Maximum Frequency based Adaptive Sensing for Particulate Matter Nodes in IoT Network

In most IoT-based monitoring applications, the data can vary at a slow rate but the variability pattern may not always be same. For example, the patterns of particulate matter (PM), one of the most dominant air pollutants, often change seasonally over a year. Therefore, having a fixed predefined sensing rate is both hard to decide and energy inefficient. This paper proposes an adaptive, non-parametric method to change the sensing rate using the maximum frequency estimate based on recent historical data. The proposed algorithm has been tested on the data collected over one year from an IoT network consisting of multiple PM sensor nodes. A performance comparison of the proposed scheme with the existing approach shows the effectiveness and performance improvement in terms of Reduction Factor (RF) and Mean Absolute Error (MAE).