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<p>Latency-sensitive and context-driven resource orchestration is needed to maintain service continuity in human-interactive ubiquitous settings. Agile Context-Adaptable Interaction Scheme (ACAIS) is a unified interaction–context–resource co-optimization system that treats resource management as a multi-objective adaptive decision process. ACAIS dynamically allocates computing resources under restricted capacity restrictions using a deep reinforcement learning–based policy optimizer and a structured state-space representation of contextual entropy, interaction intensity, and workload elasticity. In real time, a dual-mode latency inference system examines context-to-latency and interaction-to-latency mappings to manage allocation strategies. An external benchmark dataset shows significant performance gains under peak workload conditions (12 interactions/hour), including 12.68% latency reduction (p &lt; 0.01), 12.85% adaptability improvement, 8.01% resource utilization efficiency enhancement, and 15.54% context-awareness accuracy increase compared to baseline adaptive schemes. ACAIS' predictive, interaction-aware resource governance paradigm for scaled ubiquitous computing environments is confirmed by these findings.</p>

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Agile context-aware interaction scheme for optimized resource management in human-interactive ubiquitous environments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月22日

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  • Asma Aldrees,
  • Amr Yousef,
  • Sana Shahab,
  • Mohd Anjum,
  • Isabel de la Torre Díez,
  • Zaffar Ahmed Shaikh

摘要

Latency-sensitive and context-driven resource orchestration is needed to maintain service continuity in human-interactive ubiquitous settings. Agile Context-Adaptable Interaction Scheme (ACAIS) is a unified interaction–context–resource co-optimization system that treats resource management as a multi-objective adaptive decision process. ACAIS dynamically allocates computing resources under restricted capacity restrictions using a deep reinforcement learning–based policy optimizer and a structured state-space representation of contextual entropy, interaction intensity, and workload elasticity. In real time, a dual-mode latency inference system examines context-to-latency and interaction-to-latency mappings to manage allocation strategies. An external benchmark dataset shows significant performance gains under peak workload conditions (12 interactions/hour), including 12.68% latency reduction (p < 0.01), 12.85% adaptability improvement, 8.01% resource utilization efficiency enhancement, and 15.54% context-awareness accuracy increase compared to baseline adaptive schemes. ACAIS' predictive, interaction-aware resource governance paradigm for scaled ubiquitous computing environments is confirmed by these findings.