Smart equipment in a house that allows remote automation, control, and monitoring a system that links and unifies differences is called smart home network. Devices can control and accessed via a central hub and can connect with one another, i.e., smart phone app or voice assistant. Sensors, smart appliances, and other gadgets that can interact with one another and react to user or system provider commands are the elements that make up a smart home network. In order to accommodate a high number of smart devices with different distributions and heterogeneous physical access (wired and wireless), smart home networks are expanding quickly. This leads to a variety of traffic patterns. To classify heterogeneous traffic flows in smart home networks by developing a dynamic queuing model based on machine learning and utilizing the Semi-Supervised Support Vector Machine (S3VM) is the goal of the work. We evaluate the performance of the QoS-level Pair Heterogeneous-sourced traffic model (QP-SH) against that of the proposed dynamic queuing system. Higher priority traffic might not need a smaller delay than lower priority traffic, because many advance QoS-aware scheduling techniques merely use the traditional IP type of service (ToS) information to determine priority metrics for deciding how to divide bandwidth. For instance, traffic from streaming devices needs a shorter maximum latency than traffic from medical sensors, although the former has a higher priority. Proposed network was evaluated using the MATLAB simulator, which was used to conduct simulation tests to verify the suggested model's performance. The MAL-DQ algorithm was compared with an existing queuing model, QP-SH, across metrics such as classification accuracy, throughput, and delay. The results shows that MAL-DQ outperformed QP-SH by achieving higher accuracy (average of 90.57% vs. 82.27%), better throughput (8.95 kbps vs. 7.02 kbps), and lower average delay (6.62 ms vs. 15.98 ms), confirming its suitability for smart home environments.

Mehmat-AliM, Khabazian, M., & Aissa, S. (2013). Performance model of safety message broadcasting in vehicular ad hoc networks. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 14(1), 380–387. https://doi.org/10.1109/TITS.2012.2213595 A. Fladen Muller, O. Fourmaux, and R. M. Abuteir (2016). An SDN approach to adaptive video streaming in IEEE International Wireless Home Networks. 321–326, Wireless Communication. Mobile Computing. https://doi.org/Conf/(IWCMC). J. He, Y. Zhang, S. Leng, and M. Zeng (2018). A matching-theoretic approach to QoE-aware power management in vehicle-to-grid networks. IEEE Transactions on Smart Grid, 9(4), 2468–2477. Q. Huang, H. Chen, and QQ. Zhang (2020). Coordinated design of sensing and communication systems for smart homes. IEEE Transactions on Intelligent Transportation System, 34(6), 191-197. F.-R. Boyer, Y. Savaria, and I. Benacer (2018). In the proceedings of the 16th IEEE International Conference on New Circuits and Switching (NEWCAS), HPQ: A hybrid priority queue architecture with large capacity for fast network switches was presented. I.-H. Hou and P.-C. Hsieh (2018). QoE optimality for on-demand video streams over fading channels: a heavy-traffic analysis. IEEE/ACM Transaction on Intelligent Transportation Network., 26(4), 1768–1781. S. S. H. Shakir and A. Rajesh (2017). The performance analysis of two-level calendar disc scheduling in an LTE advanced system with carrier aggregation. Wireless Pers. Commun., 95(3) 2855–2871. https://doi.org/10.1007/TITS.11277-017-3967 Fan S., Zhao H. (2018). Cross-layer delay-based QoS scheme for wireless ad hoc networks for streaming video. China Commun., 15(9) 215–234. ShambelTseggaye Getaneh (2019). Enhanced Security Mechanism for VANET Sybil Attack Detection. The Paper Turned in to the Department of Information Technology to Comply with Part of the Master of Science in Computer Networks and Security Standards. Zaidi, Taskeen, and Syed Mohd Faisal (2020), Time stamp-Based Sybil Attack Detection in VANET: International Journal of Network Security, https//doi.org/10.6633/IJNS.2020 0522(3).05. Anand, A. and de Veciana, G. (2017). Measurement-based scheduler for multiclass QoE optimisation in wireless networks. IEEE INFO COM Conf. Comput. Commun., 1-9. Ghita, B., and Bakhshi (2016). User-centric traffic optimization in residential software-defined networks. IEEE 23rd International Conference on Communications Technology (ICT), 1-6; T. Bozkurt I-N. and Benson T. (2016). Contextual router: Bringing experience-oriented networking to the home, 15 Proc. ACM Symp. SDN Res. Lukman Audah, Mustafa Maad Hamdi, Sameer Alani, and Sami Abduljabbar Rashid (2020). Prediction Based Efficient Multi-hop Clustering Approach with Adaptive Relay Node Selection for VANET. Journal of Communications, 15(4). https://doi.org/10.12720/jcm.15.4.332-344 Kugali, Sandeep N. and Kadadevar, Sneha (2020). Vehicular ADHOC Network (VANET):- A Brief Knowledge. ISSN: 2278-0181 International Journal of Engineering Research & Technology (IJERT) Volume 9, Issue 06, June 2020 2020 ICASISET, May 16–17Chennai, India © 2021 eai.16-5-2020.2304038 EAIDOI10.4108. A. Mohamed, E. A. Jorswieck, and M. M. Butt (2018). A system-level model for the trade-off between energy and bursty packet loss over fading channels. IEEE System Journal, Volume 12, Issue 1, pages 527–538. H. Gao, J. Li, Z. Cai, and X. Zheng (2017). A study on wireless networks using application-aware scheduling. IEEE Trans. Mobile Comput., 16(7) 1787–1801. Taoufik Yeferny and Sofian Hamad (2020). Vehicular Ad-hoc Networks: Architecture, Applications, and Challenges. International Journal of Computer Science and Network Security, 20(2). https://doi.org/10.48550/arXiv.2101.04539. Hubaux J.P., Luo J., Capkun S. (2004). The security and privacy of smart vehicles, IEEE International Conference on Security & Privacy, 2(3) 49-55. https:/doi.org/10.1109/MSP.2004.26 Chaabnia S. and Meddeb A. (2018). Slicing aware QoS/QoE in software defined smart home network. Proc. NOMS IEEE/IFIP Netw.Oper.Manage. Symp., 1–5. By Xiao L., Greenstein L.J., Mandayam N.B., and Trappe W. (2009). Channel-based detection of Sybil attacks in wireless networks. IEEE International Conference on Information Forensics and Security, 4(3) 492-503. https//doi.org/10.1109/TIFS.2009.2026454