# An Optimization Routing Algorithm for Green Communication in Underground Mines
## 1 Introduction  
In a green communication underground mine (GCUM), installing mobile modules on the miners, as well as mobile equipment, requires numerous mobile nodes. Therefore, GCUMs are highly dynamic [1, 2]. To track the locations of miners and perform other functions, it is important to obtain accurate data from mobile nodes. In this paper, the proposed technique will serve our program of localization in underground mines in this paper. Connectivity is essential between each mobile node in a network. Furthermore, connectivity and clustering techniques can be used to optimize the structure of a network with heterogeneous mobile nodes, but these methods increase the energy consumption of the network. To optimize green communication and the performance of the network and structure, reduce energy consumption and prolong the lifetime of the network, clustering algorithms are effective solutions for networks with large numbers of mobile nodes. In a GCUM, batteries supply all the power to the mobile nodes. Battery supplement brings limited lifetime to these mobile nodes. The approach of green communication is introduced to save more energy to extend and lifespan of the network. Accidents are highly probable in underground mining [3]. In case of emergency or accident, the mobile node must use limited energy for communication with rescue forces. Therefore, green communication is important to save residual energy of mobile nodes and help to implement the functions in normal and emergency states. Anchor nodes that are fixed to the tunnel walls have wired power supplies and batteries for backup. However, long wires may have high costs and create risks since they can be damaged in accidents. Thus, anchor nodes areestablished to limit the energy consumption. In this paper, a routing algorithm is proposed to effectively reduce energy consumption from two perspectives. First, a degree constraint mine spanning tree (DCMST)-based algorithm is introduced to determine the lowest cost and most reliable routing paths. Second, a GA is introduced to optimize the cluster structure. There are many tunnels, geological structures, and environmental factors that constitute the complex environment in underground mines. Such mines are often affected by unpredictable and extensive structural changes, especially when accidents occur. In such cases, the original communication paths are often disrupted. Therefore, in a GCUM, high connectivity between nodes is essential. In this paper, the DCMST algorithm is used to determine the lowest overhead option for routing paths and to optimize the quality of routing paths. In addition, based on the degree of nodes in the GCUM, nodes with high residual energy are established as cluster head nodes to balance energy consumption and extend the network lifetime. Based on the former routing paths, nodes in the GCUM are clustered to balance the energy consumption of the green communication network. The cluster head nodes concurrently receive the data packets transmitted by all the cluster member nodes and receive the packets transmitted from other cluster head nodes or anchor nodes to each cluster member node. The timing and frequency of transmissions from the mobile nodes to the cluster head node will significantly affect the energy consumption of each node. If a mobile node exhausts its energy too early, it will lose all its data and impact the lifetime of the entire network. In addition, clustering algorithms can reduce the energy consumption caused by other factors. Clustering algorithms are effective methods of improving the transmission of data in a network and reducing energy consumption. These algorithms can improve the data transmission capacity of mobile nodes and prevent nodes from becoming cluster head nodes that consume large amounts of energy. Furthermore, these optimization processes can prevent mobile nodes with low remaining energy levels from becoming cluster heads that consume large amounts of energy, thereby extending the lifetime of the network.