## 2 Literature Survey  
### Related Work  
A concentrated survey of the literature, especially that on approaches that employ wireless sensor networks and artificial intelligence for oil pipeline leak detection systems, is presented in this section. In 2015, [PERSON] et al. [11] focused on employing the electromagnetic approach based on wireless underground sensor network (WUSN-based EM) communication techniques for oil pipeline networks. Their research explained a suitable transmission range that is optimal in terms of the intake losses of the compound underground oil pipeline network and determined the efficient communication distance between sensing nodes operating at 315 MHz in an underwater environment. In 2015, [PERSON] et al. [12] studied the problems of linear sensor placement in oil pipeline monitoring. They applied two evolutional algorithms to resolve the issue regarding deploying the optimal number of sensors, i.e., ant colony optimization (ACO) and a genetic algorithm (GA), to realize the aims of the deployment procedure and maximize the coverage of the pipeline, thus creating a connected network and extending the lifetime of the whole network. In 2020, [PERSON] et al. [13] utilized the deep learning (DL) approach with LoRa technology to achieve the optimal transmitting power, which is useful for maximizing energy efficiency (EE) in LoRa networks. In 2018, [PERSON] et al. [14] proposed a novel MAC protocol to enhance the scalability and reliability of LoRaWAN. A lightweight scheduling algorithm was implemented by dividing a huge number of nodes into several groups, while similar values of transmission powers were used in each group to reduce the capture effect. In 2018, [PERSON] et al. [15] proposed message replication and gateways with multiple receiving antennas to accomplish, respectively, the temporal and spatial diversity of a LoRa network. Their study presented the proposed schemes and assessed them through theoretical analysis and computer simulations. The final results showed that LoRa networks are very sensitive to user increases and traffic density, but the proposed technique can enhance performance. In 2020, [PERSON] et al. [16] developed a LoRaWAN simulator that integrated with the multi-armed bandit and reinforcement learning (RL) algorithms to formulate and find a professional resource allocation solution for LoRa endpoints. In 2021, [PERSON] et al. [17] presented an intelligent deep reinforcement learning algorithm for multichannel resource allocation in dense LoRa networks, known as the LoRa-DR technique, which enhanced LoRaWAN's packet delivery ratio and ensured lower power consumption; therefore, the lifetime and network capacity were increased. In 2022, [PERSON] et al. [18] proposed a LoRa-based energy consumption model that can predict the amount of energy consumed for each sensing node in a WSN. The study showed that the average energy consumption and lifespan of a battery are affected by a decreasing sensing interval and an increasing spreading factor of the standard parameters of LoRa. In 2021, [PERSON] et al. [19] introduced a smart spreading factor (SF) assignment method that depended on the different structural designs of artificial neural networks to detect collisions and selected optimal SFs to reduce the probability of collisions and improve network performance, addressing issues such as the energy consumption for the LoRa network. In 2022, [PERSON] et al. [20] introduced a new approach using the multi-armed bandit (MAB) technique to set LoRa end devices' transmission power parameters in a centralized manner over the network server side, while also improving the energy consumption. In 2023, [PERSON] et al. [21] presented a LoRaWAN-based flow-rater approach for detecting and localizing leaks in pipelines. They included an investigational setup that simulated a pipeline network with flow rate and pressure sensor measurements. In 2023, [PERSON] et al.