Recently, IoT technologies have been used to monitor critical infrastructure in remote settings. Prior research has supported active O&M campaigns, quantified groundwater extraction rates, and evaluated service delivery approaches. In this study, continuous data collection was used to examine the operating characteristics of rural water infrastructure. Three research questions motivated this work: (1) What are the operating characteristics and trends of these pumps? (2) Can water point functionality be predicted? (3) Does the instrumented water point sample accurately represent overall water system functionality in this setting? 397 randomly-selected groundwater pump sites were observed within Plateau State, Nigeria over 12 months in 2021. 200 of these sites were instrumented with in-situ sensor systems, including 100 handpump sensors, 50 AC electrical sensors, and 50 water level cistern sensors. Bi-monthly phone calls and site visits were used to observe pump functionality statuses and served as ground-truth data over the study period. An automated expert classifier system generated statuses for instrumented pumps on a weekly basis. Classifier statuses were compared to ground-truth statuses, showing overall high accuracy (82.4%), with good sensitivity (88.9%) but poor specificity (14.3%). The classifier was able to accurately detect running pumps, but did not perform well in detecting failures. Varied responses were seen in pump usage as a function of rainfall, with handpump use decreasing significantly, AC pump usage decreasing to a lesser degree, and DC pump usage increasing in response to local rainfall. A statistical comparison of the 200 instrumented to 197 non-instrumented sites showed significant overall functionality level differences due to a baseline functionality criteria for sensor installation, but similar repair and failure rates on a bi-monthly basis. This suggests that in terms of functional change, the sensor-enabled group statistically represented the larger group of water points in Plateau State.