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Machine Learning-Based Wireless Sensor Networks

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Machine Learning: Theoretical Foundations and Practical Applications

Part of the book series: Studies in Big Data ((SBD,volume 87))

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Abstract

With the progression in machine learning techniques, many applications of it, in various fields, have emerged. One of such fields is wireless sensor networks (WSNs) where machine learning is applied for various purposes. It is very much important for a wireless sensor network to adapt and work in dynamic environment and to track the target correctly. Also it needs to detect attacks and faults early. Machine learning techniques provide various solutions to the issues that are faced by WSNs. This chapter discusses some recent applications of machine learning techniques, to different areas of WSNs as well as the challenges faced.

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References

  1. Michie, D., Spiegelhalter, D. J., & Taylor, C. C. (1994). Machine learning. Neural and Statistical Classification, 13(1994), 1–298.

    Google Scholar 

  2. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.

    Article  MathSciNet  Google Scholar 

  3. Alpaydin, E. (2020). Introduction to machine learning. MIT Press.

    Google Scholar 

  4. Dietterich, T. G. (2000). Ensemble methods in machine learning. In International Workshop on Multiple Classifier Systems (pp. 1–15). Berlin, Heidelberg: Springer.

    Google Scholar 

  5. Akyildiz, I. F., Weilian, Su., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  6. Tree, S. (2014). Wireless sensor networks. Self, 1(R2), C0.

    Google Scholar 

  7. Karl, H., & Willig, A. (2007). Protocols and architectures for wireless sensor networks. Wiley.

    Google Scholar 

  8. Romer, K., & Mattern, F. (2004). The design space of wireless sensor networks. IEEE Wireless Communications, 11(6), 54–61.

    Article  Google Scholar 

  9. Sharma, S., Bansal, R. K., & Bansal, S. (2013). Issues and challenges in wireless sensor networks. In 2013 International Conference on Machine Intelligence and Research Advancement (pp. 58–62). IEEE.

    Google Scholar 

  10. Seah, W. K. G., Eu, Z. A., & Tan, H.-P. (2009). Wireless sensor networks powered by ambient energy harvesting (WSN-HEAP)-Survey and challenges. In 2009 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology (pp. 1–5). IEEE.

    Google Scholar 

  11. Sharma, K., & Ghose, M. K. (2010). Wireless sensor networks: An overview on its security threats. In IJCA, Special Issue on “Mobile Ad-hoc Networks” MANETs (pp. 42–45).

    Google Scholar 

  12. Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H.-P. (2014). Machine learning in wireless sensor networks: Algorithms, strategies, and applications. IEEE Communications Surveys & Tutorials, 16(4), 1996–2018.

    Google Scholar 

  13. Guestrin, C., Bodik, P., Thibaux, R., Paskin, M., & Madden, S. (2004). Distributed regression: an efficient framework for modeling sensor network data. In Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (pp. 1–10).

    Google Scholar 

  14. Barbancho, J., León, C., Javier Molina, F., & Barbancho. A. (2007). A new QoS routing algorithm based on self-organizing maps for wireless sensor networks. Telecommunication Systems, 36(1–3), 73–83.

    Google Scholar 

  15. Sun, R., Tatsumi, S., & Zhao, G. (2002). Q-map: A novel multicast routing method in wireless ad hoc networks with multiagent reinforcement learning. In 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering. TENCOM'02. Proceedings (Vol. 1, pp. 667–670). IEEE.

    Google Scholar 

  16. Dong, S., Agrawal, P., & Sivalingam, K. (2007). Reinforcement learning based geographic routing protocol for UWB wireless sensor network. In IEEE GLOBECOM 2007-IEEE Global Telecommunications Conference (pp. 652–656). IEEE.

    Google Scholar 

  17. Arroyo-Valles, R., Alaiz-Rodriguez, R., Guerrero-Curieses, A., & Cid-Sueiro, J. (2007). Q-probabilistic routing in wireless sensor networks. In 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information (pp. 1–6). IEEE.

    Google Scholar 

  18. Forster, A., & Murphy, A. L. (2007). FROMS: Feedback routing for optimizing multiple sinks in WSN with reinforcement learning. In 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information (pp. 371–376). IEEE.

