A Robust Lossless Data Compression Technique for Air Quality Time-Series Data using Genetic Algorithm

Due to the advancement of industrialization and urbanization, air pollution become a serious issue in recent decades. To get rid of this problem Air Quality Monitoring Stations (AQMSs) are established that can asses the air quality and provide some measures to control it. These AQMSs capture the time series data through sensors and form an IoT based network to send the data to the cloud for further analysis. These IoT devices are paired with low capacity batteries and limited memory, transmission, and computational components. Transmitting the high volume data to the cloud for successive analytical purposes demands high energy dissipation and higher bandwidth. Moreover, for storing the big volumed data the system needs larger storage space. The single solution for all these constraints is data compression. Data compression reduces the volume of the datasets. The reduced volume of data saves energy and it is easier to transmit the compressed data through the limited bandwidth. In this paper, a lossless data compression algorithm using Genetic Algorithms is proposed. On successful implementation of the algorithm, the air quality time series data is compressed with absolutely no data loss. To evaluate the efficiency of the algorithm, its performance is compared with some classical and several state of the art compression schemes. The experimental results show that the proposed compression algorithm outperforms the classical as well as state of the art models concerning the performance evaluating parameters like Compression Ratio, Compression Factor, and most importantly Power Saving.