Channel Aware Decision Fusion in Wireless Sensor Networks

TLDR

Information fusion using multiple distributed sensors is studied, but the optimal fusion rule requires perfect knowledge of local decision performance and fading channel characteristics. The study proposes two alternative fusion schemes—maximum ratio combining and a two‑stage Chair‑Varshney approach—to replace the optimal rule, and a robust equal‑gain‑combiner‑like statistic requiring minimal prior information. We extend the classical parallel fusion structure with a fading channel layer to derive a likelihood‑ratio fusion rule for fixed local decisions, propose alternative schemes (maximum ratio combining, two‑stage Chair‑Varshney, and an equal‑gain‑combiner‑like statistic), and evaluate performance analytically and via simulation.

Abstract

Information fusion by utilizing multiple distributed sensors is studied in this work. Extending the classical parallel fusion structure by incorporating the fading channel layer that is omnipresent in wireless sensor networks, we derive the likelihood ratio based fusion rule given fixed local decision devices. This optimum fusion rule, however, requires perfect knowledge of the local decision performance indices as well as the fading channel. To address this issue, two alternative fusion schemes, namely, the maximum ratio combining statistic and a two-stage approach using the Chair-Varshney fusion rule, are proposed that alleviate these requirements and are shown to be the low and high signal-to-noise ratio (SNR) equivalents of the likelihood-based fusion rule. To further robustify the fusion rule and motivated by the maximum ratio combining statistics, we also propose a statistic analogous to an equal gain combiner that requires minimum a priori information. Performance evaluation is performed both analytically and through simulation.