# Compressive Sensing-Based IoT Applications: A Review
## 3 Compressive Sensing Overview  
CS is a two-stage paradigm that acquires the data in a compressed form at the sensor and reconstructs efficiently the original data from a fewer sample signal at the receiver. Thus, CS simultaneous senses and compresses the data in one single operation. \begin{table}
\begin{tabular}{p{85.4 pt} p{284.5 pt}} \hline \hline
**IoT Layer** & **Description** \\ \hline Sensing layer & Collect physical data entities (temperature, ECG blood pressure) using different sensors and convert the collected measurements into digital signal. \\ \hline Processing layer & The Processing Layer provides mainly transient data storage for the data received from the sensors and performs local data processing, executes real-time actions, and up-links the data to the cloud. \\ \hline Application layer & The task of the Application Layer is based on the information routed from the processing layer to perform data analysis and develops diverse IoT services. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A three-layer architecture for IoT platform. Figure 1: General IoT platform architecture considered in this paper. The well-established traditional sensing and processing theory in WSNs relies mainly on the Nyquist-Shannon sampling theorem which presents the main pillar of the digital processing realm. This theorem states that, given any signal with a bandwidth of \(W\), its information is entirely preserved if it is sampled at secured sampling frequency \(f_{s}\geq 2W\). Further, with the ever-increasing number of digital sensing devices, several challenges have faced this paradigm, from the torrent amount of data generated by the different sensing devices to the high data transmission rate and unbearable constraints on the sampling/processing platforms. Such challenges would largely increase the cost of the wireless transmission and shorten the sensors lifespan. Thus, to address the challenges associated with high-dimensional data, researches depends often on compression techniques by discarding the redundant samples in the signal. The benefits of compression will apply to the whole ubiquitous computing environment, as decreasing data size means shorter communication delays, efficient usage of bandwidth and reduced battery drain. For instance, taking WSN applications, where the energy consumption in the sensing nodes is mainly dominated by wireless communication rather than the onboard processing. State-of-the-art radio transmitters exhibit energy consumption of order of \(n\!\!\!\!/\,/bit\) while all the other sensor circuits consume at most only tens of \(p\!\!\!\!/\,/bit\)[36]. This cost disparity suggests that some data reduction strategy at the sensor node should be employed to minimize the energy cost of the system. Thus, reducing the number of the transmitted samples is necessary to achieve a long lifespan. However, it should be noted that these compression techniques remain useful as long as they do not consume more energy than the one saved by reducing the transmitted samples. In fact, in WSN, this condition is fulfilled. Recently, to tackle the issue of acquiring a huge amount of redundant samples, a very interesting theory, namely CS, has been proposed [6, 7]. CS suggests that it is possible to surpass the traditional limits of sampling theory for a specific type of structured signal by reducing the sampling rate without any significant loss in the data information. The authors argued that, if the information rate of the signal is less than its bandwidth rate, i.e., the signal is either sparse or compressible, then it is possible to capture all the information without any loss using fewer samples than what the Nyquist-Shannon theorem stats. Hence, CS enables a potentially large reduction in the sampling and computation costs for sensing signals with sparse or compressible behavior. CS differs significantly from state-of-the-art compression techniques.