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![Page 1: Decomposing Data-Centric Storage Query Hot-Spots in Sensor Netwokrs Mohamed Aly, Panos K. Chrysanthis, and Kirk Pruhs University of Pittsburgh Proceeding.](https://reader035.fdocuments.in/reader035/viewer/2022062321/56649eca5503460f94bd7a2d/html5/thumbnails/1.jpg)
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
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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
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DIM
• Each sensor – knows its geographical location– has a unique nodeID– has the capacity for wireless communication
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Problem Statement
Locally
Detect and Decompose
Data Centric Storage Query Hot-Spots
in Sensor Networks
![Page 5: Decomposing Data-Centric Storage Query Hot-Spots in Sensor Netwokrs Mohamed Aly, Panos K. Chrysanthis, and Kirk Pruhs University of Pittsburgh Proceeding.](https://reader035.fdocuments.in/reader035/viewer/2022062321/56649eca5503460f94bd7a2d/html5/thumbnails/5.jpg)
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
![Page 6: Decomposing Data-Centric Storage Query Hot-Spots in Sensor Netwokrs Mohamed Aly, Panos K. Chrysanthis, and Kirk Pruhs University of Pittsburgh Proceeding.](https://reader035.fdocuments.in/reader035/viewer/2022062321/56649eca5503460f94bd7a2d/html5/thumbnails/6.jpg)
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
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Part II. Zone Partitioning
Example of zone partitioning
Local detection of query hot-spotsPartitioning Criterion (PC)GPSR modifications
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Example of Zone Partitioning
[ Before ]N0, N2, N4, N8, and N9 require dataN5 partitions the responsibility
[ After ]the donor: N5the receivers: N3 and N6
![Page 9: Decomposing Data-Centric Storage Query Hot-Spots in Sensor Netwokrs Mohamed Aly, Panos K. Chrysanthis, and Kirk Pruhs University of Pittsburgh Proceeding.](https://reader035.fdocuments.in/reader035/viewer/2022062321/56649eca5503460f94bd7a2d/html5/thumbnails/9.jpg)
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)
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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
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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
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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
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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
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Part III. Zone Partial Replication
Example of zone partial ReplicationAdditional PC RequirementZPR handling of insertions
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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
![Page 16: Decomposing Data-Centric Storage Query Hot-Spots in Sensor Netwokrs Mohamed Aly, Panos K. Chrysanthis, and Kirk Pruhs University of Pittsburgh Proceeding.](https://reader035.fdocuments.in/reader035/viewer/2022062321/56649eca5503460f94bd7a2d/html5/thumbnails/16.jpg)
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
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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
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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
![Page 19: Decomposing Data-Centric Storage Query Hot-Spots in Sensor Netwokrs Mohamed Aly, Panos K. Chrysanthis, and Kirk Pruhs University of Pittsburgh Proceeding.](https://reader035.fdocuments.in/reader035/viewer/2022062321/56649eca5503460f94bd7a2d/html5/thumbnails/19.jpg)
Energy Consumption
Node Energy Level
Node Energy Level
220 nodes0.33% hot-spot
220 nodes2.5% hot-spot
![Page 20: Decomposing Data-Centric Storage Query Hot-Spots in Sensor Netwokrs Mohamed Aly, Panos K. Chrysanthis, and Kirk Pruhs University of Pittsburgh Proceeding.](https://reader035.fdocuments.in/reader035/viewer/2022062321/56649eca5503460f94bd7a2d/html5/thumbnails/20.jpg)
Quality of Data
Network Size
![Page 21: Decomposing Data-Centric Storage Query Hot-Spots in Sensor Netwokrs Mohamed Aly, Panos K. Chrysanthis, and Kirk Pruhs University of Pittsburgh Proceeding.](https://reader035.fdocuments.in/reader035/viewer/2022062321/56649eca5503460f94bd7a2d/html5/thumbnails/21.jpg)
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
![Page 22: Decomposing Data-Centric Storage Query Hot-Spots in Sensor Netwokrs Mohamed Aly, Panos K. Chrysanthis, and Kirk Pruhs University of Pittsburgh Proceeding.](https://reader035.fdocuments.in/reader035/viewer/2022062321/56649eca5503460f94bd7a2d/html5/thumbnails/22.jpg)
Load Balancing
Network Size
Network Size