Um algoritmo online e evolutivo para compressão automática de dados em cenários de IoT
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 challenge...
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| Formato: | Dissertação |
| Idioma: | pt_BR |
| Publicado em: |
Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/43115 |
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| Resumo: | 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. |
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