Improving the Expected Performance of Self-Organization in a Collective Adaptive System of Drones using Stochastic Multiplayer Games

Date

2022-01-04

Contributor

Advisor

Department

Instructor

Depositor

Speaker

Researcher

Consultant

Interviewer

Narrator

Transcriber

Annotator

Journal Title

Journal ISSN

Volume Title

Publisher

Volume

Number/Issue

Starting Page

Ending Page

Alternative Title

Abstract

The Internet-of-Things (IoT) domain will be one of the most important domains of research in the coming decades. Paradigms continue to emerge that can employ self-organization to capitalize on the sheer number and variety of devices in the market. In this paper, we combine the use of stochastic multiplayer games (SMGs) and negotiation within two collective adaptive systems of drones tasked with locating and surveilling intelligence caches. We assess the use of an ordinary least squares (OLS) regression model that is trained on the SMG’s output. The SMG is augmented to incorporate the OLS model to evaluate integration configurations during negotiation. The augmented SMG is compared to the base SMG where drones always integrate. Our results show that the incorporation of the OLS model improves the expected performance of the drones while significantly reducing the number of failed surveillance tasks which result in the loss of drones.

Description

Keywords

Cyber Systems: Their Science, Engineering, and Security, collective adaptive systems, internet-of-things, self-organization, stochastic multiplayer games

Citation

Extent

10 pages

Format

Geographic Location

Time Period

Related To

Proceedings of the 55th Hawaii International Conference on System Sciences

Related To (URI)

Table of Contents

Rights

Attribution-NonCommercial-NoDerivatives 4.0 International

Rights Holder

Local Contexts

Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.