Um algoritmo online e evolutivo para compressão automática de dados em cenários de IoT

Abstract

With the advancement and mass adoption of solutions in the fields of Internet of Things (IoT) and connected cities, the number of devices and sensors connected to the network tends to grow exponentially. In this scenario, the transmission and storage of the growing volume of data bring new challenges. When devices transmit potentially irrelevant or redundant data, there is increased energy and processing waste, as well as unnecessary use of the communication channel. Thus, local data compression solutions on the IoT devices themselves become increasingly attractive, enabling the elimination of samples that would have little or no contribution to the application, in order to significantly reduce the volume of data needed to represent the information. However, such devices present on the market today have serious storage and processing power limitations. In order to circumvent these limitations, the TinyML field emerges, which seeks ways to implement machine learning models in low-power devices. Given this context, one of the sectors that can benefit most from these new technologies is the automobile industry, as currently all cars produced must be instrumented with a series of sensors. In this way, by connecting an intelligent device to the vehicle, it is possible to process the data locally and transmit it to a remote server later. In this context, the present work proposes the development of a new online, unsupervised, and automatically adaptable data compression algorithm for IoT applications. The proposed approach is called Tiny Anomaly Compressor (TAC) and is based on data eccentricity and does not require pre-established mathematical models or any assumptions about data distribution. To test the effectiveness of the solution and validate it, two tests were carried out with different objectives. First, a comparative analysis on two real-world datasets was developed with two other algorithms from the literature, the Swing Door Trending (SDT) and the Discrete Cosine Transform (DCT). Finally, the proposal was embedded in an IoT device based on an Arduino and connected to a car to verify the impact of the algorithm on the processing time of the system’s primary operations. The obtained results show that it is possible to achieve high compression rates without significant impacts on the generated error and system processing times.