# Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World
## 3 Ultra-Low Power MCU Architectures
### Compressive Sensing  
As briefly introduced in Section 1, not only architectural optimizations but also algorithmic optimizations are required to efficiently exploit the available energy. Usually, the energy bottleneck in ultra-low power systems is the communication subsystem, which can be in certain applications a very power-hungry block, depending on the communication standard and the hardware architecture used. Many algorithms have been proposed to manage meshes of a large number of small interconnected sensor nodes in order to limit the radio range [100; 101; 102; 103; 104; 105; 106; 107; 108], but, even in this scenario, it is not possible to just send the whole measured data to the cloud for post-processing. This is energetically too expensive and the burden of data to be processed is (and increasingly will be) a major problem in the IoT context. To cope with this problem, a large research effort has been dedicated to the compressive sensing paradigm. Indeed, it is possible, given that one can find a domain where the signal representation is sparse, to undersample the signal, i.e., not work at the Nyquist frequency, Figure 8. By approximating the signal shape, it is possible to retain the relevant information and send it instead of the whole ADC reading, or even work directly with signals compressed in the analog domain as in Analog to Information Converters (AIC) [109; 110; 111; 112; 113; 114; 115; 116; 117]. In particular, this is possible only when dealing with signals, whose waveform can be efficiently approximated resorting to a change of basis and/or by working on the transformed signal (Discrete Fourier Transform, Discrete Sine and Cosine Transforms, Wavelet Transforms and others). If in one domain the signal, or a faithful representation of it, can be compactly encoded, it is possible to send only its representation. For example, a sinusoidal signal in the time domain requires many samples to be represented correctly while, in the frequency domain, it is fully characterized by a single complex number representation. Even if periodic signals are easy to compress, it is also possible to resort to dictionaries shared by the node and the cloud as proposed in [118]. Indeed, in [118] authors cleverly set up a dictionary with redundancies to form a custom basis set for the signal representation, which adapts its content with respect to the signal shape.  
If there is no need to reconstruct the signal but only to extract some features, a different approach can be exploited in order to isolate and send features. As an example in certain bio-inspired applications, it is important to detect only some changes in the amplitude of the signal. This detection can be obtained by a thresholding mechanism, so that, when the signal crosses the threshold, the system generates a pulse or an event. In systems where only the event is important, it would be a waste of resources to send the whole signal. In [119], an event-based bio-inspired pseudo-neuromorphic approach is proposed. This system aims to mimic the behavior of neurons, by sending a pulse only when the signal crosses a defined threshold. Refs. [120; 121; 122; 123] show how this approach can be effectively coupled with an impulse radio UWB communication system. Indeed, in such a system, pulses behave as a spread spectrum signal that can be effectively sent with a very low power consumption and without further processing. This approach works well with low frequency signals, whose information can be predicted and where an event is an anomalous deviation from the expected behavior, which is conceptually similar to an asynchronous delta-sigma modulation.  
In [124], different types of compressive sensing and reconstruction algorithms are listed, along with a list of successful compressive sensing applications in the fields of imaging, biomedical, communications, pattern recognition, audio and video processing, dimensionality reduction, and very-large-scale integrated (VLSI) systems.