A Learning Framework for Bandwidth-Efficient Distributed Inference in Wireless IoT

The limited bandwidth and power resources of wireless sensors in distributed environments have resulted in new challenges in handling the ever-growing volume of transmissions generated by the Internet-of-Things (IoT) applications. To overcome these challenges, each sensor should compress and quantize its observations before sending them to a fusion center (FC) for global decision inference. Unfortunately, most of the conventional compression techniques and entropy quantizers only focus on reconstruction fidelity as a performance measure, neglecting the sensing goal. In this article, we propose a joint design of compression mechanisms and entropy quantizers with the sensing goal of machine-to-machine (M2M) communications. We define a deep learning-based framework for compressing and quantizing observations from correlated sensors. Unlike traditional methods, our proposed method not only maximizes the reconstruction fidelity but also optimally compresses sensor observations in terms of the accuracy of the inferred decision (i.e., the sensing goal) at the FC. The proposed framework is widely applicable as it does not impose any assumptions on observation distribution. Furthermore, a novel loss function has been proposed to focus on learning complementary features at each sensor. Our experimental results demonstrate that the framework outperforms other benchmark models.