    Google Scholar 

  19. Gunduz, S., Arslan, B., & Demirci. M. (2015). A review of machine learning solutions to denial-of-services attacks in wireless sensor networks. In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). IEEE.

    Google Scholar 

  20. Khan, Z. A., & Samad, A. (2017). A study of machine learning in wireless sensor network. International Journal of Computer Networks and Applications 4(4), 105–112.

    Google Scholar 

  21. Mamdouh, M., AI Elrukhsi, M., & Khattab, A. (2018). Securing the internet of things and wireless sensor networks via machine learning: A survey. In 2018 International Conference on Computer and Applications (ICCA). IEEE

    Google Scholar 

  22. Kumar, D. P., Amgoth, T., & Annavarapu, C. S. R. (2019). Machine learning algorithms for wireless sensor networks: A survey. Information Fusion 49, 1–25.

    Google Scholar 

  23. Detection C., Pankaj R., Mahalle, P. N. & Shinde, G. R. (2020). Deep learning and machine learning techniques for intrusion detection and prevention in wireless sensor networks: Comparative study and performance analysis. Design frameworks for wireless networks (pp. 95–120). Singapore: Springer.

    Google Scholar 

  24. Dwivedi, R. K., Rai, A. K., & Kumar, R. (2020). Outlier detection in wireless sensor networks using machine learning techniques: A survey. In 2020 International Conference on Electrical and Electronics Engineering (ICE3) (pp. 316–321). IEEE.

    Google Scholar 

  25. Al Aghbari, Z., Khedr, A. M., Osamy, W., Arif, I., & Agrawal, D. P. (2019). Routing in wireless sensor networks using optimization techniques: A survey. Wireless Personal Communications 1–28.

    Google Scholar 

  26. Alrajeh, N. A., Khan, S., & Shams, B. (2013). Intrusion detection systems in wireless sensor networks: A review. International Journal of Distributed Sensor Networks, 9(5), 167575.

    Google Scholar 

  27. Sandhya, G., & Julian, A. (2014). Intrusion detection in wireless sensor network using genetic K-means algorithm. In 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies (pp. 1791–1794). IEEE.

    Google Scholar 

  28. Ghezelbash, R., Maghsoudi, A., & Carranza, E. J. M. (2020). Optimization of geochemical anomaly detection using a novel genetic K-means clustering (GKMC) algorithm. Computers & Geosciences, 134, 104335.

    Google Scholar 

  29. Chandre, P. R., Mahalle, P. N., & Shinde, G. R. (2018). Machine learning based novel approach for intrusion detection and prevention system: A tool based verification. In 2018 IEEE Global Conference on Wireless Computing and Networking (GCWCN). IEEE.

    Google Scholar 

  30. Geetha, S., Dulhare, U. N., & Sivatha Sindhu, S. S. (2018). Intrusion detection using NBHoeffding rule based decision tree for wireless sensor networks. In 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) (pp. 1–5). IEEE.

    Google Scholar 

  31. Tan, X., Shaojing, Su., Huang, Z., Guo, X., Zuo, Z., Sun, X., & Li, L. (2019). Wireless sensor networks intrusion detection based on SMOTE and the random forest algorithm. Sensors, 19(1), 203.

    Article  Google Scholar 

  32. Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18–22.

    Google Scholar 

  33. de Lima Pinto, E. M., Lachowski, R., Eduardo Pellenz, M., Penna, M. C., & Souza, R. D. (2018). A machine learning approach for detecting spoofing attacks in wireless sensor networks. In 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA) (pp. 752–758). IEEE.

    Google Scholar 

  34. Shafiee, E., Mosavi, M. R., & Moazedi, M. (2018). Detection of spoofing attack using machine learning based on multi-layer neural network in single-frequency GPS receivers. The Journal of Navigation, 71(1), 169–188.

    Article  Google Scholar 

  35. Al-issa, A. I., Al-Akhras, M., ALsahli, M. S., Alawairdhi. M. (2019). Using machine learning to detect DoS attacks in wireless sensor networks. In 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT) (pp. 107–112). IEEE.

    Google Scholar 

  36. Liu, W., Chawla, S., Cieslak, D. A., & Chawla, N. V. (2010). A robust decision tree algorithm for imbalanced data sets. In Proceedings of the 2010 SIAM International Conference on Data Mining (pp. 766–777). Society for Industrial and Applied Mathematics.

