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Minji Wu, Jianliang Xu, Xueyan Tang, Wang-Chien Lee
Professor : 王鼎超
Speaker : 林育弘
Minji Wu, Jianliang Xu, Xueyan Tang, Wang-Chien Lee, “Top-k Monitoring in Wireless Sensor
Networks”, Knowledge and Data Engineering, IEEE Transactions on Volume 19, Issue 7, July
2007
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
Introduction
Top-k Monitoring
◦ FILA Overview
◦ Filter Setting (Uniform & Skewed)
◦ Filter Update (Eager & Lazy)
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Performance Evaluation
◦ Simulation
◦ Eager versus Lazy Filter Update
◦ Performance Comparison with TAG and Cache
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Conclusions
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Top-K Query
◦ Environmental Monitoring
 A top-k query is issued find out the nodes and their
corresponding areas with the highest pollution indexes
for the purpose of pollution control or research study.
◦ Network Management
 A top-k query may be issued to continuously monitor
the sensor nodes with the least residual energy.
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This paper focuses on continuously
monitoring top-k queries in sensor networks.
◦ Utilize previous top-k result to obtain a new top-k
result.
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Monitoring a Top-1 query
◦ TAG
Base Station
t1
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51
t2
38
56
t3
37
43
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t1
43
t2
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t3
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A
總共需要9次傳送
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51
45
56
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B
C
t1
51
t2
56
t3
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Monitoring a Top-1 query
◦ FILA
Base Station
probe node C
t1
35
t2
38
t3
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t1
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t2
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B
t3
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[39, 47]
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A
總共需要6次傳送
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[20, 39]
52
t1
51
C
t2
56
[47, 80]
t3
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Base station has a continuous power supply.
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Sensor nodes powered by battery.
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Each sensor node measures the local physical
phenomenon at a fixed sampling rate.
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1.
Filter Setting
◦ the base station computes a filter [li, ui] for each
sensor node i and sends it to the node for
installation.
2.
3.
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Query Reevaluation
Filter update
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Tinternal：the set of internal updates
Tjoin：the set of join updates
Tleave：the set of leave updates
T：the old top-k set
◦ If |T’|=|T|-|Tleave|+|Tjoin|≧k
 The new top-k set must be a subset of T’
◦ Otherwise, if |T’|<k
 The nodes that are not in T have to be probed.
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Uniform filter setting
◦ It is simple and favorable when the readings of
sensor nodes follow a similar changing pattern.
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
Skewed filter setting
◦ Taking into account the changing patterns of
sensor readings.
◦ Suppose the average time for the reading of node I
to change beyond is fi(δ)
 1/fi(δ): the rate of sensor-initiated updates by node i
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
Eager filter update
◦ If a new filtering windows [li’, ui’] is different from
the old one [li, ui] then the new filter [li’,ui’] is
immediately sent to node i
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Lazy filter update
◦ If a new filtering windows [li’, ui’] fully contains the
old one [li, ui], then the base station delays the
filter update until node i’s reading violates the old
filter [li, ui] .
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
Simulation Setup
◦ Energy cost in transmitting a message





s: message size
α: distance-independent term
β: coefficient
q: distance-dependent term
d: distance
◦ Energy cost in receiving a message
 γ is set at 50 nJ/b
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
A Sensor initiated update message:
◦ Sensor ID: 4 bytes
◦ Sensor Reading: 4 Bytes
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A filtering windows is characterized by 8
bytes.
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10 Sensor
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120 Sensor
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Simulated using the real traces provided by the
Live from Earth and Mars (LEM) project at the
University of Washington.
Two kinds of sensor readings are used
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Total 500000 sensor readings
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◦ Temperature (TEMP)
◦ Dew point (DEW)
◦ Logged by the station at the University of Washington
from August 2004 to August 2005
◦ Extract many subtraces stating at different dates
◦ Each subtrace contains 20000 readings
◦ The subtraces were used to simulate the physical
phenomena in the immediate surroundings of different
sensor nodes.
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Network Lifetime
◦ The network lifetimes is defined as the time
duration before the first sensor node runs out of
power.
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Average Energy Consumption
◦ It is defined as the average amount of energy
consumed by a sensor node per time unit.
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Monitoring Accuracy
◦ This is defined as the mean accuracy of monitored
results against the real results.
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
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This paper exploited the semantics of top-k
query and proposed a novel energy-efficient
monitoring approach called FILA.
Two filter setting algorithms (that is, uniform
and skewed) and two filter update strategies
(that is, eager and lazy) have been proposed.
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