Incentivizing Crowdsourced Data
for Indoor Localization
Presented by ZHANG Qi
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Indoor Positioning System(IPS)
Indoor location with a smart phone
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RFID Tags
• RFID: Radio-frequency identification
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RFID Tags
• RFID: Radio-frequency identification
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RSS Vector
• Assume there are totally m RFID tags.
• Each position p has its received signal strength
(RSS) vector
x  (x1,x2 ,...,xm )
• We can incentivize crowd users determine RSS
vectors.
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Collecting Data from the Crowd
• Users report positions, RSS vectors x , and
bids bi
• Select winner users according to their
contributions,  (x )
• Determine the payment for winners, such that
pi  bi，
p
i
B
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Determine the Winners
• Let Winner Set, S, be an empty set
• Pick the next user ,w*,with maximal unit margin
w*  arg max
(x i )
bi
• If (1) and (2) hold, add w* to S
bw*  B   bi
(1)
iS
bw* 
• Else, exclude w*
 w*
   i   w*
B
(2)
iS
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Determine the Winners
•   1 , the algorithm runs on a reduced budget,
B/ to ensure that  pi  B
bw*  B   bi
(1)
 w*
b 
   i   w*
(2)
iS
*
w
B
iS
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Determine the Payment
• If pi = bi, the system is suspicious to malicious
bid
• Payment pi is the largest bid user i can declare
before being removed from the winners
(Myerson 1981)
pi  inf{ bi' : i   (bi' , bi )}
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Determine the Payment
• S={1,…,i,…k}, winners selected from users W
S’={1,…,j,…k’}, winners selected from W’=W/ i
• bi(j) : bid that i can declare to replace j in S’
• pi = max { bi(j), j = 1,…,k’ }
bi(i)  bi
pi  inf{ bi' : i   (bi' , bi )}
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A probability Vector
• View RSS vector as probability vector
x  (x1,x2 ,...,xm )
xi  [0,1]  Prbobility that tag i is detected
xi ~ Beta (a, b)
• Each position is characterized by on-off RFID
tags
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User’s Contribution
• Degree of Certainty for a probability vector:
m
C   max(Pr( xi  0.5), Pr( xi  0.5))
i 1
max C s.t.  p  B
• User’s contribution:
i
(x)  C
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Future Work
• In indoor environments, interference, multipath
propagation, and the presence of obstacles lead
to a complex spatial distribution of the RSS
• Probability vector model is thus far from enough..
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Thanks for your time!!
Q&A
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