Top

Wireless Personal Communications

Published in:

18-03-2023

Optimized Localization Learning Algorithm for Indoor and Outdoor Localization System in WSNs

Authors: Preeti Yadav, S. C. Sharma, Omkar Singh, Vinay Rishiwal

Published in: Wireless Personal Communications | Issue 1/2023

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The localization problem in wireless sensor networks (WSN) has recently received justifiable interest from researchers. Various optimization/learning algorithms are used to determine the accurate node localization. The main aim of this paper is to develop a novel Optimized Localization Learning Algorithm (OLLA) and analyze it with well-noticed localization-based learning algorithms, namely Approximate Point in Triangle (APIT), Localization Algorithm using Expected hop Progress (LAEP), Randomized Approximate Nearest Neighbors (RANN) and Standard-based Particle Swarm Optimization (SPSO). The performance is investigated based on indoor and outdoor scenarios, on specific parameters: absolute localization error, relative localization error, root mean square error, and probability distribution on various anchor nodes. Simulation results show that the proposed learning algorithm OLLA performs well in both scenarios. Besides this, numerous localization-based learning algorithms are discussed, and comparative analyses of well-known learning algorithms with merits and limitations are also presented.

previous article Multi-population Firefly Algorithm Based Node Deployment in Underwater Wireless Sensor Networks

next article A Review: Security and Privacy for Health Care Application in Wireless Body Area Networks

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Qiao, J., Hou, J., Gao, J., & Wu, Y. (2021). Research on improved localization algorithms RSSI-based in wireless sensor networks. Measurement Science and Technology., 32, 113–125. https://doi.org/10.1088/1361-6501/ac22f1CrossRef

Brik, B., Bettayeb, B., Sahnoun, M. H., & Duval, F. (2019). Towards predicting system disruption in industry 4.0: machine learning-based approach. EDI., 151, 667–674. https://doi.org/10.1016/j.procs.2019.04.089CrossRef

Thami, S. M., Trajkovic, L., Li, K. C., Das, S., Wozniak, M., & Berretti, S. (2019). Machine learning and metaheuristics algorithms and applications. Communications in Computer and Information Scienc., 1, 203–276.

Abhale, A. B., & Manivannan, S. S. (2020). Supervised machine learning classification algorithmic approach for finding anomaly type of intrusion detection in wireless sensor network. OMNN., 29, 244–256. https://doi.org/10.3103/S1060992X20030029CrossRef

Buonanno, L., Vita, D. D., Carminati, M., & Fiorini, C. (2020). A directional gamma-ray spectrometer with microcontroller-embedded machine learning. JETCAS., 20, 51–57. https://doi.org/10.1109/JETCAS.2020.3029570CrossRef

Chowdhury, P. R., Reynolds, C., Garrett, A., Garrett, A., Feng, T., Adiga, S. P., & Ruan, X. (2020). Machine learning maximized Anderson localization of phonons in aperiodic superlattices. Nano Energy, 69, 28–44. https://doi.org/10.1016/j.nanoen.2019.104428CrossRef

Khan, A. I., & Habsi, S. A. (2020). Machine learning in computer vision. ICCIDS. Procedia Computer Science, 167, 1444–1451. https://doi.org/10.1016/j.procs.2020.03.355CrossRef

Rahman, M. A., Rashid, M. A., Ahmad, M., Kuwana, A., & Kobayashi, H. (2020). Activation modeling and classification of voluntary and imagery movements from the prefrontal fNIRS signals. IEEE Access., 8, 218215–218233. https://doi.org/10.1109/ACCESS.2020.3042249CrossRef

Staroverov, A., Yuddin, D. A., Belkin, I., Adeshkin, V., Solomentsev, Y. K., & Panov, A. I. (2020). Real-time object navigation with deep neural networks and hierarchical reinforcement learning. RMLAMR., 8, 195608–195621. https://doi.org/10.1109/ACCESS.2020.3034524CrossRef

10.

Kagi, S., & Mathapati, B. S. (2022). Localization in wireless sensor network using machine learning optimal trained deep neural network by parametric analysis. Measurement Sensors., 24, 1–5. https://doi.org/10.1016/j.measen.2022.100427CrossRef

11.

