Maximum Average Entropy-Based Quantization of Local Observations for Distributed Detection
Muath A. Wahdan, Mustafa A. Alt{\i}nkaya

TL;DR
This paper introduces a maximum average entropy-based quantization method for distributed detection in wireless sensor networks, balancing power consumption and detection accuracy, and demonstrates its effectiveness through simulations.
Contribution
It proposes a novel quantization approach based on maximizing average output entropy, improving detection performance over traditional distance measure methods.
Findings
MAE method enhances detection performance with multilevel quantization.
Performance improves with increased quantization levels, nearing non-quantized data performance.
Effective in both error-free and noisy wireless communication scenarios.
Abstract
In a wireless sensor network, multilevel quantization is necessary in order to find a compromise between the smallest possible power consumption of the sensors and the detection performance at the fusion center (FC). The general methodology is using distance measures such as J-divergence and Bhattacharyya distance in this quantization. This work proposes a different approach which is based on maximizing the average output entropy of the sensors under both hypotheses and utilizes it in a Neyman-Pearson criterion based distributed detection scheme in order to detect a point source. The receiver operating characteristics of the proposed maximum average entropy (MAE) method in quantizing sensor outputs was obtained for multilevel quantization both when the sensor outputs are available error-free at the FC and when non-coherent M-ary frequency shift keying communication is used for…
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