# An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors
## 7 Conclusions  
In this paper, a novel Energy Aware Adaptive Sampling Algorithm for WSN with power hungry sensors and energy harvesting capability is presented. It has been shown that combining a traditional approach for adaptive sampling and energy aware management technique performed at node level can save energy, facilitating self-sustainability through energy harvesting. When the battery level is above a critical level, defined by the user (\(X_{level}\)), the proposed algorithm can use the sampling rate of any ASA. As the battery level goes below the critical level, the algorithm becomes more energy conservative by reducing the sampling rate. Thus, the node manages its activity in the network according to its energy levels. The algorithm was validated using real in-field data from two deployment scenarios in two different application spaces, namely, measuring wind speed and direction and monitoring bees in a smart beehive. These scenarios involved different sensors, including ultrasonic anemometers and gas sensors. Moreover, two different energy harvesting sources are considered, namely wind  
Figure 9: (a) Deployment (I): Energy in the system (ASA _vs._ EASA) as a function of time; (b) Deployment (II): Energy in the system (ASA _vs._ EASA) and fixed sampling rate energy level (\(E_{FSR}\)) as a function of time.  
and solar. The aim is to achieve a self-sustainable system that can manage its activity according to the available energy. The error introduced due to aliasing can be accepted as a trade-off for staying active in the network, and not discharging the battery completely. Using the combination of energy harvesting, adaptive sampling algorithm, and energy aware management technique, an end user can be guaranteed a self-sustainable wireless sensor network solution. The presented approach is especially suitable for applications with energy hungry sensors, and applications where maintenance has to be reduced to a minimum.  
This material is based upon works supported by the Science Foundation Ireland under Grant No. 12/RC/2302 MaREI, Analog Devices and Irish Research Council.  
[PERSON], [PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON], and [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} WSN & Wireless Sensor Networks \\ ASA & Adaptive Sampling Algorithm \\ EASA & Energy Aware Adaptive Sampling Algorithm \\ MAC & Medium Access Control \\ SHM & Structural Health Monitoring \\ MCU & Micro Controller Unit \\ BS & Base Station \\ ADC & Analog to Digital Converter \\ RISC & Reduced Instruction Set Computer \\ CPU & Central Processing Unit \\ SRAM & Static Random Access Memory \\ DC-DC & Direct current to Direct current \\ SD & Secure Digital \\ GSM & Global System for Mobile Communication \\ GPRS & General Packet Radio Service \\
3G & Third Generation mobile telecommunication technology \\ UART & Universal Asynchronous Receiver/Transmitter \\ TSR & Tip Speed Ratio \\ FSR & Fixed Sampling Rate \\ \end{tabular}