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

Load Balancing of In-Network DCS Schemes in Sensor Networks Mohamed Aly 2 Why Data Centric Storage??

Load Balancing of In-Network DCS Schemes in Sensor Networks Mohamed Aly 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

Load Balancing of In-Network DCS Schemes in Sensor Networks Mohamed Aly 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

Load Balancing of In-Network DCS Schemes in Sensor Networks Mohamed Aly 5 DIM

Load Balancing of In-Network DCS Schemes in Sensor Networks Mohamed Aly 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)

Load Balancing of In-Network DCS Schemes in Sensor Networks Mohamed Aly 7 1. Storage Hot-Spots in DCS Schemes S1 x є [1,10] S2 x є [10,20] S3 x є [20,30] S4 x є [30,40] 50% 40% 7% 3%

Load Balancing of In-Network DCS Schemes in Sensor Networks Mohamed Aly 8 K-D Tree Based DCS (KDDCS) Scheme

Load Balancing of In-Network DCS Schemes in Sensor Networks Mohamed Aly 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

Load Balancing of In-Network DCS Schemes in Sensor Networks Mohamed Aly 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)

Load Balancing of In-Network DCS Schemes in Sensor Networks Mohamed Aly 11 Zone Partitioning [MOBIQUITOUS’06]

Load Balancing of In-Network DCS Schemes in Sensor Networks Mohamed Aly 12 Zone Partial Replication [MOBIQUITOUS’06]

Load Balancing of In-Network DCS Schemes in Sensor Networks Mohamed Aly 13 Experimental Results: QoD Result Size of a 50% Query for a network with a (80%, 10%) Hot-Spot

Load Balancing of In-Network DCS Schemes in Sensor Networks Mohamed Aly 14 Experimental Results: Quality of Data 5% hot-spot

Load Balancing of In-Network DCS Schemes in Sensor Networks Mohamed Aly 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 persistence by balancing storage responsibility among nodes Increasing the QoD by distributing the storage hot-spot events among a larger number of nodes Increasing the energy savings by achieving a well balanced energy consumption overhead among sensor nodes

Load Balancing of In-Network DCS Schemes in Sensor Networks Mohamed Aly 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.

Load Balancing of In-Network DCS Schemes in Sensor Networks Mohamed Aly 17 Thank You Questions ? Advanced Data Management Technologies Lab