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Wavelet-Based Least Common Ancestor Algorithm for Aggregate Query Processing in Energy Aware Wireless Sensor Network

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Abstract

Wireless sensor network (WSN) is developed as a network of sensors, which engage in sensing and transmitting the data to the sink node. The constraints, such as energy, memory, and bandwidth insist the researchers to develop an efficient method for data transmission in WSN. Accordingly, this paper introduces a data aggregation mechanism based on query processing, Wavelet-based Least Common Ancestor-Sliding window (WLCA-SW). The energy-loss and memory-crisis is well addressed using the proposed WLCA-SW through the successive steps of query processing, duplicate detection, data compression using the wavelet transformation, and data aggregation. The proposed WLCA-SWA is developed with the integration of the weighed sliding window and Least Common Ancestor (LCA), which enables the energy-aware aggregate query processing and de-duplication such that the duplicate records are detected potentially prior to the communication of the sensed data to the sink node. It is prominent that the weighed sliding window is the extension of the existing time-based sliding windows. The effectiveness of the proposed aggregate processing approach is evaluated based on the metrics, such as number of alive nodes, data reduction rate, data-loss percentage, and residual energy, which is found to be 33, 85%, 8.222%, and 0.0610 J at the end of 1000 rounds using 150 nodes for analysis. Moreover, the proposed method has the minimum aggregation error of 0.03, when the analysis is performed using 50 nodes.

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Authors and Affiliations

  1. IKG Punjab Technical University, Jalandhar, Punjab, 144008, India

    Reeta Bhardwaj

  2. DAVIET, Jalandhar, India

    Dinesh Kumar

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  1. Reeta Bhardwaj
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  2. Dinesh Kumar
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Correspondence to Reeta Bhardwaj.

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Bhardwaj, R., Kumar, D. Wavelet-Based Least Common Ancestor Algorithm for Aggregate Query Processing in Energy Aware Wireless Sensor Network. Wireless Pers Commun 117, 1627–1643 (2021). https://doi.org/10.1007/s11277-020-07938-3

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  • DOI: https://doi.org/10.1007/s11277-020-07938-3

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