Performance Analysis of Machine Learning Driven VANET

Performance Analysis of Machine Learning Driven Approaches for Enhancing VANET

Implementation Plan:

Step 1: Initially, we created a VANET Network with 50 Vehicles , 2 RSU and 1 server

Step 2: Then, we simulate it to view the communication between the vehicles.

Step 3: Next, we implement Q-Learning Relay Placement for transmitting alert messages in the network.

Step 4: Next, we rout the vehicles in the network using QOS routing with a deep reinforcement learning process.

Step 5: Next, we allocate Radio Resources using Multi-Agent Actor-Critic Based Approach

Step 6: Finally, we plot performance metrics for the following

6.1: Number of Vehicles vs. Packet Delivery Ratio (%)
6.2: Number of Vehicles vs. End-to-End Delay (ms)
6.3: Number of Vehicles vs. Throughput (Mbps)
6.4: Number of Vehicles vs. Energy Consumption(J)

Software Requirements:

1. Development Tool: OMNeT++ 4.6 or above with Veins
2. Operating System: Windows 10 (64-bit) or above

Note :-

1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.

2) Please note that this implementation plan does not include any further steps after it is put into implementation.

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

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

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

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