodes
9. Toward Secure and Efficient Collaborative Cached Data Auditing for Distributed Fog Computing
10. Heterogeneous Fog Computing Implementation for Internet of Things Applications
11. A Traceable Location Privacy Preserving Scheme for Data Collection in Vehicular Fog Computing
12. Contract-Based Charging Protocol for Electric Vehicles With Vehicular Fog Computing: An Integrated Charging and Computing Perspective
13. Distributed Personal OS Environments – exploring Cooperative Fog Computing
14. Authenticated Key Agreement Scheme for Fog Computing in a Health-Care Environment
15. ReLIEF: A Reinforcement-Learning-Based Real-Time Task Assignment Strategy in Emerging Fault-Tolerant Fog Computing
16. FoggyEdge: An Information-Centric Computation Offloading and Management Framework for Edge-Based Vehicular Fog Computing
17. A Review on Task Scheduling Techniques in Cloud and Fog Computing: Taxonomy, Tools, Open Issues, Challenges, and Future Directions
18. Dual Attribute-Based Auditing Scheme for Fog Computing-Based Data Dynamic Storage With Distributed Collaborative Verification
19. Optimization of Partially Offloading Mobile User Tasks to Fog Computing Networks
20. Distributed Task Management in Fog Computing: A Socially Concave Bandit Game
21. Theoretical algorithm for traffic decorrelation in Fog computing
22. Significance of Internet-of-Things Edge and Fog Computing in Education Sector
23. Blockchain Based Fog Computing Network Architecture
24. Proposal of a Fog and Cloud Computing-Based Architecture for Air Quality Monitoring in Panama
25. Scheduling Precedence Constrained Tasks for Mobile Applications in Fog Computing
26. A Cost Effective IoT-Assisted Framework Coupled with Fog Computing for Smart Agriculture
27. Task Scheduling in Fog Computing: Parameters, Simulators and Open Challenges
28. An Embedded Low-Cost Solution for a Fog Computing Device on the Internet of Things
29. Accident Detection Using Fog Computing
30. Fast Adaptive Task Offloading and Resource Allocation via Multiagent Reinforcement Learning in Heterogeneous Vehicular Fog Computing
31. FOGO – an Optimized Fog and Edge Computing Method for VANETs
32. A Methodological Review on EEG Data Reduction in Edge/Fog computing-based IoMT networks
33. Load-Aware Resource Scheduling in Fog Computing Based Delay-Sensitive IoT Networks
34. IoT and Fog-Computing-Based Predictive Maintenance Model for Effective Asset Management in Industry 4.0 Using Machine Learning
35. Distributed Design of Wireless Powered Fog Computing Networks With Binary Computation Offloading
36. FCDedup: A Two-Level Deduplication System for Encrypted Data in Fog Computing
37. Delay and Total Network Usage Optimisation Using GGCN in Fog Computing
38. Network Intrusion Detection System (NIDS) Based on Pseudo-Siamese Stacked Autoencoders in Fog Computing
39. Evaluation of Intrusion Detection System for the Distributed Denial of Service Attack on Internet of Things in Fog Computing Environment
40. Agitating Sustainability using Fog Computing for Smart Cities
41. Centralized and Collaborative RL-Based Resource Allocation in Virtualized Dynamic Fog Computing
42. Efficient Management in Fog Computing
43. Analysis of Fog Computing: An Integrated Internet of Things (IoT) Fog Cloud Infrastructure for Big Data Analytics and Cyber Security
44. The FORA European Training Network on Fog Computing for Robotics and Industrial Automation
45. Joint Task Offloading and Resource Allocation: A Historical Cumulative Contribution Based Collaborative Fog Computing Model
46. Enhancing Task Efficiency in Vehicular Fog Computing: Leveraging Mobility Prediction and Min-Max Optimization for Reduced Latency
47. Task Offloading for Cloud-Assisted Fog Computing With Dynamic Service Caching in Enterprise Management Systems
48. Secure Mutual Authentication Scheme in Fog Computing: Survey
49. Blockchain Integration with Machine Learning for Securing Fog Computing Vulnerability in Smart City Sustainability
50. A Reliable and Decentralized Trust Management Model for Fog Computing in Industrial IoT