Adaptive Decision Fusion with a Guidance Sensor in Wireless Sensor Networks

Abstract

In wireless sensor networks, the fusion center collects the dates from the sensor nodes and makes the optimal decision fusion, while the optimal decision fusion rules need the performance parameters of each sensor node. However, sensors, particularly low-cost and low-precision sensors, are usually displaced in harsh environment and their performance parameters can be easily affected by the environment and hardly be known in advance. In order to resolve this issue, we take a heterogeneous wireless sensor network system, which is composed of both low-quality and high-quality sensors. Low-quality sensors are inexpensive and consume less energy while high-quality sensors are expensive and consume much more energy but provide high accuracy. Our approach uses one high-quality sensor as the guidance sensor, which enables the fusion center to estimate the performance parameters of the low-quality sensors online during the whole sampling process, and optimal decision fusion rule can be used in practice. Through using the low-quality sensors rather than the high-quality sensor most of the time, the system can efficiently reduce the system-level energy cost and prolong the network lifetime.