Accurate and low-complexity phase estimation is crucial for optimal data recovery in coherent optical receivers, especially for applications in emerging scenarios such as low-margin optical networks, green networks, probabilistic-shaping modulation, the use of high-order modulation formats in hollow-core fibers, long-haul links operating at low signal-to-noise ratios, and free-space optical links in low-earth orbit satellites, among others. These new developments demand highly efficient and reliable data transmission methods, even under stringent conditions of minimal operational excess margin. This paper introduces an efficient pilot-assisted phase estimation strategy for coherent optical receivers, integrating robust interpolation techniques for quasi-optimal operation. Our approach significantly enhances phase estimation accuracy, addressing the unique challenges posed by these new scenarios. Through comprehensive simulations, we illustrate our method’s superiority over conventional methods, showcasing marked improvements in computational complexity and bit error rate. The results highlight the critical role of sophisticated interpolation in bolstering pilot-assisted phase estimation, offering a promising technique for optimizing performance in next-generation coherent optical receivers.

The rapid growth of Internet of Things (IoT) applications is reshaping countless sectors and, in the process, exposing the limitations of existing connectivity solutions—especially in rugged regions like South America’s Andean highlands, where conventional infrastructure networks are scarce. To address this gap, this research introduces an automated system that captures uplink and downlink data from LoRaWAN nodes in real time. The system continuously monitors essential indicators—RSSI, SNR, transmit power, spreading factor, bandwidth, device speed, and packet interval—and stores them for later analysis. Thanks to its modular design, the system adapts easily to urban, semi-urban, and challenging rural topographies. Field trials show that our tool gathers reliable performance data while cutting the time and manual effort typical of traditional measurement campaigns. These results streamline IoT roll-outs in demanding terrain and lay the foundation for scalable LoRaWAN deployments throughout the Andean region.

As smart cities evolve, integrating new technologies into school transportation is becoming increasingly important to ensure student comfort and safety. Monitoring and enhancing comfort during daily commutes can significantly influence well-being and learning readiness. However, most existing research addresses isolated factors, which limits the development of comprehensive and scalable solutions. This study presents the design and implementation of a low-cost, generalized IoT-based system for monitoring comfort in school transportation. The system processes multiple environmental and operational signals, and these data are transmitted to a cloud computing platform for real-time analysis. Signal processing incorporates standardized metrics, such as root mean square (RMS) values from ISO 2631-1 for vibration assessment. In addition, machine learning techniques, including a Random Forest classifier and ensemble-based models, are applied to classify ride comfort levels using both road roughness and environmental variables. The results show that stacked multisensor fusion achieved a significant improvement in classification performance compared with vibration-only models. The platform also integrates route visualization with commuting time per student, providing valuable information to assess the impact of travel duration on school mobility.

The increasing adoption of Internet of Things (IoT) devices, coupled with the transition to Industry 4.0, has highlighted the necessity of evaluating LoRaWAN performance in complex geographical environments. This study presents an automated system for assessing LoRaWAN coverage in mountainous regions. The system addresses critical performance parameters such as Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), transmission power, velocity, and data transmission intervals. The proposed methodology integrates qualitative and quantitative approaches in three phases: Analysis, Implementation, and Evaluation. The system is deployed in urban and rural regions of southern Ecuador, where real-time data acquisition enables both uplink and downlink performance assessment. The results demonstrate the feasibility of automating LoRaWAN coverage evaluation, minimizing manual intervention, and enhancing network monitoring efficiency. The findings support the optimization of IoT deployments in challenging terrains, facilitating improved communication reliability and scalability.