Abstract
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.
Highlights
Wireless sensor networks (WSN) gained popularity in recent years as the world embraces Internet of Things (IoT) applications as part of the 4th industrial revolution [1,2,3,4]
This paper proposes the use of data imputation methods and machine learning in WSNs to realise virtual sensors
A virtual sensor using machine learning was proposed for use in wireless sensor networks
Summary
Wireless sensor networks (WSN) gained popularity in recent years as the world embraces Internet of Things (IoT) applications as part of the 4th industrial revolution [1,2,3,4]. Intrusion Detection and Prevention Systems (IDPS) can be used in WSNs to detect and limit the damage of successful attacks by for example disregarding sensor readings from suspicious nodes [12] Another important research focus is the detection of the location of the malicious node which is not trivial in the resource constrained environments [13,14,15]. This paper proposes the use of data imputation methods and machine learning in WSNs to realise virtual sensors These virtual sensors are able to completely replace physical sensor nodes and give accurate substituted data in place of nodes with failed sensor modules. The main function of the proposed system is to ensure the robustness of WSNs by ensuring that damaged or compromised nodes in these systems can be replaced by these machine learning-based virtual sensors This intervention reduces the effects, on system performance, of not being able to use the sensor data from the affected nodes.
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