## 3 System Profile and Overall Scheme
### Aggregation Policy and Intelligent Routing Design  
For most of the monitoring requirements of chronic changes, we only need to collect their data periodically and analyze it, while some monitoring requires only a large number of repeated measurements as the train passes by. For these data with low real-time requirements, we consider data aggregation on the transmission path to reduce the transmission burden of nodes to achieve the purpose of saving energy and prolonging the network life cycle. A lossless data aggregation model is adopted in this study. This means that the original data can be reconstructed by the sink node from the received aggregated data \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Monitoring Objects** & **Sensor Type** & **Life Cycle Demands** & **Real-Time Demands** \\ \hline Longitudinal stress of steel rail & Ultrasonic sensor & high & low \\ Rail deformation & Deformation sensor & high & low \\ Rail integrity & Ultrasonic sensor & medium & medium \\ Rail wear & Video monitoring & low & low \\ Track switch extension pitch adjuster & Fiber grating strain sensor & high & high \\ Rail stiffness & Rail inspection car & low & low \\ Foreign body contamination limit & Video monitoring & high & high \\ Track foundation submerges & Leica total station monitoring system & medium & low \\ Track slope condition & Laser laser scanner and fiber grating & medium & medium \\  & strain sensor & & \\  & Infrared temperature sensor, fire & & \\ Lead power supply system & detector, temperature and & high & high \\  & humidity sensor & & \\  & Acceleration sensor, ratchet deviation & & \\ Bow net service state & Angle sensor, cable clip & high & high \\  & temperature sensor & & \\ Suspension tension, elasticity and vibration & Tension measurement sensor, wire & & \\  & vibration sensor, elastic & medium & medium \\ Pantograph image recognition & Image and Video Signal Processing & high & high \\ Geological disasters & Seismic detector, landslide detection & low & low \\ Meteorological disaster & Laser monitoring equipment, video & low & high \\  & and image processing & & \\ Meteorological watch & Temperature sensor and & high & high \\  & humidity sensor & high & high \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Monitoring objects and characteristics. packets without any damage or loss of data. The data aggregation model is expressed as follows:  
\[DA\{Q_{i}^{l}(n)\}=\ \ \ \left\{ \begin{array}{cl}U_{m}^{t}\times\log_{2}(DP_{i}(n)+1)& \text{if }0<DP_{i}(n)\\ 0&\text{if }DP_{i}(n)=0.\end{array} \right. \tag{1}\]  
The data aggregation model is defined in this paper. The data buffer area of each sensor is partitioned, and each sensor maintains storage areas of multiple data types. For the same type of data, when the real-time requirement is low, the sensor performs data aggregation before forwarding to optimize the data volume. When a packet of data type t is transmitted in the network, each node transmits to the next hop after the interval \(Sl^{t}\). Obviously, the longer the waiting time for data aggregation, the greater the delay for the data packet to reach the sink node. It can be seen from Table 2 that different types of sensors have different real-time requirements for data. Therefore, in the design of routing protocols, the real-time requirements of different nodes need to be considered when energy-saving design is carried out [29; 30]. This is explained in Section 4. The aggregation process design is shown in Figure 2 and model parameters are listed in Table 3.  
If the sensor node \(S_{i}\) is the next hop selected by \(S_{j}\), \(S_{k}\), and \(S_{l}\), the \(t1\)-type aggregated data packets \(\text{AD}_{\text{i}}^{t1}(n)\) from the node \(S_{j}\) and the \(t1\)-aggregated data packets \(\text{AD}_{\text{i}}^{t1}(n)\) of the \(S_{k}\) node are stored together with the \(t1\) data packets \(\text{OD}_{\text{i}}^{t1}(n)\) observed by the \(S_{i}\) node from the surrounding environment.