1. Revisiting Reading Rate with Mobility: Rate-Adaptive Reading in COTS RFID Systems
Qiongzheng Lin, Lei Yang, Huanyu Jia , Chunhui Duan, Yunhao Liu
The Hong Kong Polytechnic University
Tsinghua University
Dec.14, 2017
Good afternoon everyone. My name is Lei Yang, today, It is our honor to present recent work here. The title is revisiting reading rate with mobility….

2. 1. Background & Motivation
Firstly, please allow me to briefly introduce the background and motivation.
More info

3. IDTechEx: 18.2 billion tags have been sold so far in 2017.
1. What is RFID?
Air Protocol Ta
Tag
Reader
According to the statistics provide by IDTechEx, billion RFID tags have been sold so far this year. That is a really a huge number!
A typical RFID System is composed of three components: RFID reader, tag and air protocol. The air protocol defines the communications between reader and tags.
IDTechEx: 18.2 billion tags have been sold so far in 2017.
RFID Systems have been widely employed for various applications!

4. 1.1 The-State-of-Art: UHF RFID Standards
GEN 2
There are several standards on air protocol all around the world, such as AIAG, IEC, ISO 18000, and so on. However, the most widely adopted air protocol is proposed by EPCglobal organization, called EPC gen2 protocol. EPC means Electronic Product code. It is has been adopted as a major part of the ISO standard. So in this work, we are focusing on gen2 rfid systems.
EPCglobal Gen 2 has become the widely adopted air protocol.

5. 1.2 The-State-of-Art: Anti-collision
Tags
Tags
Tags 3 3 3 1 5 1 5 1 5
Reader 4 4 4 2 2 2
Query
The main purpose of a RFID system is to use a reader to collect all EPCs of tags on the spot. The reading is based on Framed Alopha Protocol.
The reader divides the procedure into many slots. Each tag randomly selects a slot to reply its EPC or ID. If there exists any collisions that multiple tags select a same slot, the reader will starts new frame to allow unread tags re-select slots. This procedure ends until all tags are successfully read.
1st frame
2nd frame
3rd frame
Reading Procedure: Framed Aloha Protocol

6. It looks like that Q-adaptive is not efficient…
1.3 The-State-of-Art: Anti-collision
It looks like that Q-adaptive is not efficient…
Actually, Q-adaptive protocol almost approaches the optimal reading rate.
The Gen2 system uses a variant of framed aloha protocol, called as Q-adaptive protocol, which would dynamically adjust the frame length. We conduct some experiments to show the reading rate of current system. The x-axis is the number of tags. The y-axis is the reading rate, which is defined as the number of tags read per second on average. From the figure, we observe that when the number of tags is over than 30, the reading rate would drop by 84% compared with the case where there is one tag. We could see that Q-adaptive protocol looks not so efficient! The gray value shows the optimal reading rate of aloha protocol. On the other hand, we say that Q-adaptive protocol almost approaches the optimal reading rate. This tells us whatever we could do, we cannot significantly improve the reading rate anymore.
Gen 2 Reading Protocol: Q-Adaptive

7. However, such approach requires many readings of the mobile tags…
1.4 Real Case
Unsorted and sorted tags can be easily distinguished by leveraging the physical layer signal patterns.
However, such approach requires many readings of the mobile tags…
Let us look at a real case. We deployed RFID systems in Airport. We hope the device tells us that one baggage is carried from the conveyor to the carriage.
RFID assisted sorting system

8. 1.5 Case Study
Four hour real trace, acquired by one reader obtained up to 367, 536 readings from 527 tags.
10% of the tags are read over 655 times
Tag #271 has been continuously read over 90,000 times!
This figure shows the 4 hour real readings acquired by the one reader. The trace totally contains up to 300 thousand readings from 527 tags. The right figure shows the distributions of the reading number. We can find that 10% of the tags are read over 655 times. In particular, we found that the one tag has been continuously read over 90,000 times! Why this tag was read so many times! Because it was statically placed nearby the reader for a long time.
Our insight here is that: a large number of readings are consumed on static tags, which are nearby the reader.
Insight: A large number of readings are consumed on static tags, which are nearby the reader.
Reading Trace
Trace Distribution
Reading Trace & Trace Distribution

