Analysis of Data Exchange Among Heterogeneous IoT Systems

Data interoperability allows data exchanges among information systems, their sub-systems and their environment. The multiplicity of these exchanges and the increasing amount of exchanged data can generate dysfunctions with negative impact on the overall performance of the communicating systems. Data interoperability should therefore be continuously assessed and improved. We propose a messaging metamodel that aggregates collected information from several pub/sub-communication protocols, and we present a work in progress which utilizes services provided by AMQP, MQTT, CoAP and Kafka to collect information in order to analyze data exchanges. Including these pub/sub-communication protocols and the data analysis platform Moose to achieve monitoring, we propose the pulse framework that provides a tracking of architecture changes in the pub/sub-systems. We analyzed the differences between the protocols to provide a generic metamodel to include all of these pieces of information in the same system. It will allow to extract precise information about the evolution of the system.