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Load Balancing of In-Network Data-Centric Storage Schemes in Sensor Networks

Load Balancing of In-Network Data-Centric Storage Schemes in Sensor Networks. Mohamed Aly In collaboration with Kirk Pruhs and Panos K. Chrysanthis Advanced Data Management Technologies Lab Dept. of Computer Science University of Pittsburgh. Why Data Centric Storage??.

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Load Balancing of In-Network Data-Centric Storage Schemes in Sensor Networks

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  1. Load Balancing of In-Network Data-Centric Storage Schemes in Sensor Networks Mohamed Aly In collaboration with Kirk Pruhs and Panos K. Chrysanthis Advanced Data Management Technologies Lab Dept. of Computer Science University of Pittsburgh

  2. Why Data Centric Storage??

  3. Why Data Centric Storage?? • Motivating application: Disaster management sensor networks • Sensors are deployed to monitor the disaster area. • First responders moving in the area issue ad-hoc queries to nearby sensors • The sensor network is responsible of answering these queries • First responders use query results to improve the decision making process in the management process of the disaster

  4. Data-Centric Storage • Quality of Data (QoD) of ad-hoc queries • Define an event owner based on the event value • Examples: • Distributed Hash Tables (DHT) [Shenker et. al., HotNets’03] • Geographic Hash Tables (GHT) [Ratnasamy et. al., WSNA’02] • Distributed Index for Multi-dimensional data (DIM)[Li et. al., SenSys’03] • Greedy Perimeter Stateless Routing algorithm (GPSR)[Karp & Kung, Mobicom’00] • Among the above schemes, DIM has been shown to exhibit the best performance

  5. DIM

  6. Problems of Current DCS Schemes • Storage Hot-Spots: • A large percentage of events is mapped to few sensor nodes • Our Solutions • The Zone Sharing algorithm on top of DIM (ZS) [DMSN’05] • The K-D Tree based DCS scheme (KDDCS) [submitted] • Query Hot-Spots: • A large percentage of queries is targeting events stored in few sensor nodes • Our Solutions [MOBIQUITOUS’06, to appear] • The Zone Partitioning algorithm on top of DIM (ZP) • The Zone Partial Replication algorithm on top of DIM (ZPR)

  7. 1. Storage Hot-Spots in DCS Schemes S1 x є [1,10] S2 x є [10,20] 50% 7% S4 x є [30,40] 40% S3 x є [20,30] 3%

  8. K-D Tree Based DCS (KDDCS) Scheme

  9. K-D Tree Based DCS (KDDCS) Scheme • Abstracted Theoretical Problem: • Weighted Split Median problem • Each sensor has an associated value • Goal: sensors to agree on a split value V such that approximately half of the values are larger than V and half of the values are smaller than V • Distributed Algorithm • O(log n) times the network diameter • O(1) times network diameter if the number of sensors is known a priori within a constant factor • KDDCS Components: • Distributed logical address assignment algorithm • Based on the usage of “dynamic split points” • Event to bit-code mapping • Using the split points stored locally in any node • Logical Stateless Routing (LSR) • KDTR: K-D Tree Re-balancing algorithm

  10. 2. Query Hot-Spots in DIM • A high percentage of queries accessing a small number of nodes • Existence of query hot-spots lead to: • Increased node death • Network Partitioning • Reduced network lifetime • Decreased Quality of Data (QoD)

  11. Zone Partitioning [MOBIQUITOUS’06]

  12. Zone Partial Replication [MOBIQUITOUS’06]

  13. Experimental Results: QoD Result Size of a 50% Query for a network with a (80%, 10%)Hot-Spot

  14. Experimental Results: Quality of Data 5% hot-spot

  15. Conclusions (1) • Storage Hot-Spots: Serious problem in current DCS schemes • Contribution: • ZS: A storage hot-spots decomposition algorithm working on top of DIM • KDDCS: A DCS scheme avoiding the formation of storage hot-spots • KDDCS Advantages: • Achieving a better data persistenceby balancing storage responsibility among nodes • Increasing the QoDby distributing the storage hot-spot events among a larger number of nodes • Increasing the energy savingsby achieving a well balanced energy consumption overhead among sensor nodes

  16. Conclusions (2) • Query Hot-Spots: Another important problem in DCS schemes • Contribution: • A query hot-spots decomposition scheme for DCS sensor nets, ZP/ZPR • Experimental validation of its practicality • Increasing energy savings by balancing energy consumption among sensors • Increasing the network lifetime by reducing node deaths • Increasing the QoD by partitioning the hot range among a large number of sensors, thus, balancing the query load among sensors and keep them alive longer to answer more queries.

  17. Thank You Questions ? Advanced Data Management Technologies Lab http://db.cs.pitt.edu

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