The rapid evolution of the Internet of Things (IoT) is making way for the development of several IoT applications that require minimal or no human involvement in the data collection, transformation, knowledge extraction, and decision-making (actuation) process. To ensure that such IoT applications (we term them autonomic) function as expected, it is necessary to measure and evaluate their quality, which is challenging in the absence of any human involvement or feedback. Existing Quality of Experience (QoE) literature and most QoE definitions focuses on evaluating application quality from the lens of human receiving application services. However, in autonomic IoT applications, poor quality of decisions and resulting actions can degrade the application quality leading to economic and social losses. In this paper, we present a vision, survey and future directions for QoE research in IoT. We review existing QoE definitions followed by a survey of techniques and approaches in the literature used to evaluate QoE in IoT. We identify and review the role of data from the perspective of IoT architectures, which is a critical factor when evaluating the QoE of IoT applications. We conclude the paper by identifying and presenting our vision for future research in evaluating the QoE of autonomic IoT applications.

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Fog computing extends the functionality of the traditional cloud data center (cdc) using micro data centers (mdcs) located at the edge of the network. These mdcs provide both computation and storage to applications. Their proximity to users makes them a viable option for executing jobs with tight deadlines and latency constraints. Moreover, it may be the case that these mdcs have diverse execution capacities, i.e. they have heterogeneous architectures. The implication for this is that tasks may have variable execution costs on different mdcs. We propose PASHE (Privacy Aware Scheduling in a Heterogeneous Fog Environment), an algorithm that schedules privacy constrained real-time jobs on heterogeneous mdcs and the cdc. Three categories of tasks have been considered: private, semi-private and public. Private tasks with tight deadlines are executed on the local mdc of users. Semi-private tasks with tight deadlines are executed on "preferred" remote mdcs. Public tasks with loose deadlines are sent to the cdc for execution. We also take account of user mobility across different mdcs. If the mobility pattern of users is predictable, PASHE reserves computation resources on remote mdcs for job execution. Simulation results show that PASHE offers superior performance versus other scheduling algorithms in a fog computing environment, taking account of mdc heterogeneity, user mobility and application security.

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The unprecedented growth of Internet of Things (IoT) and its applications in areas such as Smart Agriculture compels the need to devise newer ways for evaluating the quality of such applications. While existing models for application quality focus on the quality experienced by the end-user (captured using likert scale), IoT applications have minimal human involvement and rely on machine to machine communication and analytics to drive decision via actuations. In this paper, we first present a conceptual framework for the evaluation of IoT application quality. Subsequently, we propose, develop and validate via empirical evaluations a novel model for evaluating sensor data quality that is a key component in assessing IoT application quality. We present an implementation of the sensor data quality model and demonstrate how the IoT sensor data quality can be integrated with a Smart Agriculture application. Results of experimental evaluations conducted using data from a real-world testbed concludes the paper.