# Compressive Sensing-Based IoT Applications: A Review
## 6 Conclusions  
IoT applications require a huge number of heterogeneous smart devices to sense, communicate and collaborate with each other in order to gather the maximum information to enable numerous types of services. A large number of sensors will generate big redundant data that would cause unnecessary network traffic, thus degrading the overall system performance. Therefore, exploring CS can reduce the number of data collected and can release the pressure on the wireless communication.  
This paper provides a brief overview on CS theory, starting from the basic model of the signal acquisition to the criteria that should be satisfied while designing the sensing matrix, as well as listing some of the most considered reconstruction algorithms used in the literature.  
Moreover, this paper reviews the extant work presented in the literature that addresses the integration of CS techniques in the IoT platforms. To this end, and aiming to make the discussion clear and easy to follow, the IoT platform is divided into three layers and the related CS researches for each layer is discussed thoroughly.  
The acquisition layer consists of the huge number of sensors that communicate with each other and with a central fusion node. High power consumption presents the most important issue to tackle. Thus, CS techniques can be deployed as a compression technique to reduce the power consumption.  
In addition, compressive gathering, sparse networking and hybrid joint DCS-routing protocols can also be explored to achieve energy-efficient sensing layer.  
At the processing unit, implementing edge computing platform on FPGAs presents an appealing solution to achieve real-time performance. Several design and architectures for CS reconstruction algorithms have already been proposed and investigated in the literature.  
For the application layer, the utilization of CS can be extended from being just a compression paradigm to a broad range of applications by exploring the recovery algorithms associated with CS to enable several applications related to smart cities applications and next generation communication systems.  
While it is hard to cover all of the CS software and hardware research that can be integrated with the realm of IoT, this paper highlights the open issues and future trends that can be addressed exploring CS techniques along with some powerful computing platforms to stimulate new research orientation in the field of IoT. The realization of CS encoder that provides directly a compressed signal acquisition is a hot topic to investigate as it enables the next era of digital sampling. In addition, using structured sensing matrices would simplify the acquisition operation and allow real-time data reconstruction. Moreover, providing a CS implementation on commercial multi-core platforms can be well fitted for different IoT applications related to smart home and health care. At the top of all that, CS can be used to enable secure and encrypted transmission scheme by exploring the randomness accommodated with acquisition process without introducing any additional processing.  
This paper was funded by National Priorities Research Program (NPRP) grant No. 9-114-2-055 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.  
We would like to thank [PERSON] from Hamad Bin Khalifa University (HBKU), Qatar, for his valuable discussion related to this paper.  
The authors declare no conflict of interest.