Research Article
BibTex RIS Cite

Year 2023, Volume: 27 Issue: 3, 590 - 602, 30.06.2023

Ömer Güleç

https://doi.org/10.16984/saufenbilder.1246617
Cited By: 2

Abstract

Keywords

Wireless nano-sensor networks IoNT machine learning nano-router localization

References

  • A. O. Balghusoon, M. Saoucene, “Routing protocols for wireless nanosensor networks and internet of nano things: A comprehensive survey”,IEEE Access, 8, 200724-200748, 2020.
  • O. Gulec, “Extending lifetime of Wireless Nano-Sensor Networks: An energy efficient distributed routing algorithm for Internet of Nano-Things”, Future Generation Computer Systems, 135, 382-393, 2022.
  • A. Rizwan, A. Zoha, R. Zhang, W. Ahmad, K. Arshad, N. A. Ali, Q. H. Abbasi, “A review on the role of nano-communication in future healthcare systems: A big data analytics perspective”, IEEE Access, 6, 41903-41920, 2018.
  • A. Galal, X. Hesselbach, “Machine Learning Models for Traffic Classification in Electromagnetic Nano-Networks”, IEEE Access, 10, 38089-38103, 2022.
  • M. A. Akkaş, R. Sokullu, “Wireless Communications Beyond 5 g: Teraherzwaves, Nano-Communications and the Internet of Bio-Nano-Things”, Wireless Personal Communications, 126, 3543–3568, 2022.
  • A. Galal, X. Hesselbach, “Probability-based path discovery protocol for electromagnetic nano-networks”, Computer Networks, 174, 107246, 2020.
  • L. Zhou, G. Han, L. Liu, “Pulse-based distance accumulation localization algorithm for wireless nanosensor networks”, IEEE Access, 5, 14380-14390, 2017.
  • M. Pierobon, J. M. Jornet, N. Akkari, S. Almasri, I. F. Akyildiz, “A routing framework for energy harvesting wireless nanosensor networks in the Terahertz Band”, Wireless Networks, 20, 1169-1183, 2014.
  • P. Yadav, S. C. Sharma, “A Systematic Review of Localization in WSN: Machine Learning and Optimization‐Based approaches”, International Journal of Communication Systems, e5397, 2022.
  • M. Nain, N. Goyal, L. K. Awasthi, A. Malik, “A range based node localization scheme with hybrid optimization for underwater wireless sensor network”, International Journal of Communication Systems, 35(10), e5147, 2022.
  • S. Yu, J. Zhu, C. Lv, “A Quantum Annealing Bat Algorithm for Node Localization in Wireless Sensor Networks”, Sensors, 23 (2), 782, 2023.
  • H. M. Kanoosh, E. H. Houssein, M. M. Selim, “Salp Swarm Algorithm for Node Localization in Wireless Sensor Networks”, Journal of Computer Networks and Communications, vol. 2019, 1028723, 2019.
  • P. Sekhar, E. L. Lydia, M. Elhoseny, M. Al-Akaidi, M. M. Selim, K. Shankar, “An effective metaheuristic based node localization technique for wireless sensor networks enabled indoor communication”, Physical Communication, 48, 101411, 2021.
  • I. Javed, X. Tang, M. A. Saleem, M. U. Sarwar, M. Tariq, C. S. Shivachi, “3D localization for mobile node in wireless sensor network”, Wireless Communications and Mobile Computing, 2022)
  • O. J. Aroba, N. Naicker, T. T. Adeliyi, “Node Localization in Wireless Sensor Networks using a Hyper-Heuristic DEEC-Gaussian Gradient Distance Algorithm”, Scientific African, e01560, 2023.
  • U. Dampage, L. Bandaranayake, R. Wanasinghe, K. Kottahachchi, B. Jayasanka, “Forest fire detection system using wireless sensor networks and machine learning”, Scientific Reports, 12 (1), 46, 2022.
  • N. Bacanin, M. Sarac, N. Budimirovic, M. Zivkovic, A. A. Al Zubi, A. K. Bashir, “Smart wireless health care system using graph LSTM pollution prediction and dragonfly node localization”, Sustainable Computing: Informatics and Systems, 35, 100711, 2022.
  • H. Esmaeili, B. M. Bidgoli, V. Hakami, “CMML: Combined metaheuristic-machine learning for adaptable routing in clustered wireless sensor networks”, Applied Soft Computing, 118, 108477, 2022.
  • S. El Khediri, W. Fakhet, T. Moulahi, R. Khan, A. Thaljaoui, A. Kachouri, “Improved node localization using K-means clustering for Wireless Sensor Networks”, Computer Science Review, 37, 100284, 2020.
  • T. Mahmood, J. Li, Y. Pei, F. Akhtar, S. A. Butt, A. Ditta, S. Qureshi, “An intelligent fault detection approach based on reinforcement learning system in wireless sensor network”, The Journal of Supercomputing, 78(3), 3646-3675, 2022.
  • L. Li, Y. Qiu, J. Xu, “A K-means clustered routing algorithm with location and energy awareness for underwater wireless sensor networks”, Photonics, Vol. 9, No. 5, p. 282, MDPI, 2022.
  • M. Sathyamoorthy, S. Kuppusamy, R. K. Dhanaraj, V. Ravi, “Improved K-means based q learning algorithm for optimal clustering and node balancing in WSN”, Wireless Personal Communications, 122(3), 2745-2766, 2022.
  • J. Xu, Y. Zhang, J. Jiang, J. Kan, “A multi-hop routing protocol based on link state prediction for intra-body Wireless Nanosensor Networks”, Ad Hoc Networks, 116, 102470, 2021.
  • O. Gulec, E. Sahin, “ Red Deer Algorithm based nano-sensor node clustering for IoNT”, Journal of Network and Computer Applications, 103591, 2023.
  • A. J. Garcia-Sanchez, R. Asorey-Cacheda, J. Garcia-Haro, J. L. Gomez-Tornero, “Dynamic Multihop Routing in Terahertz Flow-Guided Nanosensor Networks: A Reinforcement Learning Approach”, IEEE Sensors Journal, vol. 23, no. 4, pp. 3408-3422, 2023.
  • P. Nayak, K. G. Swetha, S. Gupta, K. Madhavi, “Routing in wireless sensor networks using machine learning techniques: Challenges and opportunities”, Measurement, 178, 2021.
  • D. Stiawan, M. E. Suryani, M. Y. Idris, M. N. Aldalaien, N. Alsharif, R. Budiarto, “Ping flood attack pattern recognition using a K-means algorithm in an Internet of Things (IoT) network”, IEEE Access, 9, 2021.
  • H. Mahboubi, B. Stéphane, A. G. Aghdam, “A machine learning assisted method for coverage optimization in a network of mobile sensors”, IEEE Transactions on Industrial Informatics, 2022.
  • NS-3. Discrete-event network simulator for Internet systems [Online] Available: https://www.nsnam.org
  • Nano-Sim. [Online] Available: https://telematics.poliba.it
  • Networkx. Network analysis in Python [Online] Available: https://networkx.org
  • Matplotlib. Visualization with Python [Online] Available: https://matplotlib.org

