## 1 Introduction  
In recent years, several wireless sensor network (WSN) technologies have emerged to support Internet of Things (IoT) devices and relevant applications. WSNs provide wide coverage and low deployment costs compared to wired communication networks, providing reliable and scalable coverage with the inherent flexibility in relocating or moving wireless sensors within the coverage area [1]. Such WSNs support meeting the requirements of many IoT-based monitoring applications, such as those developed for smart cities. There are specific monitoring-system scenarios that involve linear sensor topologies, such as oil and gas pipeline monitoring systems [2]. In such linear topologies, the devices are arranged according to a linear layout to monitor pipelines with long lengths and, in many cases, within environments (e.g., deserts) that do not provide adequate wireline network coverage or connectivity to energy grids. Additionally, this type of deployment introduces many challenges, such as high signal delays, low reliability, and high sensor energy consumption that may impact the actual sensor performance [2]. We are considering a case study on a large-scale oil pipeline leak-detection system--one kind of monitoring system. The oil pipeline runs through the desert in the Kingdom of Saudi Arabia (KSA). In the pipeline monitoring scenario, there are various deployment lengths, which can reach hundreds of kilometers. According to the pipeline length, different WSN technologies can be used [3]. Wi-Fi, Bluetooth, and ZigBee technologies can be applied for short pipeline lengths. Long pipelines need long-range communication technologies that ensure low energy consumption. To address these challenges and limitations, many WSN technologies have been developed. Low-power wide-area networks (LPWANs) provide a low deployment cost and high energy efficiency [4]. In addition, these networks cover wide areas. Some examples of LPWANs are LoRaWAN, Sigfox, and DASH7 [4]. LoRaWAN networks demonstrate good results for environment and monitoring applications in terms of their long range and low energy consumption [5]. In addition, such networks offer a large communication range and several years of battery life while operating on unlicensed frequency bands. Also, LoRaWAN networks provide high channel capacity, meaning several devices may transmit data simultaneously over a single channel. Large-scale IoT monitoring systems have many challenges, such as the need for a large transmission range and the huge number of sensors affecting the network lifetime due to their limited battery duration. Additional challenges are network congestion and decreased throughput, which result from high data traffic, delay, packet loss, and heavy load. LoRaWAN has been designed while taking such challenges into consideration [5]. In cases of linear sensor networks that involve long end-end communication ranges, multi-hop LoRa networks may provide a more promising solution than single-hop LoRa networks, which are based on star topologies. In multi-hop communication, the sensing device acts as a relay for other nearby devices during data transmission [4;5]. However, the choice of deploying multi-hop LoRa networks needs to be studied cautiously to address possible tradeoffs, such as increased end-end delay and energy consumption on the one hand and throughput on the other hand. Another issue is that multi-hop networks have better scalability in comparison to single-hop networks; however, to support increasing network scalability, adding more devices must be carried out while setting network parameters by avoiding LoRa limitations and the challenges caused by network requirements [6; 7; 8]. Multi-hop LoRaWAN was used in our previous study [9]. The authors measured temperature and pressure for leak detection in the oil pipeline. Some of the results obtained determined the optimal distance between sensors and defined the number of hops in multi-hop scenarios.