IoT devices and sensors can generate incorrect measurements which can be attributed to software and hardware issues. Ensuring accurate datasets through fault monitoring and isolation is crucial for operational IoT deployments. As an example, if an IoT system is used to perform predictive maintenance of a smart building, the collected IoT datasets must accurately reflect the status of the monitored system. Continually monitoring and isolating faults is an important feature in IoT. We aim at experimenting, improving and validating an IoT module development for fault detection and isolation of IoT device data in a smart building environment. We intend to experiment reactive fault-tolerance based on Rule Based, Simple Moving average (SMA) algorithms, as well as proactive fault-tolerance algorithm based on Autoregressive integrated moving average (ARIMA) using the Fiesta-IoT testbeds.