Title: An IoT Analytics Embodied Agent Model based on Context-Aware Machine Learning,
Abstract: Agent-based Internet of Things (IoT) applications have recently emerged as
applications that can involve sensors, wireless devices, machines and software
that can exchange data and be accessed remotely. Such applications have been
proposed in several domains including health care, smart cities and
agriculture. However, despite their increased adoption, deploying these
applications in specific settings has been very challenging because of the
complex static and dynamic variability of the physical devices such as sensors
and actuators, the software application behavior and the environment in which
the application is embedded. In this paper, we propose a modeling approach for
IoT analytics based on learning embodied agents (i.e. situated agents). The
approach involves: (i) a variability model of IoT embodied agents; (ii)
feedback evaluative machine learning; and (iii) reconfiguration of a group of
agents in accordance with environmental context. The proposed approach advances
the state of the art in that it facilitates the development of Agent-based IoT
applications by explicitly capturing their complex and dynamic variabilities
and supporting their self-configuration based on an context-aware and machine
learning-based approach.