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Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 18

XGBoost-based congestion prediction and SDN-assisted energy-efficient routing algorithm for 6G-enabled Internet of Bio-Nano Things

6G-etkin Biyo-Nano Nesnelerin İnterneti için XGBoost tabanlı tıkanıklık tahmini ve SDN-destekli enerji verimli yönlendirme algoritması

Ömer Güleç(PAMUKKALE ÜNİVERSİTESİ)0000-0002-7657-3230
Pages
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Publication Date
2026-09-14
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Ultimately, the proposed algorithm extends network transport capacity by 2.87 times and overall network lifetime by 94%.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:While 6G technology advances the Internet of Bio-Nano Things (IoBNT) in healthcare, the limited energy of nano-sensors and high Terahertz band losses challenge reliable data transmission.
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Key Findings & Data:In 6G-enabled ultra-dense Wireless Nano-Sensor Networks, sudden traffic bursts cause severe congestion, packet loss and premature energy depletion.
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Conclusion & Impact:Ultimately, the proposed algorithm extends network transport capacity by 2.87 times and overall network lifetime by 94%.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Kablosuz Nano-Sensör Ağları · Biyo-Nano Nesnelerin İnterneti · Makine Öğrenmesi

Abstract

While 6G technology advances the Internet of Bio-Nano Things (IoBNT) in healthcare, the limited energy of nano-sensors and high Terahertz band losses challenge reliable data transmission. In 6G-enabled ultra-dense Wireless Nano-Sensor Networks, sudden traffic bursts cause severe congestion, packet loss and premature energy depletion. To address this, a Machine Learning-based Software Defined Network (SDN)-assisted routing algorithm is proposed for 3-dimensional IoBNT applications. First, k-Means clustering determines optimal nano-router placements to ensure energy balance. Next, an XGBoost model running on the SDN controller predicts congestion by analyzing real-time traffic loads and queue states. The architecture dynamically increases the routing costs of high-risk nodes, seamlessly redirecting traffic to less congested alternative routes. Simulations demonstrate that this approach drastically reduces packet loss, enhances throughput and ensures optimal load balancing compared to traditional distance-based protocols. Ultimately, the proposed algorithm extends network transport capacity by 2.87 times and overall network lifetime by 94%.

Özet

Biyo-Nano Nesnelerin İnterneti (IoBNT) 6G teknolojisi sayesinde hızla gelişirken, nano-sensörlerin sınırlı enerjiye sahip olması ve Terahertz bant kayıpları nedeniyle güvenilir veri iletimi zorlaşmaktadır. 6G-etkin ultra-yoğun Kablosuz Nano Sensör Ağları’nda ani trafik yığılmaları tıkanıklığa, paket kaybına ve enerjinin erken tükenmesine neden olmaktadır. Bu sorunları çözmek için bu makalede, 3 boyutlu IoBNT uygulamaları için makine öğrenmesi tabanlı, yazılım tanımlı ağ (SDN) destekli yönlendirme algoritması geliştirilmiştir. İlk olarak k-Means algoritması, enerji dengesini sağlamak için en uygun nano-yönlendirici konumlarını belirlemektedir. Ardından, SDN denetleyicisinde çalışan XGBoost modeli gerçek zamanlı trafik yüklerini ve kuyruk durumlarını analiz ederek tıkanıklığı tahmin etmektedir. Önerilen mimari, yüksek riskli düğümlerin yönlendirme maliyetlerini dinamik olarak artırarak, trafiği daha az tıkanık alternatif rotalara yönlendirmektedir. Benzetimler, bu yaklaşımın geleneksel mesafe tabanlı protokollere kıyasla paket kaybını önemli ölçüde azalttığını, verimliliği artırdığını ve optimum yük dengesini sağladığını göstermektedir. Sonuç olarak, önerilen algoritma ağ taşıma kapasitesini 2.87 kat, ağ yaşam süresini ise %94 oranında uzatmaktadır.

