# Battery Life Prediction for Ensuring Robust Operation of IoT Devices in Remote Metering
## 6 Conclusions  
In this paper, we propose a battery life prediction method to ensure the robust operation of IoT devices in remote metering applications. The robust battery life prediction process proposed in this paper is structured in two stages. The first stage focuses on predicting the SOC, enabling the real-time remote monitoring of battery conditions in metering devices. In the second stage, a hardware-based alerting mechanism is integrated into IoT devices to issue warnings prior to complete discharge. This mechanism utilizes a custom-designed MSD primary battery architecture to enhance the reliability and operational efficiency of IoT devices in remote metering applications. These two stages collectively contribute to improving battery life management and ensuring the robust operation of IoT systems. For the real-time prediction of SOC, we developed the C-SDFormer model. C-SDFormer uses a self-attention mechanism to process sequences of voltage, temperature, \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**MAE (\%)**} & \multicolumn{4}{c}{**RMSE (\%)**} & \multicolumn{4}{c}{**SMAPE (\%)**} & \multicolumn{4}{c}{**R\({}^{2}\)**} \\ \cline{2-13}  & **\#1** & **\#2** & **\#3** & **Average** & **\#1** & **\#2** & **\#3** & **Average** & **\#1** & **\#2** & **\#3** & **Average** & **\#1** & **\#2** & **\#3** & **Average** \\ \hline DNN [13] & 4.799 & 7.643 & 6.366 & 6.269 & 6.207 & 9.802 & 8.018 & 8.009 & 8.131 & 11.54 & 8.708 & 9.459 & 0.949 & 0.854 & 0.920 & 0.907 \\ \hline LSTM [14] & 3.745 & 6.921 & 4.360 & 5.008 & 4.406 & 8.037 & 5.558 & 6.000 & 7.471 & 10.23 & 6.744 & 8.148 & **0.974** & 0.903 & 0.960 & 0.945 \\ \hline CNN [15] & 3.793 & 5.337 & 6.169 & 5.099 & 4.840 & 6.763 & 7.035 & 6.212 & 7.366 & **8.144** & 9.441 & 8.317 & 0.964 & 0.921 & 0.928 & 0.937 \\ \hline CNN-LSTM-DNN [16] & 4.228 & **5.120** & 6.241 & 5.196 & 5.716 & 6.448 & 7.729 & 6.631 & 7.260 & 11.03 & 9.226 & 9.172 & 0.954 & 0.943 & 0.920 & 0.939 \\ \hline Bi-GRU [17] & 3.810 & 7.492 & 6.565 & 5.955 & 4.844 & 9.250 & 8.417 & 7.503 & 6.714 & 12.32 & 8.789 & 9.274 & 0.968 & 0.889 & 0.904 & 0.920 \\ \hline CNN-BWGRU [18] & 3.777 & 6.586 & 4.995 & 5.119 & 4.704 & 8.353 & 6.192 & 6.416 & 6.834 & 12.48 & 6.898 & 8.737 & 0.970 & 0.904 & 0.948 & 0.940 \\ \hline C-SDFormer (Ours) & **3.408** & 5.123 & **3.534** & **4.021** & **4.602** & **6.391** & **4.567** & **5.186** & **5.070** & 11.60 & **4.547** & **7.072** & 0.972 & **0.950** & **0.974** & **0.965** \\ \hline \hline \end{tabular} Note: Bold values indicate the best performance among the compared methods.  
\end{table} 
Table 6: Performance comparison of SOC estimation between C-SDFormer and other methods. Figure 8: Estimation error rate of SOC at primary point. and communication count data for SOC prediction. Specifically, the model uses transformer layers to effectively extract features from input and output sequences, and series decomposition is integrated into the transformer to mitigate noise. The effectiveness of this model architecture was validated through an ablation study. To evaluate the SOC estimation, we deployed smart gas meters equipped with the MSD primary battery in households and collected discharge characteristic data. The collected temperature, voltage, and communication count data were used as model inputs, while the discharge capacity was used as the ground truth for SOC estimation. As a result, C-SDFormer demonstrated superior performance compared to existing SOC prediction methods, achieving an average MAE of 4.021%, an average RMSE of 5.186%, an average SMAPE of 7.072%, and an average R\({}^{2}\) of 0.965. In addition, C-SDFormer produced results closer to the ground truth in primary point estimation than other methods. These results confirm the C-SDFormer model's robustness and accuracy in SOC estimation and primary point prediction for MSD primary batteries, offering a promising solution for real-world applications.