###### Abstract  
Mission-critical wireless sensor networks require a trustworthy and punctual routing protocol to ensure the worst-case end-to-end delay and reliability when transmitting mission-critical data collected by various sensors to gateways. In particular, the trustworthiness of mission-critical data must be guaranteed for decision-making and secure communications. However, it is a challenging issue to meet the requirement of both reliability and QoS in sensor networking environments where cyber-attacks may frequently occur and a lot of mission-critical data is generated. This study proposes a trust-based routing protocol that learns the trust elements using Q-learning to detect various attacks and ensure network performance. The proposed mechanism ensures the prompt detection of cyber threats that may occur in a mission-critical wireless sensor network and guarantees the trustworthy transfer of mission-critical sensor data. This paper introduces a distributed transmission technology that prioritizes the trustworthiness of mission-critical data through Q-learning results considering trustworthiness, QoS, and energy factors. It is a technology suitable for mission-critical wireless sensor network operational environments and can reliably operate resource-constrained devices. We implemented and performed a comprehensive evaluation of our scheme using the OPNET simulator. In addition, we measured packet delivery rates, throughput, survivability, and delay considering the characteristics of mission-critical sensor networks. The simulation results show an enhanced performance when compared with other mechanisms. mission-critical wireless sensor network; trust-based routing; Q-learning; reinforcement learning; QoS 20  
mission operation. To solve these problems, studies that consider trust evaluation, end-to-end delay, and energy as metrics are actively being conducted. Recently, research on learning these metric elements to improve reliability has also attracted attention. This study proposes a trust-based routing protocol that learns the trust elements using Q-learning to detect various attacks and ensure performance. Machine learning methods can be typically classified into supervised learning/unsupervised learning/reinforcement learning. Q-learning, a well-known reinforcement learning (RL) method, can be a useful technique for achieving adaptive trustworthiness in harsh environments [3; 4]. It considers the history of significant testing results from previous data reporting intervals. The reason we use Q-learning is that it is appropriate to receive a reward for every hop while transmitting data from the source node to the destination. As a result, the trust reputation for each sensor node is determined using Q-learning, which helps identify malicious nodes [5]. The proposed scheme is divided into local trust evaluation and global trust evaluation with Q-learning to satisfy the mission-critical application requirements and to discover the most trusted path. The proposed technique focuses on how to quickly and securely transmit mission-critical data to its destination by learning as well as calculating trustworthiness and QoS factors. We propose a distributed transmission technology that prioritizes the reliability of mission-critical data through the Q-learning result considering trustworthiness, QoS, and energy factors. This method can solve the problem of reduced network performance when data is transmitted over a single path. The proposed distributed transmission method ensures reliability by transmitting mission-critical data with the highest priority to the path of MAX-Q value and transmits data of relatively low importance through the suboptimal path that satisfies each requirement. This method can satisfy the requirements of all mission-critical data while ensuring the reliability of the most important data first.