Mohar, S. S., Goyal, S., & Kaur, R. (2022). Localization of sensor nodes in wireless sensor networks using bat optimization algorithm with enhanced exploration and exploitation characteristics. The Journal of Supercomputing., 78, 11975–12023. https://doi.org/10.1007/s11227-022-04320-xCrossRef

12.

Shen, Z., Zhang, T., Tagami, A., Tagami, A., & Jin, J. (2021). When RSSI encounters deep learning: An area localization scheme for pervasive sensing systems. Journal of Network and Computer Applications., 173, 1–13. https://doi.org/10.1016/j.jnca.2020.102852CrossRef

13.

Vij, A., Singh, V., Jain, A., Bajaj, S., Bassi, A., & Sharma, A. (2020). IoT and machine learning approaches for automation of farm irrigation system. ICCIDS., 167, 1250–1257. https://doi.org/10.1016/j.procs.2020.03.440CrossRef

14.

Lei, F., Cai, J., Dai, Q., & Zhao, H. (2019). Deep learning based proactive caching for effective WSN-enabled vision applications. Hindawi Complexity., 2019, 1–13. https://doi.org/10.1155/2019/5498606CrossRef

15.

Nain, M., Goyal, M., Awasthi, L. K., & Malik, A. (2022). A range based node localization scheme with hybrid optimization for underwater wireless sensor network. International Journal of Communication Systems., 35, 1–16. https://doi.org/10.1002/dac.5147CrossRef

16.

Sharma, A., & Chauhan, S. (2020). A distributed reinforcement learning based sensor node scheduling algorithm for coverage and connectivity maintenance in wireless sensor network. Wireless Networks., 10, 1–19. https://doi.org/10.1007/s11276-020-02350-yCrossRef

17.

Li, M., & Hu, T. (2021). Deep learning enabled localization for UAV auto landing. Chinese Journal of Aeronautic., 34, 585–600. https://doi.org/10.1016/j.cja.2020.11.011CrossRef

18.

DhirajAgarwal, A. A. A., Meruane, V., & Sangwan, K. S. (2021). Development of machine learning based model for damage detection, localization and quantification to extend structure life. CIRP., 98, 199–204. https://doi.org/10.1016/j.procir.2021.01.030CrossRef

19.

Xiao, L., Behboodi, A., & Mathar, R. (2018). Learning the localization function: machine learning approach to fingerprinting localization. Computer Networks., 21, 1–30. https://doi.org/10.48550/arXiv.1803.08153CrossRef

20.

Hesser, D. F., Kocur, G. K., & Markert, B. (2020). Active source localization in wave guides based on machine learning. Ultrasonics, 106, 44–61. https://doi.org/10.1016/j.ultras.2020.106144CrossRef

21.

Zhang, J., Xiao, W., Li, Y., Zhang, S., & Zhang, Z. (2019). Multilayer probability extreme learning machine for device-free localization. Neurocomputing, 132, 1–11. https://doi.org/10.1016/j.neucom.2018.11.106CrossRef

22.

Wu, M., Zhong, L., Xu, B., & Xiong, N. (2020). A consensus-based diffusion Levenberg-marquardt method for collaborative localization with extension to distributed optimization. IEEE Access., 8, 215650–215660. https://doi.org/10.1109/ACCESS.2020.3041491CrossRef

23.

Poornima, I. G. A., & Paramasivan, B. (2020). Anomaly detection in wireless sensor network using machine learning algorithm. Computer Communications., 151, 331–337. https://doi.org/10.1016/j.comcom.2020.01.005CrossRef

24.

Yin, F., Lin, Z., Xu, Y., Kong, Q., Li, D., Theodoridis, S., & Cui, S. (2020). FedLoc: Federated learning framework for data-driven cooperative localization and location data processing. Journal of Signal Processing., 1, 187–215. https://doi.org/10.48550/arXiv.2003.03697CrossRef

25.

Kim, M., Han, D., & Rhee, J. K. (2020). Machine learning for practical localization system using multiview CSI. IEEE Access., 8, 184575–184584. https://doi.org/10.1109/ACCESS.2020.3029598CrossRef

26.

