Machine Learning-based Variability Handling in IoT Agents

TL;DR

This paper presents a machine learning-based approach for managing variability in IoT agents, enabling self-configuration and adaptation in complex, dynamic environments across various domains.

Contribution

It introduces a novel self-configurable IoT agent framework utilizing feedback-driven machine learning to handle variability in sensors, actuators, and environment.

Findings

Effective variability modeling for IoT agents

Automated generation of customized IoT agents

Improved adaptability through feedback-based feature selection

Abstract

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.