Indoor positioning by distributed machine-learning based data analytics on smart gateway network

Real-time data analysis on sensor nodes is challenging due to limited computing resources. A changing environment where received signal strength (RSSI) varies with time makes it more complex to update position predictors for real-time indoor positioning. Based on the distributed collection and analytics of RSSI values in a gateway network, a time-efficient workload-based (WL) distributed support vector machine (WL-DSVM) algorithm is introduced in this paper to perform the indoor positioning. Experimental results show that with 5 distributed sensor nodes running in parallel, the proposed WL-DSVM can achieve a performance improvement in run time up to 3.2× with a stable positioning accuracy.