Ordering based energy efficient Neyman-Pearson classification in sensor networks

Since sensor longevity is crucial in wireless sensor networks, transmission saving is highly important. Ordering based energy efficient classification in sensor networks has shown significant savings in transmissions. The study presented in this paper improves upon the earlier work on ordered classification by extending the classification scheme to nonlinear kernel methods and introducing ordering based Neyman-Pearson (NP) classification. Given a specified level α ∈(0, 1), NP criterion ensures to keep a false alarm rate no greater than α while minimizing the miss rate. This study demonstrates transmissions can be saved, without degradation in error probability, using an ordering based NP-classification approach. The average number of transmissions saved is lower bounded by a quantity proportional to the number of sensors employed.