    Google Scholar 

  37. Cortes, C., & Vapnik, V. (1995). Support vector machine. Machine Learning, 20(3), 273–297.

    MATH  Google Scholar 

  38. Pachauri, G., & Sharma, S. (2015). Anomaly detection in medical wireless sensor networks using machine learning algorithms. Procedia Computer Science, 70, 325–333.

    Article  Google Scholar 

  39. Jain, R., & Shah, H. (2016). An anomaly detection in smart cities modeled as wireless sensor network. In 2016 International Conference on Signal and Information Processing (IConSIP) (pp. 1–5). IEEE.

    Google Scholar 

  40. Hassoun, M. H. (1995). Fundamentals of artificial neural networks. MIT Press.

    Google Scholar 

  41. Graupe, D. (2013). Principles of artificial neural networks. (Vol. 7). World Scientific.

    Google Scholar 

  42. Cauteruccio, F., Fortino, G., Guerrieri, A., Liotta, A., Mocanu, D. C., Perra, C., Terracina, G., & Vega, M. T. (2019). Short-long term anomaly detection in wireless sensor networks based on machine learning and multi-parameterized edit distance. Information Fusion, 52, 13–30.

    Google Scholar 

  43. Salem, O., Guerassimov, A., Mehaoua, A., Marcus, A., & Furht, B. (2014). Anomaly detection in medical wireless sensor networks using SVM and linear regression models. International Journal of E-Health and Medical Communications (IJEHMC), 5(1), 20–45.

    Article  Google Scholar 

  44. Warriach, E. U., & Tei, K. (2013). Fault detection in wireless sensor networks: A machine learning approach. In: 2013 IEEE 16th International Conference on Computational Science and Engineering (pp. 758–765). IEEE.

    Google Scholar 

  45. Zidi, S., Moulahi, T., & Alaya, B. (2017). Fault detection in wireless sensor networks through SVM classifier. IEEE Sensors Journal, 18(1), 340–347.

    Article  Google Scholar 

  46. Javaid, A., Javaid, N., Wadud, Z., Saba, T., Sheta, O. E., Saleem, M. Q., & Alzahrani, M. E. (2019). Machine learning algorithms and fault detection for improved belief function based decision fusion in wireless sensor networks. Sensors, 19(6), 1334.

    Google Scholar 

  47. Rashid, S., Akram, U., & Khan, S. A. (2015). WML: Wireless sensor network based machine learning for leakage detection and size estimation. Procedia Computer Science, 63, 171–176.

    Article  Google Scholar 

  48. Rish, I. (2001). An empirical study of the naive Bayes classifier. IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, 3(22), 41–46.

    Google Scholar 

  49. Reynolds, D. A. (2009). Gaussian mixture models. Encyclopedia of Biometrics, 741.

    Google Scholar 

  50. Liu, Y., Ma, X., Li, Y., Tie, Y., Zhang, Y., & Gao, J. (2019). Water pipeline leakage detection based on machine learning and wireless sensor networks. Sensors, 19(23), 5086.

    Article  Google Scholar 

  51. Shahid, N., Naqvi, I. H., & Qaisar, S. B. (2015). One-class support vector machines: Analysis of outlier detection for wireless sensor networks in harsh environments. Artificial Intelligence Review, 43(4), 515–563.

    Google Scholar 

  52. Martins, H., et al. (2015). A machine learning technique in a multi-agent framework for online outliers detection in wireless sensor networks. In IECON 2015–41st Annual Conference of the IEEE Industrial Electronics Society. IEEE.

    Google Scholar 

  53. Suykens, J. A. K., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9(3), 293–300.

    Google Scholar 

  54. Pfahringer, B., Holmes, G., & Kirkby, R. (2008). Handling numeric attributes in hoeffding trees. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 296–307). Berlin, Heidelberg: Springer.

    Google Scholar 

  55. Tominaga, Y. (1999). Comparative study of class data analysis with PCA-LDA, SIMCA, PLS, ANNs, and k-NN. Chemometrics and Intelligent Laboratory Systems, 49(1), 105–115.

