|Bibliography||Benzing, Andreas; Koldehofe, Boris; Völz, Marco; Rothermel, Kurt: Multilevel Predictions for the Aggregation of Data in Global Sensor Networks. |
In: Proceedings of the 14th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology.
pp. 169-178, english.
IEEE, October 2010.
Article in Proceedings (Conference Paper).
|CR-Schema||C.2.4 (Distributed Systems)|
|Keywords||Global Sensor Networks; Distributed Stream Processing; Predictors|
Real-time 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, 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.
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|Department(s)||University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems|
|Entry date||July 29, 2010|