Abstract
Wireless sensor networks (WSNs) are critically resource-constrained due to wireless sensor nodes’ tiny memory, low processing unit, power limitation, and narrow communication bandwidth. The data reduction technique is one of the most widely used techniques to minimize the transmitted data over the entire network and overcome the limitations mentioned above. In this paper, a reliable single prediction data reduction approach is proposed for WSNs. The proposed approach is built on two phases: the Data Reduction (DR) Phase and Data Prediction (DP) Phase. In the first phase (DR), the proposed approach aims at minimizing the total data transmission using two techniques, Data Equality (DE) and Data Change Detection (DCD). In the second phase (DP), the non-transmitted data are predicted on the sink node utilizing the well-known Kalman filter. The obtained results demonstrate that the proposed approach is efficient and effective in data reduction and data reliability.
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Yemeni, Z., Wang, H., Ismael, W.M., Ibrahim, Y., Li, P. (2021). A Reliable Single Prediction Data Reduction Approach for WSNs Based on Kalman Filter. In: Saeed, F., Mohammed, F., Al-Nahari, A. (eds) Innovative Systems for Intelligent Health Informatics. IRICT 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-030-70713-2_55
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DOI: https://doi.org/10.1007/978-3-030-70713-2_55
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