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<p>In Internet of Things environments involving autonomous drones, sustained collaboration and effective conflict mitigation are essential for reliable service delivery, particularly in mission-critical applications such as parcel logistics. This paper presents an applied-artificial-intelligence approach based on recommendation of things to support cooperation and conflict-aware decision-making among drones performing parcel-delivery tasks. Unlike existing task-allocation and optimization approaches that primarily focus on resource efficiency and assume uniform cooperation among drones, the proposed approach explicitly models inter-drone coordination relations (e.g., facilitate, constrain, and cause), along with historical interaction patterns, to capture both collaboration potential and conflict likelihood. The proposed system analyzes job-level context—including spatial proximity, drone capabilities and constraints, and historical interaction data—to model coordination factors such as inter-drone relationships, collaboration potential, conflict likelihood, and expected satisfaction. Using these factors, the system generates recommendations for drone pairings to support informed task execution. The approach is evaluated through extensive experiments on a parcel-delivery case study under controlled conditions, demonstrating improved coordination-aware recommendation behavior compared with baseline strategies, with more consistent prediction and identification of collaborative drone pairings and conflict-prone combinations across multiple time intervals.</p>

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Sustaining collaboration and mitigating conflicts in IoT-based drone systems using recommendation of things

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • Ayesha Qamar,
  • Muhammad Asim,
  • Zakaria Maamar,
  • Ali Ismail Awad,
  • Qaisar Shafi

摘要

In Internet of Things environments involving autonomous drones, sustained collaboration and effective conflict mitigation are essential for reliable service delivery, particularly in mission-critical applications such as parcel logistics. This paper presents an applied-artificial-intelligence approach based on recommendation of things to support cooperation and conflict-aware decision-making among drones performing parcel-delivery tasks. Unlike existing task-allocation and optimization approaches that primarily focus on resource efficiency and assume uniform cooperation among drones, the proposed approach explicitly models inter-drone coordination relations (e.g., facilitate, constrain, and cause), along with historical interaction patterns, to capture both collaboration potential and conflict likelihood. The proposed system analyzes job-level context—including spatial proximity, drone capabilities and constraints, and historical interaction data—to model coordination factors such as inter-drone relationships, collaboration potential, conflict likelihood, and expected satisfaction. Using these factors, the system generates recommendations for drone pairings to support informed task execution. The approach is evaluated through extensive experiments on a parcel-delivery case study under controlled conditions, demonstrating improved coordination-aware recommendation behavior compared with baseline strategies, with more consistent prediction and identification of collaborative drone pairings and conflict-prone combinations across multiple time intervals.