### Overview of the Conducted SLR  
Figure 1 depicts the top-level view of the entire SLR process conducted in this study. Using seven scientific databases (i.e., IEEE, Springer, Elsevier, SAGE, Wiley, MDPI, and Taylor and Francis), the research articles are scrutinized using a well-defined criterion.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Year** & **Focus** & **Limitations** \\ \hline
[24] & 2017 & Investigates various methods and techniques in the context of ambient energy sources and EH for SHM applications. & \(\bullet\) & Conducted without SLR guidelines. Not focused on bridge applications. Limited to 2016. \\ \hline
[25] & 2023 & Reviews the deployment of machine learning algorithms and feature extraction techniques in the SHM process for bridges. & \(\bullet\) & WSN was not explored. EH techniques, inspection scales, and response types were not explored. \\ \hline
[26] & 2013 & Provides an overview of EH techniques for SHM systems. & \(\bullet\) & Not focused on bridge applications. RF and thermal techniques were not explored. \\ \hline
[27] & 2017 & Explores the deployment of WSN platforms for the SHM. The particular focus is made on the damage detection algorithms. Another salient feature is highlighting the network challenges. & \(\bullet\) & Conducted without SLR guidelines. Not focused on bridge applications. Inspection scale and response type were not explored. \\ \hline
[28] & 2019 & Summarizes the opinion of experts for the deployment of WSN platforms as well as sensor nodes using wires in the context of SHM. The comparison is made for platform, architecture, functionality, communication, and operating system. & \(\bullet\) & Conducted without SLR guidelines and is almost 8 years old. Inspection scale and response types were not explored. \\ \hline
[29] & 2015 & Highlights the technologies and developments in the field of SHM using WSNs in bridge engineering. & \(\bullet\) & Conducted without SLR guidelines and is almost 10 years old. \\ \hline
[30] & 2013 & Reviews the WSN technology for the SHM of bridges. Various sensor types have been explored. & \(\bullet\) & EH techniques, inspection scales, and response types were not explored. \\ \hline
[31] & 2022 & Describes the methods and challenges for damage identification in bridges. The particular focus is made on the data transmission and analysis for an early damage prediction. & \(\bullet\) & Conducted without SLR guidelines. EH techniques were not compared. Inspection scale was not explored. \\ \hline \hline \end{tabular}
\end{table}
Table 1: State-of-the-art review articles on WSN platforms and EH techniques for the SHM of bridges.  
Section 2 elaborates on the methodology (criterion) used for the selection of 46 research articles. These research articles are then divided into three categories: WSN studies (28 research articles), energy harvesting studies (9 research articles), and combined studies (9 research articles). Section 3 explores the selected research articles according to various parameters, including sensor type (17 types), inspection scale (local/global), and response type (dynamic/static). While Section 3 overviews the WSN platforms and EH techniques, a comprehensive comparison is provided in Section 4. The energy harvesting techniques include solar, wind, thermal, radiofrequency (RF), and vibration-based (electromagnetic and piezoelectric material (PZT)). Section 5 provides answers to the formulated research questions. Section 6 provides a discussion of the achieved results along with the limitations of the employed process. The concluding remarks are given in Section 7.