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RFID Estimation Problem

RFID Estimation Problem. Lee, Gunhee Survey. 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

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RFID Estimation Problem

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  1. RFID Estimation Problem Lee, Gunhee Survey

  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 There is N somewhere in this interval with probability greater than α

  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 Non-empty slots (1 or more replies) Time

  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

  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

  10. Initialization Phase • We want to pick a small value for the initial contention probability p1, because if p1is 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

  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 Nmaxin MLEA. How to determine Nminand Nmaxto 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

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