9. 1.6 Insights Static tags Moving tags
Fact 1: The reading demands of mobile tags are considerably more urgent than those of static tags because the states of the static tags nearly remain unchanged
Fact 2: The participations of the static tags seriously affect the reading rates of the mobile tags.
Clearly, the reading demands of mobile tags are considerably more urgent than those of stationary tags. Because the states of the latter nearly remain unchanged!!! We do not need to read them continuously. More worse, the reading rates of mobile tags are seriously affected by the nearby static tags.
Thus, reducing the total number of participating tags is a good way of improving the IRR of mobile tags.
Reducing the total number of participating tags is a good way of improving the IRR of mobile tags.

10. More readings are given to mobile tags
1.6 Basic Idea
Exploring the reading rate from a new perspective
Mobility
So, this work explores the reading rate from a new perspective: mobility. We give more readings for mobile tags but less readings for static tags. That is to say, take real-time adjustment of the IRR according to the tag’s current motion state.
More readings are given to mobile tags
Real-time adjustment of the IRR according to tag’s current motion state.

11. 2. Design of Rate-Adaptive Reading RFID Systems
More info

12. 2.1 Overview System architecture: Two-phase reading design
To achieve the rate-adaptive reading, we design a two-phase reading system. It contains two phase.
Phase I: Motion assessment. In the first phase, the system reads all tags within a short period and then leverages the reading results to assess the motion state of each tag. Afterwards, Tagwatch selects mobile tags to be scheduled in the next phase according to the history-based immobility models.
Phase II: Target schedule. In this phase, Tagwatch rst selects a group of bitmasks to cover target tags (e.g., mobile tags) and then conducts bitmask-enabled selective reading on target tags for a relatively long period.
he two phases constitute a basic cycle, which occurs alternatively and periodically
A periodical mo- tion assessment is necessary to capture the state transitions of tags (e.g., from a moving state to a static state, or vice versa).
System architecture: Two-phase reading design

13. 2.1 Overview
The two phases constitute a basic cycle, which occurs alternatively and periodically.
A periodical motion assessment is necessary to capture the state transitions of tags.
The scheduling phase is longer than the assessment phase to guarantee target tags gain sufficient time to be read.
A periodical mo-tion assessment is necessary to capture the state transitions of tags (e.g., from a moving state to a static state, or vice versa).
System architecture: Two-phase reading design

14. Phase I: Motion Assessment
How to find out mobile tags?

15. 2.2 Motion Assessment Use of GMM to model the immobility of a tag.
We leverage the physical layer signals to determine whether a tag is moving. If it is moving, its signal changes a lot. We can use the Gaussian model to model a tag’s immobility. However, the environment may change. Such changes due to multipath force the signals jumps among different gaussian models. Thus, we further use Mixture of Guassian models.
Use of GMM to model the immobility of a tag.

16. Phase II: Target Schedule
How to read target tags only?

17. 2.3 Target Schedule: Selective ReadingSL
There is a core function named selective reading, specified in Gen2 air protocol. We can leverage a bitmask to choose a subset of tags. Only these chosen tags will take participate in the incoming inventory.
Selective reading allows us to read a subset of tags with a bitmask

18. Usually, one mask cannot cover all targets.
2.4 Target Schedule: Challenges
Usually, one mask cannot cover all targets.
More masks are expected.
Targets
Usually, one mask cannot cover all our target tags. For example, there are 5 tags in the field. We want to find a mask to select the first three tags. Unfortunately, there is no common mask covering them all.
Non-targets
Goal: find masks to cover all target tags with minimal cost

19. 2.4 Target Schedule: Challenges Plan I 0 0 1 1 1 0 0 1 0 0 1 0
Mask 1: (10,4, 2)
Mask 2: (11,3,2)
In plan I: we can use to masks: 10 and 11. All three target tags are covered by these two masks. Unfortunately, one non-target is also selected. This is not we want.
Unfortunately, one non-target tag is also selected!
Collateral tags
Goal: find masks to cover all target tags with minimal cost

