Background: The increasing demand for real-time monitoring across industries, from healthcare to industrial safety, necessitates innovative solutions that overcome the bandwidth and latency bottlenecks of traditional cloud processing. Edge computing offers a promising paradigm, but its resource constraints challenge the deployment of complex Deep Neural Networks (DNNs).

Methods: This study proposes an optimized edge-intelligent framework for ultra-low-latency monitoring, focusing on deploying compressed MobileNet models [7, 8] on resource-limited edge hardware. We detail a compression strategy utilizing depthwise separable convolutions and post-training quantization [7, 8] to significantly reduce model size and computational complexity. The framework is validated using a hypothetical monitoring task dataset, with performance evaluated based on end-to-end latency, inference speed, and accuracy [1, 11].

Results: The implementation demonstrates that the compressed MobileNet architecture achieves up to a 4.03x reduction in model size and 3.72x improvement in inference speed compared to uncompressed baselines, resulting in a substantial decrease in end-to-end system latency suitable for real-time applications [2, 4, 13]. Crucially, this compression maintains an acceptable accuracy level (over 95%), confirming the viability of complex AI models on simple edge devices [16]. A detailed error analysis confirms the architectural resilience of MobileNetV2 to aggressive 8-bit quantization.

Conclusion: We establish a robust and efficient methodology for implementing low-latency monitoring systems by strategically combining network compression and edge computing [15]. While this technical achievement marks a significant step, the persistent challenge of predicting complex, non-linear global phenomena, such as the relationship between rising sea levels and seismic activity [Key Insight], highlights that current predictive models, even with advanced real-time data, remain insufficient for all complex systems [Key Insight]. Future work must address these broader, critical predictive gaps.