Using the spatiotemporal data compression approach to developing a massive IoT-based air quality monitoring system with low transmission cost and high data fidelity

Lossy compression techniques have been widely used in digital media distribution to reduce both bandwidth and storage consumption. Although lossy compression techniques could generate more compact data, they usually sacrifice more data precision than other compression techniques. In this paper, we develop a systematic framework for a massive deployment of IoT-based PM sensing devices, in which a spatiotemporal compressing approach is proposed to reduce transmission volume and to allow the functionality with a fault tolerant mechanism for the delivered data. In addition, a comparative analysis is provided by using open dataset compared to the real measurement dataset. The experimental results show that the compressed spatiotemporal data could reduce not only the data transmission amounts but also the energy consumption. Hence, the developed system could achieve a higher data saving ratio. Concerning with the data fidelity, our method is superior to the traditional methods under a noisy environment.