Hybrid maximum depth-kNN method for real time node tracking using multi-sensor data

In this paper, a hybrid maximum depth - k Nearest Neighbour (hybrid MD-kNN) method for real time sensor node tracking and localization is proposed. The method combines two individual location hypothesis functions obtained from generalized maximum depth and generalized kNN methods. The individual location hypothesis functions are themselves obtained from multiple sensors measuring visible light, humidity, temperature, acoustics, and link quality. The hybridMD-kNN method therefore combines the lower computational power of maximum depth and outlier rejection ability of kNN method to realize a robust real time tracking method. Additionally, this method does not require the assumption of an underlying distribution under non-line-of-sight (NLOS) conditions. Additional novelty of this method is the utilization of multivariate data obtained from multiple sensors which has hitherto not been used. The affine invariance property of the hybrid MD-kNN method is proved and its robustness is illustrated in the context of node localization. Experimental results on the Intel Berkeley research data set indicates reasonable improvements over conventional methods available in literature.