Ahmadi, H., & Bouallegue, R. (2015). Comparative study of learning-based localization algorithms for wireless sensor networks: support vector regression, neural network and Naïve Bayes. International Wireless Communications and Mobile Computing Conference., 15, 1554–1558. https://doi.org/10.1109/IWCMC.2015.7289314CrossRef

27.

Saini, I., Singh, D., & Khosla, A. (2013). QRS detection using K-nearest neighbor algorithm (KNN) and evaluation on standard ECG databases. International Journal of Advanced Research., 4, 331–344. https://doi.org/10.1016/j.jare.2012.05.007CrossRef

28.

Bhatti, G. (2018). Machine learning based localization in large-scale wireless sensor networks. Sensors (Basel), 18, 1–20. https://doi.org/10.3390/s18124179CrossRef

29.

Zhang, L., Li, Y., Gu, Y., & Yang, W. (2017). An efficient machine learning approach for indoor localization. Communications Theories and Systems., 17, 141–150. https://doi.org/10.1109/CC.2017.8233657CrossRef

30.

Yadav, P., & Sharma, S. C. (2022). A systematic review of localization in WSN: Machine learning and optimization based approaches. International Journal of Communication System. https://doi.org/10.1002/dac.5397CrossRef

31.

Kusy, M., & Kowalski, P. A. (2022). Architecture reduction of a probabilistic neural network by merging k-means and k-nearest neighbour algorithms. Applied Soft Computing., 128, 87–93. https://doi.org/10.1016/j.asoc.2022.109387CrossRef

32.

Matijasevic, T., Antic, T., & Capuder, T. (2022). A systematic review of machine learning applications in the operation of smart distribution systems. Energy Reports., 8, 12379–12407. https://doi.org/10.1016/j.egyr.2022.09.068CrossRef

33.

Gao, X., Zhou, F., Xu, K. et al. (2022). A parallel algorithm for maximal cliques enumeration to improve hypergraph construction, (65), 10105–10119. https://doi.org/10.1016/j.jocs.2022.101905

34.

Anusha, K. S., Ramanathan, R., & Jayakumar, M. (2020). Link distance-support vector regression (LD-SVR) based device free localization technique in indoor environment. Engineering Science and Technology, an International Journal., 23, 483–493. https://doi.org/10.1016/j.jestch.2019.09.004CrossRef

35.

Jondhale, S. R., Mohan, V., Sharma, B. B., Lloret, J., & Athawale, S. V. (2022). Support vector regression for mobile target localization in indoor environments. Journal of Sensors., 22, 1–19. https://doi.org/10.3390/s22010358CrossRef

36.

Rishiwal, V., Singh, O., Tanwar, S., et al. (2018). Base station oriented multi route diversity protocol for wireless sensor networks. GC Wkshps. https://doi.org/10.1109/GLOCOMW.2018.8644227CrossRef

37.

Yadav, P., Sharma, S. C., et al. (2022). Hybrid localization scheme using K-fold optimization with machine learning in WSN. International Journal of Communication Systems, 12, e5206. https://doi.org/10.1002/dac.5206CrossRef

38.

Behera, A. P., Singh, A., Verma, S., & Kumar, M. (2020). Manifold learning with localized procrustes analysis based WSN localization. Sensor Applications., 04, 1–4. https://doi.org/10.1109/LSENS.2020.3025360CrossRef

Title: Optimized Localization Learning Algorithm for Indoor and Outdoor Localization System in WSNs
Authors: Preeti Yadav
S. C. Sharma
Omkar Singh
Vinay Rishiwal
Publication date: 18-03-2023
Publisher: Springer US
Published in: Wireless Personal Communications / Issue 1/2023
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-023-10304-8

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 1/2023

Integrated Signing Procedure Based Data Transfer Security and Authentication Framework for Internet of Things Applications

Improved Adaptive Beamforming Algorithms for Wireless Systems

The Effect of Miniaturized Circular Antennas on the SAR Reduction for Wireless Applications

Design and Analysis of a High Gain Millimeter-Wave Antenna Array for Dual Purpose Applications

An Opportunistic Coexistence Analysis of LTE and Wi-Fi in Unlicensed 5 GHz Frequency Band

A Distributed and Secure Software Architecture Based on Blockchain Technology for Application Software