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Redundancy Elimination During Data Aggregation in Wireless Sensor Networks for IoT Systems

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Recent Trends in Communication, Computing, and Electronics

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 524))

Abstract

Internet of things (IoT) has emerged as a natural evolution of environmental sensing systems such as wireless sensor networks (WSNs). Wireless sensor nodes being resource constrained in terms of limited energy supply through batteries, the communication overhead and power consumption are the most important issues for WSNs design. The sensor nodes have non-replaceable battery with limited amount of energy, which determines the sensor node’s life as well as network lifetime. A key challenge in the design as well as during the operation of WSNs is the extension of the network lifetime even in harsh environment. This paper presents a review on various data aggregation techniques to reduce redundancy and proposes an approach for redundancy elimination during data aggregation exploiting support vector machine (SVM) so that network lifetime can be prolonged during communication of information from sensor nodes to base station. The proposed approach is simulated, and the results are analyzed in terms of average packet delivery ratio, average residual energy, accuracy, and aggregation gain ratio with state-of-the-art techniques.

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References

  1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor network. IEEE Communications Magazine, 40(8), 102–114.

    Article  Google Scholar 

  2. T. Koskiahde, J. Kujala, T. Norolampi, & Oy, F. (2008). A sensor network architecture for military and crisis management. In IPCS International IEEE Symposium on Precision Clock Synchronization for Measurement, Control and Communication, IEEE (pp. 110–114). Michigan: IEEE Xplorer, September 2008.

    Google Scholar 

  3. White paper. http://www.iec.ch/whitepaper/pdf/iecWP-internetofthings-LR-en.pdf; July 31, 2014.

  4. Razzaque, M. A., Milojevic, M., Palade, A., & Clarke, S. (2016). Middleware for internet of things: a survey. IEEE Internet of Things Journal, 3(1), 70–96.

    Article  Google Scholar 

  5. Yadav, S., & Yadav, R. S. (2016). A review on energy efficient protocols in wireless sensor networks. Wireless Networks, Springer, 22(1), 335–350.

    Article  Google Scholar 

  6. Madamkam, S., Ramaswamy, R., & Tripathi, S. (2015). Internet of Things (IoT): A literature review. Journal of Computer and Communications, 3, 164–173.

    Google Scholar 

  7. Nakamura, E.F. et al. (2005). Using information fusion to assist data dissemination in wireless sensor networks. Telecommunication Systems, 30(1), 237–254.

    Article  Google Scholar 

  8. Fasolo, E., Rossi, M., Widmer, J., & Zorzi, M. (2007). In-network aggregation techniques for wireless sensor networks: A survey. IEEE Wireless Communications, 14(2), 70–87.

    Article  Google Scholar 

  9. Abdelgawad, A., & Bayoumi, M. (2012). Data fusion in WSN” In Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks; Lecture Notes in Electrical Engineering Series (Vol. 118, pp. 17–35). Springer.

    Google Scholar 

  10. Qurat-ul-Ain, I. T., Ahmed, S., & Zia, H. (2012). An objective based classification of aggregation techniques for wireless sensor networks. In Springer International Multi Topic Conference, IMTIC.

    Google Scholar 

  11. He, T., Blum, B. M., Stankovic, J. A., & Abdelzaher, T. (2004). AIDA: Adaptive application independent data aggregation in wireless sensor networks. ACM Transactions on Embedded Computing System, 3(2), 426–457.

    Article  Google Scholar 

  12. Pandey, V., Kaur, A., & Chand, N. (2010). A review on data aggregation techniques in wireless sensor network. Journal of Electronics and Electrical Engineering, 1, 01–08.

    Google Scholar 

  13. Xu, H., Huang, L., Zhang, Y., Huang, H, Jiang, S., Liu G. (2010). Energy-efficient cooperative data aggregation for wireless sensor networks. Elsevier Parallel Distributed Computing, 70(9), 953–961.

    Article  MATH  Google Scholar 

  14. Sampoornam, K. P., & Rameshwaran, K. (2013). An efficient data redundancy reduction technique with conjugative sleep scheduling for sensed data aggregators in sensor networks. WSEAS TRANSACTIONS on COMMUNICATIONS, 12(9), 2224–2864. E-ISSN.

    Google Scholar 

  15. Kumar, M., & Dutta, K. (2016). LDAT: LFTM based data aggregation and transmission protocol for wireless sensor networks. Journal of Trust Management, 2–20.

    Google Scholar 

  16. Kumar, S. M., Rajkumar, N. (2014). SCT based adaptive data aggregation for wireless sensor networks. Springer Wireless Personal Communication, 75, 2121–2133.

    Google Scholar 

  17. Patil, P., & Kulkarni, U. (2013). SVM based data redundancy elimination for data aggregation in wireless sensor networks. IEEE, 1309–1316. 978-1-4673-6217-7/13.

    Google Scholar 

  18. Khedo, K., Doomun, R., & Aucharuz, S. (2010). READA: redundancy elimination for accurate data aggregation in wireless sensor networks. Elsevier Computer Networks, 38(4), 393–422.

    Google Scholar 

  19. Yadav, S., Yadav, R. S. (2015). Energy efficient protocol for aggregation head selection in wireless sensor network. International Journal of Future Computer and Communication, 4(5), 311–315.

    Article  Google Scholar 

  20. Manevitz, L. M., & Yousef, M. (2001). One-class SVMs for document classification. Journal of Machine Learning Research, 2, 139–154.

    MATH  Google Scholar 

  21. Tripathi, R. K., Dhuli, S., Singh, Y. N., & Verma, N. K. (2014). Analysis of weights for optimal positioning of base station in a wireless sensor network. IEEE Transaction, 978-1-4799-2361-8/14.

    Google Scholar 

  22. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2002). An application specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.

    Article  Google Scholar 

  23. Wang, X. F., Xiang, J., & Hu, B. J. (2009). Evaluation and improvement of an energy model for wireless sensor networks. Chinese Journal of Sensors and Actuators, 22(9), 1319–1333.

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad, 211004, India

    Sarika Yadav & Rama Shankar Yadav

Authors
  1. Sarika Yadav
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  2. Rama Shankar Yadav
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Corresponding author

Correspondence to Sarika Yadav .

Editor information

Editors and Affiliations

  1. University of Allahabad, Allahabad, India

    Ashish Khare

  2. Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India

    Uma Shankar Tiwary

  3. Oakland University, Rochester, MI, USA

    Ishwar K. Sethi

  4. University of Allahabad, Allahabad, Uttar Pradesh, India

    Nar Singh

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Yadav, S., Yadav, R.S. (2019). Redundancy Elimination During Data Aggregation in Wireless Sensor Networks for IoT Systems. In: Khare, A., Tiwary, U., Sethi, I., Singh, N. (eds) Recent Trends in Communication, Computing, and Electronics. Lecture Notes in Electrical Engineering, vol 524. Springer, Singapore. https://doi.org/10.1007/978-981-13-2685-1_20

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  • DOI: https://doi.org/10.1007/978-981-13-2685-1_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2684-4

  • Online ISBN: 978-981-13-2685-1

  • eBook Packages: EngineeringEngineering (R0)

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