Machine Learning Supported Nano-Router Localization in WNSNs

Year 2023, Volume: 27 Issue: 3, 590 - 602, 30.06.2023

Ömer Güleç

https://doi.org/10.16984/saufenbilder.1246617
Cited By: 2

Abstract

Sensing data from the environment is a basic process for the nano-sensors on the network. This sensitive data need to be transmitted to the base station for data processing. In Wireless Nano-Sensor Networks (WNSNs), nano-routers undertake the task of gathering data from the nano-sensors and transmitting it to the nano-gateways. When the number of nano-routers is not enough on the network, the data need to be transmitted by multi-hop routing. Therefore, there should be more nano-routers placed on the network for efficient direct data transmission to avoid multi-hop routing problems such as high energy consumption and network traffic. In this paper, a machine learning-supported nano-router localization algorithm for WNSNs is proposed. The algorithm aims to predict the number of required nano-routers depending on the network size for the maximum node coverage in order to ensure direct data transmission by estimating the best virtual coordinates of these nano-routers. According to the results, the proposed algorithm successfully places required nano-routers to the best virtual coordinates on the network which increases the node coverage by up to 98.03% on average and provides high accuracy for efficient direct data transmission.

Keywords

Wireless nano-sensor networks IoNT machine learning nano-router localization

References

  • A. O. Balghusoon, M. Saoucene, “Routing protocols for wireless nanosensor networks and internet of nano things: A comprehensive survey”,IEEE Access, 8, 200724-200748, 2020.
  • O. Gulec, “Extending lifetime of Wireless Nano-Sensor Networks: An energy efficient distributed routing algorithm for Internet of Nano-Things”, Future Generation Computer Systems, 135, 382-393, 2022.
  • A. Rizwan, A. Zoha, R. Zhang, W. Ahmad, K. Arshad, N. A. Ali, Q. H. Abbasi, “A review on the role of nano-communication in future healthcare systems: A big data analytics perspective”, IEEE Access, 6, 41903-41920, 2018.
  • A. Galal, X. Hesselbach, “Machine Learning Models for Traffic Classification in Electromagnetic Nano-Networks”, IEEE Access, 10, 38089-38103, 2022.
  • M. A. Akkaş, R. Sokullu, “Wireless Communications Beyond 5 g: Teraherzwaves, Nano-Communications and the Internet of Bio-Nano-Things”, Wireless Personal Communications, 126, 3543–3568, 2022.
  • A. Galal, X. Hesselbach, “Probability-based path discovery protocol for electromagnetic nano-networks”, Computer Networks, 174, 107246, 2020.
  • L. Zhou, G. Han, L. Liu, “Pulse-based distance accumulation localization algorithm for wireless nanosensor networks”, IEEE Access, 5, 14380-14390, 2017.
  • M. Pierobon, J. M. Jornet, N. Akkari, S. Almasri, I. F. Akyildiz, “A routing framework for energy harvesting wireless nanosensor networks in the Terahertz Band”, Wireless Networks, 20, 1169-1183, 2014.
  • P. Yadav, S. C. Sharma, “A Systematic Review of Localization in WSN: Machine Learning and Optimization‐Based approaches”, International Journal of Communication Systems, e5397, 2022.
  • M. Nain, N. Goyal, L. K. Awasthi, A. Malik, “A range based node localization scheme with hybrid optimization for underwater wireless sensor network”, International Journal of Communication Systems, 35(10), e5147, 2022.
  • S. Yu, J. Zhu, C. Lv, “A Quantum Annealing Bat Algorithm for Node Localization in Wireless Sensor Networks”, Sensors, 23 (2), 782, 2023.
  • H. M. Kanoosh, E. H. Houssein, M. M. Selim, “Salp Swarm Algorithm for Node Localization in Wireless Sensor Networks”, Journal of Computer Networks and Communications, vol. 2019, 1028723, 2019.
