Time series data reduction of data from IoT devices

Abstract

This research concentrates on evaluating the effectiveness of Data Optimisation techniques when applied to Time Series Data generated by IoT devices. Three sample data formats were chosen, namely Video, Audio and Radar data, and for each the nature of the data series was analysed and optimisation techniques suitable for lower power mobile IoT device use were proposed and evaluated by implementing the optimisation algorithms and building related prototypes using common IoT Devices, namely ESP32 MCUs, OV7670 Camera Sensors, ILI9341 TFT Screens, MAX9814 Microphone Sensors, and Acconeer A111 Radar Sensors. This research concluded that industry standard data optimisation techniques such as MP3, JPEG and other processing and memory intensive algorithms are unsuitable for IoT use due to their heavy requirements. However, some of the main concepts behind them could be adapted to simpler and less demanding algorithms that can work with the limited resources offered by IoT embedded platforms. Even though the proposed algorithms do not reach the compression ratios achieved by their industry standard counterparts, the bandwidth and hence power savings are considerable and could lead to a tangible improvement especially in large scale IoT implementations. When optimising Video Data, the proposed techniques for video data resulted in improving the data efficiency in TCP and UDP by 64% and 46% respectively. These techniques also resulted in power consumption efficiency of 19% on the transmitting side and a 7% on the receiving side. Similarly, when optimising the audio data, the proposed data optimisation techniques resulted in a data efficiency of 38% in TCP while in UDP resulted 28%. These audio optimisation techniques also reduced the power consumption by 21% on the transmission side and a 9% on the receiving side. When optimising the Radar Data, the optimisation technique resulted in 90% data efficiency improvement and reduced the power consumption by 5%.