Analysis of Latency Minimization in 5G Vehicular Networks

Performance Analysis of Latency Minimization in 5G Heterogeneous Vehicular Networks

Implementation plan:
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Step 1: Initially, we constructed a network with 50 -> IOV Nodes, 2 -> Base Station, 1 -> RSU , and 1 -> Edge server.

Step 2: Next, we optimally distribute the clusters using the Efficient Cluster Head Selection (ECHS) scheme.

Step 3: Next, we optimize the latency and energy consumption using Swarm Optimized Non-dominated Sorting Genetic algorithm with Deep Reinforcement Learning (SONG-PO-DRL) method.

Step 4: Next, we allocate resources using Adaptive Resource Management and Scheduling with FL-DA and (DQ-EDCA).

Step 5: Next, we mitigate the network congestion using Traffic Density-based Congestion Control Algorithm (TDCCA).

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

6.1: Number of IoV nodes vs Latency (ms)

6.2: Number of IoV nodes vs Energy Consumption (J)

6.3: Number of IoV nodes vs Cluster Head Lifetime (s)

6.4: Number of IoV nodes vs Congestion Rate (%)

6.5: Number of IoV nodes vs Computational Overhead (%)

Software Requirement:
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1. Development Tool: OMNET++ with Veins (Sumo) with Python

2. Operating System: Windows 11 (64-bit) or above

Note:
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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.

We perform with an Existing Approach Ref : Title:- SOMACA: A New Swarm Optimization-Based and Mobility-Aware Clustering Approach for the Internet of Vehicles”.

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