# An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors
## 2 Related Work  
Energy management techniques to achieve long term monitoring and self-sustainability in WSN applications are widely used and explored in the literature at both node and network level [10; 11]. The most common approaches employ: communication power management [12], voltage scaling [5], adaptive IC power modes, duty cycling [13], wake up receivers [14] and data aggregation [15]. Beside them, another popular way to reduce energy consumption in WSN is using the adaptive radio frequency approach [16; 17] and also static or dynamic packet length optimization, in order to save energy on transmission [18; 19]. Another approach is the implementation of adaptive sampling algorithms [6; 20; 21], which allows a high increase in lifetime. Today, the traditional methods, with fixed sampling rate, are replaced with solutions that modify the sampling rate of the sensors according to the needs (_i.e._, temporal changes and frequency) of the phenomenon observed. In this way, the sensor node does not waste energy sampling the sensor more than the actual maximum frequency of the signal [20]. However, using only adaptive sampling can guarantee an extension of the lifetime, but may not guarantee self-sustainability (or energy neutrality) of WSN, as the battery is still a finite energy resource. Energy harvesting (EH) for WSN has been very prolific in the last decade of research [22; 23], trying to overcome the battery limitation. EH plays an important role in enhancing the life span of a node in a network that aims to achieve self-sustainability [2; 21]. A good example of an energy harvesting system that utilizes small wind turbine to harvest wind energy and to power the associated electronic circuitry as well as sensing the wind is presented in [24]. The authors give a detailed description, analysis, and evaluation of the proposed energy harvesting system for autonomous wind sensors and prove the concept of fully autonomous self-powered wireless sensing. A similar example of an autonomous self powered WSN node is analyzed and developed in [25], where the authors consider the application of monitoring wildfire spread in a remote area. As emphasized in [25], the main problem of a battery powered WSN node in fire hazard monitoring applications is that when the battery is running low the sensor nodes could stop functioning and expose the area to fire danger, which is not acceptable for such a monitoring system. To that end, authors are suggesting a solution in the form of a self powered WSN node that will measure the wind speed at the site, and at the same time, harvest the energy from the wind to sustain the remote sensing operation of the WSN node. A battery-less self powered wireless sensor, that autonomously performs a measurement of air temperature and velocity and transmits data to an external receiving unit is presented in [26]. The proposed sensor is powered by a harvesting system that exploits the airflow. The sensor continuously operates for airflow speeds greater than 3 m/s. For slower flows, the sensor is off, and the receiver assumes that the velocity of air is below the threshold. This type of application allows the battery constraints to be overcome by excluding the battery from the system. However, the main issue with the approach presented in [24; 25; 26], is the unpredictable variable nature of the energy sources in both space and time. Hence, energy harvesting systems on their own cannot ensure that the node will last [27]. Complementing the energy harvesting with an efficient energy management algorithm is often a solution to this problem [27]. An effective power management algorithm for sensor networks with sensors that do not require a high sampling rate is presented in [28]. Another interesting approach is described in [29], where the authors present an algorithm that adaptively adjusts the processor speed to achieve system-wide energy efficiency based on the workload and available energy information. In [30], the authors present an energy management technique for WSN with solar energy harvesting.