Sathupadi Kaushik, Achar Sandesh, Bhaskaran Shinoy Vengaramkode, Faruqui Nuruzzaman, Abdullah-Al-Wadud M, Uddin Jia
Google LLC, Sunnyvale, CA 94089, USA.
Department of Software Engineering, Walmart Global Tech, Sunnyvale, CA 94086, USA.
Sensors (Basel). 2024 Dec 11;24(24):7918. doi: 10.3390/s24247918.
Sensor networks generate vast amounts of data in real-time, which challenges existing predictive maintenance frameworks due to high latency, energy consumption, and bandwidth requirements. This research addresses these limitations by proposing an edge-cloud hybrid framework, leveraging edge devices for immediate anomaly detection and cloud servers for in-depth failure prediction. A K-Nearest Neighbors (KNNs) model is deployed on edge devices to detect anomalies in real-time, reducing the need for continuous data transfer to the cloud. Meanwhile, a Long Short-Term Memory (LSTM) model in the cloud analyzes time-series data for predictive failure analysis, enhancing maintenance scheduling and operational efficiency. The framework's dynamic workload management algorithm optimizes task distribution between edge and cloud resources, balancing latency, bandwidth usage, and energy consumption. Experimental results show that the hybrid approach achieves a 35% reduction in latency, a 28% decrease in energy consumption, and a 60% reduction in bandwidth usage compared to cloud-only solutions. This framework offers a scalable, efficient solution for real-time predictive maintenance, making it highly applicable to resource-constrained, data-intensive environments.
传感器网络实时生成大量数据，由于高延迟、高能耗和高带宽需求，这对现有的预测性维护框架构成了挑战。本研究通过提出一种边缘-云混合框架来解决这些限制，利用边缘设备进行即时异常检测，并利用云服务器进行深度故障预测。在边缘设备上部署了K近邻（KNN）模型以实时检测异常，减少了持续向云传输数据的需求。同时，云中的长短期记忆（LSTM）模型分析时间序列数据以进行预测性故障分析，提高维护调度和运营效率。该框架的动态工作负载管理算法优化了边缘和云资源之间的任务分配，平衡了延迟、带宽使用和能耗。实验结果表明，与仅使用云的解决方案相比，混合方法的延迟降低了35%，能耗降低了28%，带宽使用降低了60%。该框架为实时预测性维护提供了一种可扩展、高效的解决方案，使其非常适用于资源受限、数据密集型环境。
