Transferência de maior volume de dados em redes de sensores sem fio de alta latência e baixa taxa de transmissão utilizando estratégias de segmentação e compressão

Abstract

Currently, Internet of Things technologies have been used in different applications and contexts where communication can be a key aspect. However, their data throughput capacity is limited. In the context of this research—associated with a large-scale communication application aimed at issuing alerts in case of dam incidents in metropolitan areas—it is important to develop means to reduce the impacts of this limitation. To tackle this challenge, this work addresses data segmentation and compression strategies to optimize the transmission of larger amounts of data, such as waveforms, through class A LoRaWAN (Long Range Wide Area Network) sensor networks. An efficient data segmentation algorithm is proposed, characterized by a late verification of missing data segments after the transmission of all packets, followed by retransmission of the missing packets. Additionally, the implementation of a data compression algorithm based on the principle of Lossless Entropy Compression (LEC) coded in C language is presented, suitable for running in microcontroller systems with a permissive license and bindings for use in Python language, allowing portability and integration with other development platforms. The choice of LEC is justified by its ability to achieve high compression rates without information loss, a fundamental characteristic for applications demanding data fidelity. Furthermore, practical tests were carried out to prove the effectiveness of this proposal through an electronic device, with a Long Range (LoRa) standard modem and inertial sensor, developed and built in this work. The results obtained demonstrate that the combination of the proposed segmentation and compression techniques allows sensor networks, initially designed for low data transmission, to be used for transmitting larger amounts of data without compromising the fidelity of the received data.