# Compressive Sensing-Based IoT Applications: A Review
## 1 Introduction
For instance, both discrete cosine transform (DCT) and discrete wavelet transform (DWT) provide a good sparse representation for ECG, images, temperature, humidity data, etc. Moreover, it is always possible to form a sparsifying basis by means of dictionary learning methods [13]. Thus, exploring CS has been widely investigated in long-term data acquisition for large-scale wireless sensor networks (WSNs) [14; 15; 16]. The second property relies on the sporadic transmission schemes that most of the IoT platforms exhibit. In a sporadic transmission scheme, not all devices transmit their data simultaneously to the fusion node, rather only a small number of devices contribute on the aggregated signal at any given time. i.e., the rate of the active devices at each transmission time slot is very small. Thus, the architecture sparsity can be explored and the knowledge about the nodes activity can be exploited at the cloud level by means of sparsity aware and joint detection protocols to achieve a high data reliability with a small number of transmitting devices. The objective of this paper is threefold: First, it aims to present a simple and a coherent overview of the fundamentals and the mathematical models that underlie CS concept as well as its well-considered recovery algorithms. Further, the paper highlights the main difference between CS and state-of-the-art compression techniques. Additionally, the paper also reviews the distributed compressive sensing (DCS) which represents the extension of CS to the multi-channel acquisition scenario. Secondly, the paper surveys the different research efforts in the area of CS-based IoT platforms aiming to emphasize on the importance of deploying joint CS-IoT applications. Since IoT platforms are multidisciplinary paradigms that support the connection between different cross layers from the data sensing to the enabled services and applications. The paper divides the IoT into three main layers, namely, sensing, processing an application. Thereafter, for each layer, the paper delivers a thorough comprehensive discussion for the extant efforts and research directions that have been investigated and quantified in the literature to design and empower a high efficient embedded CS-based IoT platforms. To the best of our knowledge, no such review study has been presented in the literature before. Third, the paper extends the discussion to illuminate the main research orientations and the emerging trends that have been originally developed out of the IoT scope. However, they hold great potentials for future integration within the context of energy-efficient real-time response CS-based IoT applications. This review paper aims to present a significant contribution to the joint CS-IoT researches by reviewing current work, diffusing insights and highlighting the important research trends that can be devoted to the CS-based IoT applications. The remaining sections of this paper are organized as follows. Section 2 provides an overall description of general IoT framework and architecture. In Section 3, a brief overview of CS concept is provided by describing the system model, highlighting the general considerations on the design of the sensing matrix and presenting the most considered recovery algorithms associated with CS. Section 4 addresses the main issue of this paper by discussion thoroughly the joint CS-IoT efforts over the different IoT layers. The limitations facing each layer are exposed and the related CS efforts that have been made to tackle them are analyzed. Section 5 presents a discussion about open research issues and avenues for future work in IoT. Section 6 concludes the paper.