5th IEEE ICC 2020 Workshop on Convergent Internet of Things
AI-assisted Operation and Integration
Over the past 4 years, this workshop has built momentum in addressing interoperability and synergy challenges in the Internet of Things. Aiming to build a Convergent-IoT (C-IoT), there have been many dimensions of development in IoT systems, most notably in the communication and data representation layers. 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), we are soliciting contributions that address synergy and convergence with Tactile Internet applications. At this stage, the focus will be on interoperability at the Tactile Edge, and how IoT could leverage TI cognizance.
This workshop will focus on encouraging developments that transcend single-function IoT deployments. While interoperability and functional convergence remain a pressing challenge, recent trends in multi-homing IoT architectures, gateways with multiple connectivity modes, and cataloguing systems that enable rapid resource discovery, have led many advancements in IoT interoperability. These developments are building on recent strides in managing hardware-agnostic low-power networks, which are promising many new frontiers in convergent operation.
We focus the scope of C-IoT 2020 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. That is, as we are growing more able to communicate between heterogeneous IoT architectures, 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.
To this end, C-IoT 2020 topics will span architectures, frameworks and implementations that address IoT interoperability, across resource, data and information planes. That includes traditional IoT interoperability techniques (i.e. gateway based, hub-based, mixed-mode APs with heterogeneous IoT services, service-level cataloguing, etc), in addition to Machine Learning techniques that attempts to quantify QoI and QoR, as well as aid information extraction from IoT data. There is a clear demand in addressing IoT data management, and interoperability across large-scale IoT systems, and we wish to enable a dedicated venue for these pioneering directions.