@ARTICLE{Doorwar_Minaxi_QIBMRMN:_2023,
 author={Doorwar, Minaxi and Malathi, P},
 volume={vol. 69},
 number={No 4},
 journal={International Journal of Electronics and Telecommunications},
 pages={775-784},
 howpublished={online},
 year={2023},
 publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications},
 abstract={Multimedia networks utilize low-power scalar nodes to modify wakeup cycles of high-performance multimedia nodes, which assists in optimizing the power-toperformance ratios. A wide variety of machine learning models are proposed by researchers to perform this task, and most of them are either highly complex, or showcase low-levels of efficiency when applied to large-scale networks. To overcome these issues, this text proposes design of a Q-learning based iterative sleep-scheduling and fuses these schedules with an efficient hybrid bioinspired multipath routing model for largescale multimedia network sets. The proposed model initially uses an iterative Q-Learning technique that analyzes energy consumption patterns of nodes, and incrementally modifies their sleep schedules. These sleep schedules are used by scalar nodes to efficiently wakeup multimedia nodes during adhoc communication requests. These communication requests are processed by a combination of Grey Wolf Optimizer (GWO) & Genetic Algorithm (GA) models, which assist in the identification of optimal paths. These paths are estimated via combined analysis of temporal throughput & packet delivery performance, with node-to-node distance & residual energy metrics. The GWO Model uses instantaneous node & network parameters, while the GA Model analyzes temporal metrics in order to identify optimal routing paths. Both these path sets are fused together via the Q-Learning mechanism, which assists in Iterative Adhoc Path Correction (IAPC), thereby improving the energy efficiency, while reducing communication delay via multipath analysis. Due to a fusion of these models, the proposed Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks (QIBMRMN) is able to reduce communication delay by 2.6%, reduce energy consumed during these communications by 14.0%, while improving throughput by 19.6% & packet delivery performance by 8.3% when compared with standard multimedia routing techniques.},
 type={Article},
 title={QIBMRMN: Design of a Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks},
 URL={http://ochroma.man.poznan.pl/Content/129121/PDF/19-4113-Doorwar-sk.pdf},
 doi={10.24425/ijet.2023.147701},
 keywords={multimedia, network, Q-learning, GWO, GA, Adhoc, QoS, iterative, process},
}