Decomposing Data-Centric Storage Query Hot-Spots in Sensor Netwokrs Mohamed Aly, Panos K....

Decomposing Data-Centric Storage QueryHot-Spots in Sensor Netwokrs

Mohamed Aly, Panos K. Chrysanthis, and Kirk PruhsUniversity of Pittsburgh

Proceeding of Mobiquitous 2006

Jong Gun Lee (jglee_at_an.kaist.ac.kr)Advanced Networking Lab. KAIST

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

DIM

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

Problem Statement

Locally

Detect and Decompose

Data Centric Storage Query Hot-Spots

in Sensor Networks

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)

1) Increasing QualityQuality ofof DataData (QoD)(QoD) and 2) increasing energyenergy savingsaving

Table of Contents

• Part I. Background & Problem Statement

• Part II. Zone Partitioning (ZP)o Example of zone partitioning o Local detection of query hot-spotso 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

Part II. Zone Partitioning

Example of zone partitioning

Local detection of query hot-spotsPartitioning Criterion (PC)GPSR modifications

Example of Zone Partitioning

[ Before ]N0, N2, N4, N8, and N9 require dataN5 partitions the responsibility

[ After ]the donor: N5the receivers: N3 and N6

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(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)

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 loadof node x Total storage

capacity

Energy levelof node donor

Energy for receiving amsg

Energy levelof node receiver

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-spotbecause neighbors of hot-spot are falling in the hot spot

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

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

Part III. Zone Partial Replication

Example of zone partial ReplicationAdditional PC RequirementZPR handling of insertions

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 neighborsThe results are first provided by N3 and N6

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

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

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– Threshold1: 2

– E1 and E2: 0.3

– Q1: 3, Q2: 0.8, and Q3: 0.2

Energy Consumption

Node Energy Level

Node Energy Level

220 nodes0.33% hot-spot

220 nodes2.5% hot-spot

Quality of Data

Network Size

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

Load Balancing

Network Size

Network Size