# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 5 Experiment and Evaluation  
### Experimental Settings  
The low-energy adaptive clustering hierarchy (LEACH) was the first dynamic clustering protocol proposed for hierarchical sensor networks and is a routing method developed by [PERSON] of MIT [33]. The LEACH protocol has been widely acknowledged as a proactive sensor network protocol that operates in a self-organizing hierarchical manner. It utilizes adaptive clustering techniques to effectively reduce the energy consumption of network components, hence prolonging their operational lifespan [34]. The core concept of the LEACH protocol is to organize nodes into clusters to distribute energy among all nodes in a network. LEACH assumes that the sink node is fixed and located far from the sensor nodes. In addition, all the sensor nodes are homogeneous and have limited energy and memory. Consequently, inside every cluster, a central node known as a cluster head (CH) collects the data acquired from the sensors within its cluster and then sends it to the sink node. This approach serves to decrease energy consumption and mitigate the amount of data sent to the central base station. It is assumed that the sensor nodes are positioned at the same height and that the antennas are mounted in a vertical orientation. Consequently, the elevation angle \(\theta\) and tilt angle \(\tau\) of the signal are both configured to be 0, as shown in Table 2.  
Figure 7 presents a LEACH-based WSN simulated using MATLAB, with the simulation parameters summarized in Table 3. The sink node is denoted by a green asterisk, which is positioned at the fixed coordinates of \((250,460)\). The sensor nodes are scattered in a random manner within an area of 500 m by 500 m. Each iteration consists of two phases: setup and steady state. In the setup phase, two CH nodes are selected from among the sensor nodes. The CH nodes are denoted as orange circles in Figure 7. Each CH broadcasts an advertising message (ADV-CH) to the other nodes, shown as small circles without a background color, resulting in the formation of two distinct clusters. Subsequently, each member node decides which cluster it belongs to by selecting the CH node with the highest received signal strength indicator for its ADV-CH message. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Elevation angle \(\theta\) & 0 \\ Tilt angle \(\tau\) & 0 \\ Signal carrier frequency \(f\) & 1.9 GHz \\ Rain rate \(R_{\text{rain}}\) (rainfall) & 10 mm/h \\ Liquid water content \(R_{\text{s}}\) (wet snow) & 2.5 mm/h \\ Liquid water density (fog) \(\xi\) & 0.5 g/m\({}^{3}\) \\ Cloud liquid water temperature \(T\) (fog) & 288.15 K \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Environmental parameters. In the steady-state phase, nodes gather data from their respective environments and send them to their CH within their designated time slot, following a time division multiple access (TDMA) schedule. CHs receive all data from their member nodes during normal operation and transmit them to the BS. Following a pre-established temporal interval, the network initiates a further iteration by turning back to the setup and steady-state stages. In addition to constructing a simulation model for a LEACH-based WSN, we also include real sensor data gathered in Beijing City, China, from December 2013 to October 2022. The sensor member nodes gather environmental data, such as PM2.5, P10, AQL temperature, and O3. These data will be sent to the CH nodes using a wireless channel. In order to evaluate the effectiveness of the data reconstruction model, we introduce additional noise to the sensed data and attempt to effectively recover the original data. The presence of noise encompasses both internal sources, such as thermal noise, and external sources caused by adverse weather conditions, such as rain, fog, or snow, simulated according to the parameters in Table 2.  
Given that the sensor nodes are situated in close proximity, it may be assumed that they are impacted by identical weather conditions.