## 3 Research Methodology and Procedures
LoRa technology, based on the LoRaWAN protocol, has established itself as a leading wireless communication solution specifically designed to meet the needs of IoT applications. It operates in the sub-GHz frequency bands, typically at 433 MHz, 868 MHz, or 915 MHz, and offers a unique combination of long-range capabilities, low power consumption, and robustness in noisy environments. LoRa's spread spectrum modulation techniques enable it to achieve remarkable coverage, making it well-suited to urban environments with numerous obstacles and buildings that can interfere with traditional wireless communication systems. Urban environments present a variety of challenges for wireless communication systems. The presence of tall buildings, structures, and dense infrastructure can result in signal blockage, multipath propagation, and signal attenuation. NLoS conditions are common in urban environments and affect the reliability and range of conventional wireless technologies. This limitation is an obstacle to seamless data collection and communication in IIoT scenarios, especially for mobile platforms such as industrial vehicles navigating complex urban landscapes. The proposed architecture, shown in Figure 1, addresses the challenges posed by urban environments for wireless communication and data collection. By integrating two USRPs, one located in the moving vehicle and the other acting as a stationary transmitter, we aim to establish a reliable, direct communication link that effectively overcomes the obstacles of NLoS conditions. The vehicle's mobility is an advantage as it allows the USRPs to establish temporary line-of-sight connections by dynamically adjusting their antenna orientation according to the vehicle's movement. This dynamic positioning improves the chances of successful communication between USRPs, even in NLoS scenarios caused by tall buildings and structures. In addition, this vehicle-based architecture facilitates streamlined data collection as the vehicle navigates through urban areas. It provides the means to collect up-to-the-minute data from various IoT sensors and devices strategically positioned along the vehicle's trajectory. The architecture thus facilitates a continuous flow of information, enabling smart city applications, traffic monitoring, and environmental sensing in urban environments. The selection of SINR over Signal-to-Noise Ratio (SNR) is deliberate, aiming  
Figure 1: Testbed for the LoRa wireless sensor node deployment. to provide a more accurate representation of LoRa system performance in Industrial IoT under NLoS conditions. SINR's consideration of interference aligns with the challenges posed by industrial environments, where various electronic devices can impact signal quality. Additionally, we opted for Block Error Rate (BLER) over BER to evaluate the reliability of data transmission at the packet level, recognizing the practical importance of assessing entire data blocks in IIoT applications. The exclusion of Packet Error Rate (PER) stems from its limited granularity in understanding error severity within packets. The combined use of SINR and BLER offers a holistic evaluation, emphasizing robustness and reliability in challenging scenarios. This approach provides practical insights into the specific challenges faced by LoRa systems in Industrial IoT applications, ensuring a focused and comprehensive assessment of their performance under real-world conditions. This flowchart Figure 2 illustrates the comprehensive process of optimizing LoRa connectivity in urban environments, from testbed implementation to careful evaluation of system performance.