STEAM: um modelo para processamento de eventos e enriquecimento de fluxos de dados IoT na borda da rede

Abstract

CONTEXT: The Internet of Things is a fast expanding environment in which objects, animals, or people are equipped with the most diverse sensors and can automatically transfer their data through a network. Due to their limited nature, sensors and edge devices usually only relay the collected data to be processed by centralized systems in the cloud and, in many cases, wait for a response. This transfer from local to remote processing results in critical issues such as loss of connection, high response time, computer system overhead, in addition to requiring a robust and scalable structure for data communication and centralized processing. OBJECTIVE: Thus, we identified two challenges. First, devise a model capable of bringing data processing from the cloud to the network edge. Second, implement a solution that meets the constraints and heterogeneity of the IoT environment, both from a hardware and software perspective. The scientific contribution consists in the proposal of a model containing several layers, from data collection, processing, evaluation, and publication of results, in addition to the implementation of a set of classes and functions that facilitate the development of IoT applications executed by devices with few computational resources at the edge of the network. The main practical results are the optional use of the cloud, near real-time processing and simplicity in application development. METHODOLOGY: The methodology consists of proposing a model and implementing a framework called STEAM. The validation of the model takes place through the implementation of applications built with the STEAM framework, besides the evaluation of performance metrics and computational resources usages such as CPU, memory, and network. RESULTS: The experiments carried out in a semiconductor industry through the implementation of 2 applications and 4 test scenarios demonstrated the viability of both the model and the framework STEAM. Since one of the goals was to build lightweight applications in edge computing, we achieved an average of less than 1.0% CPU load and less than 436kb of memory consumption on a Raspberry Pi 3 model B+. In addition, we reached fast response times, processing up to 239 data packets per second, reducing the size of the output data to 14% the size of the raw input data when notifying events, and integrating with a remote control panel application. CONCLUSION: The proposal proved to be viable with promising results, presenting the framework STEAM as a lightweight, fast and accurate alternative for the development of IoT applications with data processing at the edge of the network, eliminating the processing dependency in the cloud.