Swinging Door Trending Compression Algorithm for IoT Environments - Correa, Juan David Arias; Pinto, Alex Sandro Roschildt; Montez, Carlos; Leão, Erico Meneses (Anais Estendidos do Simpósio Brasileiro de Engenharia de Sistemas Computacionais (SBESC), 2019)
The transmission and storage of data collected by the devices are essential components of the Internet of Things (IoT). When devices send unnecessary or redundant information, it spends more energy, unnecessarily using the communication channel, besides processing at the destination, data that make a small contribution to the application. Data compression is a possible solution for the significant quantity of information generated by IoT devices. Data compression is the process of reducing the quantity of data necessary to represent some volume of data. This paper proposes the use of Swinging Door Trending (SDT) into an IoT environment and a new calibration step to select its major parameter: the compression deviation. A prototype was built, and experimental results show the effectivity of the proposal.