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Multi-Source Temporal Data Aggregation in Wireless Sensor Networks

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Abstract

Data aggregation has been emerged as a basic approach in wireless sensor networks (WSNs) in order to reduce the number of transmissions of sensor nodes.This paper proposes an energy-efficient multi-source temporal data aggregation model called MSTDA in WSNs. In MSTDA model, a feature selection algorithm using particle swarm optimization (PSO) is presented to simplify the historical data source firstly. And then a data prediction algorithm based on improved BP neural network with PSO (PSO-BPNN) is proposed. This MSTDA model, which helps to find out potential laws according to historical data sets, is deployed at both the base station (BS) and the node. Only when the deviation between the actual and the predicted value at the node exceeds a certain threshold, the sampling value and new model are sent to BS. The experiments on the dataset which comes from the actual data collected from 54 sensors deployed in the Intel Berkeley Research lab made a satisfied performance. When the error threshold greater than 0.15, it can decrease more than 80% data transmissions.

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Authors and Affiliations

  1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China

    Wenzhong Guo, Guolong Chen & Hongju Cheng

  2. Department of Computer Science, Georgia State University, Atlanta, GA, USA

    Naixue Xiong

  3. Department of Computer and Telecommunications Engineering, University of Western Macedonia, Kozani, Greece

    Athanasios V. Vasilakos

Authors
  1. Wenzhong Guo
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  2. Naixue Xiong
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  3. Athanasios V. Vasilakos
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  4. Guolong Chen
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  5. Hongju Cheng
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Corresponding author

Correspondence to Guolong Chen.

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Guo, W., Xiong, N., Vasilakos, A.V. et al. Multi-Source Temporal Data Aggregation in Wireless Sensor Networks. Wireless Pers Commun 56, 359–370 (2011). https://doi.org/10.1007/s11277-010-9976-9

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  • DOI: https://doi.org/10.1007/s11277-010-9976-9

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