Task-Driven Optimization of Ground User Clustering and Channel Access in Unknown Environments: A Coalition-Based Optimal Stopping Approach

In emergency rescue operations, coordinating ground users (GUs) efficiently to handle dispersed tasks is crucial for saving lives and property. However, challenges such as task assignment and channel access hinder effective performance. The heterogeneity of GU abilities and the multiple ability requirements of tasks often lead to mismatched assignments, reducing rescue efficiency. Furthermore, channel access is complicated by the lack of channel state information (CSI) in disaster environments, which increases resource consumption if all channels are explored exhaustively. To address these challenges, this paper proposes a two-stage optimization framework that combines task assignment and channel access under unknown environments. First, a clustering-based method groups GUs according to multiple ability requirements. The task assignment problem is formulated as a transferable utility coalition formation game (CFG) with defined utility and preference relations. Second, a channel access mechanism is designed and modeled as an optimal stopping problem to optimize exploration time and select the optimal channel from the explored set. A task assignment and channel access optimization algorithm for cooperative rescue is proposed, where a multi-round matching preprocessing step supports coalition formation, and a one-stage look-ahead (1-SLA) rule balances exploration and data reception. Simulation results show that the proposed algorithm effectively satisfies task ability requirements, accelerates channel access, and improves the actual total utility.

• Publication year

  2026

• Venue

  Electronics

• Publication date

  2026-02-02

• Fields of study

  Not labeled

• Identifiers
  DOI 10.3390/electronics15030643
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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