International Journal of Computer Networks and Applications

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A Study of Machine Learning in Wireless Sensor Network


Affiliations
1 College of Life Science, Nanjing Agricultural University, Nanjing, China
2 University Women’s Polytechnic, Aligarh Muslim University, Aligarh, India
 

Within this Paper, a concept of machine learning strategies suggested in this investigation to address the design issues in WSNs is introduced. As can be viewed within this paper, countless endeavors have induced up to now; several layout issues in wireless sensor networks have been remedied employing numerous machine learning strategies. Utilizing machine learning based algorithms in WSNs need to deem numerous constraints, for instance, minimal sources of the network application that really needs distinct events to be tracked as well as other operational and non-operational aspects.

Keywords

Wireless Sensor Network, Machine Learning, Supervised Machine Learning, Unsupervised Machine Learning.
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  • P. Langley and H. A. Simon, “Applications of machine learning and rule induction,” Communications of the ACM, vol. 38, no. 11, pp. 54–64, 1995.

  • L. Paradis and Q. Han, “A survey of fault management in wireless sensor networks,” Journal of Network and Systems Management, vol. 15, no. 2, pp.171–190, 2007.

  • B. Krishnamachari, D. Estrin, and S. Wicker, “The impact of data aggregation in wireless sensor networks,” in 22nd International Conference on Distributed Computing Systems Workshops, 2002, pp. 575–578.

  • J. Al-Karaki and A. Kamal, “Routing techniques in wireless sensor networks: A survey,” IEEE Wireless Communications, vol. 11, no. 6, pp. 6–28, 2004.

  • K. Romer and F. Mattern, “The design space of wireless sensor networks,” IEEE Wireless Communications, vol. 11, no. 6, pp. 54–61, 2004.

  • J. Wan, M. Chen, F. Xia, L. Di, and K. Zhou, “From machine-to-machine communications towards cyber-physical systems,” Computer Science and Information Systems, vol. 10, pp. 1105–1128, 2013.

  • Y. Bengio, “Learning deep architectures for AI,” Foundations and Trends in Machine Learning, vol. 2, no. 1, pp. 1–127, 2009.

  • M. Di and E. M. Joo, “A survey of machine learning in wireless sensor netoworks from networking and application perspectives,” in 6th International Conference on Information, Communications Signal Processing, 2007, pp. 1–5.

  • A. Forster, “Machine learning techniques applied to wireless ad-hoc networks: Guide and survey,” in 3rd International Conference on Intelligent Sensors, Sensor Networks and Information. IEEE, 2007, pp. 365–370.

  • Y. Zhang, N. Meratnia, and P. Havinga, “Outlier detection techniques for wireless sensor networks: A survey,” IEEE Communications Surveys & Tutorials, vol. 12, no. 2, pp. 159–170, 2010.

  • R. Kulkarni, A. Förster, and G. Venayagamoorthy, “Computational intelligence in wireless sensor networks: A survey,” IEEE Communications Surveys & Tutorials, vol. 13, no. 1, pp. 68–96, 2011.

  • S. Kulkarni, G. Lugosi, and S. Venkatesh, “Learning pattern classification-a survey,” IEEE Transactions on Information Theory, vol. 44, no. 6, pp.2178–2206, 1998.

  • C.-H. Lu and L.-C. Fu, “Robust location-aware activity recognition using wireless sensor network in an attentive home,” IEEE Transactions on Automation Science and Engineering, vol. 6, no. 4, pp. 598–609, 2009.

  • W. Branch, C. Giannella, B. Szymanski, R. Wolff, and H. Kargupta, “Innetwork outlier detection in wireless sensor networks,” Knowledge and information systems, vol. 34, no. 1, pp. 23–54, 2013.

  • A. Moustapha and R. Selmic, “Wireless sensor network modeling using modified recurrent neural networks: Application to fault detection,” IEEE Transactions on Instrumentation and Measurement, vol. 57, no. 5, pp. 981–988, 2008.

  • Y. Wang, M. Martonosi, and L.-S. Peh, “Predicting link quality using supervised learning in wireless sensor networks,” ACM SIGMOBILE Mobile Computing and Communications Review, vol. 11, no. 3, pp. 71– 83, 2007.

  • S. R. Safavian and D. Landgrebe, “A survey of decision tree classifier methodology,” IEEE Transactions on Systems, Man and Cybernetics, vol. 21, no. 3, pp. 660–674, 1991.

  • Merlyn, A. Anuba, and A. Anuja Merlyn. "Energy Efficient Routing (EER) For Reducing Congestion and Time Delay in Wireless Sensor Network." International Journal of Computer Networks and Applications 1.1 (2014): 1-10.

  • Y. Zhang, N. Meratnia, and P. J. Havinga, “Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine,” Ad Hoc Networks, vol. 11, no. 3, pp. 1062–1074, 2013.

  • D. Tran and T. Nguyen, “Localization in wireless sensor networks based on support vector machines,” IEEE Transactions on Parallel and Distributed Systems, vol. 19, no. 7, pp. 981–994, 2008.

  • J. Barbancho, C. León, F. Molina, and A. Barbancho, “A new QoS routing algorithm based on self-organizing maps for wireless sensor networks,” Telecommunication Systems, vol. 36, pp. 73–83, 2007.

  • J. Kivinen, A. Smola, and R. Williamson, “Online learning with kernels,” IEEE Transactions on Signal Processing, vol. 52, no. 8, pp. 2165–2176, 2004.

  • G. Aiello and G. Rogerson, “Ultra-wideband wireless systems,” IEEE Microwave Magazine, vol. 4, no. 2, pp. 36–47, 2003.

  • R. Rajagopalan and P. Varshney, “Data-aggregation techniques in sensor networks: A survey,” IEEE Communications Surveys & Tutorials, vol. 8, no. 4, pp. 48–63, 2006.

  • S. Kaur and R. N. Mir, “Energy efficiency optimization in wireless sensor network using proposed load balancing approach,” International Journal of Computer Networks and Applications vol. 3, no. 5, pp.108-117, 2016.

  • H. He, Z. Zhu, and E. Makinen, “A neural network model to minimize the connected dominating set for self-configuration of wireless sensor networks,” IEEE Transactions on Neural Networks, vol. 20, no. 6, pp. 973–982, 2009.

  • J. Kho, A. Rogers, and N. R. Jennings, “Decentralized control of adaptive sampling in wireless sensor networks,” ACM Transactions on Sensor Networks (TOSN), vol. 5, no. 3, pp. 19:1–19:35, 2009.


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  • A Study of Machine Learning in Wireless Sensor Network

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Authors

Zaki Ahmad Khan
College of Life Science, Nanjing Agricultural University, Nanjing, China
Abdus Samad
University Women’s Polytechnic, Aligarh Muslim University, Aligarh, India

Abstract


Within this Paper, a concept of machine learning strategies suggested in this investigation to address the design issues in WSNs is introduced. As can be viewed within this paper, countless endeavors have induced up to now; several layout issues in wireless sensor networks have been remedied employing numerous machine learning strategies. Utilizing machine learning based algorithms in WSNs need to deem numerous constraints, for instance, minimal sources of the network application that really needs distinct events to be tracked as well as other operational and non-operational aspects.

Keywords


Wireless Sensor Network, Machine Learning, Supervised Machine Learning, Unsupervised Machine Learning.

References