The internet of things (IoT) manages a large infrastructure of web-enabled smart devices, small devices that use embedded systems, such as processors, sensors, and communication hardware to collect, send, and elaborate on data acquired from their environment. Thus, from a practical point of view, such devices are composed of power-efficient storage, scalable, and lightweight nodes needing power and batteries to operate. From the above reason, it appears clear that energy harvesting plays an important role in increasing the efficiency and lifetime of IoT devices. Moreover, from acquiring energy by the surrounding operational environment, energy harvesting is important to make the IoT device network more sustainable from the environmental point of view. Different state-of-the-art energy harvesters based on mechanical, aeroelastic, wind, solar, radiofrequency, and pyroelectric mechanisms are discussed in this review article. To reduce the power consumption of the batteries, a vital role is played by power management integrated circuits (PMICs), which help to enhance the system’s life span. Moreover, PMICs from different manufacturers that provide power management to IoT devices have been discussed in this paper. Furthermore, the energy harvesting networks can expose themselves to prominent security issues putting the secrecy of the system to risk. These possible attacks are also discussed in this review article.

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In the last decades, a huge effort has been dedicated to the development of vibration-based techniques for damage detection. In this article, an algorithm based on the wavelet packet transform and the Karhunen-Loéve transform is analysed to perform a pattern recognition application for the structural health monitoring purpose. In this article, the wavelet packet transform is used to decompose the signals coming from an accelerometer on a vibrating composite beam. The configuration of the beam has been changed and the wavelet packet transform was tested as a feature extraction tool. Then the Karhunen-Loéve transform was applied to the data to classify the different patterns and to test its capability of pattern recognition.

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