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Review & Pr esentation

Hybrid Context Inconsistency Resolution for Context-aware Services Chenhua Chen1, Chunyang Ye2, 3 and Hans-Arno Jacobsen2 Department of Computer Science, University of Saarland Middleware Systems Research Group, University of Toronto 3 Institute of Software, Chinese Academy of Sciences 1

2

Antenna ICÂ Chip Substrate Connection


Outline RF I D & D iscussion on Paper s


Outline Background ~ RFID Introduction ~ Context-awareness concepts Research Problem ~ Context Inconsistency Resolution Hybrid Solution ~ Context Correlation Model ~ Application Recovery Model Experimental Results Concluding Remarks References and Fun Thoughts


I ntr oductor y Wor ds: “ An Important Technology”

Tracking Technologies and Automatic ID Systems Various technologies are used to track and  automatically ID people, products, and  other objects – Barcodes – Optical Character Recognition  (OCR) – Biometrics • Voice recognition and ID systems • Fingerprint ID systems

– Smart cards – Memory cards – Microprocessor cards


RF I D : What is it ? RFID combines many of the features of several of these  technologies – Like barcodes, RFID is used to identify and track objects – As with OCR and biometrics, RFID enables automatic ID  and verification – RFID also can be used like smart cards, memory card, and  microprocessor cards to store information and provide  interactive data processing

Most RFID tags contain at least two parts. One is an integrated circuit for  storing and processing information, modulating and demodulating a (RF) signal,  and other specialized functions. The second is an antenna for receiving and  transmitting the signal.


H ow is RF I D unique? – It can be used to accurately locate and identify  objects from a distance using RF signals – It can be used to detect and read objects that are  not in line of sight  –Data can be interactively managed and processed  by the RFID chip and RFID system


M ar k et & Application Industrial Products

Logistics/ Trans.

Consumer Products

Retail  Products 

Homeland Security

Key Industry Drivers Leading Us Toward RFID

Other Service


Example: RF I D in L ibr ar ies

Simplifies checkout  process for staff

Inventory of  Collections

Use with new and  future technology

Item Security

Express checkout for  patrons


Cur r ent T echnology

Contexts locations, time  etc. Implicit  input/output Seamless  integrated


Context-aw ar eness An important feature of pervasive applications

Contexts locations, time etc. Implicit input/output Seamless integrated Context­awareness Sense environment     automatically Remember history Adapt to changing     situations


Context-aw ar eness Application An example


Supply Chain Scenar io The example used in the paper

Reading RFID tags

Update warehouse database


Context-aw ar eness Resolution An example of application semantics

Contexts locations, time etc. Implicit input/output Seamless integrated


Context I nconsistency •

Reasons – Environmental noise

Examples – RFID reader report wrong readings • Register incorrect number in warehouse – GPS or GSM devices report      inaccurate location • Pick wrong route


Context I nconsistency Resolution Consistency constraints

Context queue

Validate consistency constraints

Inconsistency resolution

2) Remove oldest 3) Remove all 4) User preference, heuristics etc.

Inconsistent contexts

1) Remove latest

Chen, Ye and Jacobsen, PerCom'11, Seattle

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Context I nconsistency Resolution L imitation • Difficult to identify problematic contexts – E.g.,  remove the latest, oldest,      least frequently used etc. – Counter example to remove the latest • Two RFID readers, the first one is inaccurate,      the second one is accurate

• Resolution approaches rely heavily on constraints – Accuracy and completeness of constraints are crucial – Counter example • Constraint: Two RFID readers report identical readings • Reported readings are the same but inaccurate


Resear ch Pr oblem

How to get a better Context Inconsistency Resolution for Context-aware Services


T heir Pr oposal : H ybr id Solution The big picture


Example of the pr oposal 1. Two readers report inconsistent readings

2. Postpone inconsistency resolution 3. Warehouse check in, collect weight info

4. Update profile of goods 5. Resolve inconsistent readings based on weight and profile Chen, Ye and Jacobsen, PerCom'11, Seattle

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Challenges H ow to mak e use of the application semantics in r esolution?