  • P. Sekhar, E. L. Lydia, M. Elhoseny, M. Al-Akaidi, M. M. Selim, K. Shankar, “An effective metaheuristic based node localization technique for wireless sensor networks enabled indoor communication”, Physical Communication, 48, 101411, 2021.
  • I. Javed, X. Tang, M. A. Saleem, M. U. Sarwar, M. Tariq, C. S. Shivachi, “3D localization for mobile node in wireless sensor network”, Wireless Communications and Mobile Computing, 2022)
  • O. J. Aroba, N. Naicker, T. T. Adeliyi, “Node Localization in Wireless Sensor Networks using a Hyper-Heuristic DEEC-Gaussian Gradient Distance Algorithm”, Scientific African, e01560, 2023.
  • U. Dampage, L. Bandaranayake, R. Wanasinghe, K. Kottahachchi, B. Jayasanka, “Forest fire detection system using wireless sensor networks and machine learning”, Scientific Reports, 12 (1), 46, 2022.
  • N. Bacanin, M. Sarac, N. Budimirovic, M. Zivkovic, A. A. Al Zubi, A. K. Bashir, “Smart wireless health care system using graph LSTM pollution prediction and dragonfly node localization”, Sustainable Computing: Informatics and Systems, 35, 100711, 2022.
  • H. Esmaeili, B. M. Bidgoli, V. Hakami, “CMML: Combined metaheuristic-machine learning for adaptable routing in clustered wireless sensor networks”, Applied Soft Computing, 118, 108477, 2022.
  • S. El Khediri, W. Fakhet, T. Moulahi, R. Khan, A. Thaljaoui, A. Kachouri, “Improved node localization using K-means clustering for Wireless Sensor Networks”, Computer Science Review, 37, 100284, 2020.
  • T. Mahmood, J. Li, Y. Pei, F. Akhtar, S. A. Butt, A. Ditta, S. Qureshi, “An intelligent fault detection approach based on reinforcement learning system in wireless sensor network”, The Journal of Supercomputing, 78(3), 3646-3675, 2022.
  • L. Li, Y. Qiu, J. Xu, “A K-means clustered routing algorithm with location and energy awareness for underwater wireless sensor networks”, Photonics, Vol. 9, No. 5, p. 282, MDPI, 2022.
  • M. Sathyamoorthy, S. Kuppusamy, R. K. Dhanaraj, V. Ravi, “Improved K-means based q learning algorithm for optimal clustering and node balancing in WSN”, Wireless Personal Communications, 122(3), 2745-2766, 2022.
  • J. Xu, Y. Zhang, J. Jiang, J. Kan, “A multi-hop routing protocol based on link state prediction for intra-body Wireless Nanosensor Networks”, Ad Hoc Networks, 116, 102470, 2021.
  • O. Gulec, E. Sahin, “ Red Deer Algorithm based nano-sensor node clustering for IoNT”, Journal of Network and Computer Applications, 103591, 2023.
  • A. J. Garcia-Sanchez, R. Asorey-Cacheda, J. Garcia-Haro, J. L. Gomez-Tornero, “Dynamic Multihop Routing in Terahertz Flow-Guided Nanosensor Networks: A Reinforcement Learning Approach”, IEEE Sensors Journal, vol. 23, no. 4, pp. 3408-3422, 2023.
  • P. Nayak, K. G. Swetha, S. Gupta, K. Madhavi, “Routing in wireless sensor networks using machine learning techniques: Challenges and opportunities”, Measurement, 178, 2021.
  • D. Stiawan, M. E. Suryani, M. Y. Idris, M. N. Aldalaien, N. Alsharif, R. Budiarto, “Ping flood attack pattern recognition using a K-means algorithm in an Internet of Things (IoT) network”, IEEE Access, 9, 2021.
  • H. Mahboubi, B. Stéphane, A. G. Aghdam, “A machine learning assisted method for coverage optimization in a network of mobile sensors”, IEEE Transactions on Industrial Informatics, 2022.
  • NS-3. Discrete-event network simulator for Internet systems [Online] Available: https://www.nsnam.org
  • Nano-Sim. [Online] Available: https://telematics.poliba.it
  • Networkx. Network analysis in Python [Online] Available: https://networkx.org
  • Matplotlib. Visualization with Python [Online] Available: https://matplotlib.org
There are 32 citations in total.

