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<p>Mobile Ad Hoc Networks (MANETs) have gained significant importance with the growing proliferation of mobile devices and Internet of Things (IoT) applications. Efficient routing plays a vital role in decentralized wireless networks, ensuring efficacious data transmission. However, the intrinsic constraints of limited network resources and the dynamic nature of network topologies present significant challenges for conventional routing protocols. To address these impediments, an Augmented Directing Algorithm with Machine Learning (ADAML) has been developed to accommodate intelligent and adaptive routing solutions for MANETs. This approach incorporates the selection of a Cluster Head (CH) through a Hybrid Particle Swarm Optimization (HPSO) algorithm, followed by a clustering process and the integration of an intrusion detection system utilizing k-Nearest Neighbors (k-NN) for enhanced security. The performance of ADAML is estimated through simulations conducted in Network Simulator 3 (NS-3), demonstrating its efficacity in dynamic network environments. The proposed comprehensive analysis of the network based on critical network parameters such as Routing overhead, Security, packet delivery, delay and throughput, offers a robust solution to the challenges of routing in MANETs.</p>

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Analysis and design of optimized routing algorithm for high-speed MANET using machine learning

  • 期刊论文

  • 发表日期：

    2026年2月13日

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  • Akanksha,
  • Saurabh Sharma,
  • Rajendra Prasad Mahapatra

摘要

Mobile Ad Hoc Networks (MANETs) have gained significant importance with the growing proliferation of mobile devices and Internet of Things (IoT) applications. Efficient routing plays a vital role in decentralized wireless networks, ensuring efficacious data transmission. However, the intrinsic constraints of limited network resources and the dynamic nature of network topologies present significant challenges for conventional routing protocols. To address these impediments, an Augmented Directing Algorithm with Machine Learning (ADAML) has been developed to accommodate intelligent and adaptive routing solutions for MANETs. This approach incorporates the selection of a Cluster Head (CH) through a Hybrid Particle Swarm Optimization (HPSO) algorithm, followed by a clustering process and the integration of an intrusion detection system utilizing k-Nearest Neighbors (k-NN) for enhanced security. The performance of ADAML is estimated through simulations conducted in Network Simulator 3 (NS-3), demonstrating its efficacity in dynamic network environments. The proposed comprehensive analysis of the network based on critical network parameters such as Routing overhead, Security, packet delivery, delay and throughput, offers a robust solution to the challenges of routing in MANETs.