International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

MENU
International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Prediction Model to Analyze Source Node Localization Using Machine Learning and Fault-Tolerant in Wireless Sensor Networks

Author NameAuthor Details

P. Sakthi Shunmuga Sundaram, K. Vijayan

P. Sakthi Shunmuga Sundaram[1]

K. Vijayan[2]

[1]Department of Electronics and Communication Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India.

[2]Department of Electronics and Communication Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India.

Cite this Article

DOI: 10.22247/ijcna/2023/223312

Pages : 527-542

 Download PDF

1186

Views

Abstract

Recent technological developments include wireless sensor networks in modern and intelligent environments. Finding the localization of the sensor node is a problem in the research community field. Localization on a two-dimensional plane, a key focus in WSNs, is to maximize the lifespan and overall performance of sensor nodes by minimizing their energy consumption. The compiled data that base stations receive from packets.in wireless sensor networks can be used to make decisions with the help of localization. A cost-effective method of solving the problem is not the Internet of Things with GPR tracking sensor zones. There are several approaches to locating wireless sensor networks with unclear sensor locations. The main challenge lies in accurately determining the location of the base station's sensor node with a minor localization error during wireless communication. The proposed method, Distributed clustering Distance Algorithm (DCDA) using machine learning, considers the distance estimation error, location in accuracy, and fault tolerance issue with WSNs. According to the findings, the average localization error is 11% and 11.3%, respectively. For anchor nodes 20-80 and 200-450. As a result, when compared to contemporary methods of localization with centroid weighted algorithm (LCWA), Distance vector hop algorithm (DV-Hop), Coefficient for reparation algorithm (CRA), and Weighted Distributed Hyperbolic algorithm (WDHA) methods, the demonstrated Distributed clustering Distance Algorithm (DCDA) gives greater accuracy. According to the experimental results, the suggested algorithm significantly improves the number of alive nodes compared to the LBCA and G. Gupta FT algorithms. Specifically, the proposed algorithm achieves a remarkable 96% increase in active and functional nodes within the wireless sensor network.

Index Terms

Clustering

Distributed Clustering Distance Algorithm (DCDA)

Wireless Sensor Network

Node Localization Error

Fault-Tolerant

Machine Learning.

