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An Efficient Data Aggregation Approach for Prolonging Lifetime of Wireless Sensor Network

  • Bhupesh GuptaEmail author
  • Sanjeev Rana
  • Avinash Sharma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1059)

Abstract

In today’s environment the aim of wireless sensor network is not restricted to data gathering. But also focus on extraction of useful information. Data aggregation is the term used for extraction of useful information. Data aggregation helps in gathering and aggregating data in energy-efficient way so that network lifetime is heightened. This paper presents a data aggregation approach Mutual Exclusive Sleep Awake Distributed Data Aggregation (MESA2DA). This approach merges with our previous work Mutual Exclusive Sleep Awake Distributed Clustering (MESADC). MESA2DA approach selects cluster head on the basis of MESADC protocol. After that, data aggregation is done by cluster head on its cluster members to remove redundancy, so that the packets delivered to base station are reduced which helps in prolonging lifetime of wireless sensor network. The results obtained with MESA2DA approach are compared with HEED protocols in terms of average energy, delay, throughput, and packet delivery ratio and one finds that the proposed approach is efficient in prolonging lifetime of wireless sensor network.

Keywords

Sleep awake Distributive Clustering Sensor Network 

References

  1. 1.
    Gupta B, Rana S (2018) Energy conservation protocols in wireless sensor network: a review. IJFRCSCE 4(3):16–20Google Scholar
  2. 2.
    Heinzelman WR, Chandrakasan AP, Balakrishnan H (2000) Energy-efficient communication protocol for wireless micro sensor networks. In: Proceedings of the 33rd annual Hawaii international conference on system sciences, pp 1–10Google Scholar
  3. 3.
    Ahlawat A, Malik V (2013) An extended vice cluster selection approach to improve V-leach protocol in WSN. In: 3rd international conference on advanced computing and communication technologies. IEEEGoogle Scholar
  4. 4.
    Liao Y, Qi H, Li W (2013) Load-balanced clustering algorithm with distributed self-organization for wireless sensor networks. IEEE Sens J 13(5)CrossRefGoogle Scholar
  5. 5.
    Villas LA, Boukerche A, Ramos HS (2013) DRINA: a lightweight and reliable routing approach for in-network aggregation in wireless sensor networks. IEEE Trans Comput 62(4)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Mantri D, Prasad NR (2013) Grouping of clusters for efficient data aggregation (GCEDA) in wireless sensor network. In: 3rd IEEE international advance computing conferenceGoogle Scholar
  7. 7.
    Li G, Wang Y (2013) Automatic ARIMA modeling-based data aggregation scheme in wireless sensor networks. EURASIP Wirel Commun NetwGoogle Scholar
  8. 8.
    Abirami S (2019) A complete study on the security aspects of wireless sensor networks. In: Bhattacharyya S, Hassanien A, Gupta D, Khanna A, Pan I (eds) International conference on innovative computing and communications. Lecture Notes in networks and systems, vol 55. Springer, SingaporeGoogle Scholar
  9. 9.
    Devi DC, Vidya K (2019) A survey on cross-layer design approach for secure wireless sensor networks. In: Bhattacharyya S, Hassanien A, Gupta D, Khanna A, Pan I (eds) International conference on innovative computing and communications. Lecture Notes in networks and systems, vol 55. Springer, SingaporeGoogle Scholar
  10. 10.
    Ji P, Li Y, Jiang J, Wang T (2012) A clustering protocol for data aggregation in wireless sensor network. In: International conference on control engineering and communication technologyGoogle Scholar
  11. 11.
    Gupta B et al (2012) A review on query clustering algorithm for search engine optimization. IJARCSSE 2(2)Google Scholar
  12. 12.
    Younis O, Krunz M, Ramasubramanian S (2006) Node clustering in wireless sensor networks: recent developments and deployment challenges. IEEEGoogle Scholar
  13. 13.
    Ruperee A, Nema S, Pawar S (2014) Achieving energy efficiency and increasing network life in wireless sensor network. IEEEGoogle Scholar
  14. 14.
    Younis O, Fahmy S (2004) HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor network. IEEE Trans Mob Comput 3(4)CrossRefGoogle Scholar
  15. 15.
    Gupta B, Rana S (2018) Mutual exclusive sleep awake distributive clustering (MESADC): an energy efficient protocol for prolonging lifetime of wireless sensor network. Int J Comput Sci Eng 1–7Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.CSE DepartmentMM (Deemed to be University)MullanaIndia

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