About this Research Topic
The unprecedented growth of the 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. IoT applications are typically distributed in nature and integrate multiple components such as sensors for data collection, computing for data processing and analysis, as well as the application of machine learning on the collected sensor data, to generate insights that enable decision-making and actuation.
A key challenge in enabling autonomic IoT is the ability to measure/compute/predict quality across the heterogeneous IoT ecosystem. The pervasiveness, scale, complexity, and heterogeneity of autonomic IoT lend to the increased importance of ensuring the resilient operations of such IoT applications. However, due to their distributed and heterogeneous nature, the performance and quality depend crucially on the quality of their individual components which is further influenced by the environment in which they are operating. Hence, a holistic approach towards establishing quality in IoT is key. Drawing from the principle of Industry 4.0/5.0, we proffer Quality 5.0 Autonomic IoT i.e., IoT applications that are quality-aware across the entire distributed IoT ecosystem to ensure resilient operation.
To address the growing need for understanding and using quality to ensure resilient operation of autonomic IoT, this Research Topic solicits both original research contributions and review papers on the topics including, but not limited to, the following:
- Quality of Experience (QoE) in IoT
- Quality of Autonomic IoT applications
- IoT quality metrics
- Quality of IoT in edge-fog-cloud environments
- Observability in IoT
- Management of Autonomic IoT applications
- Autonomic IoT Architectures
- Quality in IoT - Real-world case studies
- ML/AI for Quality in IoT
- Distributed Quality of Autonomic IoT applications
- Quality - Context Awareness in IoT
- Quality 5.0 for IoT
A key challenge in enabling autonomic IoT is the ability to measure/compute/predict quality across the heterogeneous IoT ecosystem. The pervasiveness, scale, complexity, and heterogeneity of autonomic IoT lend to the increased importance of ensuring the resilient operations of such IoT applications. However, due to their distributed and heterogeneous nature, the performance and quality depend crucially on the quality of their individual components which is further influenced by the environment in which they are operating. Hence, a holistic approach towards establishing quality in IoT is key. Drawing from the principle of Industry 4.0/5.0, we proffer Quality 5.0 Autonomic IoT i.e., IoT applications that are quality-aware across the entire distributed IoT ecosystem to ensure resilient operation.
To address the growing need for understanding and using quality to ensure resilient operation of autonomic IoT, this Research Topic solicits both original research contributions and review papers on the topics including, but not limited to, the following:
- Quality of Experience (QoE) in IoT
- Quality of Autonomic IoT applications
- IoT quality metrics
- Quality of IoT in edge-fog-cloud environments
- Observability in IoT
- Management of Autonomic IoT applications
- Autonomic IoT Architectures
- Quality in IoT - Real-world case studies
- ML/AI for Quality in IoT
- Distributed Quality of Autonomic IoT applications
- Quality - Context Awareness in IoT
- Quality 5.0 for IoT
Keywords: Internet of Things, Quality of Experience, Industry 5.0, Autonomic Computing, Edge Fog Computing
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
