Advertisement

Cluster-Based Systematic Data Aggregation Model (CSDAM) for Real-Time Data Processing in Large-Scale WSN

  • M. ShobanaEmail author
  • R. Sabitha
  • S. Karthik
Article

Abstract

In present decade, wireless sensor networks is applied in a variety of applications such as health monitoring, agriculture, traffic management, security domains, pollution management, and so on. Owing to the node density, the same data are collected by multiple sensors that introduce redundancy, which should be avoided by means of proper data aggregation methodology. With that note, this paper presents a cluster-based systematic data aggregation model (CSDAM) for real-time data processing. First, the network is formed into a cluster with active and sleep state nodes and cluster-head (CH) is selected based on ranking given to sensors with two criteria: existing energy level (EEL) and geographic-location (GL) to base station (BS), [i.e., Rank(EEL,GL)]. Here, the CH is the aggregator. Second, Aggregation is carried out in 3 levels where the data processing of level 3 has been reduced by aggregating the data at level 1 and level 2. If the energy of aggregator goes below the threshold, we choose another aggregator. Third, Real time application should be given more precedence than other applications, so additionally an application type field is added to each sensor node from which the priority of data processing is given first to real time applications. The simulation results show that CSDAM minimizes the consumption of energy and transmission delay effectively, thereby increasing the network lifespan.

Keywords

Wireless sensor networks (WSN) Cluster-based systematic data aggregation model (CSDAM) Data aggregation Clustering protocols Active sensors 

Notes

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Standards

This article does not contain any studies with animals performed by any of the authors.

