1. Decomposing Data-Centric Storage Query Hot-Spots in Sensor Netwokrs Mohamed Aly, Panos K. Chrysanthis, and Kirk Pruhs University of Pittsburgh Proceeding of Mobiquitous 2006 Jong Gun Lee (jglee_at_an.kaist.ac.kr) Advanced Networking Lab. 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-centric storage “After an event is detected, the data is stored by name within the sensornet.” Greedy Perimeter Stateless Routing (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. Problem Statement Locally Detect and Decompose Data Centric Storage Query Hot-Spots in Sensor Networks

5. Contribution In this paper, we propose two algorithms locally solving the query hot-spots problem in the DIM framework: a) Zone Partitioning (ZP) and b) Zone Partial Replication (ZPR) Quality of Data (QoD) 1) Increasing Quality of Data (QoD) and energy saving 2) increasing energy saving

6. Table of Contents Part I. Background & Problem Statement Part II. Zone Partitioning (ZP) o Example of zone partitioning o Local detection of query hot-spots o Partitioning criterion o Coalescing process Part III. Zone Partial Replication (ZPR) o Additional PC requirements o ZPR handling of insertions o Example of zone partial replication Part IV. Experimental Evaluation Part V. Conclusion

7. Part II. Zone Partitioning Example of zone partitioning Local detection of query hot-spots Partitioning Criterion (PC) GPSR modifications

8. Example of Zone Partitioning [ Before ] N0, N2, N4, N8, and N9 require data N5 partitions the responsibility [ After ] the donor: N5 the receivers: N3 and N6

9. Local Detection of Query Hot-Spots access frequency – This counter represents the number of queries accessing such event over a given time period (window), w Average Access Frequency, AAF(Z k ) – Average of access frequencies of events belonging to zone Z k – x decides to split the hot zone Z i into two partitions: Z i1 and Z i2 – 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. Partitioning Criterion (PC) Set of inequalities to be locally applied by the donor to select the best receiver among its neighbors Storage Safety Requirement Energy Safety Requirement I Storage Safety Requirement Energy Safety Requirement II # of traded msgs (events) Storage load of node x Total storage capacity Energy level of node donor Energy for receiving amsg Energy level of node receiver

11. Partitioning Criterion Periodic messages to share load information – In terms of storage, energy, and average query frequency – Can be piggy-backed messages A donor sends a Request to Partition (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. GPSR Modifications 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. Coalescing Process 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. Part III. Zone Partial Replication Example of zone partial Replication Additional PC Requirement ZPR handling of insertions

15. Example of zone partial replication [ Before ] N0, N2, N4, N8, and N9 require data [ After ] N5 sends the hot sub-zone events to all its direct neighbors The results are first provided by N3 and N6

16. Additional PC Requirements 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. ZPR Handling of Insertions We bound the number of hops a zone can be replicated away from its original owner to a limited number of hops

18. Experimental Testbed Settings – Number of sensors: from 50 to 300 – Initial energy: 100 units – Radio range: 40m – Storage capacity: 10 units – Uniformly distributed sensors Parameters – Threshold 1 : 2 – E 1 and E 2 : 0.3 – Q 1 : 3, Q 2 : 0.8, and Q 3 : 0.2

19. Energy Consumption Node Energy Level 220 nodes 0.33% hot-spot 220 nodes 2.5% hot-spot

20. Quality of Data Network Size

21. Conclusion We present two novel algorithms for decomposing query hot-spots in Data-Centric Storage sensor networks – Zone Partitioning (ZP) and Zone Partial Replication (ZPR) To apply the ZP/ZPR algorithms on top of the DIM scheme achieves good performance in decomposing query hot-spots of different size This improves the QoD and increses energy savings

22. Load Balancing Network Size