Fault-Tolerant 1-bit Representation for Distributed Inference Tasks in Wireless IoT
Mostafa Hussien, Kim Khoa Nguyen, Mohamed Cheriet · 2021
In IoT applications, the sensors usually have limited bandwidth and power resources. Therefore, the sensed data should be mapped to a low-bit representation by means of compression and quantization before being transmitted to a central node, called the fusion center (FC). At the FC, a global decision is inferred from this data. In many cases, this data is intended for machine consumption, not for human perception. However, the compression techniques are mainly designed for reconstruction fidelity. The accuracy of the inferred decision at the FC is less considered. In this work, we present an end-to-end framework for learning a 1-bit representation of correlated-sensors data. We also propose a novel loss function and a three-stage training algorithm for learning discriminative binary features at each sensor. Extensive experiments show the proposed framework achieves high compression ratios with a marginal loss in the inferred decision accuracy. Comparatively, the obtained results outperform other benchmark models in the literature.