# Delay-Sensitive Multi-Sensor Routing Scheduling Method for Underground IoT in Mines
## 2 Related Work
### Age of Information in IoT-Based Real-Time Monitoring  
Age of Information (AoI) is an increasingly important metric in IoT applications, particularly in scenarios that rely on timely, accurate data updates to support real-time decision-making and rapid response, such as the monitoring systems used in underground mines [15]. Unlike traditional delay metrics, AoI focuses on the \"freshness\" or \"age\" of the information that reaches the decision-making systems, measuring how current the data are at any given time. This is especially critical in high-stakes environments like mining, where outdated information on toxic gas levels, structural integrity, or water infiltration could lead to hazardous incidents and pose severe risks to personnel safety. Ensuring that the information available to the system remains as up-to-date as possible can make a significant difference in the ability to anticipate and respond to threats before they escalate. Recent research on AoI in IoT networks has concentrated on methods to optimize data update frequencies and reduce latency, with the goal of maintaining data relevance over time [16; 17]. One prominent approach is AoI-minimal clustering, which groups sensor nodes strategically to minimize the age of data received across various clusters. Byorganizing sensors into well-planned clusters, it is possible to streamline data flow from each cluster, reducing information latency and enhancing the efficiency of data retrieval. This clustering approach has shown promise in large-scale IoT networks by managing the trade-offs between data update rates and network resource constraints, though it typically requires adaptation to work effectively in underground mine environments with their unique layouts and connectivity challenges. In addition to clustering methods, other studies have explored AoI-oriented optimization strategies in different types of IoT and wireless networks. For example, techniques such as transmission and trajectory co-design for Unmanned Aerial Vehicle (UAV)-assisted Wireless Powered Communication Networks (WPCNs) [18] offer valuable insights into optimizing both the physical paths of data transmission and the frequency of updates in settings where nodes are mobile or the environment is highly dynamic. Although these strategies were originally designed for UAV applications, they present opportunities for adaptation in fixed underground sensor networks. Specifically, by applying similar principles to optimize transmission routes and adjust update intervals, it becomes possible to achieve high efficiency in data updates in the context of a mixed wired-wireless system, as is commonly found in mine networks. For underground mines, where sensor nodes may have limited energy resources and a challenging deployment layout, adapting these AoI-related techniques can be transformative. By strategically clustering sensor nodes and carefully planning transmission paths, the freshness of critical safety information can be maintained with minimal delay, thereby improving the real-time responsiveness of the IoT network in a power-efficient manner. This paper builds on the principles of AoI minimization to address the unique demands of underground mine monitoring, providing a solution that enhances both the timeliness and reliability of data collection for improved mine safety management. In addition, there are some studies focused on real-time transmission and scheduling in wireless networks [19, 20, 21, 22]. However, the application of these results in underground mines faces several challenges. In conclusion, while both energy-based and delay-based wireless sensor routing algorithms have seen significant advancements, there are still considerable challenges in applying these methods to more complex and dynamic environments like underground mines. In such settings, the network topology is not fixed, and sensors often face harsh physical conditions, making it difficult to maintain efficient, reliable communication.