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Stream Processing in Networks of Smart Devices

Stream Processing in Networks of Smart Devices. Holger Ziekow, Lenka Ivantysynova. Institute of Information Systems Humboldt University of Berlin, Germany. Stream Processing on Smart Items (Motivation).

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Stream Processing in Networks of Smart Devices

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  1. Stream Processing in Networks of Smart Devices Holger Ziekow, Lenka Ivantysynova Institute of Information Systems Humboldt University of Berlin, Germany

  2. Stream Processing on Smart Items (Motivation) • 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)

  3. Stream Query plan   S2 S1 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?

  4. Querying in the Network Challenges: • Devices vary in • Position in the network. • Free memory. • Operators network position influencesenergy consumption. • Memory influences data accuracy. • Mapping problem is NP hard.(Rectilinear Steiner Tree Problem) • Query plans may have to be modified.

  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 Energy Data quality 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

  6. Stream Processing on Smart Items (Test Results) • 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 Cost Approximation using a genetic algorithm Optimization steps

  7. Stream Processing on Smart Items (Test Results) Query: AGG(JOIN(Src1,Src2)) Memory Usage: AGG = 50 JOIN = 70 Memory

  8. Stream Processing on Smart Items (Test Results) Query: AGG(Src1,Src2,Src3,Src4) Mapped Query: AGG(AGG(Src1,Src4),AGG(Src2,Src3))

  9. Future Work • Additional optimization parameters • Message delay. • Value based errors. • Node specific energy consumption. • Tuning the optimization algorithm. • Integration of different routing algorithms.

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