1. RFID E STIMATION P ROBLEM Lee, Gunhee S URVEY

2. R EFERENCES 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. B ACKGROUND 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. P ROBLEM 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. P ROBLEM D EFINITION There is N somewhere in this interval with probability greater than α

6. S YSTEM M ODEL 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. P OLLING P ROTOCOL 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. A LGORITHM Maximum Likelihood Estimation Algorithm (MLEA) uses fixed frame size f = 1 slot. If we know lower bound N min, 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

9. M AXIMUM L IKELIHOOD E STIMATION Initialization phase –Quickly produces a coarse estimation of N Iteration phase –Refines the contention probability and generates the estimation results Let p i be the contention probability of the ith polling, and let z i be the slot state of the ith polling. The sequence of z i 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

10. I NITIALIZATION P HASE We want to pick a small value for the initial contention probability p 1, because if p 1 is too large, a lot of tags will respond, which is wasteful of energy Upper bound N max is often available in practice, such as from physical limit, financial limit, or company policy. N max can be much bigger than N If z i = 0, we multiply contention probability p i 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/p L

11. I TERATION P HASE 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

12. E FFECTS 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. D ISCUSSION 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 N min and N max in MLEA. How to determine N min and N max to be more accurate and more reasonable?

14. C ONCLUSION 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