Cloud computing is ever stronger converging with the Internet of Things (IoT) offering novel techniques for IoT infrastructure virtualization and its management on the cloud. However, system designers and operations managers face numerous challenges to realize IoT cloud systems in practice, mainly due to the complexity involved with provisioning large-scale IoT cloud systems and diversity of their requirements in terms of IoT resources consumption, customization of IoT capabilities and runtime governance. In this paper, we introduce the concept of software-defined IoT units -a novel approach to IoT cloud computing that encapsulates fine-grained IoT resources and IoT capabilities in well-defined APIs in order to provide a unified view on accessing, configuring and operating IoT cloud systems. Our software-defined IoT units are the fundamental building blocks of software-defined IoT cloud systems. We present our framework for dynamic, on-demand provisioning and deploying such software-defined IoT cloud systems. By automating provisioning processes and supporting managed configuration models, our framework simplifies provisioning and enables flexible runtime customizations of software-defined IoT cloud systems. We demonstrate its advantages on a real-world IoT cloud system for managing electric fleet vehicles.

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Abstract-Pervasive applications are based on acquisition and consumption of real-time data from various environments. The quality of such data fluctuates constantly because of the dynamic nature of pervasive environments. Although data quality has notable impact on applications, little has been done on handling data quality in such environments. On the one hand past data quality research is mostly in the scope of database applications. On the other hand the work on Quality of Context still lacks feasibility in practice, thus has not yet been adopted by most context-aware systems. This paper proposes three metric definitions-Currency, Availability and Validity-for pervasive applications to quantitatively observe the quality of real-time data and data sources. Compared to previous work, the definitions ensure that all the parameters are interpretable and obtainable. Furthermore, the paper demonstrates the feasibility of proposed metrics by applying them to real-world data sources on open IoT platform Cosm 1 (formerly Pachube).

Abstract-Cloud computing technologies have recently been intensively exploited for the development and management of large-scale IoT systems, due to their capability to integrate diverse types of IoT devices and to support big IoT data analytics in an elastic manner. However, due to the diversity, complexity and scale of IoT systems, the need to handle large volumes of IoT data in a nontrivial manner, and the plethora of domaindependent IoT controls, programming IoT applications on cloud platforms still remains a great challenge. To date, existing work neglects high-level programming models and focuses on lowlevel IoT data and device integration. In this paper, we outline PatRICIA, which aims at providing an end-to-end solution for high-level programming and provisioning of IoT applications on cloud platforms. We present a novel programming model, based on the concept of intent and intent scope. Further, we introduce its runtime for dealing with the complexity, diversity and scale of IoT systems in the cloud. Our programming model defines abstractions to enable easier, efficient and more intuitive development of cloud-scale IoT applications. To illustrate our programming model, we present a case study with real-world applications for controlling and managing electric vehicles.