Reference

  1. 1.
    Wang, L., Er, M.J. and Zhang, S., 2020. A kernel extreme learning machines algorithm for node localization in wireless sensor networks. IEEE Communications Letters, 24(7), pp.1433-1436.2020
  2. 2.
    Aroba, Oluwasegun Julius, Nalindren Naicker, and Timothy Adeliyi. "An innovative hyperheuristic, Gaussian clustering scheme for energy-efficient optimization in wireless sensor networks." Journal of Sensors 2021 (2021): pp1-12.2021
  3. 3.
    Zhao, Q., Cheng, Y. and Zhou, T., 2021. Research on Node localization Algorithm in WSN Based on TDOA. In Journal of Physics: Conference Series (Vol. 1757, No. 1, p. 012142). IOP Publishing.2021.
  4. 4.
    Pandey, O.J., Gautam, V., Jha, S., Shukla, M.K. and Hegde, R.M., 2020. Time synchronized node localization using optimal H-node allocation in a small world WSN. IEEE Communications Letters, 24(11), pp.2579-2583.2020
  5. 5.
    Farahzadi, H.R., Langarizadeh, M., Mirhosseini, M. and Fatemi Aghda, S.A., 2021. An improved cluster formation process in wireless sensor network to decrease energy consumption. Wireless Networks, 27, pp.1077-1087.2021
  6. 6.
    Aroba, O.J., Naicker, N., Adeliyi, T. and Ogunsakin, R.E., 2020. Meta-analysis of heuristic approaches for optimizing node localization and energy efficiency in wireless sensor networks. International Journal of Engineering and Advanced Technology (IJEAT), 10(1), pp.73-87.2020.
  7. 7.
    Huang, X., Han, D., Cui, M., Lin, G. and Yin, X., 2021. Three-dimensional localization algorithm based on improved A* and DV-Hop algorithms in wireless sensor network. Sensors, 21(2), p.448.2021
  8. 8.
    Rishiwal, V. and Singh, O., 2021. Energy efficient emergency rescue scheme in wireless sensor networks. International Journal of Information Technology, 13, pp.1951-1958.2021
  9. 9.
    Abdurohman, M., Supriadi, Y. and Fahmi, F.Z., 2020. A modified E-LEACH routing protocol for improving the lifetime of a wireless sensor network. Journal of Information Processing Systems, 16(4), pp.845-858.2020
  10. 10.
    Ibrahim, B.K., Mahdi, M.A. and Salman, M.A., 2020, April. Triple mobile anchors approach for localization in WSN. In 2020 International Conference on Computer Science and Software Engineering (CSASE) (pp. 174-179). IEEE.2020.
  11. 11.
    Kumar, S., Kumar, S. and Batra, N., 2021. Optimized distance range free localization algorithm for WSN. Wireless Personal Communications, 117, pp.1879-1907.2020.
  12. 12.
    Prashar, D. and Jha, N., 2021. Review of secure distributed range-free hop-based localization algorithms in the wireless sensor networks. Multimedia Security: Algorithm Development, Analysis and Applications, pp.283-302.2021.
  13. 13.
    Cheng, L., Li, Y., Xue, M. and Wang, Y., 2020. An indoor localization algorithm based on modified joint probabilistic data association for wireless sensor network. IEEE Transactions on Industrial Informatics, 17(1), pp.63-72.2020.
  14. 14.
    D. Prashar, N. Jha, in Three-Dimensional Distance-Error-Correction-Based Hop Localization Algorithm for IoT Devices, 66, CMC-Computer Materials & Continua, 2021, pp. 1529–1549.2020
  15. 15.
    A. Kaur, N. Agrawal, S. Lal, An accurate localization in wireless sensor networks, in 2020 6th International Conference on Signal Processing and Communication (ICSC) IEEE, 2020, pp. 18–22.2020.
  16. 16.
    Nagy, J., Oláh, J., Erdei, E., Máté, D. and Popp, J., 2018. The role and impact of Industry 4.0 and the Internet of things on the business strategy of the value chain—the case of Hungary. Sustainability, 10(10), p.3491.2018.
  17. 17.
    Jaiswal, K.; Anand, V. FAGWO-H: A hybrid method towards fault-tolerant cluster-based routing in a wireless sensor network for IoT applications. J. Supercomputer. 2022, 78, 11195–11227,2022
  18. 18.
    Dowlatshahi, M.B., Rafsanjani, M.K. and Gupta, B.B., 2021. An energy aware grouping memetic algorithm to schedule the sensing activity in WSNs-based IoT for smart cities. Applied Soft Computing, 108, p.107473.2021.
  19. 19.
    Naser Abdali, T.A., Hassan, R., Mohd Aman, A.H., Nguyen, Q.N. and Al-Khaleefa, A.S., 2021. Hyper-angle exploitative searching for enabling multi-objective optimization of fog computing. Sensors, 21(2), p.558.2021.
  20. 20.
    Idrees, A.K. and Al-Qurabat, A.K.M., 2021. Energy-efficient data transmission and aggregation protocol in periodic sensor networks based fog computing. Journal of Network and Systems Management, 29(1), p.4.2021.
  21. 21.
    Abdali, T.A.N., Hassan, R., Aman, A.H.M. and Nguyen, Q.N., 2021. Fog computing advancement: Concept, architecture, applications, advantages, and open issues. IEEE Access, 9, pp.75961-75980.2021.
  22. 22.
    Gao, Y., Xiao, F., Liu, J. and Wang, R., 2018. Distributed soft fault detection for interval type-2 fuzzy-model-based stochastic systems with wireless sensor networks. IEEE Transactions on Industrial Informatics, 15(1), pp.334-347.2019.
  23. 23.
    Li, L., Dai, H., Chen, G., Zheng, J., Dou, W. and Wu, X., 2019. Radiation constrained fair charging for wireless power transfer. ACM Transactions on Sensor Networks (TOSN), 15(2), pp.1-33.2019.
  24. 24.
