Event-driven Gaussian Process for Object Localization in Wireless Sensor Networks

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.