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WIREs Data Mining Knowl Discov
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Knowledge discovery for enabling smart Internet of Things: A survey

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The use of knowledge discovery on the Internet of Things (IoT) and its allied domains is undeniably one of the most indispensable ones, which results in optimized placement architectures, efficient routing protocols, device energy savings, and enhanced security measures for the implementation. The absence of knowledge discovery in IoT results in just an implementation of large‐scale sensor networks, which generates a huge amount of data, and which needs, an often under‐optimized, processing for actionable outputs. In this survey, we explore various domains of IoT for which knowledge discovery is inseparable from the application, and show how it benefits the overall implementation of the IoT architecture. This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Machine Learning Application Areas > Internet and Web‐Based Applications Algorithmic Development > Spatial and Temporal Data Mining
Heirarchy of the Internet of Things (IoT) networking ecosystem along with the various actors making‐up the ecosystem
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A comparison of the various metrics on a resource constrained edge IoT node (Raspberry Pi) for the following methods—A: KNN, B: Decision trees, C: Neural networks, D: SVM, E: Linear regression, F: Bayesian network, G: K‐means clustering, H: Affinity propagation. (a) Training time (s) for 5000 data samples, (b) Prediction time (s) for 2000 data samples, (c) CPU usage, and (d) Memory usage
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The distribution of data received at a remote server from various networked agricultural sensor nodes. Each of the four IoT nodes consist of four soil moisture sensors placed at additively increasing depths of 15 cm and a soil temperature sensor at a depth of 10 cm below the ground. (a) Node 1, (b) Node 2, (c) Node 3, (d) Node 4
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The knowledge discovery process in an IoT ecosystem
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Heirarchy of the IoT networking ecosystem along with the various actors making‐up the ecosystem
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Algorithmic Development > Spatial and Temporal Data Mining
Technologies > Machine Learning
Fundamental Concepts of Data and Knowledge > Knowledge Representation

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