About this Research Topic
We are pleased to announce this Research Topic that stems from and is developed in collaboration with the "CPD Workshop Combining Physical and Data-Driven Knowledge in Ubiquitous Computing".
Real-world internet of things and cyber-physical systems face the challenge of requiring a significant amount of data to obtain accurate information through pure data-driven approaches. The performance of these data-driven systems greatly depends on the quantity and `quality' of data. In ideal conditions, pure data-driven methods perform well due to the abundance of data. However, in real-world systems, collecting data can be costly or impossible due to practical limitations. Furthermore, the labeling of data can be laborious and difficult to scale.
Physical knowledge, on the other hand, has been studied over multiple disciplines for centuries. The concept of physical knowledge covers multiple domains in many forms, which includes and is not limited to domain knowledge from experts, heuristics from experiences, as well as for analytic models of the physical phenomena. They can be used to alleviate these issues of data and label limitations.
This Research Topic aims to explore the intersection between (and the combination of) data-driven and physical knowledge and bring together domain experts that explore the physical understanding of the data, practitioners that develop systems, and the researchers in classic data-driven domains. We welcome papers addressing these issues in different transdisciplinary applications/domains, as well as algorithmic and systematic approaches to apply physical knowledge. We further seek to develop a community that systematically analyzes the data quality regarding inference and evaluates the improvements from physical knowledge.
Topics of Interests
- Innovations in learning algorithms that combine physical knowledge or models for sensor perception and understanding
- Experiences, challenges, analysis, and comparisons of sensor data in terms of its physical properties
- Sensor data processing to improve learning accuracy
- Machine learning and deep learning with physical knowledge on sensor data
- Mobile and pervasive systems that utilize physical knowledge to enhance data acquisition
- System services such as time and location estimation enhanced by additional physical knowledge
- Heterogeneous collaborative sensing based on physical rules
- Distributed sensing for cyber-physical systems
- Advanced machine learning algorithms and solutions for efficient sensing
The application areas include but not limited to:
- Human-centric sensing applications
- Environmental and structural monitoring
- Smart cities and urban health
- Health, wellness and medical
- Smart energy systems and intelligent transportation networks
The Editors of this Article Collection are the organizers of the CPD 2020 Workshop, which, as a part of the Ubicomp 2020 conference, will be held in September 2020 in Cancun. While Topic Editors encourage manuscript submissions from all researchers, authors who have presented their work in CPD 2020 may submit their extended work to this Article Collection. Topic Editors commit to ensuring a faster, though rigorous, peer-review where possible. Please note that manuscript acceptance is fully subject to a high-quality peer-review process in line with Frontiers journal standard and therefore not guaranteed.
Keywords: physics-informed, data-driven, internet of things, cyber-physical systems, data analytics, machine learning, data mining, physical-knowledge
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.