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Cite This Article

Güleç, Ö. (2026). XGBoost-based congestion prediction and SDN-assisted energy-efficient routing algorithm for 6G-enabled Internet of Bio-Nano Things. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 18. https://doi.org/10.28948/ngumuh.1924522

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References (30)

  1. O. B. Akan, E. Dinc, M. Kuscu, O. Cetinkaya, and B. A. Bilgin, “Internet of Everything (IOE) - from molecules to the universe,” IEEE Communications Magazine, vol. 61, no. 10, pp. 122–128, May 2023, doi: https://doi.org/10.1109/mcom.001.2200594.
  2. F. Salahdine, T. Han, and N. Zhang, “5G, 6G, and Beyond: Recent advances and future challenges,” Annals of Telecommunications, vol. 78, no. 9–10, pp. 525–549, Jan. 2023, doi: https://doi.org/10.1007/s12243-022-00938-3.
  3. S. M. A. El-Atty, P. Vijayakumar, O. Alfarraj, M. Karuppiah, and F. Shawki, “Bioinspired molecular communications system for targeted drug delivery with IoBNT-based sustainable biocyber interface,” Computers & Electrical Engineering, vol. 118, p. 109452, Jul. 2024, doi: https://doi.org/10.1016/j.compeleceng.2024.109452.
  4. O. Gulec, “Distributed routing and self-balancing algorithm for Medical IoNT,” Simulation Modelling Practice and Theory, vol. 129, p. 102833, Sep. 2023, doi: https://doi.org/10.1016/j.simpat.2023.102833.
  5. O. Gulec, “Extending lifetime of Wireless Nano-Sensor Networks: An energy efficient distributed routing algorithm for Internet of Nano-Things,” Future Generation Computer Systems, vol. 135, pp. 382–393, May 2022, doi: https://doi.org/10.1016/j.future.2022.05.009.
  6. E. Sahin, M. A. Akkas, and O. Dagdeviren, “Nanonetwork-based search and rescue operations in debris areas,” Computer Networks, vol. 237, p. 110082, Nov. 2023, doi: https://doi.org/10.1016/j.comnet.2023.110082.
  7. Ö. Güleç, “Machine learning supported Nano-Router localization in WNSNs,” Sakarya University Journal of Science, vol. 27, no. 3, pp. 590–602, May 2023, doi: https://doi.org/10.16984/saufenbilder.1246617.
  8. S. Canovas-Carrasco, R. M. Sandoval, A.-J. Garcia-Sanchez, and J. Garcia-Haro, “Optimal transmission policy derivation for IONT Flow-Guided Nano-Sensor networks,” IEEE Internet of Things Journal, vol. 6, no. 2, pp. 2288–2298, Mar. 2019, doi: https://doi.org/10.1109/jiot.2019.2906015.
  9. A. O. Balghusoon and S. Mahfoudh, “Routing Protocols for wireless nanosensor networks and Internet of Nano Things: A Comprehensive survey,” IEEE Access, vol. 8, pp. 200724–200748, Jan. 2020, doi: https://doi.org/10.1109/access.2020.3035646.
  10. O. Gulec, “Hybrid FFBAT optimized multi-hop routing in Internet of Nano-Things,” Internet of Things, vol. 24, p. 100938, Sep. 2023, doi: 10.1016/j.iot.2023.100938.
  11. J. Xu, X. Li, J. Kan, and R. Wang, “Energy harvesting-based thermal aware routing protocol for lung terahertz nanosensor networks,” Nano Communication Networks, vol. 43, p. 100563, Jan. 2025, doi: https://doi.org/10.1016/j.nancom.2025.100563.
  12. K. Wang, C.-M. Yu, M.-L. Ku, L.-C. Wang, and W.-K. Jia, “GWO-PEGASIS: a Swarm-Intelligence-Based protocol for Energy-Efficient Shortest-Path construction in WSNs,” IEEE Internet of Things Journal, vol. 13, no. 5, pp. 9151–9162, Dec. 2025, doi: https://doi.org/10.1109/jiot.2025.3646180.
  13. M. Cicioğlu and A. Çalhan, “Energy-efficient and SDN-enabled routing algorithm for wireless body area networks,” Computer Communications, vol. 160, pp. 228–239, Jun. 2020, doi: https://doi.org/10.1016/j.comcom.2020.06.003.
  14. M. V. Babu, R. Sekaran, S. Kannan, and V. Ravi, “CE2RV: Commissioned energy‐efficient virtualization for large‐scale heterogeneous wireless sensor networks,” International Journal of Communication Systems, vol. 36, no. 9, Mar. 2023, doi: https://doi.org/10.1002/dac.5480.