References

  1. 1.
    Zytoune, Q., & Fakhri, Y. (2009). Aboutajdine D (2009) A balanced cost cluster- heads selection algorithm for wireless sensor networks. International Journal of Computer Science,4(1), 21–24.MathSciNetGoogle Scholar
  2. 2.
    Hill, J., Szewczyk, R., Woo, A., Hollar, S., Culler, D., & Pister, K. (2000). System architecture directions for networked sensors. ACM SIGOPS Operating Systems Review,34(5), 93–104.CrossRefGoogle Scholar
  3. 3.
    Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications.,19(2), 171–209.CrossRefGoogle Scholar
  4. 4.
    Jaseena, K. U., & David, J. M. (2014). Issues, challenges, and solutions: Big data mining’. Computer Science & Information Technology,4, 131–140.Google Scholar
  5. 5.
    Harb, H., Makhoul, A., Idrees, A. K., Zahwe, O., & Taam, M. A. (2017). Wireless n sensor networks: A big data source in Internet of Things. International Journal of Sensors Wireless Communications and Control,7(2), 93–109.Google Scholar
  6. 6.
    Braman, A., & Umapathi, G. R. (2014). A comparative study on advances in LEACH routing protocol for wireless sensor networks: A survey. International Journal of Advanced Research in Computer and Communication Engineering,3(2), 15–21.Google Scholar
  7. 7.
    Kaura, R., & Majithia, S. (2012). Efficient end to end routing using RSSI & simulated annealing. International Journal of Engineering Research and Technology,1(10), 1–5.Google Scholar
  8. 8.
    Dagar, M., & Mahajan, S. (2013). Data aggregation in wireless sensor network: A survey. International Journal of Information and Computation Technology,3(3), 167–174.Google Scholar
  9. 9.
    Dhand, G., & Tyagi, S. S. (2016). Data aggregation techniques in WSN: Survey. Procedia Computer Science,92, 378–384.CrossRefGoogle Scholar
  10. 10.
    Randhawa, S., & Jain, S. (2017). Data aggregation in wireless sensor networks: Previous research, current status and future directions. Wireless Personal Communications,97, 3355–3425.CrossRefGoogle Scholar
  11. 11.
    Khudonogova, L.I., & Muravyov, S. V. (2016). Energy-accurcay aware active node selection in wireless sensor network. In IEEE.Google Scholar
  12. 12.
    Patil, N. S., & Patil, P. R. (2010). Data aggregation in wireless sensor network. In IEEE international conference on computational intelligence and computing research.Google Scholar
  13. 13.
    Andreu-Perez, J., Poon, C. C. Y., Merrifield, R. D., Wong, S. T. C., & Yang, G. Z. (2015). Big data for health. IEEE Journal of Biomedical and Health Informatics,19(4), 1193–1208.CrossRefGoogle Scholar
  14. 14.
    Chao, W., Birch, D., Silva, D., Tsinalis, C.-H., Lee, O., & Guo, Y. (2014). Concinnity: A generic platform for big sensor data applications. Cloud Computing,1(2), 42–50.CrossRefGoogle Scholar
  15. 15.
    Chen, J., Xu, W., He, S., Sun, Y., Thulasiraman, P., & Shen, X. (2010). Utility-based asynchronous flow control algorithm for wireless sensor networks. IEEE Journal on Selected Areas in Communications,28(7), 1116–1126.CrossRefGoogle Scholar
  16. 16.
    Lin, C., Chiu, M.-J., Hsiao, C.-C., Lee, R.-G., & Tsai, Y.-S. (2006). Wireless health care service system for elderly with dementia. IEEE Transactions on Information Technology in Biomedicine,10(4), 696–704.CrossRefGoogle Scholar
  17. 17.
    Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences (Vol. 2, p. 10).  https://doi.org/10.1109/hicss.2000.926982.
  18. 18.
    Ding, M., Cheng, X., & Xue, G. (2003). Aggregation tree construction in sensor networks. In Vehicular technology conference, 2003. VTC 2003-Fall. 2003 (Vol. 4, pp. 2168–2172).Google Scholar
  19. 19.
    Tan, H. Ö., & Körpeoǧlu, I. (2003). Power efficient data gathering and aggregation in wireless sensor networks. ACM Sigmod Record,32(4), 66–71.CrossRefGoogle Scholar
  20. 20.
    Ahmed, A.A., Shi, H., & Shang, Y. (2003). Survey on network protocols for wireless sensor networks. In Proceedings of the international conference on information technology: Research and education, 11–13 Aug. 2003.Google Scholar
  21. 21.
    Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications,1(4), 660–670.CrossRefGoogle Scholar
  22. 22.
    Yuan, X. X., & Zhang, R. H. (2011). An energy-efficient mobile sink routing algorithm for wireless sensor networks. In IEEEWiCOM. Wuhan, China: IEEE, Sep 2011.Google Scholar
  23. 23.
    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.CrossRefGoogle Scholar
  24. 24.
    Ma, Y., Guo, Y., Tian, X., & Ghanem, M. (2011). Distributed clustering-based aggregation algorithm for spatial correlated sensor networks. IEEE Sensors Journal,11(3), 641–648.CrossRefGoogle Scholar
  25. 25.
    Sinha, A., & Lobiyal, D. K. (2013). Performance evaluation of data aggregation forcluster-based wireless sensor network. Human-Centric Computing and Information Sciences,3(1), 1–17.CrossRefGoogle Scholar
  26. 26.
    Yang, M. (2017).Data aggregation algorithm for wireless sensor network based on time prediction. In 2017 IEEE 3rd information tech and mechatronics engineering conference (ITOEC), Chongqing (pp. 863–867).Google Scholar
  27. 27.
    Hamzeloei, F., & Khalilydermany, M. (2016). A TOPSIS based cluster head selection for wireless sensor network (pp. 8–15). Amsterdam: Elsevier.Google Scholar
  28. 28.
    Gavhale, M., & Saraf, P. D. (2016). Survey on algorithms for efficient cluster formation and cluster head selection in MANET (pp. 477–482). Amsterdam: Elsevier.Google Scholar
  29. 29.
    Pal, V., Singh, G., & Yadav, R. P. (2015). Cluster head optimization based on genetic algorithm to prolong the lifetime of WSN (pp. 1417–1423). Amsterdam: Elsevier.Google Scholar
  30. 30.
    Zhao, L., Qu, S., & Yi, Y. (2018). A modified Cluster Head selection algorithm in WSN based on LEACH. EURASIP Journal on Wireless Communication and Networking,1, 1–8.Google Scholar
  31. 31.
    Zahedi, A. (2018). An efficient clustering method using weighting coefficients in homogeneous wireless sensor network (pp. 695–710). Amsterdam: Elsevier.Google Scholar
  32. 32.
    Mantri, D. S., & Prasad, R. (2015). Bandwidth efficient cluster based data aggregation for wireless sensor networks (pp. 256–264). Amsterdam: Elsevier.Google Scholar
  33. 33.
    Khan, F., Gul, T., Ali, S., et al. (2018). Energy aware cluster head selection for improving network lifetime in wireless sensor network (pp. 581–593). Berlin: Springer.Google Scholar
  34. 34.
    Rao, P. C. S., Jana, P. K., & Banka, H. (2017). A particle swarm optimization based energy efficient cluster head selection algorithm for WSN. Berlin: Springer.Google Scholar
  35. 35.
    Shankar, T., Karthikeyan, A., Sivasankar, P., & Rajesh, A. (2017). Hybrid approach for optimal cluster head selection in WSN using LEACH and monkey search algorithms. Journal of Engineering Science and Technology,12, 506–517.Google Scholar
  36. 36.
    Abbasi-Daresari, S., & Abouei, J. (2016). Toward cluster based weighted compressive data aggregation in WSN. Amsterdam: Elsevier.Google Scholar
  37. 37.
    Sran, S.S., & Kaur, L. et al. (2015). Energy aware chain based data aggregation scheme for WSN. In 2015 international conference on energy systems and applications (pp. 113–117).Google Scholar
  38. 38.
    Srivenkateswaran, C., & Sivakumar, D. (2019). Secure cluster based data aggregation in WSN with aid of ECC. International Journal of Business Information Systems,31, 153–169.CrossRefGoogle Scholar
  39. 39.
    Mistry, Y., & Rana, A. (2018). A survey on data aggregation cluster based technique in WSN for modern railway track monitoring. International Research Journal of engineering and Technology,5, 70–74.Google Scholar
  40. 40.
    Ebrahimi, D., & Assi, C. (2014). Compressive data gathering using random projection for energy efficient wireless sensor networks. Ad Hoc Networks,16, 105–119.CrossRefGoogle Scholar
  41. 41.
    Deng, J., Han, Y. S., & Heinzelman, W.B., & Varshney, P. K. (2004). Balanced-energy sleep scheduling scheme for high density cluster-based sensor networks. In 4th workshop on applications and services in wireless networks, 2004 (pp. 99–108).Google Scholar
  42. 42.
    Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000). Energy efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Hawaii international conference on system sciences, Jan 2000.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSNS College of TechnologyCoimbatoreIndia

Personalised recommendations