BATTERY SENSORY DATA COMPRESSION FOR ULTRANARROW BANDWIDTH IOT PROTOCOLS

Abstract: Internet of Things (IoT) communication technology is an essential parameter in modern embedded systems. Demand for data throughput drastically increases, as well as the request for transmission over considerable distance. Considering cost-eficiency in the form of power consumption is unavoidable, it usually requires nu-merous optimization and trade-os. This research tends to oer a solution basedon data compression techniques. In this way, problems caused by data through-put are mostly eliminated, still varying with the eld of application. Regardless ofhaving both lossy and lossless techniques, the focus is on lossy algorithms due toimmensely larger compression ratio (CR) factor, which is not the only but usuallythe most crucial factor. There are also numerous other quality metrics described.In the experiment part, LoRa long-range wireless communication protocol is used,with an accent on battery sensory data transmission. Temperature and current are the signals of interest. The research oers detailed information of the impact on compression parameters by four target algorithms: fractal resampling (FR), critical aperture (CA), fast Fourier transform (FFT) and discrete cosine transform (DCT).