Decomposing Data-Centric Storage Query Hot-Spots in Sensor Netwokrs

1. DecomposingData-CentricStorageQueryHot-SpotsinSensorNetwokrs MohamedAly,PanosK.Chrysanthis,andKirkPruhs UniversityofPittsburgh ProceedingofMobiquitous2006 JongGunLee(jglee_at_an.kaist.ac.kr) AdvancedNetworkingLab.KAIST

2. Background • Possible sensornet approaches [1] • External storage “Upon detection of events, the relevant data is sent to external storage where it can be further processed as needed.” • Local storage “Event information is stored locally upon detection of an event.” • Data-centricstorage “After an event is detected, the data is stored by name within the sensornet.” • GreedyPerimeterStatelessRouting(GPSR) • Efficient routing protocol for mobile, wireless network [1] Sylvia Ratnasamy, Deborah Estrin, Ramesh Govindan, Brad Karp, Scott Shenker, Li Yin, Fang Yu, Data-Centric Storage in Sensornets, HotNets 2002

3. DIM • Each sensor • knows its geographical location • has a unique nodeID • has the capacity for wireless communication

4. ProblemStatement Locally Detect and Decompose Data Centric Storage Query Hot-Spots in Sensor Networks

5. Contribution Inthispaper,weproposetwoalgorithmslocallysolvingthequeryhot-spotsproblemintheDIMframework: a)ZonePartitioning(ZP)and b)ZonePartialReplication(ZPR) 1)IncreasingQualityofData(QoD)and 2)increasingenergysaving

6. TableofContents • PartI.Background&ProblemStatement • PartII.ZonePartitioning(ZP) oExampleofzonepartitioningoLocaldetectionofqueryhot-spots oPartitioningcriterionoCoalescingprocess • PartIII.ZonePartialReplication(ZPR) oAdditionalPCrequirementsoZPRhandlingofinsertions oExampleofzonepartialreplication • PartIV.ExperimentalEvaluation • PartV.Conclusion

7. PartII.ZonePartitioning Exampleofzonepartitioning Localdetectionofqueryhot-spots PartitioningCriterion(PC) GPSRmodifications

8. ExampleofZonePartitioning [Before] N0,N2,N4,N8,andN9requiredata N5partitionstheresponsibility [After] thedonor:N5 thereceivers:N3andN6

9. LocalDetectionofQueryHot-Spots • access frequency • This counter represents the number of queries accessing such event over a given time period (window), w • Average Access Frequency, AAF(Zk) • Average of access frequencies of events belonging to zone Zk • x decides to split the hot zone Zi into two partitions: Zi1 and Zi2 • x keeps one of the partitions and a selected node of its neighbors, which name is the receiver, takes another one (traded zone T)

10. PartitioningCriterion(PC) • Setofinequalitiestobelocallyappliedbythedonortoselectthebestreceiveramongitsneighbors Storageload ofnodex StorageSafetyRequirement Totalstorage capacity #oftraded msgs(events) Energyfor receivingamsg EnergySafetyRequirementI EnergySafetyRequirementII Energylevel ofnodedonor StorageSafetyRequirement Energylevel ofnode receiver

11. PartitioningCriterion • Periodicmessagestoshareloadinformation • Intermsofstorage,energy,andaveragequeryfrequency • Canbepiggy-backedmessages • A donor sends a RequesttoPartition(RTP)message, and a receiver sends a Accept to Partition (ATP) message • Hot-spot decomposition starts from the border of hot-spot because neighbors of hot-spot are falling in the hot spot

12. GPSRModifications • A receiver can re-apply the PC to partition a previously trades zone • The original donor in all insertions and queries concerning any of the k traded zones • We augmented GPSR to recognize that a zone has been traded and moved away from its original owner • Traded Zones List (TZL) • zone address / original donor / final receiver

13. CoalescingProcess • In case any of zones is not accessed for a complete time window, d, this is considered as an indication that the hot-spot has stopped to exist • At such point, the receiver transfers the responsibility of the received zone back to its original owner • That zone are directed to the original donor based on the original DIM and GPSR schemes

14. PartIII.ZonePartialReplication ExampleofzonepartialReplication AdditionalPCRequirement ZPRhandlingofinsertions

15. Exampleofzonepartialreplication [Before] N0,N2,N4,N8,andN9requiredata [After] N5 sends the hot sub-zone events to all its direct neighbors The results are first provided by N3 and N6

16. AdditionalPCRequirements • In the node is only able to satisfy the first 4 PC inequalities, it proceeds in applying ZP • A node which satisfy all 6 PC inequalities chooses to apply ZPR • Two more Access Frequency Requirement inequalities

17. ZPRHandlingofInsertions • We bound the number of hops a zone can be replicated away from its original owner to a limited number of hops

18. ExperimentalTestbed • Settings • Number of sensors: from 50 to 300 • Initial energy: 100 units • Radio range: 40m • Storage capacity: 10 units • Uniformly distributed sensors • Parameters • Threshold1: 2 • E1 and E2: 0.3 • Q1: 3, Q2: 0.8, and Q3: 0.2

19. EnergyConsumption 220 nodes 2.5% hot-spot NodeEnergyLevel 220 nodes 0.33% hot-spot NodeEnergyLevel

20. QualityofData NetworkSize

21. Conclusion • Wepresenttwonovelalgorithmsfordecomposingqueryhot-spotsinData-CentricStoragesensornetworks • ZonePartitioning(ZP)andZonePartialReplication(ZPR) • ToapplytheZP/ZPRalgorithmsontopoftheDIMschemeachievesgoodperformanceindecomposingqueryhot-spotsofdifferentsize • ThisimprovestheQoDandincresesenergysavings

22. LoadBalancing NetworkSize NetworkSize