Water quality is a key factor in environmental and agronomic sustainability. Due to the influence of human activity and industrial development, the composition of rivers or lakes can experience significant variations both immediately and over time. In order to obtain a more accurate and documented assessment of these data, distributed monitoring with multiple sampling points is necessary. This paper presents the design and implementation of a scalable monitoring network based on long range (LoRa) and Message Queuing Telemetry Transport (MQTT), integrating a submersible sensor module (SSM) that works as a static measuring station or as a complement to sediment collectors, capable of measuring key water quality parameters such as TDS, turbidity, pH, temperature, and river kinematics with a gyroscope. The system includes a LoRa repeater (LRR) and a gateway, in addition to the SSM, which manages information transmission to a monitoring server (MS) using a tree topology. This configuration allows for dynamic antenna power adjustment based on the Received Signal Strength Indicator (RSSI) between the LRR and the gateway. Evaluations were performed on the Chil River in Arequipa, Peru, a rapid river that demonstrated ideal characteristics for validating the system’s efficacy. The results confirm the design’s efficacy and its capacity for real-time remote water quality monitoring.

Inertial navigation systems (INS) are widely used in commercial aviation, maritime navigation, and unmanned vehicle guidance. However, these systems are often sensitive, costly, and challenging to access. To address these limitations, an open-source, low-cost platform named INS OpenNavSense has been developed. This platform is built using FreeRTOS, an open-source real-time operating system (RTOS) that enables the microcontroller to run parallel individual threads (tasks), providing a practical and effective tool for implementing estimation algorithms that compensate for the use of low-cost microelectromechanical systems (MEMS) sensors instead of high-end sensors in professional INS. The main contribution of this work is the introduction of a FreeRTOS-based platform, which facilitates independent management of computational and processing tasks. The platform incorporates accelerometers, gyroscopes, magnetometers, Global Positioning System (GPS) module, and barometer sensors. Sensor data is calibrated and filtered to enhance accuracy, offering researchers a robust and reliable tool for testing their estimation algorithms. To validate this platform, the open-source Mahony library was used for attitude and heading reference system estimation, demonstrating the types of algorithms that can be tested. Tests were conducted with a drone carrying the platform as payload, and results from this low-cost INS were compared to the drone’s INS, showing both similarity and viability as a development platform.