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<p>Mobile Wireless Sensor Networks (MWSNs) with dual mobility enable dynamic data collection in smart agriculture, but lack efficient spatiotemporal query support. We propose AI-SQF, a lightweight framework integrating LSTM-based predictive clustering, Q-learning-guided multi-sink mobility, NSGA-II optimization, and grid-based indexing for real-time SQL-like queries (e.g., “soil moisture in Zone A, past 10 min”). Cluster heads maintain a 50 × 50&#xa0;m metadata index; sinks resolve queries locally, reducing transmissions. Evaluated in a 2000 × 2000&#xa0;m field using NASA SMAP/MODIS data, AI-SQF achieves 190 ± 10 ms latency, 13.5 ± 0.6 pkt/s throughput, 95.5% coverage, &gt; 91% accuracy, and 2.3 ± 0.25&#xa0;s freshness — outperforming static, KNN, and DRL baselines by 22–50% (<i>p</i> &lt; 0.01). Fault tolerance sustains 91 ± 2.5% delivery after 12% node failure.</p>

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Artificial intelligence driven spatiotemporal query framework for mobile wireless sensor networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月4日

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  • Akanksha Gupta,
  • Santosh Soni

摘要

Mobile Wireless Sensor Networks (MWSNs) with dual mobility enable dynamic data collection in smart agriculture, but lack efficient spatiotemporal query support. We propose AI-SQF, a lightweight framework integrating LSTM-based predictive clustering, Q-learning-guided multi-sink mobility, NSGA-II optimization, and grid-based indexing for real-time SQL-like queries (e.g., “soil moisture in Zone A, past 10 min”). Cluster heads maintain a 50 × 50 m metadata index; sinks resolve queries locally, reducing transmissions. Evaluated in a 2000 × 2000 m field using NASA SMAP/MODIS data, AI-SQF achieves 190 ± 10 ms latency, 13.5 ± 0.6 pkt/s throughput, 95.5% coverage, > 91% accuracy, and 2.3 ± 0.25 s freshness — outperforming static, KNN, and DRL baselines by 22–50% (p < 0.01). Fault tolerance sustains 91 ± 2.5% delivery after 12% node failure.