# Hypergraph-Aided Task-Resource Matching for Maximizing Value of Task Completion in Collaborative IoT Systems

arXiv: https://arxiv.org/abs/2405.20055 · HTML: https://mlsanity.com/paper/2405.20055/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2024.

## Built on by (most cited first, 5 of 5)

- [Chain-of-Trust: A Progressive Trust Evaluation Framework Enabled by Generative AI](https://mlsanity.com/paper/2506.17130/) (2025)
- [Rapid and Continuous Trust Evaluation for Effective Task Collaboration Through Siamese Model](https://mlsanity.com/paper/2506.17128/) (2025)
- [Semantic Chain-of-Trust: Autonomous Trust Orchestration for Collaborator Selection via Hypergraph-Aided Agentic AI](https://mlsanity.com/paper/2507.23565/) (2025)
- [Social and Physical Attributes-Defined Trust Evaluation for Effective Collaborator Selection in Human-Device Coexistence Systems](https://mlsanity.com/paper/2511.11578/) (2025)
- [Long-Term Behavioral Evaluation for Trusted Collaborator Selection via Bidirectional Mamba](https://mlsanity.com/paper/2608.25232/) (2026)

## Abstract (arXiv)

With the growing scale and intrinsic heterogeneity of Internet of Things (IoT) systems, distributed device collaboration becomes essential for effective task completion by dynamically utilizing limited communication and computing resources. However, the separated design and situation-agnostic operation of computing, communication and application layers create a fundamental challenge for rapid task-resource matching, which further deteriorate the overall task completion effectiveness. To overcome this challenge, we utilize hypergraph as a new tool to vertically unify computing, communication, and task aspects of IoT systems for an effective matching by accurately capturing the relationships between tasks and communication and computing resources. Specifically, a state-of-the-art task-resource matching hypergraph (TRM-hypergraph) model is proposed in this paper, which is used to effectively transform the process of allocating complex heterogeneous resources to convoluted tasks into a hypergraph matching problem. Taking into account computational complexity and storage, a game-theoretic hypergraph matching algorithm is proposed via considering the hypergraph matching problem as a non-cooperative multi-player clustering game. Numerical results demonstrate that the proposed TRM-hypergraph model achieves superior performance in matching of tasks and resources compared with comparison algorithms.