  15. A. Zila, A. Ouchatti, and Y. Mouzouna, “Exploring node failure and packet loss in wireless sensor networks: A comprehensive simulation analysis,” International Journal of Communication Systems, vol. 38, no. 13, Jul. 2025, doi: https://doi.org/10.1002/dac.70174.
  16. H. H. Saleh, A. A. Hussein, D. S. Ibrahim, O. A. Mahmood, and M. S. A. Muthanna, “An SDN-based clustering approach for energy-efficient and intelligent wireless sensor network management,” Telecommunications and Radio Engineering, vol. 84, no. 11, pp. 95–108, Jan. 2025, doi: https://doi.org/10.1615/telecomradeng.2025057828.
  17. H. Moudoud, Z. A. E. Houda, L. Khoukhi, and H. T. Mouftah, “An SDN-based adaptive ensemble learning framework for intrusion mitigation in wireless networks.”, IEEE International Conference on Communications (ICC), 2025, pp. 554–559. doi: https://doi.org/10.1109/icc52391.2025.11161745.
  18. V. Tyagi, S. Singh, H. Wu, and S. S. Gill, “Load balancing in SDN-Enabled WSNs toward 6G IOE: Partial Cluster Migration Approach,” IEEE Internet of Things Journal, vol. 11, no. 18, pp. 29557–29568, May 2024, doi: https://doi.org/10.1109/jiot.2024.3402266.
  19. M. Shujairi, “Developing IoT performance in healthcare through the integration of machine learning and Software-Defined Networking (SDN),” Babylonian Journal of Internet of Things, vol. 2025, pp. 77–88, Feb. 2025, doi: https://doi.org/10.58496/bjiot/2025/003.
  20. M. U. Farooq, X. Wang, A. Hawbani, L. Zhao, A. Al-Dubai, and O. Busaileh, “SDORP: SDN based opportunistic routing for asynchronous wireless sensor networks,” IEEE Transactions on Mobile Computing, vol. 22, no. 8, pp. 4912–4929, Mar. 2022, doi: https://doi.org/10.1109/tmc.2022.3158695.
  21. V. Tyagi and S. Singh, “MS-EAR: A mobile sink based energy aware routing technique for SDN enabled WSNs,” Peer-to-Peer Networking and Applications, vol. 17, no. 5, pp. 3314–3334, Jul. 2024, doi: https://doi.org/10.1007/s12083-024-01757-0.
  22. A. Saghir, M. Ashraf, B. A. Bhatti, K. Ahmed, A. A. Siddiqui, and A. Hassan, Performance evaluation of “Software defined networking (SDN) for traffic management in wireless sensor networks,” 2024 Global Conference on Wireless and Optical Technologies (GCWOT), 2024, pp. 1–6. doi: https://doi.org/10.1109/gcwot63882.2024.10805646.
  23. R. Samadi, A. Nazari, and J. Seitz, “Intelligent Energy-Aware routing Protocol in mobile IoT networks based on SDN,” IEEE Transactions on Green Communications and Networking, vol. 7, no. 4, pp. 2093–2103, Jul. 2023, doi: https://doi.org/10.1109/tgcn.2023.3296272.
  24. D. S. Rao and A. J. Emerson, “Cyberattack defense mechanism using deep learning techniques in software-defined networks,” International Journal of Information Security, vol. 23, no. 2, pp. 1279–1291, Dec. 2023, doi: https://doi.org/10.1007/s10207-023-00785-w.
  25. J. Lv, H. Babbar, and S. Rani, “AI-Driven resource management for Energy-Efficient aerial computing in Large-Scale Healthcare SDN–IoT systems,” IEEE Internet of Things Journal, vol. 12, no. 13, pp. 23536–23549, Apr. 2025, doi: https://doi.org/10.1109/jiot.2025.3556943.
  26. V. Thalapala, A. K. Reddy, and K. Guravaiah, “MLADAD: Machine Learning Algorithms Analysis on DDOS attack Detection in SDN-IoT Networks,” Procedia Computer Science, vol. 260, pp. 1121–1128, Jan. 2025, doi: https://doi.org/10.1016/j.procs.2025.03.297.
  27. P. Valizadeh, M. H. Yaghmaee, and Y. Sedaghat, “Reliability and bandwidth aware routing in SDN-based fog computing for IoT applications,” Ad Hoc Networks, vol. 172, p. 103803, Feb. 2025, doi: https://doi.org/10.1016/j.adhoc.2025.103803.
  28. W. Bekri, R. Jmal, and L. C. Fourati, “Distributed secured and trustworthy IoT framework based on SDN, AI, and blockchain,” Cluster Computing, vol. 28, no. 16, Oct. 2025, doi: https://doi.org/10.1007/s10586-025-05770-7.
  29. ns-3. https://www.nsnam.org/.
  30. Nano-Sim. https://telematics.poliba.it/nanosim-tool/.