Quantized Fusion Rules for Energy-Based Distributed Detection in Wireless Sensor Networks

TL;DR

This paper investigates quantized soft decision fusion in bandwidth-limited wireless sensor networks for intruder detection, proposing practical suboptimal rules and optimal resource allocation strategies, validated through simulations.

Contribution

It introduces quantized fusion rules that mitigate quantization effects by increasing samples and derives optimal power and bit allocation for improved detection performance.

Findings

More bits are allocated to sensors with better channels.

Increasing the number of samples compensates for quantization.

Optimal power and bit allocation enhances detection accuracy.

Abstract

We consider the problem of soft decision fusion in a bandwidth-constrained wireless sensor network (WSN). The WSN is tasked with the detection of an intruder transmitting an unknown signal over a fading channel. A binary hypothesis testing is performed using the soft decision of the sensor nodes (SNs). Using the likelihood ratio test, the optimal soft fusion rule at the fusion center (FC) has been shown to be the weighted distance from the soft decision mean under the null hypothesis. But as the optimal rule requires a-priori knowledge that is difficult to attain in practice, suboptimal fusion rules are proposed that are realizable in practice. We show how the effect of quantizing the test statistic can be mitigated by increasing the number of SN samples, i.e., bandwidth can be traded off against increased latency. The optimal power and bit allocation for the WSN is also derived. Simulation results show that SNs with good channels are allocated more bits, while SNs with poor channels are censored.