Engineering Cooperative Smart Things based on Embodied Cognition

TL;DR

This paper presents a novel approach to designing cooperative smart devices using embodied cognition and evolutionary algorithms, enabling decentralized, autonomous behavior in IoT applications like smart street lights.

Contribution

It introduces a new model of smart things as embodied agents with neural networks, evolved for autonomous cooperation without centralized control.

Findings

Feasible modeling of decentralized smart things with self-coordination.

Successful prototype of smart street lights using evolved neural networks.

Demonstrated reduction in energy consumption through cooperation.

Abstract

The goal of the Internet of Things (IoT) is to transform any thing around us, such as a trash can or a street light, into a smart thing. A smart thing has the ability of sensing, processing, communicating and/or actuating. In order to achieve the goal of a smart IoT application, such as minimizing waste transportation costs or reducing energy consumption, the smart things in the application scenario must cooperate with each other without a centralized control. Inspired by known approaches to design swarm of cooperative and autonomous robots, we modeled our smart things based on the embodied cognition concept. Each smart thing is a physical agent with a body composed of a microcontroller, sensors and actuators, and a brain that is represented by an artificial neural network. This type of agent is commonly called an embodied agent. The behavior of these embodied agents is autonomously configured through an evolutionary algorithm that is triggered according to the application performance. To illustrate, we have designed three homogeneous prototypes for smart street lights based on an evolved network. This application has shown that the proposed approach results in a feasible way of modeling decentralized smart things with self-developed and cooperative capabilities.