Zone Sharing: A Hot-Spots Decomposition Scheme for Data-Centric Storage in Sensor Networks

Zone Sharing: A Hot-Spots Decomposition Scheme for Data-Centric Storage in Sensor Networks Mohamed Aly Nicholas Morsillo Panos K. Chrysanthis Kirk Pruhs Advanced Data Management Technologies Lab Dept. of Computer Science University of Pittsburgh DMSN’05

Roadmap • Background • Problem Statement: Storage Hot-spots • Algorithms: Zone Sharing • Experimental Results • Conclusions

Sensor Networks Data Characteristics • Monitoring Applications: • One or more phenomenon • Sensor readings: events • An event contains one or more attributes for each phenomenon under concern • Querying load variations • Continuous queries: e.g. habitat monitoring applications • Ad-hoc queries: e.g. disaster management applications

Sensor Networks Data Storage Options • Base station storage • Events are sent to base stations where queries are issued and evaluated • Best suited for continuous queries • In-Network storage • Events are stored in the sensor nodes • Best suited for ad-hoc queries

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

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

Zone Sharing in DIM S2 x є [10,20] y є [1,10] 5% S1 x є [1,10] y є [1,20] 70% Z = 10 S3 x є [10,20] y є [10,20] 25% Z = 0 Z = 11 S1 A = 0 S2 S3 A = 10 A = 11

Zone Sharing in DIM S2 x є [1,10] y є [1,10] 35% (migrator) Z = 00 (receiver) S3 x є [10,20] y є [1,20] S1 x є [1,10] y є [10,20] 35% 30% (donor) Z = 01 Z = 1 S3 A = 1 S2 S1 A = 00 A = 01

Hot-Spot Decomposition in Zone Sharing

Storage Safety Requirement (1) • Pre-migration load (donor) >> post-migration load (receiver) • ldonor / (lmigrator + lreceiver) ≥ C1 • C1 should be greater than or equal to 2 to make sure that the donor is really falling in a hot-spot • Evaluated by donor and receiver

Storage Safety Requirement (2) • Post-migration load (migrator) >> pre-migration load (migrator) • T / lmigrator ≥ C2 • C2 should be greater than or equal to 2 to avoid cyclic migrations • Applied solely by migrator

Energy Safety Requirement (1) • Energy consumed (donor) << total energy (donor) • T / edonor ≤ E1 • E1 must be less than or equal to 0.5 • Applied only by donor

Energy Safety Requirement (2) • Energy consumed (migrator) << total energy (migrator) • (lmigrator + re * T) / emigrator ≤ E2 • E2 must be less than or equal to 0.5 • Applied only by migrator

Energy Safety Requirement (3) • Energy consumed (receiver) << total energy (receiver) • lmigrator * re / ereceiver ≤ E3 • E3 must be less than or equal to 0.5 • Applied only by migrator

Distributed Migration Criterion (DMC) • ldonor / (lmigrator + lreceiver) ≥ C1 • T / lmigrator ≥ C2 • T / edonor ≤ E1 • (lmigrator + re * T) / emigrator ≤ E2 • lmigrator * re / ereceiver ≤ E3

Single-Hop Zone Sharing (SHZS) • Goal: Overall minimal changes to the original DIM • Single Hop Zone Sharing: • A zone can be traded at most once • Periodic exchange of neighbors information • DMC applied locally by nodes • No changes needed to GPSR • Applicability: Small Hot Spots

Single-Hop Zone Sharing (SHZS) • Problems: • Large hot-spots: overloaded neighbors  DMC hard to be satisfied • Zone traded only once  nodes still in hot-spots after migration process • Messages pass by original destination (donor) before going to migrator  energy consumption overhead • Solution: • Allow a zone to be traded more than once

Multi-Hop Zone Sharing (MHZS) • A zone can be traded more than once • A new data structure: Traded Zoned List • Keeps track of the traded zones to redirect messages to their new destinations • An entry is composed of 3 values: (zone address, original owner, final owner) • GPSR changed to check the list first and update the destination field in the message

Roadmap • Background • Problem Statement: Storage Hot-spots • Algorithms: Zone Sharing • Experimental Results • Conclusions

Simulation Description • Compare: DIM, SHZS, and MHZS. • Simulator similar to the DIM’s [Li et. al., SenSys’03] • Two phases: insertion & query. • Insertion phase • Each sensor initiates 5 events • Events forwarded to owners • Query phase • Queries of sizes 10% to 100% of the attributes ranges

Experimental Setup

Experimental Results: Data Persistence Dropped Events for a network with a (50%, 10%) Hot-Spot

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

Experimental Results: Load Balancing Overloaded Nodes for a network with a (40%, 10%)Hot-Spot Note:An overloaded node is a node reaching its max. capacity

Experimental Results: Energy Consumption Average Node Energy for a network with a (50%, 10%)Hot-Spot

Conclusions • Contribution: • A storage hot-spots decomposition scheme for DCS sensor nets • Two versions: SHZS & MHZS • Experimental validation of its practicality • Current ZS extensions: • Hot-spots incremental avoidance scheme (submitted for publication) • Possibility of ZS generalizations: • Non-uniform loads for individual sensors • Upper bound for ZS max. trading hops (nMHZS)

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

Zone Sharing: A Hot-Spots Decomposition Scheme for Data-Centric Storage in Sensor Networks