International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Paula Santi Rudati *, Feriyonika, Muhammad Faturrahman

Department of Electrical Engineering, Politeknik Negeri Bandung, West Bandung Regency, West Java, 40559, Indonesia

 

Download Citation: |
Download PDF


ABSTRACT

The most significant challenge in Wireless Sensor Networks (WSNs) is determining the location of sensor nodes for localization and tracking moving targets. There are some approaches to determining a location. One consideration is using a global positioning system (GPS) for each sensor node deployed. We report the implementation of RVI to determine the localization of the WSN model, which uses the actual hardware equipped with GPS to deal with the physical parameter constraints, including hardware characteristics and environmental conditions. We investigated two critical parameters: the Received Signal Strength Indicator (RSSI) and the location coordinated by GPS. The measurement results show that the environmental conditions influence the value of RSSI, like shadowing, which causes path loss. The verification experiment shows that the RVI algorithm significantly gives correction in determining the localization using RSSI. The increasing iteration number decreases error: iteration 70 resulted in an error of about 26% compared with iteration 15, with an error of about 64%. This method can also determine the location coordinates five times faster than the used GPS. In summary, the RVI method, by using RSSI data, can inform the location coordinate close to GPS information. Therefore, the location coordinate can be determined without using GPS modules.


Keywords: RVI, RSSI, GPS, Localization, Sensor node.


Share this article with your colleagues

 


REFERENCES

  1. Al-Quayed, F., Soudani, A., Al-Ahmadi, S. 2021. Design of a new lightweight accurate grid-based localization method for WASN. IEEE Access, 9, 42660–42673.

  2. Ali, A., Jadoon, Y.K., Changazi, S.A., Qasim, M. 2020. Military operations: Wireless sensor networks-based applications to reinforce future battlefield command systems. Proceeding of 2020 IEEE 23rd International Multitopic Conference (INMIC), 1–6.

  3. Anh Khoa, T., Quang Minh, N., Hai Son, H., Nguyen Dang Khoa, C., Ngoc Tan, D., Van Dung, N., Hoang Nam, N., Minh Duc, D.N., Trung Tin, N. 2021. Wireless sensor networks and machine learning meet climate change prediction. International Journal of Communication Systems, 34, e4687.

  4. Benzerbadj, A., Bouabdellah, K., Bounceur, A., Hammoudeh, M. 2018. Surveillance of sensitive fenced areas using duty-cycled wireless sensor networks with asymmetrical links. Journal of Network and Computer Applications, 112, 41–52.

  5. Gao, L., Zhang, G., Yu, B., Qiao, Z., Wang, J. 2020. Wearable human motion posture capture and medical health monitoring based on wireless sensor networks. Measurement, 166, 108252.

  6. Gupta, M., Sinha, A. 2021. Distributed temporal data prediction model for wireless sensor network. Wireless Personal Communications, 119, 3699–3717.

  7. Ismail, M.N., Shukran, M.A., Isa, M.R.M., Adib, M., Zakaria, O. 2018. Establishing a soldier Wireless Sensor Network (WSN) communication for military operation monitoring. International Journal of Informatics and Communication Technology (IJ-ICT), 7, 89–95.

  8. Lee, J., Cho, K., Lee, S., Kwon, T., Choi, Y. 2006. Distributed and energy-efficient target localization and tracking in wireless sensor networks. Computer Communications, 29, 2494–2505.

  9. Liu, F., Chen, Z., Wang, J. 2018. Intelligent medical IoT system based on WSN with computer vision platforms. Concurrency and Computation: Practice and Experience, 33, 5036.

  10. Luo, T., Nagarajan, S.G. 2018. Distributed anomaly detection using autoencoder neural networks in WSN for IoT. Proceeding of 2018 IEEE International Conference on Communications (ICC), 1–6.

  11. Madani, B.E., Yao, A.P., Lyhyaoui, A. 2013. Combining kalman filtering with zigbee protocol to improve localization in wireless sensor network. International Scholarly Research Notices, 1–7.

