Arithmetic optimization based secure intelligent clustering algorithm for Vehicular Adhoc Network

Area of Science:

• Computer Science
• Network Security
• Artificial Intelligence

Background:

• Vehicular Adhoc Networks (VANETs) face challenges like data loss and link disruption due to high vehicle mobility and dynamic topologies.
• Network reliability is compromised by malicious vehicles spreading fake messages, endangering lives and efficiency.
• Secure and efficient communication is crucial in dense vehicular networks, making vehicular clustering a potential solution.

Purpose of the Study:

• To design a secure and intelligent vehicular clustering algorithm for VANETs.
• To optimize cluster formation and prevent the inclusion of malicious nodes.
• To enhance the reliability and security of communication in dense vehicular networks.

Main Methods:

• The Arithmetic Optimization Algorithm (AOA) was employed to achieve optimal vehicular cluster formation.
• A performance value system was implemented, rewarding legitimate transmissions (+1) and penalizing fake messages (-1) to filter nodes.
• Vehicles with a performance value above a threshold (0) were admitted as cluster members.

Main Results:

• The proposed AOACNET algorithm demonstrated superior performance in simulations.
• The algorithm achieved up to 25% improvement over existing methods in key optimization objectives.
• Evaluated metrics included cluster count, load balancing, computational time, network overhead, and delay.

Conclusions:

• AOACNET effectively enhances security and reliability in VANETs through intelligent clustering.
• The AOA-based approach successfully optimizes cluster formation and mitigates risks from malicious nodes.
• The proposed method offers a significant improvement for secure and efficient vehicular communication.