Runtime Composition in Dynamic System of Systems: A Systematic Review of Challenges, Solutions, Tools, and Evaluation Methods

2025-10-14

AI Summary

Dynamic Systems of Systems (SoSs) in smart cities and autonomous driving require runtime composition for adaptive collaboration, yet existing research lacks a systematic synthesis of core challenges, solution paradigms, and evaluation frameworks. Method: We conduct a systematic literature review and thematic analysis of SoS runtime composition. Contribution/Results: We propose a taxonomy of seven technical pathways—including semantic ontologies, digital twins, and AI-driven resilience—revealing the fundamental tension between autonomy and coordination, as well as the modeling-reality gap. We identify four major challenge categories, survey mainstream tools and evaluation methodologies, and highlight critical gaps: the absence of standardized benchmarks and cross-domain architectural frameworks. This work delivers the first comprehensive theoretical framework and practical guideline for runtime composition of dynamic SoSs.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Coordination and Collaboration
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Context: Modern Systems of Systems (SoSs) increasingly operate in dynamic environments (e.g., smart cities, autonomous vehicles) where runtime composition -- the on-the-fly discovery, integration, and coordination of constituent systems (CSs)--is crucial for adaptability. Despite growing interest, the literature lacks a cohesive synthesis of runtime composition in dynamic SoSs. Objective: This study synthesizes research on runtime composition in dynamic SoSs and identifies core challenges, solution strategies, supporting tools, and evaluation methods. Methods: We conducted a Systematic Literature Review (SLR), screening 1,774 studies published between 2019 and 2024 and selecting 80 primary studies for thematic analysis (TA). Results: Challenges fall into four categories: modeling and analysis, resilient operations, system orchestration, and heterogeneity of CSs. Solutions span seven areas: co-simulation and digital twins, semantic ontologies, integration frameworks, adaptive architectures, middleware, formal methods, and AI-driven resilience. Service-oriented frameworks for composition and integration dominate tooling, while simulation platforms support evaluation. Interoperability across tools, limited cross-toolchain workflows, and the absence of standardized benchmarks remain key gaps. Evaluation approaches include simulation-based, implementation-driven, and human-centered studies, which have been applied in domains such as smart cities, healthcare, defense, and industrial automation. Conclusions: The synthesis reveals tensions, including autonomy versus coordination, the modeling-reality gap, and socio-technical integration. It calls for standardized evaluation metrics, scalable decentralized architectures, and cross-domain frameworks. The analysis aims to guide researchers and practitioners in developing and implementing dynamically composable SoSs.

Problem

• Systematically reviews runtime composition challenges in dynamic Systems of Systems
• Identifies solutions and tools for on-the-fly system integration and coordination
• Analyzes evaluation methods for adaptable systems in dynamic environments

Innovation

• Systematic literature review for runtime composition synthesis
• Service-oriented frameworks for dynamic system integration
• AI-driven resilience and adaptive architectures solutions