Expecting the Unexpected: Fast and Reliable Detection of Missing RFID Tags in the Wild Muhammad Shahzad Alex X. Liu Dept. of Computer Science and Engineering Michigan State University East Lansing, Michigan, 48824, USA

2011: Retailers lost 34.5 billion USD Motivation Shop Lifting Employee Theft 2011: Retailers lost 34.5 billion USD

Problem Statement Input Objective Set of IDs of expected tags RFID tag population containing: some or all expected tags unexpected tags Threshold on number of missing tags, T Required reliability, α ∈ [0,1) Objective Detect the event: missing tags ≥T Event detection probability ≥α Minimize detection time

Limitations of Prior Art Assume there are no unexpected tags ICDCS 2008: How to monitor for missing RFID tags; Tan, Sheng, and Li MobiHoc 2010: Identifying the missing tags in a large RFID system; Li, Sheng, and Lin SECON 2011: Fast identification of the missing tags in a large RFID system; Zhang, Liu, and Sun IEEE ToC 2013: Completely pinpointing the missing RFID tags in a time-efficient way; Liu et. al. However, in reality, there are unexpected tags Airline baggage Multi-tenant warehouse

Naïve Solutions Identification protocol Estimation protocol Slow: fastest RFID identification protocol is 14.3 times slower compared to our scheme SIGMETRICS 2013: Probabilistic Optimal Tree Hopping for RFID Identification; Shahzad and Liu Estimation protocol Inaccurate: if new tags join, can not tell whether some tags went missing

Communication Protocol Overview 1 2 3 4 5 6 7 1 C 1 1 C 1 1 3 2 6 4 7 4 Frame size 𝑓=7 Seed 𝑅 Faster to distinguish between empty and non-empty slots Singleton and collision » non-empty At the end of frame, reader gets a sequence of 0s and 1s 011C011 becomes 0111011

RUN: Missing Tags Detection 1 1 4 11 5 6 Expected tags to be monitored 1 2 3 4 5 6 7 8 9 10 11 Frame size 𝑓=11 Seed 𝑅 1 Pre-computed frame 1 Executed frame Unexpected false positive Unexpected tag detected Missing tag event detected Unexpected tags 8 4 10 10

RUN: Handling Unexpected FPs Repeat frame 𝑛 times 1 1 1 1 1 1

RUN: Parameter Selection Three unknown parameters Frame size 𝑓 Number of frames 𝑛 Persistence probability 𝑝 Two equations 𝑓= 𝑝(𝑇−|𝐸|−|𝑈|) ln {1−𝑞 }− ln {𝑝} where 𝑞= (1−α) 1/𝑛𝑇 obtained using the expression of false positive probability |𝐸| : number of expected tags |𝑈| : number of unexpected tags 𝑝=(1−𝑞) (1−α) 𝑞 𝑛𝑇(𝑞−1) Obtained using the required reliability condition Need the number of unexpected tags |𝑈|

RUN: Estimating Unexpected Tags 1 1 4 11 5 6 1 2 3 4 5 6 7 8 9 10 11 8 4 10 10 Number of total slots in frame 𝑓 Number of grey slots in frame 𝑘 Number of white slots that become green slots: 𝑁 01 𝑈 =− 𝑓 𝑝 ln 1− 𝑁 01 𝑓−𝑘

RUN: Experimental Evaluation Implemented 4 protocols in addition to RUN TRP (ICDCS, 2008) IIP (MobiHoc, 2010) MTI (SECON, 2011) SFMTI (IEEE ToC, 2013) TH (SIGMETRICS, 2013)

Actual Reliability vs. Missing Tags Number of expected tags = 1,000 Number of unexpected tags = 10,000

Actual Reliability vs. Unexpected Tags Number of expected tags = 1,000 Number of missing tags = 200

Effect of Threshold T Number of expected tags = 1,000 Number of unexpected tags = 10,000 Threshold = 200 Required reliability = 0.99

RUN vs. RFID Identification Compared RUN with TH (SIGMETRICS 2013) RUN is 14.3 times faster than TH for Number of expected tags = 1,000 Number of unexpected tags = 10,000 Threshold = 200 Required reliability = 0.99 TH is faster than RUN when Required reliability > 0.99999, OR Threshold < 0.001 tags, which is impossible

Conclusion Proposed a protocol to reliably detect missing tags in presence of unexpected tags Reliable Fast C1G2 compliant Handles multiple readers

Questions?