# Compressive Sensing-Based IoT Applications: A Review
## 1 Introduction  
Internet of Things (IoT) refers to the large set of smart embedded devices connected to the Internet to provide specific services to meet the users demands [1]. IoT presents the shift from only connecting the end-user devices to the Internet to using the Internet itself to interconnect smart objects (known also as IoT devices) and to communicate with each other and/or with humans to offer a wide range of applications and services [2; 3]. IoT platforms usually deploy a large scale of smart objects including wearable sensors, actuators and radio frequency identification (RFID) devices to remotely monitor different physical, environmental and physiological quantities to improve the everyday activities of the end-users [4; 5]. In fact, the IoT devices often operate in a long-term mode and communicate wirelessly with each other and with a central fusion node in order to empower diverse remote monitoring platforms. In general, remote sensing devices are battery-driven, hence their performance is prone to the limited battery lifetime leading to both poor integration and user adherence. Thus, to overcome those limitations, the acquired data should be first compressed and then transmitted through optimized paths to the fusion centre to minimize the high energy. However, applying advanced data compression and transmission techniques may also consume considerable onboard energy. Therefore, the adopted compression technique has to sustain a long-term efficient monitoring along with an optimized power consumption. Compressed sensing (CS) is an emerging signal processing paradigm which aims to acquire directly a compressed form of signals with sparse behavior at the sensing stage and enables a high reconstruction quality at the receiver stage [6; 7]. CS presents an alternative paradigm to the traditional acquisition fundamentals that state that the number of measured samples should be at least equal to the number of samples in the original signal to ensure an exact recovery. However, these conditions do not take the structure of the signal into consideration. Thus, if the signal of interest is sparse, i.e., the signal can be represented by a smaller number of non-zero coefficients than its original dimension, CS claims that taking only a few numbers of random linear measurements (projections) of the sparse signal is enough to capture the salient information in the signal to provide an acceptable reconstruction quality. CS aims to shift the complexity from the sensors which are usually resources constrained and self-powered to the receiver side which is usually installed on computing platforms with relaxed constraints. For most real-world applications, it is always possible to find a sparse or compressible representation for signals of interest using the appropriate transformation. Thus, CS has spread widely in various applications such as radar, image processing, bio-signal compression, wireless communications and many others. For instance, the healthcare sector has witnessed a tremendous efforts to explore CS in different applications. Experts believe that CS would be beneficial for a wide range application in medical laboratory and pathology testing, particularly where many data are generated. CS may also improve wearable health monitoring platforms, making it smaller, cheaper, and more energy efficient [8]. CS is expected to optimize power and energy used in wireless ambulatory devices, hence extending the sensor lifespan and significantly simplifying hardware design and reducing both the size and cost of the entire healthcare platform. CS-based healthcare applications include medical imaging [9], electrocardiogram (ECG) monitoring [10], EEG compression [11], biometric solutions [12], etc. Subsequently, IoT platforms have also witnessed the integration of CS into its various application based on two properties that most of the IoT platforms exhibit. First, a wide range of real-world data can be well approximated by a sparse signal using the appropriate transform.