Hybrid Intelligent Routing with Optimized Learning (HIROL) for Adaptive Routing Topology management in FANETs
<|reference_start|>Hybrid Intelligent Routing with Optimized Learning (HIROL) for Adaptive Routing Topology management in FANETs: Enhancing the routing efficacy of Flying AdHoc Networks (FANETs), a network of numerous Unmanned Aerial Vehicles (UAVs), in which various challenges may arise as a result of the varied mobility, speed, direction, and rapid topology changes. Given the special features of UAVs, in particular their fast mobility, frequent topology changes, and 3D space movements, it is difficult to transport them through a FANET. The suggested study presents a complete hybrid model: HIROL (Hybrid Intelligent Routing with Optimized Learning) that integrates the ABC (Artificial Bee Colony) algorithm, DSR (Dynamic Source Routing) by incorporating Optimized Link State Routing (OLSR) and ANNs (Artificial Neural Networks) to optimize the routing process. The HIROL optimizes link management by ABC optimization algorithm and reliably analyses link status using characteristics from OLSR and DSR; at the same time, an ANN-based technique successfully classifies connection state. In order to provide optimal route design and maintenance, HIROL dynamically migrates between OLSR and DSR approaches according to the network topology conditions. After running thorough tests in Network Simulator 2 (NS-2), when compared to more conventional DSR and OLSR models, the hybrid model HIROL performs far better in simulations and tests. An increase in throughput (3.5 Mbps vs. 3.2-3.4 Mbps), a decrease in communication overhead (15% vs. 18-20%), and an improvement in Packet Delivery Ratio (97.5% vs. 94-95.5%). These results demonstrate that the suggested HIROL model improves FANET routing performance in different types of networks.<|reference_end|>
@article{reddy2024hybrid,
    title={Hybrid Intelligent Routing with Optimized Learning (HIROL) for Adaptive
  Routing Topology management in FANETs},
    author={Ch. Naveen Kumar Reddy, M. Anusha},
    journal={arXiv preprint arXiv:2406.15105},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15105},
    primaryClass={cs.NI}
}