Performance Analysis of V2X Network Intelligent Traffic

Performance Analysis of V2X Network for Intelligent Traffic Management

Implementation plan:

Step 1: Initially, we constructed a V2x network using 30 – Vehicles, 4 Edge Servers, 1 Blockchain Node, and 2 RSUs.

Step 2: Then, we simulate and collect network traffic data from vehicles.

Step 3: Next, we integrate edge computing nodes into the blockchain network for distributed processing and reduce latency in data sharing.

Step 4: Next, we implement smart contracts for secure data access control, privacy preservation, and tamper-proof storage in the blockchain.

Step 5: Next, we train deep learning models using DRL for traffic flow prediction and congestion detection using the collected data.

Step 6: Next, we develop a Deep reinforcement learning algorithm for adaptive traffic signal control and vehicle routing while assessing network load adaptability under varying traffic volumes.

Step 7: Finally, we plot performance for the following metrics:

7.1: Number of Vehicles vs. End-to-End Latency (ms)
7.2: Number of Vehicles vs. Transaction Confirmation Latency (ms)
7.3: Number of Vehicles vs. Edge Processing Latency (ms)
7.4: Number of Vehicles vs. Transaction Throughput (Mbps)
7.5: Number of Vehicles vs. Edge data processing rate (%)
7.6: Number of Vehicles vs. Network Scalability (%)
7.7: Number of Vehicles vs. Data Storage Scalability (%)
7.8: Number of Vehicles vs. Communication Overhead (transactions)
7.9: Number of Vehicles vs. Computational Cost ($)
7.10: Number of Vehicles vs. Security Overhead (%)
7.11: Number of Vehicles vs. Attack detection rate (%)
7.12: Number of Vehicles vs. False Positive rate (%)
7.13: Number of Vehicles vs. Network Uptime(ms)
7.14:Number of Vehicles vs. Packet Loss Rate (%)
7.15: Number of Vehicles vs. Energy Consumption (J)
7.16: Number of Vehicles vs. Transaction Operational Cost($)
7.17: Number of Transactions vs. Energy Consumption (J)
7.18: Number of Epochs vs. Accuracy (%)
7.19: Number of Epochs vs. Precision (%)
7.20: Number of Epochs vs. Recall (%)
7.21: Number of Epochs vs. F1 Score (%)
7.22: Number of Epochs vs. AUC Curve

Software Requirements:

1. Development Tool: OMNeT++ 6.1 with Veins
2. Operating System: Windows 10 (64-bit) or above

Note

1) If the plan does not meet your requirements, provide detailed steps, parameters, models, or expected results in advance. Once implemented, changes won’t be possible without prior input; otherwise, we’ll proceed as per our implementation plan.

2) If the plan satisfies your requirement, Please confirm with us.

3) Project based on Simulation only, not a real time project.

4) Please understand that any modifications made to the confirmed implementation plan will not be made after the project development.

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