MONITORING

Expanding noncontact techniques for monitoring groundwater level variations to include laser based devices has been limited in the past by the low reflectivity of water to laser light The advances made in the realization of highly sensitive receivers has greatly enabled Laser devices with the prospect for effective noncontact monitoring of groundwater levels. In this work a real-time groundwater level monitoring devices was implemented using a LiDAR-lite v3HP laser-based sensor, an Arduino Uno rev 3 microcontroller, and an ESP8266 Wi-Fi module. Hardware implementation involved interfacing the microcontroller with the LiDAR-lite, the Wi-Fi module, a 16 x 2 liquid crystal display and other necessary basic electronic components. The software implementation involved writing, editing, compiling and uploading codes through the Arduino ide unto the microcontroller. The implemented prototype was powered by a 20,000mAH, 5V power bank and deployed on two artesian wells to collect depth to water surface data which were uploaded automatically to ThingSpeak platform. Measurements at various times taken with the device were compared with manual measurements from a calibrated tape. A correction factor was applied to correct measurement residuals. Results of the validation showed that measured values were uploaded to the ThingSpeak platform an average of 33 seconds which could be reconfigured to longer timeframes. The results were remotely accessed on the platform. A correlation graph of measurement before and after applying the correction revealed a near perfect correlation coefficient of 0.99995 for the LiDAR readings for both pre-correction and post correction measurements; confirming a strong linear relationship with tape measurements. The coefficient of variation, root mean square error and limits of agreement from the Bland Altman’s plots all ascertained the improvement in post-correction measurements.

Rapid urbanization, particularly in places like Nigeria, intensifies waste management challenges due to the inefficiency and high cost of traditional collection. While current smart waste monitoring and management systems offer improvements through IoT monitoring, they often lack advanced sorting and comprehensive management features. This project
directly addresses these gaps by designing an innovative smart waste monitoring and management system that critically incorporates the automated segregation of metals and plastics, alongside enhanced automation and real-time data capabilities. Our prototype is a sophisticated solution that integrates various sensors—including ultrasonic and load sensors for fill-level monitoring and compaction, plus specialized inductive (for metals) and optical/capacitive (for plastics) detectors for sorting—with linear actuators for automated processes and a GSM/GPS module for wireless communication. This setup allows the system to not only monitor bin levels, automatically compact waste, and control the lid, but most importantly, to accurately sort plastic and metal materials at the source. This ensures more efficient resource recovery and recycling. Through rigorous testing of both hardware and software, we will verify the reliability of sensor performance, the effectiveness of automation, the accuracy of communication, and the precision of the waste segregation mechanism. The anticipated outcome is a fully integrated system that successfully demonstrates its capacity to accurately detect fill levels, initiate automated compaction, send prompt location-based alerts, and effectively sort waste. This proven dependability under real-world conditions highlights the system's potential to ignificantly alleviate urban waste management issues by reducing overflow, boosting collection efficiency, increasing recycling rates, and providing a scalable, sustainable solution for residential, commercial, and municipal settings. settings.