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Stream engines meet wireless sensor networks: cost-based planning and processing of complex queries in AnduIN

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Abstract

Wireless sensor networks are powerful, distributed, self-organizing systems used for event and environmental monitoring. In-network query processors like TinyDB offer a user friendly SQL-like application development. Due to the sensor nodes’ resource limitations, monolithic approaches often support only a restricted number of operators. For this reason, complex processing is typically outsourced to the base station. Nevertheless, previous work has shown that complete or partial in-network processing can be more efficient than the base station approach. In this paper, we introduce AnduIN, a system for developing, deploying, and running complex in-network processing tasks. In particular, we present the query planning and execution strategies used in AnduIN, a system combining sensor-local in-network processing and a data stream engine. Query planning employs a multi-dimensional cost model taking energy consumption into account and decides autonomously which query parts will be processed within the sensor network and which parts will be processed at the central instance.

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Authors and Affiliations

  1. Databases and Information Systems Group, Ilmenau University of Technology, Ilmenau, Germany

    Daniel Klan, Liz Ribe-Baumann & Kai-Uwe Sattler

  2. Max-Planck-Institut für Informatik, Saarbrücken, Germany

    Katja Hose

  3. DERI, NUI Galway, Galway, Ireland

    Marcel Karnstedt

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  1. Daniel Klan
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  2. Marcel Karnstedt
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  3. Katja Hose
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  4. Liz Ribe-Baumann
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  5. Kai-Uwe Sattler
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Correspondence to Daniel Klan.

Additional information

Communicated by Erik Buchmann.

This work was in parts supported by the BMBF under grant 03WKBD2B and by the Science Foundation Ireland under Grant No. SFI/08/CE/I1380 (Lion-2) and 08/SRC/I1407 (Clique).

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Klan, D., Karnstedt, M., Hose, K. et al. Stream engines meet wireless sensor networks: cost-based planning and processing of complex queries in AnduIN. Distrib Parallel Databases 29, 151–183 (2011). https://doi.org/10.1007/s10619-010-7071-6

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