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This paper presents a novel energy-efficient framework integrating Gallium Nitride (GaN) hardware accelerators with real-time stream processing for sustainable smart city IoT applications. The proposed system combines high-speed modulation characteristics of scaled GaN laser diodes operating at 2.4 GHz with reconfigurable Multiple-In Multiple-Out (MIMO) antenna arrays to enable efficient processing of intensive data streams from urban IoT sensors. The hybrid architecture leverages both edge and cloud computing paradigms, achieving statistically significant improvements of 47.7% in energy efficiency (95% CI: 44.2–51.3%, \( p < 0.001 \) ) and 68.0% latency reduction (95% CI: 65.1–70.9%, \( p < 0.001 \) ) compared to traditional approaches. The system incorporates resonant-cavity light-emitting diode technology for high-bandwidth data transmission and employs machine learning-based adaptive stream processing algorithms optimized for urban infrastructure monitoring. Experimental validation across 14 international deployments in Singapore, Barcelona, Toronto, and Dubai demonstrates consistent performance improvements while maintaining 99.8% system reliability and processing throughput of 8.7 Gbps.

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Energy-Efficient GaN-Accelerated Stream Processing Framework for Smart City IoT Applications

  • 会议论文

  • 发表日期：

    2026年

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  • Akey Sungheetha,
  • R. Rajesh Sharma,
  • John Blake,
  • Sheila Mahapatra,
  • Nilanga Abeysinghe,
  • Komal Parashar

摘要

This paper presents a novel energy-efficient framework integrating Gallium Nitride (GaN) hardware accelerators with real-time stream processing for sustainable smart city IoT applications. The proposed system combines high-speed modulation characteristics of scaled GaN laser diodes operating at 2.4 GHz with reconfigurable Multiple-In Multiple-Out (MIMO) antenna arrays to enable efficient processing of intensive data streams from urban IoT sensors. The hybrid architecture leverages both edge and cloud computing paradigms, achieving statistically significant improvements of 47.7% in energy efficiency (95% CI: 44.2–51.3%, \( p < 0.001 \) ) and 68.0% latency reduction (95% CI: 65.1–70.9%, \( p < 0.001 \) ) compared to traditional approaches. The system incorporates resonant-cavity light-emitting diode technology for high-bandwidth data transmission and employs machine learning-based adaptive stream processing algorithms optimized for urban infrastructure monitoring. Experimental validation across 14 international deployments in Singapore, Barcelona, Toronto, and Dubai demonstrates consistent performance improvements while maintaining 99.8% system reliability and processing throughput of 8.7 Gbps.