In order to reduce unnecessary data transmissions from Internet of Things (IoT) sensors, this paper proposes a multivariate time series prediction-based adaptive data transmission period control (PBATPC) algorithm for IoT networks. Based on the spatio-temporal correlation between multivariate time series data, we developed a novel multivariate time series data encoding scheme utilizing the proposed time series distance measure ADMWD
Composed of two significant factors for a multivariate time series prediction, i.e., the absolute deviation from the mean (ADM) and the weighted differential distance (WD), the ADMWD considers both the time distance from a prediction point and a negative correlation between the time series data concurrently.
Utilizing the convolutional neural network (CNN) model, a subset of IoT sensor readings can be predicted from encoded multivariate time series measurements, and we compared the predicted sensor values with actual readings to obtain the adaptive data transmission period. Extensive performance evaluations show a substantial performance gain of the proposed algorithm in terms of the average power reduction ratio (approximately 12%) and average data reconstruction error (approximately 8.32% MAPE). Finally, this paper also provides a practical implementation of the proposed PBATPC algorithm via the HTTP protocol under the IEEE 802.11-based WLAN network.
