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 Internet of Things (IoT) involves the connectivity of a number of different physical and virtual devices, allowing the development of new services and applications. These intelligent objects, along with their tasks, constitute domain-specific applications (vertical markets), while ubiquitous and analytical services form domain-independent services (horizontal markets). The development of these applications and services in these markets brings challenges such as deployment, scalability, integration, interoperability, mobility and performance. Recent researches indicates that Microservices have been adopted in several domains such as IoT. This paper provides an overview of the current state of the art (studies selected by May 2018) regarding the adoption of Microservices in the development of IoT applications by means of the mapping study methodology. In this context, we present the eighteen selected studies, the existing contributions and the future research perspectives.