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Internet of Things (IoT) systems are typically made up of several devices that may undergo hardware failures, lack of battery, unavailability of network connection, etc. Moreover, data collected by these devices may become inaccurate or suffer from anomalies as indicatives of potential failures or poor calibration of sensors. To avoid unavailability or undesirable behavior in the system, it is essential monitoring the state of devices, detecting anomalies, and issuing alerts that allow for quick corrections. This paper presents an alert module for a real-world IoT system that automatically controls air conditioners towards contributing to reduce energy consumption in a university. The architecture of this module relies on the MAPE-K reference model to identify anomalies through data monitoring, analysis, planning, and execution phases, all supported by a knowledge base composed of configurable rules. Computational experiments carried out to evaluate the efficacy of the developed alert module pointed out a high precision in anomaly detection.

Monitorando Dados e Gerenciando Alertas em um Sistema para Controle de Aparelhos de Ar Condicionado