Abstract The Internet of Things (IoT) has been playing an important role in the technology scenario due to its high potential and impact on different society segments. Estimates suggest a trend for an increase in the number of IoT devices connected to the Internet for the next few years. Hence, the volume of data produced by IoT devices will follow this growth perspective, and there will be a demand for systems that can process, store, and promote access to large amounts of data. The data collected from sensors in typical IoT systems is stored and processed in cloud servers. However, some IoT solutions use edge devices to perform specific actions, such as processing, storage, and access, using only local infrastructure for low latency requirements. Fog computing has been used to improve IoT solutions and to transfer some of the complexity from the cloud to the edge of the network, that is, closer to devices, applications and users, working as a kind of “local and private cloud.” The cooperation of devices and applications between edge and cloud creates a need for an interplay to enable data flow among the layers of IoT systems deployed on edge and in the cloud. Thus, IoT data systems should support the data life cycle through its collection, analysis, and use. Performance efficiency is a quality factor of systems and software engineering, which measures “performance relative to the number of resources used under stated conditions.” In particular, in IoT systems that involve a large volume of data, the performance efficiency of data interplay is a relevant requirement. This work proposes a data interplay model of IoT to define and deploy the IoT data life cycle in the collection, analytics, and data use stages. This data interplay proposal aims to improve performance efficiency in IoT data life cycle operations: collection, analytics, and use among devices and applications in edge and cloud infrastructures.

The progress towards proving full deployable Internet of Things solutions for cross-domain data exchange is moving slowly and in todays IoT's challenges the need for integrating data across different software platforms and its use over heterogeneous technology remains open. The use of Cloud computing for data sharing as part of the IoT solution(s) design is provided on the basis that sharing data is enabled by the cloud. The emerging of Fog computing is generating that new scenarios are being re-defined, and in particular cases the need for combining both technologies enabling interoperability is explored/studied. This paper presents an approach of the Data Interplay for the Fog of Things. The need for a more structured way to exchange data seamlessly between edge applications and the cloud is required. The data interplay between Fog and Cloud infrastructures addresses the need for big volume generation and storage of data and the way on how to transfer the data from the edge to the cloud and viceversa.

The Internet of Things (IoT) allows for the communication of a large number of physical or virtual objects through different technologies, protocols and patterns. Several organizations have tried to predict the number of IoT devices that will be connected to the Internet by 2020. Although they have not yet to agree on an exact `magical' number of such devices (in billions), we can expect this huge number of devices to be active which may pose several challenges regarding connectivity and information exchange over network infrastructures. And despite the fact that Cloud and Fog computing addresses some of these challenges, it can also create other problems due to the high demand for storage as well as high network traffic at the edge level. In this paper, we model the IoT data traffic in order to execute several experiments related to the demand for storage and network traffic. As a consequence, the results of our experiments, which have been validated in a modeling based on the Fog of Things paradigm, can be used to evaluate the impact that variation in IoT data traffic patterns has on IoT/Fog environments.