Machine Learning Supported Nano-Router Localization in WNSNs

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

1. 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.
2. 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.
3. 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.
4. A. Galal, X. Hesselbach, “Machine Learning Models for Traffic Classification in Electromagnetic Nano-Networks”, IEEE Access, 10, 38089-38103, 2022.
5. 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.
6. A. Galal, X. Hesselbach, “Probability-based path discovery protocol for electromagnetic nano-networks”, Computer Networks, 174, 107246, 2020.
7. L. Zhou, G. Han, L. Liu, “Pulse-based distance accumulation localization algorithm for wireless nanosensor networks”, IEEE Access, 5, 14380-14390, 2017.
8. 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.

9. 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.
10. 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.
11. S. Yu, J. Zhu, C. Lv, “A Quantum Annealing Bat Algorithm for Node Localization in Wireless Sensor Networks”, Sensors, 23 (2), 782, 2023.
12. 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.
13. 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.
14. 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)
15. 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.
16. 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.
17. 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.
18. 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.
19. 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.
20. 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.
21. 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.
22. 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.
23. 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.
24. O. Gulec, E. Sahin, “ Red Deer Algorithm based nano-sensor node clustering for IoNT”, Journal of Network and Computer Applications, 103591, 2023.
25. 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.
26. P. Nayak, K. G. Swetha, S. Gupta, K. Madhavi, “Routing in wireless sensor networks using machine learning techniques: Challenges and opportunities”, Measurement, 178, 2021.
27. 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.
28. 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.
29. NS-3. Discrete-event network simulator for Internet systems [Online] Available: https://www.nsnam.org
30. Nano-Sim. [Online] Available: https://telematics.poliba.it
31. Networkx. Network analysis in Python [Online] Available: https://networkx.org
32. Matplotlib. Visualization with Python [Online] Available: https://matplotlib.org