Computational Models of Human Decision-Making with Application to the Internet of Everything

TL;DR

This paper reviews computational models of human decision-making and applies one to optimize task offloading in fog computing within the Internet of Everything, considering human behavior in network management.

Contribution

It introduces a novel application of human decision-making models to IoE network optimization, specifically in fog computing task offloading.

Findings

Including humans in the loop affects network performance.

The proposed model improves task offloading efficiency.

Modeling human irrationality enhances decision-making accuracy.

Abstract

The concept of the Internet of Things (IoT) first appeared a few decades ago. Today, by the ubiquitous wireless connectivity, the boost of machine learning and artificial intelligence, and the advances in big data analytics, it is safe to say that IoT has evolved to a new concept called the Internet of Everything (IoE) or the Internet of All. IoE has four pillars: Things, human, data, and processes, which render it as an inhomogeneous large-scale network. A crucial challenge of such a network is to develop management, analysis, and optimization policies that besides utility-maximizer machines, also take irrational humans into account. We discuss several networking applications in which appropriate modeling of human decision-making is vital. We then provide a brief review of computational models of human decision-making. Based on one such model, we develop a solution for a task offloading problem in fog computing and we analyze the implications of including humans in the loop.