    Article  Google Scholar 

  56. Piramuthu, S., Shaw, M. J., & Gentry, J. A. (1994). A classification approach using multi-layered neural networks. Decision Support Systems, 11(5), 509–525.

    Article  Google Scholar 

  57. Mathur, A., & Foody, G. M. (2008). Multiclass and binary SVM classification: Implications for training and classification users. IEEE Geoscience and Remote Sensing Letters, 5(2), 241–245.

    Article  Google Scholar 

  58. Jordan, M. I., Ghahramani, Z., & Saul, L. K. (1997). Hidden Markov decision trees. In Advances in Neural Information Processing Systems (pp. 501–507).

    Google Scholar 

  59. Karamizadeh, S., Abdullah, S. M., Manaf, A. A., Zamani, M., & Hooman, A. (2013). An overview of principal component analysis. Journal of Signal and Information Processing, 4(3B), 173.

    Article  Google Scholar 

  60. Mahfouz, S., et al. (2014). Target tracking using machine learning and Kalman filter in wireless sensor networks. IEEE Sensors Journal, 14(10), 3715–3725.

    Google Scholar 

  61. Welch, G., & Bishop, G., (1995). An introduction to the Kalman filter.

    Google Scholar 

  62. Julier, S. J., & Uhlmann, J. K. (1997). New extension of the Kalman filter to nonlinear systems. In Signal Processing, Sensor Fusion, and Target Recognition VI (Vol. 3068, pp. 182–193). International Society for Optics and Photonics.

    Google Scholar 

  63. Zhang, Y., Duchi, J., & Wainwright, M. (2013). Divide and conquer kernel ridge regression. In Conference on learning theory (pp. 592–617).

    Google Scholar 

  64. Jondhale, S. R., & Deshpande, R. S. (2018). Kalman filtering framework-based real time target tracking in wireless sensor networks using generalized regression neural networks. IEEE Sensors Journal, 19(1), 224–233.

    Article  Google Scholar 

  65. Specht, D. F. (2006). GRNN with double clustering. In The 2006 IEEE International Joint Conference on Neural Network Proceedings (pp. 5074–5079). IEEE.

    Google Scholar 

  66. Ahmadi, H., Viani, F., & Bouallegue, R. (2018). An accurate prediction method for moving target localization and tracking in wireless sensor networks. Ad Hoc Networks, 70, 14–22.

    Article  Google Scholar 

  67. Wang, X., Liu, X., Wang, Z., Li, R., & Yiguang, Wu. (2020). SVM+ KF target tracking strategy using the signal strength in wireless sensor networks. Sensors, 20(14), 3832.

    Article  Google Scholar 

  68. Singh, P., & Agrawal, S., (2013). TDOA based node localization in WSN using neural networks. In 2013 International Conference on Communication Systems and Network Technologies (pp. 400–404). IEEE.

    Google Scholar 

  69. Jin, W., Li, Z. J., Wei, L. S., & Zhen, H. (2000). The improvements of BP neural network learning algorithm. In WCC 2000-ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000 (Vol. 3, pp. 1647–1649). IEEE.

    Google Scholar 

  70. Wilamowski, B. M., & Jaeger, R. C. (1996) Implementation of RBF type networks by MLP networks. In Proceedings of International Conference on Neural Networks (ICNN'96) (Vol. 3, pp. 1670–1675). IEEE.

    Google Scholar 

  71. Bhatti, G. (2018). Machine learning based localization in large-scale wireless sensor networks. Sensors, 18(12), 4179.

    Article  Google Scholar 

  72. Mahfouz, S., Mourad-Chehade, F., Honeine, P., Farah, J., & Snoussi, H. (2015). Kernel-based machine learning using radio-fingerprints for localization in WSNs. IEEE Transactions on Aerospace and Electronic Systems, 51(2), 1324–1336.

    Article  Google Scholar 

  73. Awad, M., & Khanna, R. (2015). Support vector regression. In Efficient learning machines (pp. 67–80). Berkeley, CA: Apress.

    Google Scholar 

  74. Kim, W., Park, J., Yoo, J., Kim, H. J., & Park, C. G. (2012). Target localization using ensemble support vector regression in wireless sensor networks. IEEE Transactions on Cybernetics., 43(4), 1189–1198.

