KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#dsh #dsh-plugin #ai-agents #developer-tools #codex #claude-code #mcp #rust #deepseek-harness #typescript #ai-agent #cli

EC-GWO

★ 31 Open GitHub ↗

EC-GWO: A Hybrid Clustering and Routing Protocol for IoV Leveraging Edge Computing and Bio-inspired Gray Wolf Optimization

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# EC-GWO
EC-GWO: A Hybrid Clustering and Routing Protocol for IoV Leveraging Edge Computing and Bio-inspired Gray Wolf Optimization
Internet of Vehicles (IoV) is a quickly emerging paradigm enabled by dynamic network topologies, specific vehicle movement patterns, fluctuating node densities and speeds. Designing efficient routing algorithms for these networks remains a critical challenge. In VANETs, RSUs are edge computing devices placed along roads and at intersections for routing support. Bio-inspired optimization algorithms are very adaptive, highly concurrent in parameters, and really apt for application in this domain. The clustering of vehicles also reduces the complexity in network management.

This study proposes a new routing approach of applications in IoV, namely edge computing and cluster-based routing with Gray Wolf Optimization. Evaluations based on these criteria are performed to obtain a feasible solution based on total delay, number of hops, and cluster size; thus, a feasible solution is the one by which the delay is minimized, while the hop count is reduced and the number of vehicles in a cluster is maximized. Then, RSUs manage the process of optimization that is executed as EC capabilities in edge computing. From the simulation results, it is noted that the proposed EC-GWO achieves better performance compared to the existing methods in respect to some of the crucial metrics: distance traversed, number of hops, latency, packet delivery ratio, and convergence time. Therefore, the proposed method gives a promising solution for effective routing in IoV networks.

In this research, the following article is used, which is one of my previous works: https://www.springerprofessional.de/en/ec-mopso-an-edge-computing-assisted-hybrid-cluster-and-mopso-bas/20023032

For a better understanding of this article and explanations of the MATLAB codes of this project and the results obtained in it, you can view the following link :https://drive.google.com/drive/folders/166BGVTLfkcFsFl6XwHkBEMQSZ-PGNR9q

We are going to evaluate using other metrics such as relative speed .and being close to the shortest path , utilizing other evolutionary algorithms, considering access control , intrusion detection ,and prioritization  as our future work.
If you have any questions or encounter issues with the implementation of the code, feel free to send an email to the following address:
Melodyjamalzadeh@gmail.com

EC-GWO

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README