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 Lübeck, 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

The Vision of the Web of Things mashing up sensors and actuators with services and data available on the Web 3

Sensor Search in WoT: Start-of-the-art GSN publish Internet a textual description 傳感器 sensor capteur 4

Sensor Search in WoT: Start-of-the-art  complex for end user! 5

Sensor Similarity Search: An Illustration Places that have Pick a climate • What to type in similar climate and sensor in Key West, search engine? oceanic condition and search for • How to describe to Key West in the similar sensors search criteria? last year? Not easy! Hmm! Marathon Key West Fishery owner 6

Sensor Similarity Search: Architecture crawls crawls Internet crawls local database search for: 7

Questions to be addressed How to define and compute similarity I. between two sensors? How to construct a fuzzy set from II. historical sensor readings? How to minimize the cost of storing such III. fuzzy sets? How to efficiently compute a similarity IV. score between a pair of sensors? How to objectively evaluate the V. approach? 8

I. Similarity Definition (1) similar reading curves 20 10 similar 21 (2) similar reading ranges 11 different 125 what about me? 12 9

I. Similar Reading Curves: Captured by Fuzzy Set F 1 K x x 0.9 kitchen library 0.6 48 44 28 time F 35 time L x 0 38 44 48 28 35  Degree of membership of elements of fuzzy set  F K (38) = 0.9  F L (38) = 0.6  Key idea: Same value, different degree of memberships in different fuzzy sets 10

I. Similar Reading Curves: Captured by Fuzzy Set F 1 K x x 0.9 kitchen library 0.6 48 44 28 time F 35 time L x 0 38 44 48 28 35  The reading 38 is likely read by sensor in kitchen:  F K (38) = 0.9 > 0.6 = F L (38)  Given a sensor S with set of readings X = {x}, S is likely located in kitchen if: 11

I. Similar Reading Ranges captured by the reading range difference 12

I. Similarity Computation  Given a sensor V , and a sensor S whose set of readings is X = {x}  Combining the two above mentioned similarity conditions:  Similar reading curves (defined by fuzzy set)  Similar reading ranges (defined by reading range difference) 13

Questions to be addressed How to define and compute similarity I. between two sensors? How to construct a fuzzy set from II. historical sensor readings? How to minimize the cost of storing such III. fuzzy sets? How to efficiently compute a similarity IV. score between a pair of sensors? How to objectively evaluate the V. approach? 14

II. Fuzzy Set Construction  Temperature sensor S has been monitoring a room for 24 hours from 00:00 -> 23:59 F S (x) x 1 S 30 + + + 24 + + + time x 15 0 00:00 23:59 24 15 30 15

Questions to be addressed How to define and compute similarity I. between two sensors? How to construct a fuzzy set from II. historical sensor readings? How to minimize the cost of storing such III. fuzzy sets? How to efficiently compute a similarity IV. score between a pair of sensors? How to objectively evaluate the V. approach? 16

III. Efficient Fuzzy Set Storage: Approximation + +  Fuzzy set‘s storage overhead  Membership function is smooth Approximation using set of line segments 17

Questions to be addressed How to define and compute similarity I. between two sensors? How to construct a fuzzy set from II. historical sensor readings? How to minimize the cost of storing such III. fuzzy sets? How to efficiently compute a similarity IV. score between a pair of sensors? How to objectively evaluate the V. approach? 18

III. Efficient Similarity Score Computation f(x) x 19

Questions to be addressed How to define and compute similarity I. between two sensors? How to construct a fuzzy set from II. historical sensor readings? How to minimize the cost of storing such III. fuzzy sets? How to efficiently compute a similarity IV. score between a pair of sensors? How to objectively evaluate sensor V. similarity search? 20

V. Evaluation: Approach  For a search, a list of sensors is returned  Ranked by decreasing similarity score  Similar sensors are ranked on top  Issue: „ Similarity “ is highly subjective!  no ground truth  Fact: Sensors close to each other have similar readings  Approach: Group sensors based on location and annotated group with its location 21

V. Evaluation: Approach perform search bedroom List ranked by kitchen similarity score 22

V. Ranked List: Degree of Accuracy 23

V. Evaluation: Multiple Real Data Sets  For each data set, group sensors based on location, and define a search trial as  Picking a sensor and perform search  Compute DOA value of the obtained ranked list  For each sensor  Last 24 hours of readings are used  Evaluation is done on a PC  Java VM  Intel Core i5 CPU at 2.4 Ghz clock rate 24

IntelLab Data Set  http://db.csail.mit.edu/labd ata/labdata.html  12 sensors in 3 groups  1500 data points/24 hours  Performance: 222 μ s / pair  4505 sensors / second (brute force) 25

NOAA Data Set  http://tidesandcurrents.no aa.gov/gmap3  23 sensors in 5 groups  200 data points/24 hours  Performance: 28 μ s / pair  35741 sensors / second (brute force) 26

MavPad Data Set  http://ailab.wsu.edu/m avhome/index.html  8 sensors in 2 groups  500 data points / 24 hours  Performance: 70 μ s / pair  14285 sensors / second (brute force) 27

Summary  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  Paralellize search  More efficient similarity computation  Index and lookup of fuzzy sets at server side  Incremental search accuracy 28