A Reference Model for IoT Embodied Agents Controlled by Neural Networks

TL;DR

This paper introduces a reference model based on statecharts for designing IoT embodied agents controlled by neural networks, facilitating high-level abstraction and supporting autonomous applications like street lights.

Contribution

It presents a novel reference model for IoT embodied agents using statecharts, including neural network training and reconfiguration, enhancing design and implementation processes.

Findings

Model supports autonomous street lights

Provides high-level abstractions for IoT agent design

Includes neural network reconfiguration process

Abstract

Embodied agents is a term used to denote intelligent agents, which are a component of devices belonging to the Internet of Things (IoT) domain. Each agent is provided with sensors and actuators to interact with the environment, and with a 'controller' that usually contains an artificial neural network (ANN). In previous publications, we introduced three software approaches to design, implement and test IoT embodied agents. In this paper, we propose a reference model based on statecharts that offers abstractions tailored to the development of IoT applications. The model represents embodied agents that are controlled by neural networks. Our model includes the ANN training process, represented as a reconfiguration step such as changing agent features or neural net connections. Our contributions include the identification of the main characteristics of IoT embodied agents, a reference model specification based on statecharts, and an illustrative application of the model to support autonomous street lights. The proposal aims to support the design and implementation of IoT applications by providing high-level design abstractions and models, thus enabling the designer to have a uniform approach to conceiving, designing and explaining such applications.