Decomposing Data-Centric Storage Query Hot-Spots in
Sensor Networks
Mohamed Aly In collaboration with
Panos K. Chrysanthis and Kirk Pruhs
Advanced Data Management Technologies LabDept. of Computer Science
University of Pittsburgh
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Motivating Application: Disaster Management
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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.
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Data Storage Options in Sensor Networks
Base Station Storage: Events are sent to base stations where queries are
issued and evaluated. Best suited for continuous queries.
In-Network Storage (INS): Events are stored in the sensor nodes. Best suited for ad-hoc queries. All previous INS schemes were Data-Centric Storage
(DCS) schemes.
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Data-Centric Storage (DCS)
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’02]
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.
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The DIM DCS Scheme
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Problems of Current DCS Schemes Storage Hot-Spots:
A large percentage of events is mapped to few sensor nodes.
Our Solutions The Zone Sharing (ZS) algorithm on top of DIM
[DMSN’05] The K-D Tree based DCS scheme (KDDCS) [submitted]
Query Hot-Spots: A large percentage of queries target events stored in
few sensor nodes. Our Solutions [MOBIQUITOUS’06]
The Zone Partitioning (ZP) algorithm The Zone Partial Replication (ZPR) algorithm
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Query Hot-Spots in DIM
Definition: A high percentage of queries accessing a “hot zone” stored by a small number of nodes.
Existence of query hot-spots leads to: Increased node deaths Network Partitioning Reduced network lifetime Decreased Quality of Data (QoD)
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Query Hot-Spots Decomposition Algorithms
Uniform vs. skewed distribution of the number of accesses among the hot-zone events: The Zone Partitioning (ZP) algorithm The Zone Partial Replication (ZPR) algorithm
Basic Idea: Each sensor keeps track of the Average Querying
Frequency (AQF) of its stored events Periodically compares its AQF to its neighbors’ AQFs In case a large difference is detected, the node
(donor) selects the Best neighbor (receiver) that can receive part of its responsibility range
Donor locally determines receiver Partitioning Criterion (PC)
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The Zone Partitioning (ZP) Algorithm
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The Zone Partial Replication (ZPR) Algorithm
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Query Hot-Spots Decomposition Algorithms
Uniform vs. skewed distribution of the number of accesses among the hot-zone events: The Zone Partitioning (ZP) algorithm The Zone Partial Replication (ZPR) algorithm
Basic Idea: Each sensor keeps track of the Average Querying
Frequency (AQF) of its stored events Periodically compares its AQF to its neighbors’ AQFs In case a large difference is detected, the node
(donor) selects the Best neighbor (receiver) that can receive part of its responsibility range
Donor locally determines receiver Partitioning Criterion (PC)
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Query Hot-Spots Decomposition Algorithms
Uniform vs. skewed distribution of the number of accesses among the hot-zone events: The Zone Partitioning (ZP) algorithm The Zone Partial Replication (ZPR) algorithm
Basic Idea: Each sensor keeps track of the Average Querying
Frequency (AQF) of its stored events Periodically compares its AQF to its neighbors’ AQFs In case a large difference is detected, the node
(donor) selects the Best neighbor (receiver) that can receive part of its responsibility range
Donor locally determines receiver Partitioning Criterion (PC)
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PC: Storage Safety Requirement
The sum of the pre-partitioning load of the receiver and the traded zone should be less than the receiver’s storage capacity
T + lreceiver ≤ S
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PC: Energy Safety Requirement (1)
The energy consumed by the donor in the partitioning process should be much less than the total energy of the donor
T / edonor ≤ E1
E1 ≤ 0.5
