# An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors
## 1 Introduction  
One of the major obstacles for Wireless Sensor Networks (WSN) technology to expand even further and ultimately replace, wherever possible, wired sensor solutions, is their limited energy availability, which significantly reduces their lifetime, as batteries are the primary energy source for WSN nodes. The rate of improvement in battery capacity in the last decades has not increased significantly [1], when compared to advances in processing, sensing, and memory technologies. However, novel applicationsas well as miniaturization requirements put significant constraints on energy management systems. To that end, industrial and academic researchers are trying to explore several innovative solutions while waiting for a breakthrough in energy storage technology. The most successful approaches are to develop more energy efficient designs (for both hardware and software), [2; 3] and also to harvest energy from the environment often exploiting heterogeneous sources [4] in order to satisfy the energy needs for prolonged periods of time. Harvested energy is variable in time and not easily predictable. Low levels of available energy might also affect the fidelity and accuracy of the sensed/processed data, and might significantly impact on the reliability of the communication [5]. During the last two decades, research in WSN has been extensively focused on developing different Medium Access Control (MAC) and routing protocols, with the ultimate aim of effectively managing the activity of the transceiver, based on the assumption that data acquisition and processing have significantly lower energy consumption than wireless communication [6]. However, there are many applications where it can be wrong to assume that this is true. For instance, in cases of structural health monitoring (SHM) of civil infrastructures, the latest generation of sensors used by geomonitoring experts are extremely expensive with respect to energy requirements. In some applications, the energy cost of sensor sampling ranges from approximately double, up to few orders of magnitude more, than the energy required to wirelessly transmit data [7]. This is due to the fact that some of the sensors (e.g., gas and pressure sensors) have an associated warm up period, or that measurement of the phenomenon requires sampling in the range of seconds. In such applications, where the acquisition times are longer than data transmission, the energy consumption of the sensor is often higher than the energy spent for data transmission, which demands the MAC protocols to be complemented with techniques that implement an efficient energy management mechanism for the sensors [6]. In this paper, we present a novel energy aware adaptive sampling (EASA) approach that combines an adaptive sampling algorithm (ASA) with an energy management technique optimized for energy harvesting WSN. The main goal of this work is to show that combining energy harvesting from the environment and effectively managing node activity (_i.e._, the sampling rate of the sensors) according to the energy levels and the dynamics of the phenomenon observed, can ensure an enhanced lifespan of the nodes in the network, thereby achieving self-sustainability. The suggested energy aware adaptive sampling method allows each node in the network to adapt the sampling rate to its sensors according to the energy available in the system. The model proposed for adjusting the sampling rate, and the proposed power model of an EH WSN node, are designed to be generic and easy to implement with any specific WSN platform. Data collected from two different deployments are used to evaluate the proposed approach. Two different scenarios with different power hungry sensors and with different sources for harvested energy are used to collect real field data. In the first deployment (I), wind speed and direction are measured with power hungry ultrasonic wind sensors that are connected to a custom made platform based on a Texas Instruments MSP430 microcontroller and a CC2520 radio transceiver.