Event-Driven Neuromorphic Processing for Smart Building Sensor Networks
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Abstract
This study presents an event-driven neuromorphic framework for intelligent indoor air quality (IAQ) monitoring in smart building environments. The proposed system employs biologically inspired spiking computation using the Leaky Integrate-and-Fire (LIF) neuron model integrated with Spike-Timing-Dependent Plasticity (STDP) learning for adaptive environmental processing. Real-world IAQ parameters—including particulate matter (PM₁, PM₂.₅, PM₁₀), carbon dioxide (CO₂), carbon monoxide (CO), total volatile organic compounds (TVOC), and ozone (O₃)—were acquired from nine building zones and encoded as asynchronous spike events. A fractional-order Kalman filter (FOKF) achieved an average 5.6% noise reduction, stabilizing the signal prior to spike encoding. The neuromorphic model achieved a mean detection accuracy of 10.93%, an average response time of 10.43 steps, and an energy efficiency score of 8.85, reflecting selective sparse firing and low computational overhead. When compared with Long Short-Term Memory (LSTM) and regression models, the neuromorphic system delivered faster response times and significantly higher energy efficiency—nearly nine times lower computational cost—while maintaining comparable event responsiveness. These results demonstrate that event-driven neuromorphic computation offers a scalable, low-power, and adaptive solution for real-time IAQ monitoring in smart building systems.
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