Fuzzy-based Sensor Search in the Web of Things by Cuong Truong - - PowerPoint PPT Presentation
Fuzzy-based Sensor Search in the Web of Things by Cuong Truong University of Lbeck, Germany truong@iti.uni-luebeck.de 1 The Vision of the Internet of Things real world objects will be uniquely identifiable and connected to the Internet 2
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real world objects will be uniquely identifiable and connected to the Internet
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mashing up sensors and actuators with services and data available on the Web
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Internet
sensor capteur 傳感器
publish a textual description
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search engine?
search criteria? Not easy! Hmm!
Places that have similar climate and
to Key West in the last year? Pick a climate sensor in Key West, and search for similar sensors
Key West Marathon Fishery owner
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local database
crawls crawls crawls search for:
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10 20 11 21 12 125
(2) similar reading ranges (1) similar reading curves
Degree of membership of elements of fuzzy set
Key idea: Same value, different degree of
x
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kitchen
time
x
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F
K 28 48 35 44
F
L 35 44
library
time
x
0.9 0.6
38
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The reading 38 is likely read by sensor in kitchen:
Given a sensor S with set of readings X = {x}, S is
x
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kitchen
time
x
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F
K 28 48 35 44
F
L 35 44
library
time
x
0.9 0.6
38
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Given a sensor V, and a sensor S whose set of
readings is X = {x}
Combining the two above mentioned similarity
conditions:
difference)
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II.
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V.
24 00:00 23:59
time
x
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Temperature sensor S has been monitoring a room
FS(x)
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15 30 1 24
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+ + + + + +
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I.
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Fuzzy set‘s storage overhead Membership function is smooth
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For a search, a list of sensors is returned
Issue: „Similarity“ is highly subjective! no
Fact: Sensors close to each other have similar
Approach: Group sensors based on location and
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kitchen bedroom
perform search
List ranked by similarity score
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For each data set, group sensors based on
For each sensor
Evaluation is done on a PC
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http://db.csail.mit.edu/labd
ata/labdata.html
12 sensors in 3 groups 1500 data points/24 hours Performance: 222 μs / pair
4505 sensors / second (brute force)
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http://tidesandcurrents.no
aa.gov/gmap3
23 sensors in 5 groups 200 data points/24 hours Performance: 28 μs / pair
35741 sensors / second (brute force)
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http://ailab.wsu.edu/m
avhome/index.html
8 sensors in 2 groups 500 data points / 24
hours
Performance: 70 μs /
pair 14285 sensors / second (brute force)
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Sensor similarity search and distributed
architecture to realize it
Fuzzy-based approach to efficiently
compute similarity score
Evaluation metric for ranked list Accurate results of evaluation Outlook: Scalability
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