Distributed Sampling Design and Data Fusion for Signal Detection in Cluster-Based Sensor Networks

This work proposes a distributed sampling design for the signal detection application in the cluster-based wireless sensor networks (WSNs). Considering the energy saving requirement in the cluster-based WSNs, a linear weighting data fusion scheme for data reduction at the cluster head is also developed in this paper. Both the distributed sampling and the data reduction schemes are designed based on Ali-Silvey distance measures. The objective functions are derived in a closed form and two numerical examples are presented to illustrate our distributed sampling design and data reduction scheme. Numerical results show that our sampling design outperforms the uniform sampling and is insensitive to the sampling jitter. The results also show that the performance loss caused by the data reduction is quiet small. Therefore, the proposed schemes are very suitable for the detection applications in battery-powered WSNs.