# Compressive Sensing-Based IoT Applications: A Review
## 5 Challenges and Research Trends
### Data Security  
Enabling secure communications is an imperative task in IoT applications. In the design of IoT platforms, the sensitive data (image, video, Medical records, etc.) transmitted over the wireless channel have to be secured against unauthorized access. Subsequently, data privacy is a paramount challenge that has to be addressed in IoT applications. A reliable solution can be realized by means of sophisticated state-of-the-art data encryption techniques. Nevertheless, CS can also be well suited to act as an encryption mechanism by exploiting the fact that randomly mapping the original data to another one would encrypt the information. Exploring CS as an encryption technique by using the random sensing matrix as the key has been investigated in [161], where the authors showed that even-though a perfect secrecy is not guaranteed, computational secrecy can be achieved as long as the sensing matrix satisfies the RIP and the number of random projections is at least twice the sparsity of the signal. In addition, [PERSON] et al. [162] showed that the design of an optimal sensing matrix in the context of full secrecy is not universal but it depends mainly on the structure of the signal of interest.  
In addition, WSNs have witnessed some applications where CS has been used as both acquisition and encryption scheme. [PERSON] et al. proposed a two-level CS-based security platform to transmit the data from the sensors to the cloud [163]. The first level of encryption is performed by acquiring data following the CS concept. Afterwards, the sensors further encrypt the compressed data without changing its dimension so no additional bandwidth cost is required. Recently, [PERSON] et al. [164] proposed a chaotic CS (CCS) scheme to tackle both energy saving and data security problems in large-scale wireless body area networks (WBANs). The aforementioned schemes are designed mainly to prevent external access to the data. However, to face attacks that can occur within the system (inference attacks) such as controllable event triggering attacks (CETA) and random event triggering attacks (RETA), [PERSON] et al. [165] presented a method called secure compressive data gathering (SCDG). The presented approach is based on changing the sensing matrix coefficients at each time slot.  
In addition, cloud security present as well a hot topic to address [166] and CS techniques have shown to disclose great opportunities. For instance, a CS-based encryption scheme for crowd sensing to enable efficient remote sensing system (RSS) maps are presented in [167]. In addition, [PERSON] et al. [168] presented a CS-based framework for preserving the privacy of reconstructed image when operating in outsourcing mode. Cloud outsourcing refers to shifting the data management from the embedded servers to the cloud servers. This framework would be very beneficial for application of connected health-care where the medical images with diagnostic results for different patients are privacy-sensitive. Reader should refer to [169; 170; 171; 172; 173] for variety of CS-based frameworks to enable secure cloud operations.