2. REFERENCES
• Energy Efficient Algorithms for the RFID Estimation
Problem
– Tao Li, Samuel Wu, Shigang Chen and Mark Yang
– University of Florida, Gainesville, FL, USA
– IEEE INFOCOM 2010
• Fast and Reliable Estimation Schemes in RFID
Systems
– M. Kodialam and T. Nandagopal
– Bell Labs, Lucent Technologies
– ACM MOBICOM 2006
3. BACKGROUND
• RFID technology is widely used in various commercial
applications, including inventory control, object
tracking, and supply chain management
• It is very desirable to have a quick way of counting
the number of items in the warehouse or in each
section of the warehouse
• To timely detect theft or management errors, such
counting may be performed frequently
4. PROBLEM
• It is both time and energy consuming to read the actual
IDs of all tags (what if there are thousands of tags)
• Kodialam and Nandagopal showed that the reading time
can be greatly reduced through probabilistic methods that
estimate the number of tags(N) with an accuracy that can
be arbitrarily set
• This is called RFID estimation problem
• Tao Li, et al. suggested an energy efficient solution of RFID
estimation problem
5. PROBLEM DEFINITION
ˆ ˆ
) )
((1 ) (1
N N
P N
rob
Estimate whose accuracy is specified by a confidence interval
with two parameters, a probabili
ˆ
and an erro
ty va r bou
lue nd .
N
ˆ ˆ
( )
N N
ˆ ˆ
( )
N N
There is N somewhere in this interval
with probability greater than α
N
6. SYSTEM MODEL
• Only interested in Active RFID
• Because a reader should move around whole area
which is very time consuming, and there is no energy
consumption constraint in Passive RFID
• There is a reader and tags, and estimation is based on
a polling protocol
• Slotted time frame contention polling is used
7. POLLING PROTOCOL
• Polling procedure uses three types of slots
– Empty slots
– Singleton slots
– Collision slots
• Contention probability p and frame size f should be chosen
carefully to estimate N succesfully
• This protocol only counts empty or non-empty, so 1-bit reply is
enough (this reduces energy and time consumption)
} Non-empty slots
Time
Non-empty slots (1 or more replies)
8. ALGORITHM
• Maximum Likelihood Estimation Algorithm (MLEA) uses
fixed frame size f = 1 slot.
• If we know lower bound Nmin , we can estimate more
efficiently and accurately
• At the beginning of a polling, each tag makes a
probabilistic decision:
– Sleep with probability
– Wake up with probability ,
and respond with probability
1
1
min
N
1
min
N
min
p N
9. MAXIMUM LIKELIHOOD ESTIMATION
• Initialization phase
– Quickly produces a coarse estimation of N
• Iteration phase
– Refines the contention probability and generates the estimation results
• Let pi be the contention probability of the ith polling, and let zi
be the slot state of the ith polling.
• The sequence of zi forms the response vector.
• 0 means empty slot and 1 means non-empty slots.
• As will be discussed shortly, authors analysis shows that the
optimal contention probability is
{ 1} 1 (1 ) 1 i
Np
N
i i
Prob z e
p
lim(1 )
x
n
n
e
1.594 /
i
p N
10. INITIALIZATION PHASE
• We want to pick a small value for the initial contention
probability p1, because if p1 is too large, a lot of tags will
respond, which is wasteful of energy
• Upper bound Nmax is often available in practice, such as
from physical limit, financial limit, or company policy. Nmax
can be much bigger than N
• If zi = 0, we multiply contention probability pi by C(>1)
after each polling until a non-empty slot is observed
• When that happens, say at the Lth polling, we have a
coarse estimation of N to be 1/pL
11. ITERATION PHASE
• This phase iteratively refines the estimation results after each
polling, and terminates when the specified accuracy requirement
is met
• The reader performs three tasks
– Sets contention probability based on previous estimate of N
– Based on the received zi , the reader finds the new estimate of N
that maximizes the following likelihood function
– After computing new estimate of N, the reader has to determine if
the confidence interval of the estimation meets the requirement
1 1
1.5
ˆ
94
ˆ
i
i i
N N
p
(1 )
1
(1 ) (1 (1 ) )
j j
i
N z z
N
i j j
j
L p p
arg ax{ }
ˆ m
i i
N L
N
ˆ
2
1 ˆ
· ·
i i
i
p
N
i
e
Z
ip
N
2
2
1.544·Z
i
12. EFFECTS
• We can estimate the total number of RFID tags by using a
probabilistic method
• This enables frequent monitoring of the number of stocks
in faster time and lower energy consumption
• It can prevent theft and managerial mistakes and has many
other advantages
• This method can be modified to fit other networks easily
(e.g., wireless sensor networks, adhoc networks)
13. DISCUSSION
• The authors only considered Active RFIDs. How about Passive
RFID? Moving receivers can adopt similar approach to the
number of estimate RFID tags.
• How to synchronize the timers of whole tags and readers?
Synchronizing or pre-determined timer is essential to use time-
slotted frame communication. And how to detect collision if
there are too many transmitted signals? Its SINR may be very low.
• We need context-sensitive information such as Nmin and Nmax in
MLEA. How to determine Nmin and Nmax to be more accurate and
more reasonable?
14. CONCLUSION
• This paper successfully applied probabilistic methods on
RFID technology
• In networks such as RFID network and Wireless Sensor
Network, time and energy consumption of polling
protocol should be regarded as main constraints
• There are advanced algorithms such as Average Sum
Estimation and Enhanced Maximum Likelihood Estimation in
the paper
• But due to highly complicated mathematical reasoning and
not significant performance difference, we skipped these
advanced variants of MLEA