    Article  Google Scholar 

  75. Zheng, J., & Dehghani, A. (2012). Range-free localization in wireless sensor networks with neural network ensembles. Journal of Sensor and Actuator Networks., 1(3), 254–271.

    Article  Google Scholar 

  76. Raj, A. B., Ramesh, M. V., Kulkarni, R. V., & Hemalatha, T. (2012). Security enhancement in wireless sensor networks using machine learning. In 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems (pp. 1264–1269). IEEE.

    Google Scholar 

  77. Yu, Z, Tsai J. J. (2008). A framework of machine learning based intrusion detection for wireless sensor networks. In 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC 2008) 2008 Jun 11 (pp. 272–279). IEEE.

    Google Scholar 

  78. Khan, F., Memon, S., & Jokhio, S. H. (2016). Support vector machine based energy aware routing in wireless sensor networks. In 2016 2nd International Conference on Robotics and Artificial Intelligence (ICRAI) (pp. 1–4). IEEE.

    Google Scholar 

  79. Ali, B., Mahmood, T., Abbas, M., Hussain, M., Ullah, H., Sarker, A., & Khan, A. (2019). LEACH robust routing approach applying machine learning. IJCSNS, 19(6), 18–26.

    Google Scholar 

  80. 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.

    Google Scholar 

  81. Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379.

    Article  Google Scholar 

  82. Liu, J.-L., & Ravishankar, C. V. (2011). LEACH-GA: Genetic algorithm-based energy-efficient adaptive clustering protocol for wireless sensor networks. International Journal of Machine Learning and Computing, 1(1), 79.

    Article  Google Scholar 

  83. Maulik, U., & Bandyopadhyay, S. (2000). Genetic algorithm-based clustering technique. Pattern Recognition, 33(9), 1455–1465.

    Article  Google Scholar 

  84. Fan, X., & Yulin, S. (2007). Improvement on LEACH protocol of wireless sensor network. In 2007 International Conference on Sensor Technologies and Applications (SENSORCOMM 2007) (pp. 260–264). IEEE.

    Google Scholar 

  85. Zhang, T., Zhao, Q., Shin, K., & Nakamoto, Y. (2018). Bayesian-optimization-based peak searching algorithm for clustering in wireless sensor networks. Journal of Sensor and Actuator Networks, 7(1), 2.

    Article  Google Scholar 

  86. Wang, J., Gao, Y., Wang, K., Sangaiah, A. K., & Lim, S.-J. (2019). An affinity propagation-based self-adaptive clustering method for wireless sensor networks. Sensors, 19(11), 2579.

    Google Scholar 

  87. Kotary, D. K., & Nanda, S. J. (2020). Distributed robust data clustering in wireless sensor networks using diffusion moth flame optimization. Engineering Applications of Artificial Intelligence, 87, 103342.

    Google Scholar 

  88. Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249.

    Article  Google Scholar 

  89. Sheng, W., & Liu, X. (2004). A hybrid algorithm for k-medoid clustering of large data sets. In Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No. 04TH8753) (vol. 1, pp. 77–82). IEEE.

    Google Scholar 

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Authors and Affiliations

  1. School of Computer Engineering, KIIT (Deemed to be University), Bhubaneswar, Odisha, India

    Lipika Mohanty, Junali Jasmine Jena, Manjusha Pandey & Siddharth Swarup Rautaray

  2. Wipro Limited, WIPRO BHDC, Bhubaneswar, Odisha, India

    Sushovan Jena

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  1. Lipika Mohanty
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  1. School of Computer Engineering, KIIT (Deemed to be University), Bhubaneswar, Odisha, India

    Manjusha Pandey

  2. School of Computer Engineering, KIIT (Deemed to be University), Bhubaneswar, Odisha, India

    Siddharth Swarup Rautaray

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Mohanty, L., Jena, J.J., Pandey, M., Rautaray, S.S., Jena, S. (2021). Machine Learning-Based Wireless Sensor Networks. In: Pandey, M., Rautaray, S.S. (eds) Machine Learning: Theoretical Foundations and Practical Applications. Studies in Big Data, vol 87. Springer, Singapore. https://doi.org/10.1007/978-981-33-6518-6_6

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