Machine Learning-based Variability Handling in IoT Agents
This addresses deployment difficulties in IoT applications for domains like health care and smart cities, but it appears incremental as it builds on existing agent-based approaches with machine learning enhancements.
The paper tackles the challenge of deploying agent-based IoT applications due to complex variability in devices, software, and environments by proposing a self-configurable IoT agent approach using feedback-evaluative machine learning, which includes a variability model, customized agent generation, and feature selection methods.
Agent-based IoT applications have recently been proposed in several domains, such as health care, smart cities and agriculture. Deploying these applications in specific settings has been very challenging for many reasons including 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 self-configurable IoT agent approach based on feedback-evaluative machine-learning. The approach involves: i) a variability model of IoT agents; ii) generation of sets of customized agents; iii) feedback evaluative machine learning; iv) modeling and composition of a group of IoT agents; and v) a feature-selection method based on manual and automatic feedback.