20. Set-cover optimization problem
2.4 Target Schedule:
Challenges
Plan II
Mask 1: (11,2, 2)
Mask 2: (01,0,2)
Set-cover optimization problem
In the second plan, we choose another groups of masks: 11 and 01. This is a perfect plan, which can cover all targets without collateral non-targets.
Actually, we can convert this problem into the classic set-cover optimization problem
Goal: find masks to cover all target tags with minimal cost

21. 4.Return to Step 1 for the next iteration.
2.5 Target Schedule:
Algorithm
Greedy algorithm based schedule:
4.Return to Step 1 for the next iteration.
3.The input indicator bitmap is updated.
2.The bitmask with the highest relative gain is selected.
1.The relative gain for each bitmask is calculated.
We define a relative gain to check a mask’s goodness. The numerator is the hamming distance that indicates how many target tags it can cover. The denominator is the number of collateral tags, namely the cost. We design a greedy algorithms to find the best masking plan. Due to the time limit, I don’t want to details. Welcome to read our paper.
Hamming distance
𝑅 𝑆 𝑖 = | 𝑉 𝑖 &𝑉| 𝐶(| 𝑉 𝑖 |)
Collateral tags

22. 3. Implementation & Evaluation
More info

23. 3.1 Implementation Gen2 v.s. LLRP
We use the LLRP to manipulate the reader for broadcasting the corresponding commands, such as the select
Gen2 v.s. LLRP

24. Completely using COTS RFID devices
3.2 Implementation
ImpinJ R420 reader.
Directional antenna
Reader
Tag
Alien 2 × 2 Inlay
Alien Squiggle Inlay
Software
EPCglobal LLRP
Java
Completely using COTS RFID devices

25. Detection sensitivity
3.3 Evaluation of Phase I
Detection ROC
Detection sensitivity
True Positive Rate
Detection rate
First, we study the ROC curve, which studies how the threshold affect the both false positive and true positive rate. The result suggests that we can find an appropriate detection threshold to achieve more than 0.9 TPR while less than 0.1 FPR.
Second, we further study the detection sensitivity to motion. Our objective is the timely and accurate monitoring of tag movement, even if its displacement is minimal. During this stage, we move a tag away in a random direction with a displacement ranging from 1cm ∼ 5cm. We conduct the experiment 20 times with the same displacement setting. The gure shows that we can successfully detect 87% and 99% of the movement events using RF phase when the tag is moved away from 2cm and 3cm,
False Positive Rate
Distance
We can find an appropriate detection threshold to achieve ≥ 0.95 TPR while ≤ 0.1 FPR using Phase-MoG.

26. ‘Quick start’ 3.4 Evaluation of Phase I Learning curve Accuracy
Third, we conduct a group of experiments to answer the question: how long does the system need to build a stable Gaussian model? It seems like the system requires a “slow” and “cold” start. It suggests that we can achieve 70% and 90% of detection accuracy when fed with 1.49s trace (i.e., including 67 readings) and 2.9s trace (i.e., including 130 readings). Therefore, one-cycle readings (i.e., 5s) are sucient to stably create a newly emerging Gaussian model, providing a “quick start” for the self-learning.
Learning time
Learning curve

27. 3.4 Evaluation of Phase II
2/40
5/40
Schedule Feasibility

28. 3.4 Evaluation of Phase II
IRR Gain
Schedule Cost & IRR Gain

29. Without Rate-adaptive
3.5 Case Study
With Rate-adaptive
Without Rate-adaptive
Real-time Tracking

30. Conclusion
We present a system (aka Tagwatch ) for the rate-adaptive reading of mobile tags through selective reading.
Tagwatch can successfully identify mobile tags with a mean probability of 80% as long as a tag moves beyond 1cm. It also achieves 95% of accuracy for motion detection, whereas the false positive rate (FPR) is maintained at below 10%.
Tagwatch can outperform the individual reading rates of mobile tags by a median of 3.2X and 1.9X when there are 5% and 10% mobile tags.

31. Thank You!