Bio-inspired deep residual neural network learning model for QoS routing enhancement in mobile ad-hoc networks
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems
Link
https://link.springer.com/content/pdf/10.1007/s11276-023-03424-3.pdf
Reference41 articles.
1. Bapu, T., Anitha, R., Soundararajan, S., Nagaraju, V., Nagappan, P., & Daniel, A. (2022). GAMAN–genetic algorithm in mobile adhoc networks for creating systematic QoS. ECS Transactions, 107(1), 3599.
2. Chandrasekaran, K., & Selvaraj, T. (2022). Differential evolution CapsNet model for QoS routing enhancement in wireless networks. International Journal of Communication Systems, 35(2), e4146.
3. Hasan, N., Mishra, A., & Ray, A. K. (2022). Fuzzy logic based cross-layer design to improve quality of service in mobile ad-hoc networks for next-gen cyber physical system. Engineering Science and Technology, an International Journal, 35, 101099.
4. Sucharitha, K., & Latha, R. (2022). Designing an ML-based congestion detection algorithm for routing data in MANETs. In International conference on computing, communication, electrical and biomedical systems (pp. 3–13). Springer.
5. Chintalapalli, R. M., & Ananthula, V. R. (2018). M-LionWhale: Multi-objective optimisation model for secure routing in mobile ad-hoc network. IET Communications, 12(12), 1406–1415.
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