Model Based Adaptive Data Acquisition for Internet of Things

In many IoT applications, sensor nodes are distributed over a region of interests and collect data at a specified time interval. With the development of hardware, the monitoring tasks become diversity. The specified acquisition strategy can not adaptively adjust the sampling interval. Due to the measurement error and the uncertainty of the environment, equi-frequency sampling technique may result in misunderstandings to the physical world. Based on Taylor expansion and time series analysis, this paper presents a sensed data model. The model can be considered as a unified approach, where linear regression or spline interpolation is a special case of our model. A mathematical method for parameter estimation is proposed, which can minimize the measurement error. And we prove the estimation is unbias. An adaptive data acquisition algorithm is proposed. Performance evaluation on the real data set verifies that the proposed algorithms have high performance in terms of accuracy and effectiveness.