SoS Meta-Architecture Selection for Infrastructure Inspection System using Aerial Drones
Abstract
Infrastructure inspection using unmanned aerial drones has a great potential to support complex inspection tasks especially where inspection task can be dangerous, dull or dirty. The increased number of systems in this type of inspection process makes it a very complex systems-of-systems (SoS) which is hard to assess. As a result, it becomes very difficult to satisfy all stakeholder needs and requirements. Therefore, an assessment system is required that can efficiently assess the meta-architecture of drone based inspection system. This paper presents a method to generate and evaluate systems of systems (SoS) architecture model for aerial inspection with drones. Where, a meta-architecture containing system component and a system to system interface is presented. To map the desired SoS attributes from stakeholders, different characteristics of the architecture capabilities are evaluated using some linguistic terms called key performance attributes (KPA). KPAs are combined in a Fuzzy Inference System (FIS) to evaluate an overall fitness value that is optimized using a Genetic Algorithm (GA) for the SoS within the meta-architecture. The integrated evaluation method presented in this paper utilizes the SoS explorer to evaluate the SoS meta-architecture using synthetic parameter values.
Recommended Citation
M. M. Karim and C. H. Dagli, "SoS Meta-Architecture Selection for Infrastructure Inspection System using Aerial Drones," Proceedings of the 15th International Conference of System of Systems Engineering (2020, Budapest, Hungary), pp. 23 - 28, Institute of Electrical and Electronics Engineers (IEEE), Jun 2020.
The definitive version is available at https://doi.org/10.1109/SoSE50414.2020.9130538
Meeting Name
15th International Conference of System of Systems Engineering, SoSE 2020 (2020: Jun. 2-4, Budapest, Hungary)
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Aerial drone; Fuzzy Inference System; Genetic Algorithm; Meta-architecture; Systems-of-Systems
International Standard Book Number (ISBN)
978-172818050-2
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
04 Jun 2020