<p>Although cloud computing has introduced valuable capabilities and features for the IoT, several challenges still persist in this field. The long distance between IoT devices and cloud platforms often leads to data transmission delays, resulting in degraded service quality, particularly for latency-sensitive applications. In the fog platform, the negative impact of this distance is significantly reduced by performing certain data management operations, such as decreasing the volume of data sent to the cloud. In this research, we proposed a prediction-based scheme in fog computing to reduce the volume of time-series IoT data transmitted to the cloud. The proposed scheme consists of <b>two approaches: data filtering and prediction</b>. In the <b>data filtering approach</b>, the amount of data received from a sensor node is reduced by discarding its redundant measurements. In the <b>prediction approach</b>, a fog-based prediction model is employed to reduce data transmission to the cloud. In this approach, historical IoT time-series data are modeled; then, based on the state space model approach associated with the Kalman Filter, modeled historical data, and a machine learning method, a prediction model is developed to accurately predict the future values of time-series data. This model is deployed on both fog and cloud layers, and similar predictions are made simultaneously in both layers. In both layers, in place of the new measurement at time <Emphasis Type="BoldItalic">t</Emphasis>, the predicted value at time <Emphasis Type="BoldItalic">t</Emphasis> is used; just the new measurement, outside the confidence range, is transmitted from fog to cloud. The experimental results indicate that the proposed scheme reduces the volume of data transmitted to the cloud by 50.30% to 97.30%, depending on the nature of the IoT data and the prediction method, while preserving data accuracy and integrity, thereby significantly lowering bandwidth consumption.</p>