# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))). [PERSON]  
1 Department of Computer Science and Engineering, Chungnam National University,  
Daejeon 34134, Republic of Korea 1, [PERSON]  
1 Department of Computer Science and Engineering, Chungnam National University,  
Daejeon 34134, Republic of Korea 1, [PERSON]  
2 Smart Power Distribution Lab, Korea Electric Power Research Institute, Daejeon 34056, Republic of Korea 2, [PERSON]  
2 Smart Power Distribution Lab, Korea Electric Power Research Institute, Daejeon 34056, Republic of Korea 2, [PERSON]
exhausted, it leads to a decrease in the network lifetime. Thus, it is necessary to reduce the additional energy consumption owing to network maintenance or to balance energy consumption between sensors. Conventional ad hoc routing protocols [1; 2] can ensure reliable transmission and satisfy service requirements of source nodes in WSNs; however, they involve a high computational cost (i.e., control-message overhead) for sensors to maintain routing paths. To reduce the energy consumption from the control overhead, tree-based routing can be a feasible solution in WSNs. The parent selection is a key role of a tree-based routing protocol because the links between parent and child nodes are used as routing paths. Conventional tree routing [3] uses the number of hops to the sink as a decision criterion for parent selection. Each source node can build the shortest path to the sink by choosing a node with the least number of hops as a parent node. This approach can provide a low latency; however, it cannot ensure reliable transmission because the link quality between the parent node and the child node is not considered in the parent selection process. To build a stable tree topology, some studies [4; 5] adopt the link quality as a decision criterion. However, they do not consider the load balancing during the parent selection; hence, an excessive load can be placed on a specific node, resulting in significant packet loss. To prevent congestion, some studies [6; 7; 8] propose a load-aware parent selection algorithm; however, they do not jointly consider link quality and energy efficiency. Although energy-aware parent selection schemes are proposed [9; 10; 11; 12; 13], they also cannot jointly achieve the above service requirements. These tree-based routing protocols address diverse parent selection problems independently; hence, they cannot jointly improve multiple performance metrics, such as the transmission reliability, end-to-end delay, and energy consumption. To address this problem, some studies [14; 15; 16; 17; 18] attempt to select the best parent node taking into account multiple cognitive metrics. To jointly achieve multiple objectives, they employ a linear weighted-sum method in which each node calculates the weighted cost by integrating multiple metrics. However, the linear weighted-sum method adopts subjective weights, which are often less objective and reduce their acceptance within the scientific community. As a result, they cannot provide a flexible trade-off between performance metrics. To overcome this limitation, in our previous work [19], we proposed a multi-criteria decision making (MCDM)-based parent selection scheme. The proposed scheme finds the best parent node by determining the relative importance of multiple decision criteria using the analytical hierarchy process (AHP) [20] and then derives the weighted cost using the simple additive weighting (SAW) method [21]. However, the decision maker needs to calculate the weights of each metric based on its preference or background knowledge of the target network.