Cengiz, KorhanKumari, RaniSah, Dinesh KumarIvkovic, NikolaSalah, Bashir2024-05-192024-05-1920231874-4907https://doi.org10.1016/j.phycom.2023.102132https://hdl.handle.net/20.500.12713/5696In this paper, we propose SOHCL-RDT'' which stands for a self-organized hybrid cross-layer design for reliable data transmission in wireless network. The communication paradigm is changing and new approach related to machine learning or other optimization algorithms are being introduce rapidly. The TCP/IP or OSI model is not at all equipped to accommodate such a vast changes in its established protocol stacks. Considering this, we have proposed the hybrid cross layer design where the communication or transmission will be handle using two set of protocol stack. One set for the established classical network, and another using cross layer approach. Our design leverages the strengths of both the physical and MAC layers to optimize packet transmission and minimize energy consumption. An optimization algorithm based on gradient descent is also developed to adjust transmission parameters in real-time. The objective is to invoke the classical model only when it needed; it means until unless gradient descent is able to make classification regarding the node scheduling and achieve the acknowledgment, the TCP/IP protocol stack will be in deactivation. Using this method, we have performed our experiments mainly on two parameters named as packet delivery ratio (PDR), end-to-end delay (E2ED); because these are important aspect of reliability. In addition to that, the energy consumption of network is also compared with the existing algorithms. The results show that the proposed hybrid cross-layer design outperforms the existing algorithms. The performance gain can be attributed to the cross-layer approach and the use of the optimization algorithm. Overall, the proposed hybrid cross-layer design is a promising solution for reliable data transmission in wireless sensor networks, with the potential to improve network performance and prolong network lifetime by reducing energy consumption.& COPY; 2023 Published by Elsevier B.V.eninfo:eu-repo/semantics/closedAccessWsnsHuman-Centric SystemsCldsMachine LearningHybridizationConvolutional Neural NetworksRlArticle60WOS:0010520876000012-s2.0-85169818417N/A10.1016/j.phycom.2023.102132Q2