This work introduces a novel approach to improve the precision of distance estimation in localization systems by using existing LoRaWAN and RSSI-based techniques. Despite the benefits of range and power efficiency, these systems exhibit limited accuracy in practical situations. To address the limitation, this study provides an innovative technique that greatly improves the precision of distance estimations, particularly in urban environments. The fundamental basis of this approach lies in the use of a dynamic path loss model. An additional element is to accommodate the varied and dynamic conditions of signal transmission in metropolitan areas. A better Kalman filter is also used in the study. This is important because it reduces the effects of multipath fading and environmental noise that often make RSSI-based localization in LoRaWAN networks less accurate. The study further examines the influence of the environmental exponent, also known as the path loss exponent, on the RSSI results and the precision of the distance measurements. This methodology achieves the average error under 1 meters for indoor environments and under 7 meters for outdoor environments. Finally, the Cumulative Density Function (CDF) shows 90 % of the distance estimation algorithm error for indoor environment is lower than 1.08 meters while for outdoor environment is lower than 7.55 meters. Based on these improvements, the introduced methodology not only enhances and improves existing approaches but also optimizes the precision and dependability of urban localization technologies, with substantial implications for a variety of practical applications.

Wireless Sensor Network (WSN) position is required in many applications and the Global Positioning System (GPS) is not compatible with battery life management issues. In this article, we propose an autonomous, low-cost and energy-efficient localization solution based on LoRa wireless network. Our design uses a low-power relay system to extend the coverage of a typical LoR-WAN gateway to improve the accuracy of Received Signal Strength Indicator (RSSI) based approaches. We then apply Artificial Intelligence (AI) techniques, mainly regression algorithms, to obtain accurate location estimates of the target node. The proposed ML-MTL (Machine Learning-Multilateration) algorithm-based localization approach better meets the demands of IoT (Internet of Things) applications and could achieve accuracy comparable to more intricate and energy intensive methods like Angle of Arrival (AoA), Time of Arrivals (ToA), and Time Difference of Arrive (TDoA).

The advent of Long Range Wide Area Network (LoRaWAN) has significantly enhanced Internet of Things (IoT) applications, particularly in Smart Cities, with its broad coverage and energy efficiency. However, in areas with blind spots and obstacles, the conventional star network has limitations in coverage. To overcome this, researchers conducted a study to explore the potential of LoRaWAN extenders with multiple relays, focusing on coverage, performance, and power consumption. The study involved designing and deploying a relay system aligned with protocol of LoRaWAN to extend the reach of a standard LoRaWAN gateway. Strategically placed relays addressed areas beyond the gateway's coverage. Performance was measured by the longest communication distance achieved by each relay, packet loss ratio, and latency. Power consumption was also monitored. The results demonstrate the practicality and effectiveness of the proposed multiple relay design, expanding the capabilities of LoRaWAN network.

This paper demonstrates that, using multiple data points around each gateway allows for the creation of accurate coverage heat maps by using a proposed LoRaWAN coverage estimation algorithm. This method effectively identifies areas with varying signal strength across Da Nang City, highlighting where additional gateways are needed to improve network reliability. Unlike previous studies, this research considers environmental factors such as topography and urban structures, enhancing prediction accuracy. This technique would be essential for optimizing essential for optimizing LPWAN deployments and ensuring efficient resource utilization. Future efforts will focus on refining neural network models and integrating real-time data to support scalable Smart City solutions and a more robust connectivity infrastructure, with this work planned for future research.