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.

A Testbed and an Experimental Public Dataset for EnergyHarvested IoT Solutions The Internet of Things (IoT) paradigm poses a great variety of application domains where million of devices work uninterruptedly to improve some aspect of our lives. To support the continuous execution of the applications working on the devices, energy harvesting systems enable to extract the energy found naturally in the environment (for instance from the sun or from the wind) and convert it into energy able to either sustain the devicexe2x80x99s operation and recharge its batteries which, in conjunction with an appropriate scheduling strategy, led to the device to an electrically sustainable state (i.e. an energyneutral state). Most of the works found in literature oriented to achieve energy neutrality are however evaluated by means of simulation which means that, in spite of precisely modeling hardware features and energy productions, lack of the realism that we find in a real deployment. A minor part of the works are based on a real deployment but do not share the collected data that permit to replicate the analysis. With this purpose in mind, in this article we describe a testbed designed for outdoor monitoring purposes in the IoT context, equipped with several sensors for weather conditions monitoring and with a solar panel to provide application lifetimes potentially infinite. The testbed was deployed on the roof of a building and it executed uninterruptedly an application able to generate a dataset with the collected information over a period of more than two months. This dataset has been online published to be used for different researching purposes, as for instance, prediction models of the energy production.