# Energy-Aware Control of Error Correction Rate for Solar-Powered Wireless Sensor Networks
## 2 Related Work
### Energy Optimization for Solar-Powered WSNs  
The first research issue that has been studied on energy optimization for solar-powered WSNs is to increase energy utilization in order to make the node operate constantly while utilizing most of the harvested energy. [PERSON] et al. [6] introduced an energy model for solar-powered nodes, which determines the bounds of energy that allow a node to survive indefinitely. [PERSON] et al. [7] proposed a transmission energy consumption model considering transmission distance, data length and path loss.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{\begin{tabular}{c} **Energy** \\ **Source** \\ \end{tabular} } & \multirow{2}{*}{**Characteristics**} & \multicolumn{2}{c}{**Amount of Energy**} & \multirow{2}{*}{\begin{tabular}{c} **Harvesting** \\ **Technology** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Conversing** \\ **Efficiency** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Amount of Energy** \\ **Harvested** \\ \end{tabular} } \\ \hline \multirow{2}{*}{\begin{tabular}{c} Solar \\ \end{tabular} } & Ambient, & \multirow{2}{*}{\(100\) mW/cm\({}^{2}\)} & \multirow{2}{*}{\(15\)\%} & \multirow{2}{*}{\(15\)\%} & \multirow{2}{*}{\(15\) mW/cm\({}^{2}\)} \\  & Uncontrollable, & & & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} Wind \\ \end{tabular} } & Ambient, & \multirow{2}{*}{\(-\)} & \multirow{2}{*}{Anemometer} & \multirow{2}{*}{\(-\)} & \multirow{2}{*}{\(1200\) mW/day} \\  & Uncontrollable, & & & & \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Finger \\ motion \\ \end{tabular} } & Active Human & \multirow{2}{*}{\(19\) mW} & \multirow{2}{*}{Piezoelectric} & \multirow{2}{*}{\(11\)\%} & \multirow{2}{*}{\(2.1\) mW} \\  & power, Fully & & & & \\ \hline
\begin{tabular}{c} Vibrations \\ in indoor \\ environments \\ \end{tabular} & Ambient, & \multirow{2}{*}{\(-\)} & Electromagnetic & \multirow{2}{*}{\(-\)} & \multirow{2}{*}{\(0.2\) mW/cm\({}^{2}\)} \\  & Uncontrollable, & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Types and characteristics of harvesting sources (Data from [1]).  
Then, the prediction of solar-energy harvested has been studied. The amount of energy harvested has to be accurately predicted and managed in order to ensure the maximized utilization. [PERSON] et al. [8] and [PERSON] et al. [9] introduced a harvested energy prediction scheme using a moving average approach and daily weather change, respectively. [PERSON] et al. [10] proposed pro-energy, which can facilitate more precise prediction using long-term and short-term predictions according to weather and time.  
Recently, the methods for allocating energy have been studied, in order to efficiently use time-variant solar energy independent of time. [PERSON] et al. [11] proposed a scheme to allocate the necessary amount of energy to time slots considering the historic data according to time and weather [PERSON] et al. [12] proposed a data gathering rate control scheme by allocating energy to the nodes and dynamically sensing and routing. [PERSON] et al. [13] proposed an adaptive data aggregation and compression to improve energy utilization. In this scheme, each node in a WSN periodically determines the amount of energy that it is allocated, which takes into account its residual energy and its likely acquisition and consumption. According to the allocated energy, it is determined whether the sensed data are aggregated with data from other nodes, compressed and transmitted. Our main goal is to allocate solar energy periodically to collect data stably and to use the surplus energy for recovering the broken data to improve data reliability.