Maximum Average Entropy-Based Quantization of Local Observations for Distributed Detection

In a wireless sensor network, multilevel quantization is necessary to find compromise between minimizing the power consumption of sensors and maximizing detection performance at fusion center (FC). The previous methods have been using distance measures such as J-divergence Bhattacharyya in this quantization. This work proposes different approach based on maximum average entropy output under both hypotheses utilizes it Neyman-Pearson criterion-based distributed scheme detect point source. receiver operating characteristics proposed (MAE) method quantizing outputs evaluated for when are available error-free FC non-coherent M-ary frequency shift keying communication used transmitting MAE quantized over Rayleigh fading channel. simulation studies show success cases where effect channel has incorporated. As expected, improves level increases with six-level approaches non-quantized data transmission.