Sensor networks have received increasing attention in the past two decades, with applications for smart cities and environmental monitoring. In the first half of this talk, we give an overview of one of our projects that will monitor Canadian oceans for noise and traffic. The platform we will build is based on data science methods, techniques and tools. The infrastructure will support data integration and interoperability, interactive data visualization and data analysis for streaming data.

In the second half of the talk, we focus on query processing for wireless sensor networks. In particular, we consider the situation when the sensors are mobile and their sensing ranges may overlap. The latter complicates the processing of aggregate queries, because the same object may be detected by multiple sensors. We develop algorithms that can identify such duplicate objects efficiently, and that can perform aggregation as early as possible. We show that the energy consumption of our algorithms are much smaller than those of baseline methods.