| Title |
Iot-Qwatch: A Novel Framework To Support The Development Of Quality-Aware Autonomic Iot Applications |
| ID_Doc |
34113 |
| Authors |
Fizza K.; Jayaraman P.P.; Banerjee A.; Auluck N.; Ranjan R. |
| Year |
2023 |
| Published |
IEEE Internet of Things Journal, 10, 20 |
| DOI |
http://dx.doi.org/10.1109/JIOT.2023.3278411 |
| Abstract |
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 article, we propose an agent-based IoT-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 realizing IoT-QWatch in real-world deployment, while the framework itself offers significant extensibility to include new models for measuring IoT quality metrics. © 2014 IEEE. |
| Author Keywords |
Actuation; autonomic Internet of Things (IoT); IoT; IoT quality; machine-to-machine; Quality of Experience (QoE) |
