# A Routing Protocol for Multisink Wireless Sensor Networks in Underground Coalmine Tunnels
## 2 Related Studies  
Most of the existing clustering routing protocols for WSNs, including LEACH (Low Energy Adaptive Clustering Hierarchy) [5], TEEN (Threshold sensitive Energy Efficient sensor Network) [6], and PEGASIS (Power-Efficient GAthering in Sensor Information Systems) [7], adopt a single-sink network structure with a cluster head node and one hop direction communication to the sink. This cluster routing protocol is suitable for large-scale WSNs [8; 9]. However, when the protocol  
Figure 2: Architecture of multisink WSNs. applied to a large-scale coalmine network that has a long strip structure, the cluster head that is located far from the sink needs to operate at a large power status, which increases the energy consumption and also causes problems of interference with data transmission over long distances. The concept of using a clustering and multisink network structure can thus be used to solve such issues in a large-scale long-strip network within a coalmine. Many of the existing studies on multisink WSNs have aimed to prolong network lifetime by studying the optimal number and best deployment location of the sink in the network. In [10], the k-means iterative clustering technique is used to obtain the optimal sink number and to confirm the best location of each sink. A new method based on gene expression for solving the problem of the best deployment location of a sink in a multisink network (GEP-MSN) was suggested by [11]. Recently, [PERSON] et al. [12] introduced a multisink deployment optimization algorithm based on the greedy algorithm, which minimizes data delay to effectively support real-time application. Similarly, [PERSON] et al. [13] presented the Centroid of the Nodes in a Partition (CNP) and Candidate Location with Minimum Hop algorithms to optimize sink deployment; out of the two ideas the assumption and simulation inherent in CNP has a greater similarity to that presented in our study. Another hotspot of multisink WSN research focuses on how to achieve load balance in the network, thus focusing on how to reduce packet loss caused by congestion and collision in the transmission process. In this respect, [PERSON] et al. [14] suggested a network load balancing protocol based on cross-layer partitioning that combines the load balancing and clustering techniques in the whole network, thereby enabling the multisink network to effectively transmit data by constructing the best routing tree based on the local metric. A routing algorithm to achieve load balance between multisinks was further introduced by [15], where each data packet randomly selects only one sink as its target node. In this, the quotient of the least number of hops reaching the target node, divided by the residual energy of the neighbor nodes, is used as the positive factor to determine the route of the next hop. In addition, a gradient-based load balancing algorithm was created by [16], which allows dynamic selection of data-forwarding nodes, based on the residual energy of the next hop campaign node as the forwarding data. The algorithm presented in [17] was then based on this gradient, and by regarding the accumulated load in the routing pathway as the gradient the algorithm is used to solve the inability of the sensor node to avoid selecting the route with the heaviest load. The multisink routing algorithm, based on ant colony optimization, is also similar to the algorithm based on the gradient. For example, the algorithm presented in [18] selects the next hop node according to the pheromone level stored in the routing table. Moreover, in [19], the data are transferred to the multisink by means of the pheromone level. Other multisink routing protocols include research based on fuzzy theory by [20], research based on self-adaptive learning by [21], and research based on partition coverage by [22]. Most algorithms in existing studies that relate to wireless sensor routing focus on energy consumption.