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CoCoDa - Data Management in Mobile Ad-Hoc Networks

Joos Böse, Katharina Hahn, Manuel Scholz, Heinz Schweppe http://www.inf.fu-berlin.de/inst/ag-db/projects/cocoda Databases and Information Systems Group Freie Universität Berlin. CoCoDa - Data Management in Mobile Ad-Hoc Networks. Research topic: guarantees for

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CoCoDa - Data Management in Mobile Ad-Hoc Networks

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  1. Joos Böse, Katharina Hahn, Manuel Scholz, Heinz Schweppe http://www.inf.fu-berlin.de/inst/ag-db/projects/cocoda Databases and Information Systems Group Freie Universität Berlin CoCoDa -Data Management in Mobile Ad-Hoc Networks

  2. Research topic: guarantees for computations in unstable environments • Distributed transactions in mobile environments? • Most are client/server oriented • MANETs: Atomic commit more important than isolation • Replication • Information freshness • Conflicting operations • Consistency of local updates? • Application Scenario • E-Commerce • Cooperative documents (e.g. mobile WiKi)

  3. CoCoDa System Architecture MANET Info TA Processor Replication SLS Core Engine Data Retrieval DataDissemination Communication Interface J2ME CDC OSGI JINI Ad-Hoc Wireless Connectivity (IEEE 802.11, Bluetooth)

  4. Replication in MANETs • Goal: Manage distributed state on mobile nodes (update anywhere) • Need for Data Dissemination • Reach as many nodes as possible • Minimize costs for dissemination • Consistent Update • Transactional consistency • Optimistic synchronization • Minimize cost

  5. Fully replicated system • Transactions only access local resources (not distributed TA) • Local pre-comitt • TAlocal (op1,…,opn) is sent to all replica • Order of TA excecution defined by time stamps • Different conflict resolution strategies • Syntactic (e.g. time), not semantic conflict resolution

  6. Dependencies of TAs Conflictingtransactions: both updated the same value established by TA12 timestamp

  7. Dependencies and conflict resolution a) and b): two conflict resolutions "value lineage": b(a), c(a), d(b,a), e(b,a) product byconflicting transaction

  8. Guarantees in unstable systems?? Are we solving the right problems?

  9. Heathland experiments • Field test of sensor network by Thurau (TU HH) • 24 nodes, 16 days, outside in the forestgoal: analysis of algorithms under realistic conditions • Many interesting results, one of them: Loss of packets significant, in particular in case of large multi-hop depth

  10. Heathland (Thurau) success rate  100*0.8depth

  11. Do not try to avoid uncertainty but make the best out of it!

  12. The Home, Year 2020 • 1000s of sensors (light, temp, sound, motion, location, …) • 100s of actuators (locks, switches, heating, water, …) • Masses of data (“Traditional” data plus sensor streams) • Does a lot for you: security, HVAC, energy management & demand-response, entertainment, … • Use Alice’s motion patterns to activate electrical devices (e.g., water heating) • Correlate user motions with existing patterns to detect “suspicious” behavior Intel's vision Berkeley Lab, HeisenData project

  13. Smart home must... Handle uncertainty and correlation ( • P( sensor 2455 fired accurately ) > .8 • P( someone in den | behavior of sensors ) > .95 • ... A hierarchy of inferences, from minute to abstract Recognize, manage, and exploit correlations (spatial, temporal)

  14. Approaches today • Interesting processing done outside DB • Lose key benefits of DBMS (declarative processing, persistence, optimization)

  15. Location... ... is always inexact Uncertain regions of moving objects from B. Yu: A spatiotemporal Uncertainty Model, SAC 06

  16. So what ? Uncertainty of data: a principle problem of mobile data mangement and radio communication • What is needed? Mechanisms dealing with uncertainty at all levels • Adaptation of algorithms to hostile environments • Management of data lineage • Graceful degradation of systems depending on degree of uncertainty • ....

  17. Example: Unreliable RFID sensors Integrity constraints for sensor data cleaning Library example (D. Suciu et al. 06):FOR ALL Sightings S CHECK COUNT(SELECT * FROM SIGHTING T WHERE T.l = S.t AND T.bid = S.bid) <= 1 " A particular book exist only once in the library " Other constraints: " A checked-out book cannot appear in the library " ...

  18. Are we solving the right problems? • to some extent: Yes sensors, data streaming, ... • No? • MANETs are not really needed when it is easy to access the fixed net through access points • Client - Server mobile interaction will dominate • Integrating methods from uncertainty management (ML, probabilistic db,..) into mobile data management seems appropriate

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