  12. Mostafaei, H., Chowdhury, M.U., Obaidat, M.S. 2018. Border surveillance with WSN systems in a distributed manner. IEEE Systems Journal, 12, 3703–3712.

  13. Muduli, L., Mishra, D.P., Jana, P.K. 2018. Application of wireless sensor network for environmental monitoring in underground coal mines: A systematic review. Journal of Network and Computer Applications, 106, 48–67.

  14. Onasanya, A., Elshakankiri, M. 2019. Secured cancer care and cloud services in IoT/WSN based medical systems. Smart Grid and Internet of Things: Second EAI International Conference, SGIoT 2018, 256, 23–35.

  15. Ouni, R., Saleem, K. 2022. Framework for sustainable wireless sensor network based environmental monitoring. Sustainability, 14, 8356.

  16. Park, J., Kim, H. 2013. Ratiometric GPS iteration localization method combined with the angle of arrival measurement. International Journal of Smart Home, 7, 197–206.

  17. Poudel, S., Moh, S., Shen, J. 2021. Residual energy-based clustering in UAV-aided wireless sensor networks for surveillance and monitoring applications. Journal of Surveillance, Security and Safety, 2, 103–16.

  18. Pragadeswaran, S., Madhumitha, S., Gopinath, S. 2021. Certain investigations on military applications of wireless sensor networks. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), 3, 14–19.

  19. Qureshi, U.M., Shaikh, F.K., Aziz, Z., Shah, S.M.Z.S., Sheikh, A.A., Felemban, E., Qaisar, S.B. 2016. RF path and absorption loss estimation for underwater wireless sensor networks in different water environments. Sensors, 16, 890.

  20. Singh, P., Mittal, N. 2020. Efficient localization approach for WSNs using hybrid DA-FA algorithm. IET Communications, 14, 1975–1991.

  21. Strumberger, I., Minovic, M., Tuba, M., Bacanin, N. 2019. Performance of elephant herding optimization and tree growth algorithm adapted for node localization in wireless sensor networks. Sensors, 19, 2515.

  22. Thangaramya, K., Kulothungan, K., Logambigai, R., Selvi, M., Ganapathy, S., Kannan, A. 2019. Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Computer Networks, 151, 211–223.

  23. Ullo, S., Gallo, M., Palmieri, G., Amenta, P., Russo, M., Romano, G., Ferrucci, M., Ferrara, A., Angelis, M.D. 2018. Application of wireless sensor networks to environmental monitoring for sustainable mobility. Proceeding of 2018 IEEE International Conference on Environmental Engineering (EE), 1–7.

  24. Zhang, H., Pan, Z. 2018. Multi-targets localization based on ARMA and GA in WSNs. MATEC Web of Conferences, 189, 04015.

  25. Zhang, Z., Glaser, S., Bales, R., Conklin, M., Rice, R., Marks, D. 2017. Insights into mountain precipitation and snowpack from a basin‐scale wireless‐sensor network. Water Resources Research, 53, 6626–6641.

  26. Zhu, H., Liu, S., Yao, Z., Okonkwo, M.C., Peng, Z. 2021. A novel method for asynchronous source localization based on time of arrival measurements. International Journal of Distributed Sensor Networks, 17, 15501477211053706.


ARTICLE INFORMATION

Received: 2023-04-12
Revised: 2023-06-14
Accepted: 2023-08-27
Available Online: 2023-10-16


Cite this article:

Rudati, P.S., Feriyonika, Faturrahman, M. 2023. The hardware simulation of ratio metric vector iteration algorithm in wireless sensor networks. International Journal of Applied Science and Engineering, 20, 2023086. https://doi.org/10.6703/IJASE.202312_20(4).005

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Other people also read ...

doaj logo

 

2.3
2022CiteScore
 
 
48th percentile
Powered by  Scopus
 
 

SCImago Journal & Country Rank

IJASE - Most Read Articles

IJASE - Most popular articles