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dc.contributor.authorWang, Haibin
dc.contributor.authorYemeni, Zaid
dc.contributor.authorIsmael, Waleed M.
dc.contributor.authorHawbani, Ammar
dc.contributor.authorAlsamhi, Saeed H.
dc.identifier.citationWang, H., Yemeni, Z., Ismael, W.M., Hawbani, A. (2021). A reliable and energy efficient dual prediction data reduction approach to WSNs based on Kalman filter. IET Communications. First published: 29 July 2021. 1-15.
dc.description.abstractWireless sensor networks (WSNs) are critically resource-constrained due to wireless sensor nodes' tiny memory, low processing units, power limitations, and narrow communication bandwidth. The data reduction technique is one of the most widely used techniques to reduce transmitted data over the wireless sensor networks and to minimize the sensor nodes' energy consumption, particularly, the entire network in general. This paper proposes a reliable dual prediction data reduction approach for WSNs. This approach performs data reduction through two phases: the data reduction phase (DRP) and data prediction phase (DPP). The DRP is mainly to decrease the number of transmissions between the sensor node and the sink node, thereby minimizing energy consumption. It also detects faulty data and discards them at the sensor node. The discarded faulty data at the sensor nodes are replaced by estimated values at the sink node to maintain data reliability. DPP runs at the sink node or base station, which works in synchronization with the sensor nodes. This phase is responsible for predicting the non-transmitted data based on the Kalman filter. The simulation results demonstrate that the proposed approach is efficient and effective in data reduction, data reliability, and energy consumption.en_US
dc.relation.ispartofIET Communicationsen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectData reductionen_US
dc.titleA reliable and energy efficient dual prediction data reduction approach to WSNs based on Kalman filteren_US
dc.contributor.affiliationAthlone Institute of Technologyen_US
dc.contributor.sponsorThis work is in part supported by the Fundamental Research Funds for the Central Universities (B200202216) and in part supported by Innovation Foundation of Radiation Application, China Institute of Atomic Energy (KFZC2020010401).en_US
dc.identifier.orcid 0000-0003-2857-6979en_US

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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International