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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXII pp 165–183Cite as

An Analysis of Variance-Based Methods for Data Aggregation in Periodic Sensor Networks

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Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 9430))

Abstract

Given the vast area to be covered and the random deployment of the sensors, wireless sensor networks (WSNs) require scalable architecture and management strategies. In addition, sensors are usually powered by small batteries which are not always practical to recharge or replace. Hence, designing an efficient architecture and data management strategy for the sensor network are important to extend its lifetime. In this paper, we propose energy efficient two-level data aggregation technique based on clustering architecture with which data is sent periodically from nodes to their appropriate Cluster-Heads (CHs). The first level of data aggregation is applied at the node itself to eliminate redundancy from the collected raw data while the CH searches, at the second level, nodes that generate redundant data sets based on the variance study with three different Anova tests. Our proposed approach is validated via experiments on real sensor data and comparison with other existing data aggregation techniques.

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Notes

  1. 1.

    In our experiments we used the binary search.

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Authors and Affiliations

  1. FEMTO-ST Laboratory, DISC Department, University of Franche-Comté, Belfort, France

    Hassan Harb, Abdallah Makhoul & David Laiymani

  2. Department of Physics and Electronics, Lebanese University, Beirut, Lebanon

    Oussama Bazzi

  3. Department of Computer Science, Lebanese University, Beirut, Lebanon

    Hassan Harb & Ali Jaber

Authors
  1. Hassan Harb
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  2. Abdallah Makhoul
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  3. David Laiymani
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  4. Oussama Bazzi
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  5. Ali Jaber
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Corresponding author

Correspondence to Hassan Harb .

Editor information

Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz, Linz, Austria

    Josef Küng

  3. FAW, University of Linz, Linz, Austria

    Roland Wagner

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Harb, H., Makhoul, A., Laiymani, D., Bazzi, O., Jaber, A. (2015). An Analysis of Variance-Based Methods for Data Aggregation in Periodic Sensor Networks. In: Hameurlain, A., Küng, J., Wagner, R. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXII. Lecture Notes in Computer Science(), vol 9430. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48567-5_6

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  • DOI: https://doi.org/10.1007/978-3-662-48567-5_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-48566-8

  • Online ISBN: 978-3-662-48567-5

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