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PC: Energy Safety Requirement (2)
The energy consumed by the receiver in the partitioning process should be much less than the total energy of the receiver
(T * re) / ereceiver ≤ E2
E2 ≤ 0.5
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PC: Access Frequency Requirement
The average access frequency of the donor is much larger than that of the receiver
AQF(donor) / AQF(receiver) ≥ Q1 Q1 should be greater than 2 to avoid cyclic
migrations
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ZPR Initiation Requirements
In case all previous requirements are satisfied: ZP initiated
If a hot sub range of small size exists within the hot range ZPR initiated instead of ZP
AQF(hot sub range) / AQF(total range) ≥ Q2
Q2 should be close to 1, for e.g. 0.9 size(hot sub range) / size(total range) ≤ Q3
Q3 should be close to 0, for e.g. 0.2
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Partitioning Criterion (PC)
1. T + lreceiver ≤ S
2. T / edonor ≤ E1
3. (T * re) / ereceiver ≤ E2
4. AQF(donor) / AQF(receiver) ≥ Q1
5. AQF(hot sub range) / AQF(total range) ≥ Q2
6. size(hot sub range) / size(total range) ≤ Q3
1:4 satisfied ZP initiated
1:6 satisfied ZPR initiated
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More about the Algorithms
Mechanism to lower messaging overhead GPSR Modifications
Traded Zone List (TZL) Coalescing Process Insertion process in ZPR Bound on the replication hops of ZPR
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Roadmap
Background Problem Statement: Query Hot-spots Algorithms: ZP, ZPR Experimental Results Conclusions
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Simulation Description
Compare: DIM, ZP/ZPR. Simulator similar to the DIM’s [Li et. al., SenSys’03] Two phases: insertion & query. Insertion phase (to achieve a steady state of network
storage) Each sensor initiates 5 events Events forwarded to owners
Query phase Each sensor generates 20 single-event queries
(worst case scenario)
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Experimental Setup
Parameter Value
Network size 50 to 300 sensors
Initial energy 100 units
Energy unit energy needed to send one event
E1 & E2 2
Q1 , Q2 , and Q3 3, 0.8, and 0.2
Number of hot-spots 1
Hot-spot sizes 0.05% - 10% of attribute ranges
Sensor node storage capacity 10 units (events)
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Experimental Results: Quality of Data (QoD)
5% hot-spot
0
1
2
3
4
5
6
50 100 150 200 250 300
Th
ou
san
ds
Network Size
No
. U
nan
swer
ed Q
uer
ies
DIM
ZP-ZPR
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Experimental Results: Balancing Energy Consumption
200 nodes, 0.33% hot-spot
020
4060
80100
120140
160180
200
10 20 30 40 50 60 70 80 90 100
Node Energy Level
Nu
mb
er o
f N
od
es
DIM
ZP-ZPR
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Experimental Results: ZP/ZPR Strengths
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.
Increasing energy savings by balancing energy consumption among sensors.
Increasing the network lifetime by reducing node deaths.
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Acknowledgment
This work is part of the “Secure CITI: A Secure Critical Information Technology Infrastructure for Disaster Management (S-CITI)” project funded through the ITR Medium Award ANI-0325353 from the National Science Foundation (NSF).
For more information, please visit: http://www.cs.pitt.edu/s-citi/
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Conclusions and Extensions
Query Hot-Spots: An important problem in current DCS schemes.
Contribution: A query hot-spots decomposition scheme for DCS
sensor nets, ZP/ZPR, working on top of the DIM DCS scheme.
Experimental validation of the ZP/ZPR practicality Work under submission:
KDDCS: A unified DCS scheme for load balancing storage and query loads.
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Thank You
Questions ?
Advanced Data Management Technologies Labhttp://db.cs.pitt.edu
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Experimental Results: Load Balancing
0.05% hotspot
1.5
2
2.5
3
3.5
4
4.5
50 100 150 200 250 300
Network Size
Avg
. N
od
e S
tora
ge
DIM
ZP-ZPR
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Experimental Results: Load Balancing
0.05% hot-spot
5
10
15
20
25
30
35
50 100 150 200 250 300
Network Size
No
. o
f F
ull
No
des
DIM
ZP-ZPR