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Similarity aware query processing in sensor
networks
PingXia, PanosK.Chrysanthis, and AlexandrosLabrinidis
Proceedings of the 14th International Workshop on Parallel and Distributed Real-Time Systems, April 2006.
(WPDRTS'06)
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Outline• Introduction• Similarity aware query processing
– Query processing scheme– Split a query– Candidate selection
• Simulation• Conclusion
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Base station architecture
BSBS
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Data Centric Storage (GHT)
(11, 28)
(11,28)=Hash(“temp”)
Get(“temp”)PDA
Put(“temp”)(11,28)=Hash(“temp”)
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Motivate
PDA(11, 28)
Q1 : Temp > 200 and 20 < light level < 40 12p.m.-12:30p.m.
Q2 : Temp > 250 and 25 < light level < 3512p.m.-12:30p.m.
Some queries are similar
The basic idea
The results (events) for previously issued queries as materialized views
Utilized the materialized view to answer similar queries
PDA
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System model
(36, 40)
Use a hash function to determine the index node
O-nodeID attributes (range) timestamp
10 temp 12p.m.
Index entry
M-nodeID attributes (range) timestamp
2 100-200 12:30p.m.
M-view directory entry
Mobile agent
Location
Index node--I-node
Original storage node--O-node
Materialized view node--M-node
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Query Processing scheme
(36, 40)
52
20
15
9
10
O-nodeID attributes timestamp
10 temp 12p.m.
15 temp 12:30p.m.
20 temp 12:30p.m.
Index entry
M-nodeID attributes timestamp
5 25-35 12:00p.m.-12:10p.m.
9 100-250 12:00p.m.-12:30p.m.
2 100-200 12:15p.m.-12:30p.m.
M-view directory entry
Mobile agent
Location
Index node--I-node
Original storage node--O-node
Materialized views node--M-node
Event : fireRange: 100-200Time: 12:00p.m.-12:30p.m.
Candidates of O-node : 15, 20Candidates of M-view-node : 9, 2
Selecting a set of nodes as responders
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Split a query• Expect to find an M-view entry
– Answer the query completely• In most case the range of an M-view entry
– Partially overly• Split the original query
– Avoid duplicatesQuery rangeQuery range 2020 4040
M-view entry 1M-view entry 1 1515 3030
M-view entry 2M-view entry 2 2525 4040
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Split a query1. [a, b] contains [x, y]
Query rangeQuery rangea b
The range of an The range of an M-view entryM-view entry
x yQ1: on range [x, y]
Q2: on range [a, x] v [b, y]
2. [x, y] contains [a, b]
Query rangeQuery rangea b
The range of an The range of an M-view entryM-view entry
x yNeedn’t to split the query
3. [a, b] intersection [x, y]
Query rangeQuery rangea b
The range of an The range of an M-view entryM-view entry
x y
Q1: on range [a, y]
Q2: on range [y, b]
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Candidate selectionO-nodeID attributes timestamp
10 temp 12p.m.
O-nodeID attributes timestamp
1 temp 12: 03p.m.
10 temp 12p.m.
O-node candidate setO-node candidate set
M-nodeID attributes timestamp
2 200-240 12:30p.m.
5 100-200 12:30p.m.
24 150-250 12:00p.m.
M-node candidate setM-node candidate set
orderorderDistance
5 → 2 → 24close far
high lowPriority
Select this M-node to be a responder
M-nodeID attributes timestamp
2 200-240 12:30p.m.
24 150-250 12:00p.m.
Event : fireRange: 100-250Time: 12:00p.m.-12:30p.m.
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Candidate selectionO-nodeID attributes timestamp
10 temp 12p.m.
O-node candidate setO-node candidate set
M-node candidate setM-node candidate set
orderorderDistance
5 → 2 → 24close far
high lowPriority
Select this M-node to be a responder
M-nodeID attributes timestamp
2 200-240 12:30p.m.
24 150-250 12:00p.m.
RespondersResponders1. Selected M-nodes (5, 2)
2. Remaining O-node candidates
(10)
CompareCompare1. Cost with M-view
2. Cost without M-view
resultresult
Event : fireRange: 100-250Time: 12:00p.m.-12:30p.m.
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Candidate selectionnodeID cost
N1 C1
N2 C2
N3 C3
: :Nn Cn
responder setresponder set
CostCost1. The sum of the energy cost of forwarding
the query to the node
2. The energy cost of returning the results back to Q-node
MinimizeMinimizeTotal_costTotal_cost = x1 * C1 + x2 * C2 + ... + xn * Cn
where x1, x2,…,xn represent the percentage of the range in a M-view answer a query
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Simulation• Esend = Etrans × k + Eamp × d2
– Etrans : Transmitter electronics– 50nJ/bit– Eamp : Transmit amplifier– 0.1nJ/bit/m2
• Ereceive = Erec × k– Erec : Receiver electronics– 50nJ/bit
• Sensing region : 400m × 400m• Num of sensor : 400• Num of events : 100• Num of queries : 100• Event size : 8bytes• Range size : 4bytes• Index size : 4bytes• Skewness of zipf distribution : 0.5
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Simulation– Event size
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Simulation– Query skewness
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Simulation– Total queries
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Simulation– Total event
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Conclusion• Propose a similarity-aware query
processing scheme – Creates materialized views– To answer future queries that are similar to
past ones– Reduces energy consumption