Abstract
The incoming Internet of Things revolution requires the adoption of innovative paradigms for the design of low-power ubiquitous sensor nodes. This can be achieved by exploiting Compressed Sensing (CS), that is a recently introduced approach capable of simultaneously sampling and compressing an input signal with a limited amount of resources. While the underlying basic theory is well developed, in recent years we have seen a flourishing of CS techniques capable of exploiting some additional priors on the input signal to improve performance. In this paper, we propose a survey and a comparison of the most promising ones. We use a classification mechanism based on which prior is used and which processing block is modified with respect to the standard CS.