D 2023

Digital Twins for Trust Building in Autonomous Drones through Dynamic Safety Evaluation

IQBAL, Danish, Barbora BÜHNOVÁ and Emilia CIOROAICA

Basic information

Original name

Digital Twins for Trust Building in Autonomous Drones through Dynamic Safety Evaluation

Authors

IQBAL, Danish (586 Pakistan, guarantor, belonging to the institution), Barbora BÜHNOVÁ (203 Czech Republic, belonging to the institution) and Emilia CIOROAICA (276 Germany)

Edition

Neuveden, 18th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE, p. 629-639, 11 pp. 2023

Publisher

SciTePress

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10200 1.2 Computer and information sciences

Country of publisher

Portugal

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

RIV identification code

RIV/00216224:14330/23:00131090

Organization unit

Faculty of Informatics

ISBN

978-989-758-647-7

ISSN

DOI

UT WoS

001119034200064

Keywords in English

Trust; Digital Twins; Safety; Autonomous Drones; Run-time Compliance Checking; Autonomous Ecosystem.

Tags

Tags

International impact, Reviewed
Změněno: 17/10/2024 21:09, Danish Iqbal, M.Sc.

Abstract

V originále

The adoption process of innovative software-intensive technologies leverages complex trust concerns in different forms and shapes. Perceived safety plays a fundamental role in technology adoption, being especially crucial in the case of those innovative software-driven technologies characterized by a high degree of dynamism and unpredictability, like collaborating autonomous systems. These systems need to synchronize their maneuvers in order to collaboratively engage in reactions to unpredictable incoming hazardous situations. That is however only possible in the presence of mutual trust. In this paper, we propose an approach for machine-to-machine dynamic trust assessment for collaborating autonomous systems that supports trust-building based on the concept of dynamic safety assurance within the collaborative process among the software-intensive autonomous systems. In our approach, we leverage the concept of digital twins which are abstract models fed with real-time data used in the run-time dynamic exchange of information. The information exchange is performed through the execution of specialized models that embed the necessary safety properties. More particularly, we examine the possible role of the Digital Twins in machine-to-machine trust building and present their design in supporting dynamic trust assessment of autonomous drones. Ultimately, we present a proof of concept of direct and indirect trust assessment by employing the Digital Twin in a use case involving two autonomous collaborating drones.

Links

CZ.02.1.01/0.0/0.0/16_019/0000822, interní kód MU
(CEP code: EF16_019/0000822)
Name: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur (Acronym: C4e)
Investor: Ministry of Education, Youth and Sports of the CR, CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence, Priority axis 1: Strengthening capacities for high-quality research
MUNI/A/1389/2022, interní kód MU
Name: Aplikovaný výzkum na FI: Bezpečnost počítačových systémů, softwarových architektur kritických infrastruktur s forenzními aspekty, zpracování dat pokročilých sensorů a algoritmy plánování v dopravě a logistice
Investor: Masaryk University
MUNI/A/1586/2023, interní kód MU
Name: Aplikovaný výzkum na FI: Forenzní aspekty kritických infrastruktur, aplikovaná kryptografie, kyberbezpečnostní cvičení, algoritmy plánování v logistice a pro zpracování dat z fyzikálních sensorů
Investor: Masaryk University, Applied research at FI: Forensic aspects of critical infrastructures, applied cryptography, cybersecurity trainings, scheduling algorithms logistics and algorithms for physical sensors
MUNI/IGA/1254/2021, interní kód MU
Name: Modelling and Runtime Assessment of Trust in Automotive Autonomous Systems
Investor: Masaryk University