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Optoelectronic-aided machine learning-based stable routing protocol for MANET and beyond massive MIMO systems in 5G networks

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Abstract

Mobile Ad Hoc Networks (MANETs) are dynamic and self-organizing networks where nodes constantly change positions and communicate wirelessly. The stability of routing protocols in MANETs is crucial for effective data transmission, considering factors like node mobility, bandwidth, and signal strength. This research introduces an innovative approach to enhance MANET routing stability by integrating optoelectronic devices and machine learning. Optoelectronic components are leveraged to optimize signal detection in 5G networks and beyond, particularly in Massive Multiple-Input, Multiple-Output (MIMO) systems. The proposed protocol, "Optoelectronic-Aided Machine Learning-Based Stable Routing Protocol," utilizes machine learning algorithms to intelligently select routes based on real-time data, improving network efficiency. Moreover, the integration of optoelectronic devices enhances signal detection and quality. Comprehensive evaluations were conducted to validate the effectiveness of this approach, comparing it with conventional routing algorithms such as AODV. The Network Simulator 2 evaluated key performance indicators such as round-trip latency, packet delivery ratio, and route lifetime. Specifically for 5G networks and Massive MIMO systems, the results show that this Optoelectronic-Aided Machine Learning-Based Stable Routing Protocol has the potential to improve the stability and efficiency of MANETs considerably. This study aids in the development of cutting-edge wireless network communication protocols.

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References

  • AlAamri, H., Abolhasan, M., Franklin, D., Lipman, J.: Optimized relay selection for route discovery in reactive routing. Ad Hoc Netw.netw. 11, 70–88 (2013)

    Article  Google Scholar 

  • Alslaim, M. N., Alaqel, H. A., Zaghloul, S. S.: A comparative study of MANET routing protocols. In. IEEE Third International Conference on e-Technologies and Networks for Development (ICeND2014), (2014). https://doi.org/10.1109/ICeND.2014.6991375

  • Arivudainambi, D., Varun Kumar, K.A., Sibi, C.S., Visu, P.: Malware traffic classification using principal component analysis and artificial neural network for extreme surveillance. Comput. Commun.. Commun. 147, 50–57 (2019)

    Article  Google Scholar 

  • Basurra, S.S., De Vos, M., Padget, J., Ji, Y., Lewis, T., Armour, S.: Energy efficient zone based routing protocol for MANETs. Ad Hoc Netw.netw. 25, 16–37 (2015)

    Article  Google Scholar 

  • Chen, C.-W., Weng, C.-C., Kuo, Y.-C.: Signal strength based routing for power saving in mobile ad hoc networks. The J. Syst. Softw. 83, 1373–1386 (2010)

    Article  Google Scholar 

  • Gin-Xian, K.O.K., Chee-Onn, C.H.O.W., Yi-Han, X.U., Hiroshi, I.S.H.I.I.: Sensor-free route stability metric for mobile ad hoc networks. Comput. Netw.. Netw. 100, 166–178 (2016)

    Article  Google Scholar 

  • Gnanasekaran, P., Rangaswamy, T.R.: Transition rate, trend and signal strength based stable routing protocol for mobile ad hoc networks. Int. J. Wirel. Mob. Comput.wirel. Mob. Comput. 15(1), 52–57 (2018)

    Article  Google Scholar 

  • Hsi, O.C., Abdullah, A.: Performance evaluation of stable weight-based on demand routing protocol for mobile ad hoc network. J. Comput. Sci.comput. Sci. 5, 732–737 (2009)

    Google Scholar 

  • Jim, L.E., Islam, N., Gregory, M.A.: Enhanced MANET security using artificial immune system based danger theory to detect selfish nodes. Comput. Sec. (2022). https://doi.org/10.1016/j.cose.2021.102538

    Article  Google Scholar 

  • Khalaf, M.B., Al-Dubai, A.Y., Min, G.: New efficient velocity-aware probabilistic route discovery schemes for high mobility Ad hoc networks. J. Comput. Syst. Sci.comput. Syst. Sci. 81, 97–109 (2015)

    Article  Google Scholar 

  • Liu, W., Kim, W.: A stability-considered density-adaptive routing protocol in MANETs. J. Syst. Architect. 59, 767–775 (2013)

    Article  Google Scholar 

  • Merkel, S., SanazMostaghim, H.S.: Hop count-based distance estimation in mobile ad hoc networks – challenges and consequences. Comput. Commun. Ad Hoc Netw. 15, 39–52 (2014)

    Google Scholar 

  • Moussaoui, A., FouziSemchedine, A.B.: A link-state QoS routing protocol based on link stability for Mobile Ad hoc Networks. J. Netw. Comput. Appl.netw. Comput. Appl. 39, 117–125 (2014)

    Article  Google Scholar 

  • Song, Q., Ning, Z., Wanga, S., Jamalipour, A.: Link stability estimation based on link connectivity changes in mobile ad-hoc networks. J. Netw. Comput. Appl.netw. Comput. Appl. 35, 2051–2058 (2012)

    Article  Google Scholar 

  • von Mulert, J., Welch, I., Seah, W.K.G.: Security threats and solutions in MANETs: a case study using AODV and SAODV. J. Netw. Comput. Appl.netw. Comput. Appl. 35(4), 1249–1259 (2012). https://doi.org/10.1016/j.jnca.2012.01.019

    Article  Google Scholar 

  • Weng, L.-N., Yang, J.: A cross-layer stability-based routing mechanism for ultra wideband networks. Comput. Commun.. Commun. 33, 2185–2194 (2010)

    Article  Google Scholar 

  • Xia, H., Xia, S., Jia, Yu., Jia, Z., Sha, E.-M.: Applying link stability estimation mechanism to multicast routing in MANETs. J. Syst. Architect. 60, 467–480 (2014)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology, B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, India

    P. Gnanasekaran & N. Rajendran

  2. Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India

    K. A. Varunkumar

  3. School of Computer Science and Engineering , Vellore Institute of Technology, Chennai, India

    R. Priyadarshini

  4. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District, Andhra Pradesh, 522502, India

    Sivudu Macherla

Authors
  1. P. Gnanasekaran
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  2. K. A. Varunkumar
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  3. N. Rajendran
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  4. R. Priyadarshini
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  5. Sivudu Macherla
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Contributions

Gnanasekaran P led the research project, oversaw optoelectronic implementation, and conducted data analysis. Varun Kumar K A developed machine learning algorithms and integrated them with the routing protocol. Rajendran N contributed to routing protocol design and simulation. R. Priyadarshini and Sivudu Macherla assisted in experimentation and provided valuable insights in manuscript preparation.

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Correspondence to K. A. Varunkumar.

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Gnanasekaran, P., Varunkumar, K.A., Rajendran, N. et al. Optoelectronic-aided machine learning-based stable routing protocol for MANET and beyond massive MIMO systems in 5G networks. Opt Quant Electron 56, 480 (2024). https://doi.org/10.1007/s11082-023-06106-8

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  • DOI: https://doi.org/10.1007/s11082-023-06106-8

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