Title: Cloud-enabled internet of things for environmental data collection: prototype and evaluation

Authors: Gary Sun; Jing Zhang

Addresses: Clear Lake High School, 2929 Bay Area Blvd., Houston, TX 77058, USA ' Department of Computer Science, Lamar University, Beaumont, TX 77710, USA

Abstract: Accurately and inexpensively collecting relevant flooding and water quality monitoring data is essential for sustainable environmental management and disaster resilience. To address this challenge, this paper aims at investigating the feasibility of a cloud-enabled internet of things (IoT) for ubiquitous environmental data collection. We present the design of a cloud-enabled IoT, which is implemented using Raspberry Pi 3 Model B and a variety of 30-party sensor probes for data collection. A camera sensor is also utilised to capture images of the surrounding environment to help with decision making. The collected water parameters and images are transmitted to a cloud platform hosted at ThingSpeak. Our experimentation demonstrates an effective prototype with acceptable inaccuracies in the data, considering the inherent discrepancies incurred by sensor hardware devices and the calibration process. The proposed data collection framework may serve as a model for designing a remote environmental monitoring system to enable research across various disciplines.

Keywords: internet of things; IoT; cloud; environmental data collection.

DOI: 10.1504/IJSNET.2023.134303

International Journal of Sensor Networks, 2023 Vol.43 No.2, pp.78 - 87

Received: 25 Jul 2023
Accepted: 29 Jul 2023
Published online: 17 Oct 2023 *

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