Matusowsky, MRamotsoela, DTAbu-Mahfouz, Adnan MI2020-10-052020-10-052020-05Matusowsky, M., Ramotsoela, D.T. and Abu Mahfouz, A.M.I. 2020. Data imputation in wireless sensor networks using a machine learning-based virtual sensor. Journal of Sensor and Actuator Networks, v9(2), 20pp.2224-2708https://doi.org/10.3390/jsan9020025https://www.mdpi.com/2224-2708/9/2/25http://hdl.handle.net/10204/11596Copyright: 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) licenseData integrity in wireless sensor networks (WSN) is very important because incorrect or missing values could result in the system making suboptimal or catastrophic decisions. Data imputation allows for a system to counteract the effect of data loss by substituting faulty or missing sensor values with system-defined virtual values. This paper proposes a virtual sensor system that uses multi-layer perceptrons (MLP) to impute sensor values in a WSN. The MLP was trained using a genetic algorithm which efficiently reached an optimal solution for each sensor node. The system was able to successfully identify and replace physical sensor nodes that were disconnected from the network with corresponding virtual sensors. The virtual sensors imputed values with very high accuracies when compared to the physical sensor values.enData imputationMachine learningNeural networksVirtual sensorsWireless sensor networksArticleMatusowsky, M., Ramotsoela, D., & Abu Mahfouz, A. M. (2020). Data imputation in wireless sensor networks using a machine learning-based virtual sensor. http://hdl.handle.net/10204/11596Matusowsky, M, DT Ramotsoela, and Adnan MI Abu Mahfouz "Data imputation in wireless sensor networks using a machine learning-based virtual sensor." (2020) http://hdl.handle.net/10204/11596Matusowsky M, Ramotsoela D, Abu Mahfouz AM. Data imputation in wireless sensor networks using a machine learning-based virtual sensor. 2020; http://hdl.handle.net/10204/11596.TY - Article AU - Matusowsky, M AU - Ramotsoela, DT AU - Abu Mahfouz, Adnan MI AB - Data integrity in wireless sensor networks (WSN) is very important because incorrect or missing values could result in the system making suboptimal or catastrophic decisions. Data imputation allows for a system to counteract the effect of data loss by substituting faulty or missing sensor values with system-defined virtual values. This paper proposes a virtual sensor system that uses multi-layer perceptrons (MLP) to impute sensor values in a WSN. The MLP was trained using a genetic algorithm which efficiently reached an optimal solution for each sensor node. The system was able to successfully identify and replace physical sensor nodes that were disconnected from the network with corresponding virtual sensors. The virtual sensors imputed values with very high accuracies when compared to the physical sensor values. DA - 2020-05 DB - ResearchSpace DP - CSIR KW - Data imputation KW - Machine learning KW - Neural networks KW - Virtual sensors KW - Wireless sensor networks LK - https://researchspace.csir.co.za PY - 2020 SM - 2224-2708 T1 - Data imputation in wireless sensor networks using a machine learning-based virtual sensor TI - Data imputation in wireless sensor networks using a machine learning-based virtual sensor UR - http://hdl.handle.net/10204/11596 ER -