Nowadays, robots (including non-humanoid ones, like self-driving cars) are part of the most promising technologies, and they rise various computing requirements. Some of those requirements came from the fact that the robots are involved in highly dynamic environments and have to execute complex decision algorithms in real-time, while other requirements ask for batch processing of big data compatible datasets. In this paper, we propose a cloud architecture for optimizing data processing using a cloud-edge infrastructure. Besides the computational architecture, we develop a mathematical model for each type of entity in our proposal and a formal description of a data capsule, which represents a generic and flexible representation for unstructured units of data in time series databases. The architecture includes multiple processing platforms. We evaluate the proposed model in edge-cloud computing platforms designed for robots that run machine learning tasks.INDEX TERMS Edge computing, data communication, data representation, cloud computing, big data applications, data aggregation.
With the evolution of technology, developed systems have become more complex and faster. Thirty years ago, there were no protocols or databases dedicated to developing and implementing IoT projects. We currently have protocols such as MQTT, AMQP, CoAP, and databases such as InfluxDB. They are built to support a multitude of data from an IoT system and scale very well with the system. This paper presents the design and implementation of an IoT alert system that uses MQTT and InfluxDB to collect and store data. We design a scalable system to display assertive alerts on a Raspberry Pi. Each user can select a subset of alerts in our system using a web interface. We present a bibliographic study of SoTA, the proposed architecture, the challenges posed by such a system, a set of tests for the performance and feasibility of the solution, and a set of conclusions and ideas for further developments.
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