# Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
## 3 Findings
Not only that the learning will be successfull, but the results are interpretable based on the understanding that the wireless channels with less contention perform better than those found in populated areas. depend on exact hardware setup (e.g., voltage supply), they are all constant, as opposed to variable terms, as detailed next. The ranges of the variables in Equation (8) are shown in Table 8. Note that \"pessimistic\" refers to the boundary of the interval that results in higher average consumption \(E_{attempt}\), while \"optimistic\" refers to the boundary of the interval with lower average consumption. A value denoted as optimistic or pessimistic does not necessarily mean a value judgement for system operation. For example, a lower \(r_{attempt}\) means less frequent transmission attempts, which is good for energy consumption, but causes high average time to transmit and lost data. Furthermore, note the difference between average energy consumption and the value for \(E_{attempt}\) given by Equation (8); while a low \(r_{attempt}\) will result in a higher \(E_{attempt}\), it will result in lower average power, as shown by Equation (10) below. The term in the denominator is the average time elapsed during a complete cycle. \[P_{avg}=\frac{E_{attempt}}{\frac{1}{r_{attempt}}+t_{GPS}+p_{success}e_{pass}t_{ pass}+(1-p_{success})t_{pass}} \tag{10}\]  
The results of these four variables on average modern power consumption are shown in Figure 13. The two dominant factors in determining average power consumption are the success rate and average time between attempts, while the smaller proportion of time \(\epsilon_{pass}\) idiled in Receive mode helps to keep the power low irrespective of other variables. The impact of the success rates on power and battery requirements is shown in Table 9 for the three preference models. Note that the \(p_{success}\) and \(r_{attempt}\) values are taken from the simulations, and \(e_{pass}\) and \(t_{pass}\) are taken as 0.5 and 25 min, respectively. For each preference model, three results are shown: (1) a baseline based on taking the earliest available passes and no scheduling, (2) another baseline based on the same average \(r_{attempt}\) as the simulated results but no scheduling, and (3) the test case with scheduling and simulated average \(r_{attempt}\). \begin{table}
\begin{tabular}{l l l} \hline \hline
**Constant** & **Meaning** & **Value** \\ \hline \(P_{SL}\) & Sleep Power & 550 \(\mu\) W \\ \(P_{GPS}\) & GPS Power & 230 mW \\ \(t_{GPS}\) & Time to GPS Fix & 30 s \\ \(P_{RX}\) & Receive Power & 130 mW \\ \(E_{TX}\) & Transmit Energy & 12.24 J \\ \(r_{pH}\) & Packet Rate & \(\frac{1}{3}\) h\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Constants used for transmission attempt energy model. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Variable** & **Pessimistic Value** & **Optimistic Value** \\ \hline Attempt rate & \(r_{attempt}\) & 1 h\({}^{-1}\) & \(\frac{1}{48}\) h\({}^{-1}\) \\ Success probability & \(p_{success}\) & 0.0 & 1.0 \\ Portion of time in & \(e_{pass}\) & 1.0 & 0.0 \\ Aerod mode & \(t_{pass}\) & 60 min & 10 min \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Sample variable ranges for transmission attempt energy model. Figure 13: Average power of the Swarm modem under different pass variable values.