Performance Analysis of Optimizing Traffic Safety in IoV

Performance Analysis of Optimizing Traffic Safety in IoV with Deep Reinforcement Learning

Implementation plan:
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Step 1: Initially, we created a VANET Network with 50- IOV, 2- Road side Units (RSU), 2-Base Stations, 1-fog Node and 1- cloud.

Step 2: Next, we perform offloading and optimal resource allocation using Deep Reinforcement Learning (DRL) Method.

Step 3: Next, we implement Fog Computing to process large volumes of data at low latency.

Step 4: Next, we Optimize the Network using deep RL algorithms to reduce traffic.

Step 5: Next, we reduce energy consumption using DRL-based optimization techniques.

Step 6: Next, we allocate the resources for optimized energy consumption using Round Robin Algorithm.

Step 7: Next, we prevent V2V and V2I communication in VANETs from false messages.

Step 8: Finally, Performance Metrics will be plotted for the following:

8.1: Number of IOV vs Energy Consumption (%)

8.2: Number of IOV vs Latency (ms)

8.3: Number of IOV vs Resource Utilization (%)

8.4: Number of IOV vs Throughput (%)

Software Requirements:
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1. Development Tool: OMNeT++ 4.6 or above with Veins
2. Operating System: Windows 10 (64-bit) or above
3.Programing Language: C++

Note: –
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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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