In recent years, water quality observing technology has allowed scientists to identify and mitigate poor water quality by providing cost-effective tools that autonomously measure essential biogeochemical variables. Despite these technological advances, there is a disconnect between data quality, data gathering, and data analysis. In this regard, autonomous sensors and autonomous methods of sensor deployment may help. For instance, installing multisensory electronic skin-like patches on a water-based robot or drone could enrich data gathering from inaccessible locations of a water body. Such patches are equipped with low-cost disposable sensors to improve data quality by moving the sensor around. Furthermore, real-time sensor data could be used to steer these robots or drones toward desired locations in a water body. Some of these approaches, currently being explored in Aquasense and Muses projects, will be covered in this lecture.