Compressed Sensing Signal and Data Acquisition in Wireless Sensor Networks and Internet of Things

The emerging compressed sensing (CS) theory can significantly reduce the number of sampling points that directly corresponds to the volume of data collected, which means that part of the redundant data is never acquired. It makes it possible to create standalone and net-centric applications with fewer resources required in Internet of Things (IoT). CS-based signal and information acquisition/compression paradigm combines the nonlinear reconstruction algorithm and random sampling on a sparse basis that provides a promising approach to compress signal and data in information systems. This paper investigates how CS can provide new insights into data sampling and acquisition in wireless sensor networks and IoT. First, we briefly introduce the CS theory with respect to the sampling and transmission coordination during the network lifetime through providing a compressed sampling process with low computation costs. Then, a CS-based framework is proposed for IoT, in which the end nodes measure, transmit, and store the sampled data in the framework. Then, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and lower energy efficiency. Performance is evaluated with respect to network size using datasets acquired by a real-life deployment.

I. Introduction

Researchers found that, in information systems, wireless sensor networks (WSNs), and Internet of Things (IoT), many types of information have a property called sparseness in the transformation process which allows a certain number of samples enabling capturing all required information without loss of information [1]–[4]. IoT has emerged as a technological revolution in the information industry [1], [2]. IoT is expected to be a worldwide network of interconnected objects, and its development depends on a number of new technologies, such as WSNs, cloud computing, and information sensing [2]–[4]. In IoT-based information systems, a low-cost data acquisition system is necessary to effectively collect and process the data and information at IoT end nodes [2], [3], [5], [6]. WSNs have the potential of a wide range of applications in many industrial systems. WSNs can be integrated into the IoT, which consists of a number of interconnected sensor nodes [3]–[5].