Today, most industrial contenders are advocating for their versions of IoT, while mainstream research efforts address singular views of scalable sensing, massive RFID-based identification, and other topological remedies to handle the ensuing Big Data communication and sense-making processes. The IoT architectures need to adopt a systems approach; “sensing systems” instead of “things” will take center stage in the next generation IoT. This will facilitate the understanding, and subsequent optimization, of interdependencies of complex IoT systems.
The scope of C-IoT 2020 will focus on topological remedies to handle Big Data communication and scalable IoT services. While many hurdles face synergistic IoT development, we will focus on techniques from Machine Learning to aid IoT convergence on data and information planes. As communication between heterogeneous IoT architectures is becoming a reality, it is ever more pressing to address data compatibility and information extraction from heterogeneously sourced data. This includes challenges with data representation, meta-data tagging practices, establishing quality of resource (QoR) and quality of information (QoI) measures in heterogeneously sourced IoT data. More importantly, scaling such IoT systems is inherently tied with trusting such data, and our inference in deriving knowledge from data.
In its 5th iteration, this workshop will focus on machine learning (ML) techniques that will aid interoperability across IoT’s operational spectrum. That is, building on ML to aid in all stages of IoT operation, from heterogeneous resource discovery, calibration, verification, functional augmentation, and sustenance, all the way to communication/interference management and data collection, pruning and homogeneous representation. As IoT is proving to be integral to recent developments in the Tactile Internet (TI), this workshop solicits contributions that address synergy and convergence with Tactile Internet applications, interoperability at the Tactile Edge, and how IoT could leverage TI cognizance.
Specific topics include, but are not limited to:
- Machine Learning techniques for IoT data modeling and analysis
- Enabling tactile Internet applications over C-IoT
- CloudML engines for integrating IoT devices with cloud analytics
- Data management in Convergent IoT systems
- Data and organization Interoperability challenges for IoT systems
- Resource identification, discovery, and profiling in heterogeneous IoT
- Resource sharing and actuation conflicts resolution
- Quality of Information and Quality of Resource quantification in heterogeneous IoT
- Agile frameworks for IoT Interoperability
- Crowd-solicited IoT proliferation
- IoT systems collaboration and cooperation mechanisms
- Innovative IoT incentive schemes
- Convergent paradigms in the Internet of Things
- Non-proprietary standardization frameworks for a heterogeneous IoT
- Convergent services on malleable IoT infrastructures (i.e. based on Information/data planes)
- IoT edge analytics
- IoT service orchestration and scheduling
- Industrial Internet – Value creation and challenges
- Legal and governance frameworks for IoT regulation
January 20, 2020
February 20, 2020
March 1, 2020
We seek original contributions that have neither been previously published nor currently under review. Authors can submit a full paper (up to 6 pages) that describes complete work in a self-contained manner with the intent to deliver an oral presentation.
Submission link: https://edas.info/N26817
All accepted submissions will be published in the ICC’20 workshop proceedings and the ieeeXplore portal.