The rapid evolution of the Internet of Things (IoT) facilitates the development of IoT applications in domains such as manufacturing, smart cities, retail, agriculture, etc. Such IoT applications collect data, analyze, and extract insightful information to enable decision-making and actuation. There is an unprecedented growth of IoT applications that automate decision-making and actuation without requiring human intervention, which we term autonomic IoT applications. The increasing scale of such applications necessitates holistic measurement and evaluation of application quality. Existing literature has evaluated quality from an end-user perspective, which may be unsuitable when dealing with the complexity of modern IoT applications, especially when they are autonomic. In this paper, we refer to IoT application quality as the aggregate quantitative value of various IoT quality metrics measured at each stage of the autonomic IoT application life cycle. We present an in-depth survey of current state-of-the-art techniques and approaches for evaluating quality of IoT applications. In particular, we survey various definitions to identify the factors that contribute to understanding and evaluating quality in IoT. Furthermore, we present open issues and identify future research directions towards realizing fine-grained quality evaluation of IoT applications. We envision that the identified research directions will, in turn, enable real-time diagnostics of IoT applications and make them quality-aware. This survey can serve as the basis for designing and developing modern, resilient quality-aware autonomic IoT applications.

Fog computing brings the cloud closer to a user with the help of a micro data center ( $mdc$ ), leading to lower response times for delay sensitive applications. RT-SANE ( R eal- T ime S ecurity A ware scheduling on the N etwork E dge) supports batch and interactive applications, taking account of their deadline and security constraints. RT-SANE chooses between an $mdc$ (in proximity to a user) and a cloud data center ( $cdc$ ) by taking account of network delay and security tags. Jobs submitted by a user are tagged as: private, semi-private and public, and $mdcs$ and $cdcs$ are classified as: trusted, semi-trusted and untrusted. RT-SANE executes private jobs on a user’s local $mdcs$ or pre-trusted $cdcs$ , and semi-private and public jobs on remote $mdcs$ and $cdcs$ . A security and performance-aware distributed orchestration architecture and protocol is made use of in RT-SANE. For evaluation, workload traces from the CERIT-SC Cloud system are used. The effect of slow executing straggler jobs on the Fog framework are also considered, involving migration of such jobs. Experiments reveal that RT-SANE offers a higher “success ratio” (successfully completed jobs) to comparable algorithms, including consideration of security tags.

The unprecedented growth of Internet of Things (IoT) is leading to its increased usage in various domains, such as manufacturing, health, and smart cities. A majority of IoT applications are autonomic, i.e., they operate under minimal/no human intervention, and make decisions/actuations based on machine-to-machine communication and data analytics. A key challenge in the development of such applications is the ability to measure their quality while they are working in a diverse and heterogeneous IoT ecosystem. In this paper, we propose an agent based Internet of Things-Quality Watch (IoT-QWatch) framework that provides the ability to measure IoT quality metrics at each stage of the autonomic IoT application life cycle running in the IoT ecosystem. We envision that IoT-QWatch will enable the development of a new generation of quality-aware autonomic IoT applications that are able to be resilient to the heterogeneous and uncertain nature of IoT ecosystems. We present architectural details and implementation of IoT-QWatch, and corresponding models used to measure IoT quality metrics at different stages. We conduct extensive experiments using a real-world IoT test bed from the domain of manufacturing to validate the efficacy of IoT-QWatch. Experimental outcomes provide promising results in realising IoT-QWatch in real-world deployment, while the framework itself offers significant extensibility to include new models for measuring IoT quality metrics.

Fog computing is a promising option for time sensitive vehicular over-the-air (OTA) updates, as it can offer enhanced network durability and lower communication delays, versus the cloud. Fog node utilization is non-deterministic, largely owing to vehicular traffic patterns. The resultant over provisioning of resources manifests itself in increased communication and handover delays. Based on an analysis of regional traffic patterns, our proposed algorithm determines the optimal number of fog nodes required for OTA updates. The efficacy of our proposed approach is demonstrated using a case study that considers handover delay, propagation delay, transmission rate and vehicular mobility to predict the OTA update time. We employ a machine learning model for predicting the communication delay between fog devices and vehicles. Using European WiFi hotspot signal strength NYC dataset and 5G dataset, we observe that the proposed approach increases net reserve fog resources by 26.57% on average, and reduces the OTA update time by 5.34%. We test the scalability of our approach by analyzing the performance in terms of average throughput while varying the number of vehicles and OTA update size. The performance of the proposed OTA update scheme on simulations has been corroborated by implementation on a real-world testbed.