## 2 Related Work
Although DORA optimizes energy usage by dynamically adjusting clusters and link configurations, these dynamic adjustments add to the algorithm's execution overhead. Frequent adjustments, in particular, can increase communication and computation costs, impacting overall efficiency. In [25], a balanced multipath routing and hybrid transmission approach is proposed. This method jointly optimizes multipath, multihop, and single-hop transmissions to design multiple nonoverlapping shortest paths, ensuring that the load is evenly distributed within hot-spot areas and effectively avoiding energy concentration. The algorithm first calculates the optimal transmission path from each node to the receiver and determines the best path selection and transmission periods to achieve balanced energy distribution. However, in practical applications, some nodes may still bear excessive load because they are located at the intersection of multiple shortest paths, leading to localized energy depletion and affecting the overall lifespan of the network. In [26], an Improved Adaptive Ranking Energy-efficient Opportunistic Routing (I-AREOR) protocol is proposed to optimize energy efficiency in WSNs within Internet of Things (IoT) environments. The I-AREOR protocol selects optimal cluster heads by considering regional density, relative distance, and residual energy, effectively balancing energy consumption and maximizing network lifespan. The protocol adjusts energy parameters based on dynamic thresholds to extend the time until First Node Death (FND), Half Node Death (HND), and Last Node Death (LND). However, I-AREOR needs to dynamically adjust cluster head selection and energy parameters based on multiple factors (such as regional density, relative distance, and residual energy). This can increase computational overhead in resource-constrained sensor nodes, potentially affecting energy savings. Selecting the optimal relay node is also crucial for balancing network energy. In [27], a relay selection algorithm for energy harvest WSNs is proposed. This algorithm optimizes the cooperation probability and uses an outage probability threshold as a constraint to control the relay nodes involved in cooperative transmission, thus conserving system energy. A multicriteria relay selection strategy is employed to choose the optimal relay node by considering the nodes' solar energy status, network energy balance, signal-to-noise ratio, and outage probability. However, in environments with unstable lighting conditions or insufficient solar energy, the solar energy status of nodes may not reliably reflect their actual energy levels, thereby affecting the accuracy of relay selection. In summary, railway tunnel monitoring faces challenges such as high algorithm complexity, significant communication and computation overhead, and high parameter sensitivity. Therefore, this paper proposes a self-organizing network and routing method specifically for railway tunnel monitoring to balance network energy and extend network lifespan:  
1. Given that a large number of nodes are randomly deployed in railway tunnel monitoring and that there is significant overlap in communication ranges, the precision requirements for localization are lower. In the topology discovery, this paper uses a small number of anchor nodes. It locates unknown nodes through geometric relationships while introducing a method for calculating the minimum number of anchor nodes. This approach significantly reduces communication losses between nodes and lowers network deployment costs, thus improving deployment efficiency. 2. In the network clustering phase, this method builds on existing uneven clustering by applying a timing mechanism for cluster head selection. The timing duration is determined based on the node's remaining energy, adjacency (degree), and position. This approach effectively balances energy consumption between clusters and results in smoother changes in the number of clusters, thereby achieving better network stability. 3.