January 20, 2020
February 20, 2020
March 01, 2020
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. IoT architectures need to expand on the functional capacity and interaction of “things”; beyond sheer sensors and actuators, as they 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.