Compressive Sensing-Based IoT Applications: A Review

Abstract

The Internet of Things (IoT) holds great promises to provide an edge cutting technology that enables numerous innovative services related to healthcare, manufacturing, smart cities and various human daily activities. In a typical IoT scenario, a large number of self-powered smart devices collect real-world data and communicate with each other and with the cloud through a wireless link in order to exchange information and to provide specific services. However, the high energy consumption associated with the wireless transmission limits the performance of these IoT self-powered devices in terms of computation abilities and battery lifetime. Thus, to optimize data transmission, different approaches have to be explored such as cooperative transmission, multi-hop network architectures and sophisticated compression techniques. For the latter, compressive sensing (CS) is a very attractive paradigm to be incorporated in the design of IoT platforms. CS is a novel signal acquisition and compression theory that exploits the sparsity behavior of most natural signals and IoT architectures to achieve power-efficient, real-time platforms that can grant efficient IoT applications. This paper assesses the extant literature that has aimed to incorporate CS in IoT applications. Moreover, the paper highlights emerging trends and identifies several avenues for future CS-based IoT research.

Classification of multiple power quality events via compressed deep learning

“…CS empowers a theoretically great reduction in the sampling and computation burdens for sensing signals that have a sparse or compressible illustration while in Shannon sampling theorem, the sampling rate must be more than twice the maximum frequency component of the signal being measured. Based on the CS, the sampling rate can be reduced more than half of original signal, and signals can be directly compressed with sparse behavior at the sensing stage . It is the fundamental fact that the computational density is directly related with sampling frequency .…”

“…The computational load of a signal processing can be reduced using a signal compression approach . CS procedure shrinks computational complexity and storage load, and AE can be employed to any of dimensionality reduction . In this paper, the PQ disturbance signals have been compressed 1/10 of the original signal using CS technique.…”

Special Issue: Empowering eHealth with Smart Internet of Things (IoT) Medical Devices

“…Six papers have been selected in this special issue that respond to questions raised [1][2][3][4][5][6]. The first paper, entitled "Design, Fabrication, and Testing of an Internet Connected Intravenous Drip Monitoring Device" submitted by researchers from German universities, presents a monitoring system retro-fittable for existing Intravenous (IV) infusion setup that leverages human based estimations of time required by an IV bottle to empty, which makes the IV therapy vulnerable to human error [1].…”

“…Paper 5 is entitled "Compressive Sensing-Based IoT Applications: A Review" is another review paper submitted by researchers from Qatar University. The aim of this paper is to highlight emerging trends and venues to optimize data transmission while optimizing energy consumption in IoT [5]. In particular, it advocates the compressive sensing (CS) as an attractive paradigm to be incorporated in the design of IoT platforms.…”

“…In particular, it advocates the compressive sensing (CS) as an attractive paradigm to be incorporated in the design of IoT platforms. CS is a novel signal acquisition and compression theory that exploits the sparsity behavior of most natural signals and IoT architectures to achieve power-efficient, real-time platforms that can grant efficient IoT applications [5].…”