Stream processing has evolved as a paradigm for efficiently sharing and integrating a massive amount of data into applications. However, the integration of globally dispersed sensor data imposes challenges in the effective utilization of the IT infrastructure that forms the global sensor network. Especially, simulations require the integration of sensor streams at widely differing spatial and temporal resolutions. For current stream processing solutions it is necessary to generate a separate data stream for each requested resolution. Therefore, these systems will suffer from high redundancy in data streams, wasting a significant amount of bandwidth and limiting their scalability.

This paper presents a new approach to scalable distributed stream processing of data which stems from globally dispersed sensor networks. The approach supports applications in establishing continuous queries for sensor data at different resolutions and ensures efficient bandwidth usage of the data distribution network. Unlike existing work in the context of video stream processing which provides multiple resolutions by establishing separate channels for each resolution, this paper presents a stream processing system that can efficiently split/combine data streams in order to decrease/increase their resolution without loss in data precision. In addition the system provides mechanisms for load balancing of sensor data streams that allow efficient utilization of the bandwidth of the global sensor network.