Master of Science / Department of Biological and Agricultural Engineering / Naiqian Zhang / A real time wireless, optical sensor network was tested for long-term, remote monitoring of suspended sediment concentrations (SSC) in streams. The sensor and control board assembly was calibrated using a two-stage calibration procedure, including a pre-calibration conducted in the laboratory to adjust the sensitivity of the sensor and a field calibration using grab samples to establish an effective statistical model to predict SSC from the sensor signals. The assembly was installed in three military bases around the United States. These bases were Fort Riley, Kansas; Fort Benning, GA; and Aberdeen Proving Ground, MD. The types of water bodies and watersheds varied greatly among the sites, which allowed the sensor to be tested under versatile conditions for potential widespread use. The results show that the sensor was capable of measuring SSC at each watershed independently. The calibration model developed for each sensor can be used to predict SSC from real-time sensor data. A data processing algorithm was developed to lessen the effect of fouling and clogging on sensor signals, along with eliminating anomalies in the data gathered. The results of this study displayed meaningful prediction data that can be used to estimate SSC in a stream over a long period of time. Information obtained in this study can be used as a launching point for future work and understanding of stream processes.