Embedded fog models for remote aquatic environmental monitoring

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Rawlins, Blake
Trevathan, Jarrod
Sattar, Abdul
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2022
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Abstract

Fog computing is used in Internet of Things (IoT) applications to allow end node processing and storage to facilitate device autonomy and other benefits in light of unstable/unreliable cloud network connections. This paper investigates how a cloud-based IoT remote aquatic environmental monitoring platform can be adapted using a fog architectural approach to provide embedded condition assessment and forecasting functionality. An equivalent IoT hydroponic garden water quality monitoring test platform is developed which is capable of accommodating embedded fog models using a node-level microcontroller (i.e., ESP8266). The test platform is deployed in a hydroponics setting to gather environmental data and evaluate the feasibility of fog intelligence functionality on an embedded system. The first model involves a node-level Fuzzy Logic water quality condition assessment using the current sensor readings. The second model is a Support Vector Machine that makes short-term predictions of water quality parameters based on a history of locally stored sensor readings. The third model contrasts Long Short-Term Memory for water quality parameter prediction. The fog test platform and embedded models are evaluated according to performance, functionality and ease-of-implementation on a microcontroller. Finally, we propose a framework detailing the future modifications for the remote aquatic environmental monitoring platform to achieve embedded fog capabilities based on the experience with the hydroponics test platform.

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Internet of Things

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20

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LP190101083�

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Marine and estuarine ecology (incl. marine ichthyology)

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Rawlins, B; Trevathan, J; Sattar, A, Embedded fog models for remote aquatic environmental monitoring, Internet of Things, 2022, 20, pp. 100621

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