# Compressive Sensing-Based IoT Applications: A Review
## 4 CS-Based IoT Applications
### Sensing Layer
Following the several results presented in the literature, the radio frequency transmission of the data over the wireless channel is the most power consuming process [36] and it can highly decrease the performance of the system, mainly if the sensors used are self-powered. Thus, determining the best compression technique in term of minimizing power consumption is the first step to develop an energy-efficient platform. Besides CS, many research groups have developed low power, low-cost compression techniques exploring state-of-the-art compression algorithms [68, 69, 70]. Subsequently, various efforts have been made to establish different frameworks to compare CS with state-of-the-art compression algorithms for different applications to determine the optimum compression technique. First, [PERSON] et al. [71] developed a non-adaptive CS-based platform for ECG compression on the Shimmer mode. In addition, they provided a comparison study between the performance of CS and DWT-based ECG compression in terms of reconstruction quality, node lifetime and CPU execution time. Although the results show that DWT outperforms the non-adaptive CS in terms of reconstruction quality (to achieve very good reconstruction quality, DWT requires \(M=0.3\times N\) samples in the compressed signal, whereas, to achieve the same reconstruction quality, CS requires \(M=0.5\times N\)), CS-based compression has shown to provide a better energy-efficient performance, with a node lifetime extension of 37.1% over DWT and an execution time 20 times faster than the one taken by DWT. [PERSON] et al. [36] implemented and quantified two different implementation approaches for CS, namely, digital CS and analog CS. The analysis showed that for real WSN applications, digital CS provides a better energy efficiency performance. Moreover, they compared CS with the Lempel-Ziv-Welch (LZW) compression technique; the CS compression system offers six times higher compression factor and over ten times lower implementation (storage/power) cost. Moreover, [PERSON] et al. [69] provided a more detailed comparative study including CS, DCS, transform coding (TC), predictive coding (PC) and adaptive sampling (AS) approach [72]. The work has investigated three different datasets including temperature, seismic signals and CO\({}_{2}\) emission data. The overall obtained results reveal that CS improves the energy saving by a factor of 79.4% compared to 62.43% and 34% obtained by TC and AS, respectively. In addition, [PERSON] et al. [73] conducted a comparative study in terms of reconstruction quality and computation time between CS and the segmentation and labelling technique [74] which has been considered for the definition of the new standard of the IEEE 1451 [75]. The obtained results show that, even though CS is more complex, it outperforms remarkably the segmentation and labelling technique in terms of data reconstruction and robustness to noise. Furthermore, a comparison between CS and transform coding (TC) and transmission without compression in terms of energy consumption is presented in [15]. First, the authors established energy dissipation models for both approaches to investigate the energy consumption in a unified framework. The obtained results indicate that CS extends the network lifetime up to four times compared to no transmission without compression processing if the data are highly sparse and the transmission range is between 50 m and 100 m. All these results prove that incorporating CS in IoT applications will provide more improvements in terms of energy-efficiency. Thus, with the relative superiority of conventional CS over classical compression techniques being established, a fundamental question is how to raise the standards and further enhance the exciting results of CS to minimize power consumption. To this end, various approaches have been proposed in the literature to answer this fundamental question.