Distributed-neuron-network based machine learning on smart-gateway network towards real-time indoor data analytics

Abstract

Indoor data analytics is one typical example of ambient intelligence with behaviour or feature extraction from environmental data. It can be utilized to help improve comfort level in building and room for occupants. To address dynamic ambient change in a large-scaled space, real-time and distributed data analytics is required on sensor (or gateway) network, which however has limited computing resources. This paper proposes a computationally efficient data analytics by distributed-neuron-network (DNN) based machine learning with application for indoor positioning. It is based on one incremental L2-norm based solver for learning collected WiFi-data at each gateway and is further fused for all gateways in the network to determine the location. Experimental results show that with multiple distributed gateways running in parallel, the proposed algorithm can achieve 50x and 38x speedup during data testing and training time respectively with comparable positioning accuracy, when compared to traditional support vector machine (SVM) method.