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<p>The deployment of advanced reinforcement learning algorithms in edge computing environments presents significant challenges for real-time aquaculture management, particularly in resource-constrained recirculating aquaculture systems (RAS). Building upon our previous work demonstrating superior performance of DDPG controllers in commercial RAS operations, this research introduces a lightweight DDPG architecture specifically optimized for edge computing deployment in recirculating aquaculture systems. The Edge-DDPG framework reduces computational complexity by 85% while maintaining 92% of the original model’s performance accuracy. The lightweight architecture employs compact neural networks with reduced layer dimensions (64→32→1 neurons vs. 400→300→1 in the original), memory-efficient replay buffers (5,000 vs. 100,000 capacity), and CPU-optimized operations suitable for ARM-based edge devices. Experimental validation demonstrates consistent performance with average inference times of 15.2 ± 3.1 ms on Raspberry Pi 4B, enabling real-time control within 50 ms system response requirements. The edge-optimized controller achieved 94.3% feeding accuracy and 96.1% water quality stability while consuming only 47 ± 8&#xa0;MB of system memory. Economic analysis demonstrates deployment cost reductions from $56,900 to $8,400 for large-scale implementations, enabling widespread adoption of intelligent feeding control in small to medium-scale aquaculture operations.</p>

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Lightweight deep deterministic policy gradient for edge computing in recirculating aquaculture systems: real-time feeding control with reduced computational requirements

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月30日

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  • Wael M. Elmessery,
  • Mahmoud Y. Shams,
  • Tarek Abd El-Hafeez,
  • Péter Szűcs,
  • Mohamed Hamdy Eid,
  • M. Alhumedi,
  • Atef Fathy Ahmed,
  • Abdallah Elshawadfy Elwakeel

摘要

The deployment of advanced reinforcement learning algorithms in edge computing environments presents significant challenges for real-time aquaculture management, particularly in resource-constrained recirculating aquaculture systems (RAS). Building upon our previous work demonstrating superior performance of DDPG controllers in commercial RAS operations, this research introduces a lightweight DDPG architecture specifically optimized for edge computing deployment in recirculating aquaculture systems. The Edge-DDPG framework reduces computational complexity by 85% while maintaining 92% of the original model’s performance accuracy. The lightweight architecture employs compact neural networks with reduced layer dimensions (64→32→1 neurons vs. 400→300→1 in the original), memory-efficient replay buffers (5,000 vs. 100,000 capacity), and CPU-optimized operations suitable for ARM-based edge devices. Experimental validation demonstrates consistent performance with average inference times of 15.2 ± 3.1 ms on Raspberry Pi 4B, enabling real-time control within 50 ms system response requirements. The edge-optimized controller achieved 94.3% feeding accuracy and 96.1% water quality stability while consuming only 47 ± 8 MB of system memory. Economic analysis demonstrates deployment cost reductions from $56,900 to $8,400 for large-scale implementations, enabling widespread adoption of intelligent feeding control in small to medium-scale aquaculture operations.