# Delay-Sensitive Multi-Sensor Routing Scheduling Method for Underground IoT in Mines
## 2 Related Work
### Delay-Based Wireless Sensor Routing Algorithms  
Another significant research focus in WSNs involves minimizing transmission delays, a crucial requirement in many IoT applications such as real-time monitoring and emergency response systems. Low-latency communication is particularly essential in environments like underground mines, where timely data transmission directly impacts both safety and operational efficiency.  
Various delay-optimized routing protocols have been proposed to address these challenges. For instance, the delay-sensitive routing algorithm in [13] employs relay nodes strategically to minimize transmission delays and extend network lifetime. Using game-theoretical principles to optimize relay node selection, this method reduces the number of hops and routing overhead, thereby enhancing throughput and achieving low-latency performance. This approach is particularly relevant in settings like mines, where even minor delays in data transmission could lead to safety hazards.  
Location-aware and opportunistic routing strategies also offer significant benefits for delay reduction in dynamic networks. As seen in [7], a location-based opportunistic routing protocol leverages node positions to minimize transmission delays in underwater sensor networks. By ensuring that data packets take the shortest available path, the protocol reduces both delay and energy consumption while dynamically adapting to changing network conditions. This adaptability makes it suitable for environments requiring high responsiveness and energy efficiency, as is often the case in WSNs deployed in underground or challenging terrains.  
Similarly, cluster-based methods have proven effective in delay and energy management. The cluster-based data aggregation scheme in [14] constructs an aggregation tree and uses time-slot scheduling to mitigate retransmissions and waiting times. This structure enables faster transmission, reduced congestion, and lower overhead, making it particularly advantageous in networks with high sensor density and simultaneous data transmissions.  
Analyzing these approaches reveals that optimizing the Medium Access Control (MAC) layer is a common strategy for addressing delay issues. Techniques such as Time-Division Multiple Access (TDMA) are frequently employed to manage traffic flow in a controlled manner, allowing for predictable data scheduling and minimizing transmission delays. However, TDMA requires some advance knowledge of data flow characteristics, such as flow count, deadlines, and data sizes, which may not always be feasible in real-world scenarios with dynamic data requirements.  
On the other hand, contention-based methods, like Carrier-Sense Multiple Access (CSMA), are also widely used for data access and routing in WSNs. While CSMA is flexible and effective in managing unscheduled traffic, it may introduce packet collisions and delays, especially in high-density sensor environments such as underground IoT networks. In such cases, where reliable and low-latency communication is essential, contention-based approaches often struggle to meet stringent real-time requirements due to the increased likelihood of transmission conflicts and congestion.  
The methods discussed highlight a range of strategies for reducing transmission delays across different WSN applications. However, many of these approaches require specific network configurations or assume relatively stable conditions, which limits their effectiveness in complex environments like underground mines. This study builds on these foundations by adapting delay-optimized routing strategies to handle the unique challenges of underground IoT networks, where both delay minimization and robust performance are critical.