© HU-IWI 2006 · Holger Ziekow Stream Processing in Networks of Smart Devices Institute of Information Systems Humboldt University of Berlin, Germany Holger.

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Presentation transcript:

© HU-IWI 2006 · Holger Ziekow Stream Processing in Networks of Smart Devices Institute of Information Systems Humboldt University of Berlin, Germany Holger Ziekow, Lenka Ivantysynova

© HU-IWI 2006 · Holger Ziekow Page 2 Business Applications aim to integrate data from smart devices (e.g. sensors and RFID data) Sensor and RFID data have the properties of data streams (Stream, Aurora) Processing streams on the device layer can extend device lifetime by reducing communication (Courgar) Stream Processing on Smart Items (Motivation)

© HU-IWI 2006 · Holger Ziekow Page 3 Stream Processing on Smart Items (Motivation) Smart devices are commonly battery powered and therefore very energy constrained Communicating is much more energy consuming than calculations (sending 1 bit 1000 CPU instructions)  Data processing in the network is favorable How to map? S1S1 S2S2 Stream Query plan  

© HU-IWI 2006 · Holger Ziekow Page 4 Querying in the Network Challenges: Devices vary in Position in the network. Free memory. Operators network position influences energy consumption. Memory influences data accuracy. Mapping problem is NP hard. (Rectilinear Steiner Tree Problem) Query plans may have to be modified.

© HU-IWI 2006 · Holger Ziekow Page 5 Stream Processing on Smart Items (Optimized Mappings) Finding an optimal mapping is an NP-hard problem We define a metric to measure a mappings quality. This metric can be used in optimization algorithms Parameters to mutate: Target devices for the query operator (m) for the given query plan (q) Operators in the query plan (q) which can subsequently be calculated EnergyData quality

© HU-IWI 2006 · Holger Ziekow Page 6 Stream Processing on Smart Items (Test Results) Cost Optimization steps Optimization steps Approximation using a genetic algorithm Approximation using a genetic algorithm We used our metric and a genetic algorithm to find good mappings of query plans Tests show that good results can be found relatively fast In manual checks the generated mapping can be proven as reasonable

© HU-IWI 2006 · Holger Ziekow Page 7 Stream Processing on Smart Items (Test Results) Query: AGG(JOIN(Src1,Src2)) Memory Usage: AGG = 50 JOIN = 70 Memory

© HU-IWI 2006 · Holger Ziekow Page 8 Stream Processing on Smart Items (Test Results) Query: AGG(Src1,Src2, Src3,Src4) Mapped Query: AGG(AGG(Src1,Src4), AGG(Src2,Src3))

© HU-IWI 2006 · Holger Ziekow Page 9 Future Work Additional optimization parameters Message delay. Value based errors. Node specific energy consumption. Tuning the optimization algorithm. Integration of different routing algorithms.