DefinePK

Research Objective

To develop an optimal number of clusters in Vehicular Ad Hoc Networks (VANETs) using a Salp Swarm Optimization-Enhanced Clustering Algorithm (SSOCANET) to reduce communication overhead and end-to-end latency.

Methodology

The study proposes SSOCANET, a bio-inspired clustering algorithm that leverages the Salp Swarm Algorithm (SSA) to optimize cluster formation in VANETs. The algorithm models the foraging behavior of salps to determine the optimal number of clusters. It employs objective functions, including delta difference (C1) and distance neighbor (C2), to evaluate cluster formation. The performance of SSOCANET is compared against benchmark algorithms like CLPSO, MOPSO, and GWOCNET through simulations.

Methodology Flowchart

                        graph TD
    A[Initialize Vehicles and Search Agents] --> B[Generate Clustered Matrix];
    B --> C[Evaluate Solutions using Objective FunctionsC1, C2];
    C --> D[Update Search Agent Positions Exploration/Exploitation];
    D --> E[Compare SSOCANET with Benchmark Algorithms];
    E --> F[Analyze Results End-to-End Delay, Overhead, CH Count];
    F --> G[Conclude on SSOCANET's Performance];                    

Discussion

The SSOCANET algorithm effectively optimizes vehicular cluster formation in VANETs by utilizing the Salp Swarm Algorithm. The balance between exploration and exploitation in SSA helps prevent local optima and enhances the search for global optima. The objective functions (C1 and C2) used in SSOCANET contribute to minimizing the number of clusters, thereby reducing communication delays and improving message transmission reliability. The simulation results validate the algorithm's efficiency and scalability under different network conditions.

Key Findings

SSOCANET demonstrates superior performance compared to CLPSO, MOPSO, and GWOCNET in VANET clustering. It consistently produces fewer clusters across various transmission ranges and numbers of vehicles, leading to reduced end-to-end delays and lower communication overhead. The algorithm achieves a high convergence rate and a proper equilibrium between exploration and exploitation.

Conclusion

The proposed SSOCANET approach significantly enhances VANET performance by optimizing cluster formation using Salp Swarm Optimization. It effectively mitigates end-to-end communication delays and minimizes communication overhead, making it a promising solution for efficient vehicular network management.

Fact Check

1. Publication Date: The paper is published in June 2024. (Confirmed by journal title and date on pages).
2. Number of Nodes in Simulation: Simulations involved 30-60 nodes. (Confirmed by Table 1).
3. Algorithm Comparison: SSOCANET is compared with CLPSO, MOPSO, and GWOCNET. (Confirmed in Results and Discussion sections).

Mind Map