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Internet of Things (IoT) has been found pervasive use cases and become a driving force to constitute a digital society. The ultimate goal of IoT is data and the intelligence generated from data. With the progress in public cloud computing technologies, more and more data can be stored, processed and analyzed in cloud to release the power of IoT. However, due to the heterogeneity of hardware and communication protocols in the IoT world, the interoperability and compatibility among different link layer protocols, subsystems , and back-end services have become a significant challenge to IoT practices. This challenge cannot be addressed by public cloud suppliers since their efforts are mainly put into software and platform services but can hardly be extended to end devices. In this paper, we propose a data-centric IoT framework that incorporates three promising protocols with fundamental security schemes, i.e., WiFi, Thread, and LoRaWAN, to cater to massive IoT and broadband IoT use cases in local, personal, and wide area networks. By taking advantages of the Azure cloud infrastructure, the framework features a unified device management model and data model to conquer the interoperability challenge. We also provide implementation and a case study to validate the framework for practical applications. INDEX TERMS Internet of Things, framework, cloud, azure, IoT hub, thread, WiFi, lorawan.

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The indoor climate is closely related to human health, well-being, and comfort. Thus, an understanding of the indoor climate is vital. One way to improve the indoor climates is to place an aesthetically pleasing active plant wall in the environment. By collecting data using sensors placed in and around the plant wall both the indoor climate and the status of the plant wall can be monitored and analyzed. This manuscript presents a user study with domain experts in this field with a focus on the representation of such data. The experts explored this data with a Line graph, a Horizon graph, and a Stacked area graph to better understand the status of the active plant wall and the indoor climate. Qualitative measures were collected with Think-aloud protocol and semi-structured interviews. The study resulted in four categories of analysis tasks: Overview, Detail, Perception, and Complexity. The Line graph was found to be preferred for use in providing an overview, and the Horizon graph for detailed analysis, revealing patterns and showing discernible trends, while the Stacked area graph was generally not preferred. Based on these findings, directions for future research are discussed and formulated. The results and future directions of this research can facilitate the analysis of multivariate temporal data, both for domain users and visualization researchers.

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