Event-driven Gaussian process for object localization in wireless sensor networks
Jae Hyun Yoo, Woo Jin Kim, H. J. Kim · 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems · 2011
Object localization using wireless sensor networks (WSN) often requires data from many sensor nodes and different types of sensors for position estimation. This incurs a heavy communication load, which can cause packet loss, communication delay and much energy consumption, deteriorating the performance of object localization. Here we employ an event-driven Gaussian process in order to learn the position of an unknown object using WSN with multiple types of sensors. In the event-driven framework, each sensor node transmits data only when decision criteria are satisfied. We consider the error-bounded algorithm as the decision criteria based on the measurement history of each sensor node. The overall communication between sensor nodes is reduced, thus increasing energy-efficiency of the network and relieving the concentration of communication traffic at the base node. Experiments to track the position of a mobile robot are conducted using a multi-sensor WSN, and the comparison is made between the event-driven framework and the conventional approach in which sensors transmit data at a constant sampling rate. Experimental results demonstrate the efficiency and accuracy of the proposed event-driven Gaussian process approach.