Zhu et al. present in this research paper an interesting approach to the application of fusion threshold rules for target detection in wireless sensor networks (WSNs). They “propose a binary decision fusion rule that reaches a global decision on the presence of a target by integrating local decisions made by multiple sensors.”

Specifically, the authors “present a model-based, high-level, hard-fusion scheme, also known as decision fusion, where a final global decision is reached by integrating local binary decisions made by multiple sensor nodes that detect the same target from different distances.” This approach presents a centralized fusion scheme that uses Chebyshev’s inequality to derive threshold bounds, to ensure a better system performance compared with the weighted averages of all individual sensors.

The authors claim that, based on this approach, they have achieved better system performance than corresponding weighted averages. The determined threshold bounds can be quickly computed at the fusion center, and they seem to allow users a certain freedom in fine-tuning between sensitivity and specificity. The simulation results are based on the Monte Carlo method and show improvements on target detection performance, which can be used to guide the actual threshold selection in practical sensor network implementation, under certain error-rate constraints.

In general, this scientific work is very specialized. It is appropriate for researchers who are interested in solving detection and identification problems in WSNs.