IoT System for the Continuous Electrical and Environmental Monitoring into Mexican Social Housing Evaluated under Tropical Climate Conditions

This work presented the design, development, and implementation of a low-cost IoT system for real-time monitoring of electrical consumption and environment parameters inside social housing. The IoT monitoring system is composed of a set of remote measurement nodes that wirelessly sense and transmit...

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Veröffentlicht in:Journal of sensors 2022-02, Vol.2022, p.1-20
Hauptverfasser: Mendez-Monroy, P. E., Cruz May, E., Jiménez Torres, M., Gómez Hernández, J. L., Canto Romero, M., Sanchez Dominguez, I., May Tzuc, O., Bassam, A.
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Sprache:eng
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Zusammenfassung:This work presented the design, development, and implementation of a low-cost IoT system for real-time monitoring of electrical consumption and environment parameters inside social housing. The IoT monitoring system is composed of a set of remote measurement nodes that wirelessly sense and transmit records of the air temperature and relative humidity inside the house as well as the voltage, current, and power consumed by several electrical devices. A server coordinator composed of a Raspberry Pi was used for the interaction with the sensors through the Message Queue Telemetry Transport (MQTT) protocol and allowed the measured data packaging, fragmentation, transfer, storage, and cloud upload for remote multiuser visualization. The system was experimentally evaluated in an inhabited single-story house under tropical climate conditions. Operation tests indicate a successful performance of the protocols implemented for remote visualization and cloud storage. Moreover, an analysis of measured data allows the feasibility of identifying the occupants’ energy consumption patterns and their relationship with the search for comfortable environmental conditions. Thus, the proposed friendly framework is a promising alternative to energy management. Its implementation in embedded Linux-based systems provides the flexibility to integrate control strategies based on artificial intelligence.
ISSN:1687-725X
1687-7268
DOI:10.1155/2022/5508713