## 7 SMEP Algorithms
### AuxDataQueue Compression Algorithm
\begin{table}
\begin{tabular}{c|c c c c|c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Sensor Data Sets** \\ \end{tabular} } & \multicolumn{4}{c|}{**Relative Humidity**} & \multicolumn{4}{c}{**Air Temperature**} \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Methods \\ \end{tabular} } & 1 & 2 & 3 & 4 & 1 & 2 & 3 & 4 \\  & CR: 50\% & CR: 25\% & CR: 12.5\% & CR: 6.25\% & CR: 50\% & CR: 25\% & CR: 12.5\% & CR: 6.25\% \\ \hline _winavg_ & 4.60\% & 9.93\% & 14.30\% & 21.35\% & 5.70\% & 10.05\% & 19.66\% & 31.10\% \\ _delta_ & 4.62\% & 12.61\% & 24.03\% & 38.40\% & 5.70\% & 15.29\% & 27.27\% & 50.44\% \\ _CQP_ & 3.23\% & 6.57\% & 10.75\% & 21.57\% & 2.11\% & 4.55\% & 14.80\% & 34.02\% \\ _2 MC_ & 3.32\% & 7.43\% & 12.15\% & 25.25\% & 1.76\% & 4.39\% & 6.34\% & 29.99\% \\ \hline \(\underline{SMEP}\) & 2.80\% & 5.72\% & 10.75\% & 17.66\% & 1.24\% & 3.92\% & 5.83\% & 31.94\% \\ \hline \multicolumn{2}{c|}{**Sensor Data Sets**} & \multicolumn{4}{c|}{**Underwater pH**} & \multicolumn{4}{c}{**Underwater Temperature**} \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Methods \\ \end{tabular} } & 1 & 2 & 3 & 4 & 1 & 2 & 3 & 4 \\  & CR: 50\% & CR: 25\% & CR: 12.5\% & CR: 6.25\% & CR: 50\% & CR: 25\% & CR: 12.5\% & CR:6.25\% \\ \hline _winavg_ & 0.46\% & 0.62\% & 0.82\% & 0.84\% & 0.22\% & 0.31\% & 0.44\% & 0.59\% \\ _delta_ & 0.49\% & 0.94\% & 1.83\% & 3.39\% & 0.22\% & 0.51\% & 0.82\% & 1.44\% \\ _CQP_ & 0.36\% & 0.68\% & 1.09\% & 1.84\% & 0.20\% & 0.34\% & 0.53\% & 0.71\% \\ _2 MC_ & 0.29\% & 0.44\% & 0.68\% & 0.81\% & 0.17\% & 0.34\% & 0.45\% & 0.82\% \\ \hline _SMEP_ & 0.24\% & 0.39\% & 0.62\% & 0.62\% & 0.16\% & 0.32\% & 0.45\% & 0.69\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average error rate (%) for each sensor data set in round compressions (CR: Compression Ratio (%)). in all methods, the underwater pH and underwater temperature samples with infrequent and low value changes show the AER difference between the 4 th round compression and each of the other round compressions is so small that we can generally ignore it. In contrast to this, every method shows the 4 th round compression is not feasible in the relative humidity and air temperature samples with frequent and high value changes. Of course, even if the maximum feasible number of round compressions depends on sensor data properties and sample periods, in these cases, Table 2 shows that the SMEP method is generally feasible up to 3 rd round compressions in the case of the samples with frequent and high value changes. In fact, the maximum feasible number of round compressions depends entirely on how finely or frequently sensor data are sampled. The more frequently sampled sensor data are, the higher the maximum feasible number of round compressions is. #### 9.2.2 Average Error Rates in _Zones_ Value Patterns

One among major merits of the SMEP method is _Zones_ compression. Such a merit is based on _Zones_ rules, in which the numbers of compressions can be different among zones of _DataQueue_. In fact, the round compressions in the experiments described in Section 9.2.1 are the special cases in which values of the non-zero value zones are all the same and there is no ground sensor data in zero-value zones. Hence, these cases are rare. The cases of _Zones_ value patterns introduced in this section are much more general and frequent than the cases of round compressions. From now on, we let _m-n-o-p Zones_ value pattern or, shortly, _m-n-o-p Zones pattern_ mean that values of _Zones[0]_, _Zones[1]_, _Zones[2]_, and _Zones[3]_ are \(m\), \(n\), \(o\), and \(p\), respectively, in the _DataQueue[0..16]_. In other words, _m-n-o-p Zones pattern_ means that sensor data corresponding to _Zones[0]_, _Zones[1]_, _Zones[2]_, and _Zones[3]_ in _DataQueue[0..16]_ are compressed \(m\), \(n\), \(o\), and \(p\) times, respectively. We tried to carry out experiments for investigating the merits in the virtue of _Zones_ patterns. For doing these, we prepared _DataQueue[0..16]_.