Q8KNN: A Novel 8-Bit KNN Quantization Method for Edge Computing in Smart Lighting Systems with NodeMCU

The emergence of the edge computing concept, such as in smart lighting control systems, is because the computing system proposed by the cloud concept often causes delays. The problem is that the end device of the Internet of things (IoT), such as NodeMCU as the device target for placing edge computing programs, has limited resources. This paper proposes a Quantized 8-bit K-Nearest Neighbor (Q8KNN), a novel quantization method that performs model compression on KNN with our case study, the smart lighting control systems, using NodeMCU. Firstly, we have created a novel and accurate smart lighting design that we proposed. The design uses edge computing and KNN models to predict the control data. Then we developed a quantization method to make the KNN model fit into the NodeMCU. Finally, we test the performance of our novel compression model using the Accuracy and Compression Ratio (CR) metrics. The test results show that the number of unique values that change due to quantization does not cause a cardinality problem. Then Q8KNN can provide CR up to 1.6 times. With a smaller model size, Q8KNN can increase the number of training samples in the NodeMCU memory. So, using the same model size, the original KNN gives an accuracy of 94% while Q8KNN can provide up to 98%.