    Hayat, H., Griffiths, T., Brennan, D., Lewis, R.P., Barclay, M., Weirman, C., Philip, B. and Searle, J.R., 2019. The state-of-the-art of sensors and environmental monitoring technologies in buildings. Sensors, 19(17), p.3648.2019.
  25. 25.
    Abdali, T.A.N., Hassan, R., Muniyandi, R.C., Mohd Aman, A.H., Nguyen, Q.N. and Al-Khaleefa, A.S., 2020. Optimized particle swarm optimization algorithm for the realization of an enhanced energy-aware location-aided routing protocol in manet. Information, 11(11), p.529.2020.
  26. 26.
    Abdulrab, H., Hussin, F.A., Abd Aziz, A., Awang, A., Ismail, I. and Devan, P.A.M., 2022. Reliable fault tolerant-based multipath routing model for industrial wireless control systems. Applied Sciences, 12(2), p.544.2022.
  27. 27.
    Biradar, M. and Mathapathi, B., 2021, February. Secure, reliable and energy efficient routing in WSN: A systematic literature survey. In 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) (pp. 1-13). IEEE.2021.
  28. 28.
    Menaria, V.K., Jain, S.C., Raju, N., Kumari, R., Nayyar, A. and Hosain, E., 2020. NLFFT: A novel fault tolerance model using artificial intelligence to improve performance in wireless sensor networks. IEEE Access, 8, pp.149231-149254.2020.
  29. 29.
    Mostafaei, H. and Menth, M., 2018. Software-defined wireless sensor networks: A survey. Journal of Network and Computer Applications, 119, pp.42-56.2018
  30. 30.
    Chouikhi, S., El Korbi, I., Ghamri-Doudane, Y. and Saidane, L.A., 2017. Recovery from simultaneous failures in a large scale wireless sensor network. Ad Hoc Networks, 67, pp.68-76.2017.
  31. 31.
    Mitra, S., & Das, A. Distributed fault-tolerant architecture for wireless sensor network. Informatica, 41(1), 47, 2017.
  32. 32.
    Singh, S.K., Singh, M.P. and Singh, D.K., 2010. Routing protocols in wireless sensor networks–A survey. International Journal of Computer Science & Engineering Survey (IJCSES), 1(2), pp.63-83.2017.
  33. 33.
    Aadri, A. and Idrissi, N., 2017. An energy efficient hierarchical routing scheme for wireless sensor networks. Computer science & information technology, pp.137-148.2017.
  34. 34.
    Moridi, E., Haghparast, M., Hosseinzadeh, M. and Jafarali Jassbi, S., 2020. Novel fault-tolerant clustering-based multipath algorithm (FTCM) for wireless sensor networks. Telecommunication Systems, 74, pp.411-424.2018.
  35. 35.
    Wang, P. and Tu, G., 2020. Localization algorithm of wireless sensor network based on matrix reconstruction. Computer Communications, 154, pp.216-222.2020.
  36. 36.
    Li, J., Gao, M., Pan, J.S. and Chu, S.C., 2021. A parallel compact cat swarm optimization and its application in DV-Hop node localization for wireless sensor network. Wireless Networks, 27, pp.2081-2101.2021.
  37. 37.
    Wang, Y., Yan, Y., Li, Z. and Cheng, L., 2020. A mobile localization method in smart indoor environment using polynomial fitting for wireless sensor network. Journal of Sensors, 2020.
  38. 38.
    Hao, Z., Dang, J., Yan, Y. and Wang, X., 2021. A node localization algorithm based on Voronoi diagram and support vector machine for wireless sensor networks. International Journal of Distributed Sensor Networks, 17(2), p.1550147721993410.2021.
  39. 39.
    Khattak, S.B.A., Jia, M., Marey, M., Nasralla, M.M., Guo, Q. and Gu, X., 2022. A novel single anchor localization method for wireless sensors in 5G satellite-terrestrial network. Alexandria Engineering Journal, 61(7), pp.5595-5606.2022.
  40. 40.
    Agoramoorthy, M. and Praveen Joe, I.R., 2022. Hybrid cuckoo–red deer algorithm for multiobjective localization strategy in wireless sensor network. International Journal of Communication Systems, 35(4), p.e5042.2022.
  41. 41.
    Vijayan, K. and Raaza, A., 2016. A novel cluster arrangement energy efficient routing protocol for wireless sensor networks. Indian Journal of science and Technology, 9(2), pp.1-9.2016.
  42. 42.
    Vijayan, K., Ramprabu, G., Samy, S.S. and Rajeswari, M., 2020. Cascading model in underwater wireless sensors using routing policy for state transitions. Microprocessors and Microsystems, 79, p.103298.2020.
  43. 43.
    Sakthi Shunmuga Sundaram, P. and Vijayan, K., 2022, February. Sleeping Node Scheduling Method Based Redundant Node Energy Reduction in Wireless Sensor Networks. In International Conference on Computing in Engineering & Technology,Singapore: Springer Nature Singapore. (pp. 602-609).2022
  44. 44.
    Sundaram, P.S.S. and Vijayan, K., 2022. Comparison of the Routing Algorithms Based on Average Location Error and Accuracy in WSN. In Sensing Technology: Proceedings of ICST 2022,Cham: Springer International Publishing.(pp. 411-423).2022.
  45. 45.
    Sankaran, K.S., Vijayan, K., Yuvaraj, S., Periasamy, J.K., Balasaraswathi, M., Verma, J.P.N. and Tyagi, C.S., 2021. Weighted-based path rediscovery routing algorithm for improving the routing decision in wireless sensor network. Journal of Ambient Intelligence and Humanized Computing, pp.1-9.2021.
SCOPUS
SCImago Journal & Country Rank

Announcements

Archives

Downloads

Contact Us

Dr.M. Milton Joe
EverScience Publications,
Nagercoil, Tamilnadu,India

Talk to Us

+91 9442777224

Email Us

editor@ijcna.org

info@ijcna.org

info@everscience.org

Quick Links

Follow Us

Copyright © 2023; EverScience Publications

Designed & Maintained By Shiro Software Solutions