Details

Primary Language English
Subjects Software Engineering, Software Architecture, Software Testing, Verification and Validation
Journal Section Research Articles
Authors

Ömer Güleç 0000-0002-7657-3230

Early Pub Date June 22, 2023
Publication Date June 30, 2023
Submission Date February 2, 2023
Acceptance Date March 6, 2023
Published in Issue Year 2023 Volume: 27 Issue: 3

Cite

APA Güleç, Ö. (2023). Machine Learning Supported Nano-Router Localization in WNSNs. Sakarya University Journal of Science, 27(3), 590-602. https://doi.org/10.16984/saufenbilder.1246617
AMA Güleç Ö. Machine Learning Supported Nano-Router Localization in WNSNs. SAUJS. June 2023;27(3):590-602. doi:10.16984/saufenbilder.1246617
Chicago Güleç, Ömer. “Machine Learning Supported Nano-Router Localization in WNSNs”. Sakarya University Journal of Science 27, no. 3 (June 2023): 590-602. https://doi.org/10.16984/saufenbilder.1246617.
EndNote Güleç Ö (June 1, 2023) Machine Learning Supported Nano-Router Localization in WNSNs. Sakarya University Journal of Science 27 3 590–602.
IEEE Ö. Güleç, “Machine Learning Supported Nano-Router Localization in WNSNs”, SAUJS, vol. 27, no. 3, pp. 590–602, 2023, doi: 10.16984/saufenbilder.1246617.
ISNAD Güleç, Ömer. “Machine Learning Supported Nano-Router Localization in WNSNs”. Sakarya University Journal of Science 27/3 (June 2023), 590-602. https://doi.org/10.16984/saufenbilder.1246617.
JAMA Güleç Ö. Machine Learning Supported Nano-Router Localization in WNSNs. SAUJS. 2023;27:590–602.
MLA Güleç, Ömer. “Machine Learning Supported Nano-Router Localization in WNSNs”. Sakarya University Journal of Science, vol. 27, no. 3, 2023, pp. 590-02, doi:10.16984/saufenbilder.1246617.
Vancouver Güleç Ö. Machine Learning Supported Nano-Router Localization in WNSNs. SAUJS. 2023;27(3):590-602.

Cited By

Distributed routing and self-balancing algorithm for Medical IoNT
Simulation Modelling Practice and Theory
https://doi.org/10.1016/j.simpat.2023.102833
Hybrid FFBAT optimized multi-hop routing in Internet of Nano-Things
Internet of Things
https://doi.org/10.1016/j.iot.2023.100938
 Download Cover Image

Sakarya University Journal of Science (SAUJS)