LAUSR

This article presents a real-time FPGA (field-programmable gate array) based fog node architecture with a remote dynamically reconfigurable application plane. The proposed FPGA-based fog node exhibits high architectural flexibility to enable fog analytics dynamic redeployment, which is critical for adapting to application dynamicity and prolonging product lifespan. The proposed fog node architecture is implemented in an FPGA without GPP (general-purpose processor) and tested experimentally in the NetFPGA development board with a case study on anomaly detection analytics that is commonly used in IoT (Internet-of-Things) time-series monitoring applications. The Windowed Gaussian and KNN CAD implementations in FPGA have been benchmarked with the NAB (Numenta Anomaly Benchmark) environment for performance assessment. Significantly, the latency and throughput improvement for the KNN CAD in FPGA implementation over software is approximately $61\times $ , while Windowed Gaussian is $42\times $ and thus, both analytics attained <1 ms real-time responsiveness. The implemented FPGA-based Windowed Gaussian and KNN CAD anomaly detection analytics has the respective energy efficiency of $0.06~\mu \text{J}$ and 0.12 mJ for each detection. In short, the proposed FPGA-based fog node exhibits high energy efficiency, low latency, and high throughput in network packet and data analytics processing for greener fog networks and real-time IoT applications.