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<p>This work addresses critical security challenges in mobile ad-hoc networks (MANETs), where dynamic node mobility and decentralized structure increase vulnerability to security breaches. Previous solutions in MANETs often rely on centralized approaches that consume significant energy. To enhance security and efficiency, we propose a novel framework integrating advanced techniques: Zone-Based Clustering (ZBC) dynamically adjusts zones based on real-time conditions, while Reinforcement Learning Cluster Head Selection (RL-CHS) optimizes cluster head choice through continuous learning. For node validation, the Quantum-Resistant cryptography PUF (QR-PUF) mechanism ensures long-term security against quantum threats. Our Trust Management System (TMS) accurately evaluates node behavior using deep learning, enhancing trustworthiness in data transmission. Data security is further reinforced with the Lightweight Encryption Algorithm (ALEA), which provides robust protection with minimal computational overhead. The Hybrid Optimization-Based Routing Protocol (HORP) combines genetic algorithms and particle swarm optimization to minimize energy consumption while ensuring reliable data transmission. Comprehensive Performance Evaluation (CPE) involves extensive simulations and real-world tests, analyzing metrics such as energy consumption, security level, latency, packet delivery ratio, throughput, and resilience to attacks. Results indicate that the proposed framework significantly outperforms existing methods, achieving higher security, lower energy consumption, reduced latency, and improved packet delivery and throughput. This innovative approach sets a new benchmark for MANET security and efficiency, addressing both current and emerging challenges in mobile networking environments.</p>

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Advanced Security and Efficiency Framework for Mobile Ad-Hoc Networks Using Adaptive Clustering and Optimization Techniques

  • 期刊论文

  • 发表日期：

    2025年1月9日

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  • S. Suga Priya,
  • R. Vijayabhasker,
  • A. Rajaram

摘要

This work addresses critical security challenges in mobile ad-hoc networks (MANETs), where dynamic node mobility and decentralized structure increase vulnerability to security breaches. Previous solutions in MANETs often rely on centralized approaches that consume significant energy. To enhance security and efficiency, we propose a novel framework integrating advanced techniques: Zone-Based Clustering (ZBC) dynamically adjusts zones based on real-time conditions, while Reinforcement Learning Cluster Head Selection (RL-CHS) optimizes cluster head choice through continuous learning. For node validation, the Quantum-Resistant cryptography PUF (QR-PUF) mechanism ensures long-term security against quantum threats. Our Trust Management System (TMS) accurately evaluates node behavior using deep learning, enhancing trustworthiness in data transmission. Data security is further reinforced with the Lightweight Encryption Algorithm (ALEA), which provides robust protection with minimal computational overhead. The Hybrid Optimization-Based Routing Protocol (HORP) combines genetic algorithms and particle swarm optimization to minimize energy consumption while ensuring reliable data transmission. Comprehensive Performance Evaluation (CPE) involves extensive simulations and real-world tests, analyzing metrics such as energy consumption, security level, latency, packet delivery ratio, throughput, and resilience to attacks. Results indicate that the proposed framework significantly outperforms existing methods, achieving higher security, lower energy consumption, reduced latency, and improved packet delivery and throughput. This innovative approach sets a new benchmark for MANET security and efficiency, addressing both current and emerging challenges in mobile networking environments.