Real-time diagnostic simulations are one challenging application domain that is expected to introduce high requirements to global sensor applications. Besides having hard constraints on latency bounds at which data needs to be processed, such simulation applications will impose high requirements with respect to available bandwidth. Predictors, originally introduced in the domain of wireless sensor networks for energy saving, are one appealing solution to provide real-time estimates and at the same time significantly reduce the data rates. While in the setting of wireless sensor networks many prediction models have been analyzed, their behavior and use is unclear when applied to distributed data streams where aggregation results are typically processed over multilevel hierarchies.In the context of weather simulations, we propose a distributed R-Tree-based aggregation algorithm that allows for efficient reuse of aggregate queries. In the setting of real temperature readings taken from weather stations during one month, we study the trade-off between updates of the prediction model and the precision of the predicted values. Our evaluations indicate that even in situations where complex prediction models are expected to perform best, simple prediction models give higher benefits with respect to saving bandwidth while providing similar data accuracy.

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 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.

Data processing tasks are increasingly spread across the internet to account for the spatially distributed nature of many data sources. In order to use network resources efficiently, subtasks need to be distributed in the network so data can be filtered close to the data sources. Previous approaches to this operator placement problem relied on various heuristics to constrain the complexity of the problem. In this paper, we propose two generic integer constrained problem formulations: a topology aware version which provides a placement including the specific network links as well as an end-to-end delay aware version which relies on the routing capabilities of the network. A linear programming relaxation for both versions is provided which allows exact and efficient solution using common solvers.

Abstract. Global sensor networks (GSN) allow applications to integrate huge amounts of data using real-time streams from virtually anywhere. Queries to a GSN offer many degrees of freedom, e.g. the resolution and the geographic origin of data, and scaling optimization of data streams to many applications is highly challenging. Existing solutions hence either limit the flexibility with additional constraints or ignore the characteristics of sensor streams where data points are produced synchronously.In this paper, we present a new approach to bandwidth-minimized distribution of real-time sensor streams in a GSN. Using a distributed index structure, we partition queries for bandwidth management and quickly identify overlapping queries. Based on this information, our relay strategy determines an optimized distribution structure which minimizes traffic while being adaptive to changing conditions. Simulations show that total traffic and user perceived delay can be reduced by more than 50%.

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