Close to T 0: Semantic infor mation is of limited usefulness

Close to T 2: When to unacceptable r esolve? r ecover y cost

Chen, Ye and Jacobsen, PerCom'11, Seattle

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Example of Application Semantics • Previous Location: (2, 3) • Current Location: (4, 5) • Inconsistency found! • The probability of each context being inaccurate is 50% warehouse

• Continue move one step • New Location: (4, 4)

• (2, 3) is more likely to be inaccurate, since it is impossible to move from (2, 3) to (4, 4) in two steps. Chen, Ye and Jacobsen, PerCom'11, Seattle

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Context-cor r elation M odel

f e(

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Current contexts

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Contexts after invoking action a

fe(CL, a): | NL – CL|≤ 1

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At least one of C3 and C8 is inaccurate! Chen, Ye and Jacobsen, PerCom'11, Seattle

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Application Er r or Recover y Inconsistency detection

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Inconsistency resolution

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Backward recovery Forward recovery


Example of Er r or Recover y

• Backward recovery – Backtrack the movement

• Forward recovery – Select a different path warehouse

Chen, Ye and Jacobsen, PerCom'11, Seattle

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COST M OD EL

• Compensation cost (cpc) – For backward recovery – Cost of compensating a task

• Execution cost (ecc) – For forward recovery – Cost of executing a task

• Total cost for an error recovery plan

n i =1

cpc(ai ) + ∑i =1 ecc(b j ) m


Resolution Algor ithm


Resolution Algor ithm Inconsistency detected

Resolve inconsistency

Postpone resolution

Compute error recovery cost

Application continues

Collect application semantics

Calculate probability

Build correlation graph

Error recovery

Chen, Ye and Jacobsen, PerCom'11, Seattle

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Exper iment XXXXXX


Exper iment Setup

• 16 X 16 Map • cpc = ecc = 1 • Search the target in a heuristic way • Random placement of goods • Metrics: – Accuracy of resolution – Cost of error recovery Chen, Ye and Jacobsen, PerCom'11, Seattle

warehouse

29


Results L-RL: Remove latest L-RO: Remove oldest M-H: Hybrid solution

Higher error rate

Chen, Ye and Jacobsen, PerCom'11, Seattle

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Results L-RL: Remove latest L-RO: Remove oldest M-H: Hybrid solution

Location-aware Higher threshold Chen, Ye and Jacobsen, PerCom'11, Seattle

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Results L-RL: Remove latest L-RO: Remove oldest M-H: Hybrid solution H-ER: Error recovery only

Higher error rate

Chen, Ye and Jacobsen, PerCom'11, Seattle

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Results L-RL: Remove latest L-RO: Remove oldest M-H: Hybrid solution H-ER: Error recovery only

Location-aware Higher threshold Chen, Ye and Jacobsen, PerCom'11, Seattle

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Scalability Randomly generate correlation graph Calculate probability of each context being inaccurate Record the time needed

Chen, Ye and Jacobsen, PerCom'11, Seattle

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Some T houghts & Refer ences RF I D & D iscussion


Concluding r emar k s

• A novel approach to resolve context inconsistency – Combine low-level inconsistency resolution with high-level error recovery – Correlation model to reason about inaccurate contexts – Cost model to calculate recovery cost – Algorithm to trade off accuracy against recovery cost

• Future work – More real-life experiments


Society’s Concer ns Privacy

­ Tracking individuals ­ Illicit or inappropriate use of personal data ­ Tracking personal activities (e.g., purchase habits, travel)

Security

­ Unsanctioned readers ­ Theft of information ­ Inadequate encryption

Global differences

­ Regulations around collecting data ­ Standards ­ Ownership of data


Back U p Slides & Refer ences RF I D & D iscussion


Back U p Slide RF I D T ags: Passive vs. Active


Benefits of RF I D •Automatic reads •Active chips can be written •Many chips can be read simultaneously •Standardized and unique encoding •Better process specific data collection

