(Do not) trust in ecosystems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F19%3A00110162" target="_blank" >RIV/00216224:14330/19:00110162 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ICSE-NIER.2019.00011" target="_blank" >http://dx.doi.org/10.1109/ICSE-NIER.2019.00011</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICSE-NIER.2019.00011" target="_blank" >10.1109/ICSE-NIER.2019.00011</a>

Jazyk výsledku

angličtina

Název v původním jazyce

(Do not) trust in ecosystems

Popis výsledku v původním jazyce

In the context of Smart Ecosystems, systems engage in dynamic cooperation with other systems to achieve their goals. Expedient operation is only possible when all systems cooperate as expected. This requires a level of trust between the components of the ecosystem. New systems that join the ecosystem therefore first need to build up a level of trust. Humans derive trust from behavioral reputation in key situations. In Smart Ecosystems (SES), the reputation of a system or system component can also be based on observation of its behavior. In this paper, we introduce a method and a test platform that support virtual evaluation of decisions at runtime, thereby supporting trust building within SES. The key idea behind the platform is that it employs and evaluates Digital Twins, which are executable models of system components, to learn about component behavior in observed situations. The trust in the Digital Twin then builds up over time based on the behavioral compliance of the real system component with its Digital Twin. In this paper, we use the context of automotive ecosystems and examine the concepts for building up reputation on control algorithms of smart agents dynamically downloaded at runtime to individual autonomous vehicles within the ecosystem.

Název v anglickém jazyce

(Do not) trust in ecosystems

Popis výsledku anglicky

In the context of Smart Ecosystems, systems engage in dynamic cooperation with other systems to achieve their goals. Expedient operation is only possible when all systems cooperate as expected. This requires a level of trust between the components of the ecosystem. New systems that join the ecosystem therefore first need to build up a level of trust. Humans derive trust from behavioral reputation in key situations. In Smart Ecosystems (SES), the reputation of a system or system component can also be based on observation of its behavior. In this paper, we introduce a method and a test platform that support virtual evaluation of decisions at runtime, thereby supporting trust building within SES. The key idea behind the platform is that it employs and evaluates Digital Twins, which are executable models of system components, to learn about component behavior in observed situations. The trust in the Digital Twin then builds up over time based on the behavioral compliance of the real system component with its Digital Twin. In this paper, we use the context of automotive ecosystems and examine the concepts for building up reputation on control algorithms of smart agents dynamically downloaded at runtime to individual autonomous vehicles within the ecosystem.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 41st International Conference on Software Engineering: New Ideas and Emerging Results

ISBN

9781728117584

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

9-12

Název nakladatele

IEEE Press

Místo vydání

USA

Místo konání akce

Montreal, Canada

Datum konání akce

1. 1. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000557879900003