A review of vibrational energy harvesters underpinned by a flexible snapthrough buckling beam Energy Harvesting (EH) is becoming mandatory in autonoumus measurement systems. EH encompasses all of the technologies and processes aimed at scavenging energy from alternative sources and, in particular, unused energy dispersed in the environment. In particular, the application to power directly or prolong the battery longevity of electronic devices (e.g., sensor nodes in a wireless sensors network or wearable sensors) has come to be recognized as very promising, even more so when the devices are installed in hazardous zones, embedded in infrastructure, or where the mains power is unavailable. Recently, it has also been acknowledged that the lack of selfpowered devices and the limited life time of batteries represent the main barriers to the diffusion of the Internet of Things (IoT) 1 and to other measurement related applications.

Systems that Sustain Themselves: Energy Harvesting Sensor Nodes for Monitoring the Environment Batteryless sensor networks, that harvest energy from the ambient, have attracted much attention in the last few years due to the promise of low maintenance and untethered perpetual operation. However, the major challenge in such networks is that the availability of nodes in the network depends on the energy profile of their harvesting sources. This might affect network reliability. In this work, we study the suitability of Energy Harvesting Sensor (EHS) nodes, powered using light and vibrations, for a simple temperature monitoring application. We evaluate whether such an EHS nodebased system can sustain itself and compare its performance with that of a traditional batterybased system. To economize on energy expenditure in the EHS system, we implement an Autoregressive (AR) model based adaptive sampling algorithm on the EHS nodes. After thorough experimental investigations, we conclude that the EHS nodebased system fares quite well. Results show that adaptive sampling helps achieve energy savings of 62.12% and a 52.33% reduction in the amount of sampled data.