I nnovative Applications SUPPLY CHAIN TRACKING RETAIL AND INVENTORY MANAGEMENT BAGGAGE HANDLING CREDIT CARDS HEALTH CARE ID AND MEDICAL DATA SMART PASSPORTS IMPORT/EXPORT PROCESSES AUTO ID FOR TOLLS, IGNITION, PARKING CHILD AND PET TRACKING


Refer ence Books RFID security Rockland, MA : Syngress, c2006 Frank Thornton ... [et al.]. RFID implementation New York : McGraw-Hill, c2007. Dennis E. Brown. RFID essentials Beijing ; Sebastopol, CA : O'Reilly, 2006 Bill Glover and Himanshu Bhatt. RFID for dummies Hoboken, N.J. ; Chichester : Wiley, 2005. by Patrick J. Sweeney.

Websites Managing RFID data http://portal.acm.org/citation.cfm?id=1316791 http://portal.acm.org/citation.cfm?id=1083592.1083723 http://www.informaworld.com/index/768428270.pdf http://portal.acm.org/citation.cfm?id=1107548.1107603 http://www.ingentaconnect.com/content/mcb/089/2003/00000031/00000010/art00005 Temporal management of RFID data http://portal.acm.org/citation.cfm?id=1083592.1083723 http://www.springerlink.com/index/f32p7n6t32q71703.pdf http://portal.acm.org/citation.cfm?id=1164127.1164143 http://millennium.cs.ucla.edu/~zaniolo/papers/ICDE07RFID.pdf http://www.itee.uq.edu.au/~xueli/RoozbehDerakhshan.pdf Mining compressed commodity workflows from massive RFID data sets http://portal.acm.org/citation.cfm?id=1183641 http://daisy.cs.uiuc.edu/hector/research.pdf http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4368141 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4401085 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4529801 Warehousing and Analyzing Massive RFID Data Sets http://xavier.ceng.calpoly.edu/ime312/RFID_warehouse.pdf http://portal.acm.org/citation.cfm?id=1083592.1083723 http://doi.ieeecomputersociety.org/10.1109/ICDE.2006.171 http://www.springerlink.com/index/r7y7wnmwqtfpx92t.pdf http://portal.acm.org/citation.cfm?id=1080148.1080164


Context-cor r elation M odel

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1

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7

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Context Ci1

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Contexts Contexts Cin Ci2

C 2

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p2

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C 3

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p3

p2

p1 ≥ 1- p2 * p3


RF I D in Action


A Ver y Active T ag


Back U p Slide

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the Guard channel policy rejects all new calls until the channel occupancy goes below threshold

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Some Other T houghts Sky is still the limit but RFID did not reached half of its limit yet Some L ink s (if time permits)

RFID - Technology Video (Detailed) http://www.youtube.com/watch?v=4Zj7txoDxbE RFID Demonstration http://www.youtube.com/watch?v=FVmD4iTXRLE That´s how we will shop within a couple of years! http://www.youtube.com/watch?v=sDyqhcy1L-0 Future Store (Smart Check Out) http://www.youtube.com/watch?v=zBz3aoikLpU


F U n T houghts & food for thoughts


F U N T houghts or F ood for thought How can we redesign this earth from fresh? What will be the goal? What kind of systems we need to establish?

Some L ink s (if time permits)

Redesigning the human civilization (Detailed) http://www.youtube.com/watch? v=eK2KBFjShVs&annotation_id=annotation_25083&sr c_vid=uOw5eW52vRk&feature=iv Resource based economy http://www.youtube.com/watch? v=uOw5eW52vRk&feature=mfu_in_order&list=UL

Hybrid Context Inconsistency Resolution for Context-aware Services  

Hybrid Context Inconsistency Resolution for Context-aware Services

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