RESUMO
Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effective data compression algorithms to make the data manageable among tiny and battery-powered devices and, more importantly, shareable across the network. Additionally, considering that, very often, wireless communications (e.g., low-power wide-area networks) are adopted to connect field devices, user payload compression can also provide benefits derived from better spectrum usage, which in turn can result in advantages for high-density application scenarios. As a result of this increase in the number of connected devices, a new concept has emerged, called TinyML. It enables the use of machine learning on tiny, computationally restrained devices. This allows intelligent devices to analyze and interpret data locally and in real time. Therefore, this work presents a new data compression solution (algorithm) for the IoT that leverages the TinyML perspective. The new approach is called the Tiny Anomaly Compressor (TAC) and is based on data eccentricity. TAC does not require previously established mathematical models or any assumptions about the underlying data distribution. In order to test the effectiveness of the proposed solution and validate it, a comparative analysis was performed on two real-world datasets with two other algorithms from the literature (namely Swing Door Trending (SDT) and the Discrete Cosine Transform (DCT)). It was found that the TAC algorithm showed promising results, achieving a maximum compression rate of 98.33%. Additionally, it also surpassed the two other models regarding the compression error and peak signal-to-noise ratio in all cases.
RESUMO
In the last decade, the growth of the automotive market with the aid of technologies has been notable for the economic, automotive and technological sectors. Alongside this growing recognition, the so called Internet of Intelligent Vehicles (IoIV) emerges as an evolution of the Internet of Things (IoT) applied to the automotive sector. Closely related to IoIV, emerges the concept of Industrial Internet of Things (IIoT), which is the current revolution seen in industrial automation. IIoT, in its turn, relates to the concept of Industry 4.0, that is used to represent the current Industrial Revolution. This revolution, however, involves different areas: from manufacturing to healthcare. The Industry 4.0 can create value during the entire product lifecycle, promoting customer feedback, that is, having information about the product history throughout it is life. In this way, the automatic communication between vehicle and factory was facilitated, allowing the accomplishment of different analysis regarding vehicles, such as the identification of a behavioral pattern through historical driver usage, fuel consumption, maintenance indicators, so on. Thus, allowing the prevention of critical issues and undesired behaviors, since the automakers lose contact with the vehicle after the purchase. Therefore, this paper aims to propose a customer feedback platform for vehicle manufacturing in Industry 4.0 context, capable of collecting and analyzing, through an OBD-II (On-Board Diagnostics) scanner, the sensors available by vehicles, with the purpose of assisting in the management, prevention, and mitigation of different vehicular problems. An intercontinental evaluation conducted between Brazil and Italy locations shown the feasibility of platform and the potential to use in order to improve the vehicle manufacturing process.
