Efficient support for multi-resolution queries in global sensor networks
Proceedings of the 5th International Conference on Communication System Software and Middleware
Language expressiveness and quality of service for publish/subscribe systems
Proceedings of the 9th Middleware Doctoral Symposium of the 13th ACM/IFIP/USENIX International Middleware Conference
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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.