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Outline. Introduction Resource Discovery in Pervasive Environments EPCglobal RFID technology architecture Semantic-enhanced RFID Infrastructure Semantic enhanced Bluetooth SDP RFID object discovery Ontology support Prototype (design and implementation) ‏ Conclusion and Future Work.

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  1. Outline • Introduction • Resource Discovery in Pervasive Environments • EPCglobal RFID technology architecture • Semantic-enhanced RFID • Infrastructure • Semantic enhanced Bluetooth SDP • RFID object discovery • Ontology support • Prototype (design and implementation)‏ • Conclusion and Future Work 2 of 43

  2. Resource Discovery in ubiquitous computing service provider user useragent serviceagent directory agent serviceagent serviceagent serviceagent • Mobile Resource Discovery Protocols (RDPs) have been adapted from protocols designed for wired contexts • Common resource discovery paradigms are centralized and registration-oriented • Each service advertises and registers itself to a service lookup • The directory service keeps track of resources in the network • The lookup server attempts to match the query pattern with resource descriptions in its DB • Current RDPs require a continuous and robust network connectivity • Existing mobile resource discovery methods use a simple string-matching 3 of 43

  3. Radio Frequency IDentification • Currently RFID technology focus on supply chain management • identification • tracking • RFID components • tags attached to objects • readers deployed in the field • supportinfrastructure for data collection and processing 4 of 43

  4. RFID - stateoftheart • Objects identified by a unique ID code stored in the tag • Code is: • read by an RFID reader • used as a key to retrieve relevant information about the object from a support infrastructure • Information about an item is dynamically updated only on information servers of the backend infrastructure • object location • ongoing processes 5 of 43

  5. EPCglobal RFID technology architecture • RFID tag data specification for EPC(Electronic Product Code) families • derived from GS1 barcode ID schemes • unambiguous identification at item level • RFID Class 1 Generation 2 UHF protocol • tag memory organization for storing EPC value • tag-reader communication • Multiple readers management • Event filtering and collection • EPCIS (EPCglobal Information Services)‏ • manages authoritative information about products • access through a WAN • ONS(Object Naming Service)‏ • translation of EPCs into URIs for querying EPCIS via the Internet 6 of 43

  6. Basics of EPCglobal RFID Protocol • Tag memory content • Optional user application data • IDs for manufacturer and model of tags • Protocol control info and EPC code • Kill and access passwords • Reader-tag communication protocol steps • Selection of a subset of all tags in range (Selectcommand provides basic discovery features)‏ • Inventory of the preselected tag population • Access to individual tags (Read, Write, other commands)‏ 7 of 43

  7. Main issues (1/2)‏ • Centralized approach for object/resource identification • Stable and dependable WAN infrastructures are required • EPC code stored on tag provides minimal information on the object • Manufacturer • Merchandise class • Logistic unit type • Serial number • Discovery features rely on exact match only encoded as bit strings without proper semantics 8 of 43

  8. Main issues (2/2)‏ RFID is effective to bridge the gap between physical and digital world in pervasive computing (business, home), but: • the need for a backend infrastructurerestrictstheexploitation of current RFID solutions to really mobile scenarios • syntactic matching is largelyinefficient in mobile ad-hocenvironments for advanced applications There is the need to cope also with non exact matches to: • carry out resource discovery covering requester’s needs as much as possible • grant also a partial satisfaction of the user request 9 of 43

  9. Vision • The availability of RFID tags with more memory and/or onboard sensors discloses new features: • Tag storing a full object description in addition to ID • Data being annotated in machine-understandableformats with well-grounded semantics • Resourcesself-exposingtheir properties in RFID-enabled digital ad hoc environments • On-tag descriptionevolving along each step of product lifecycle • Technical challenges call for advanced knowledge representation technologies in order to • go beyond pure identification • enable rich yet efficient objectdescriptionanddiscovery in resource-constrained environments 10 of 43

  10. Approach GOALS • Exploit ideas and technologies of Semantic Web vision • Re-use EPCglobal RFID Class 1 Generation 2 UHF protocol • Backward compatibility with legacy identification applications • Integrate with semantic-enhanced Bluetooth Service Discovery Protocol MEANS • Annotation of resource stored within the tag • Exploitation of the strict correspondence OWL-DL - DLs • Use of DL-based reasoners to infer new information • Reference syntax: DIG (syntactic variant of OWL-DL), less verbose and more compact • Use of compression algorithms 11 of 43

  11. SHOP Infrastructure Product description is shown on the touchscreen as a hint to the user for composing her request for more products Hotspot is endowed with a MatchMaker to compute matchmaking between request and available offers measuring a “semantic distance” reader Each resource in the m-marketplace has an EPC and is annotated by a DIG description stored in its RFID tag hotspot RFID tag Hotspot classifies resource contents by means of an OWL-DL ontology middleware Smart shopping cart integrates RFID reader, touchscreen and Bluetooth device Hotspot has previously collected DIG resource descriptions from shopping mall server, used for inventory and supply chain management User submits her DIG resource request to the zone hotspot via Bluetooth When a user puts a product in her shopping cart, a sensor triggers RFID reading of its information A pair of ranked lists of discovered resources is returned; both the most similar products and the most suitable for a combination with the user request are provided 12 of 43

  12. Semantic-enhanced Bluetooth SDP [Ruta et al., IJWGS, 2006] (1/2)‏ • Semantic-based Service Discovery capabilities into the existing Bluetooth SDP • Support to matchmaking of semantically annotated resources • Full backwardcompatibility with standard Bluetooth SDP • OUUID(Ontology Universally Unique IDentifier)‏ • 128-bit UUIDs in the original SDP are associated to specific service classes • Unused ranges of UUID values are exploited to mark each ontology • Each OUUID so defines a resource context, excluding supply descriptions that do not refer to the same ontology as the request • Integration of a semantic layer at service discovery level • For each semantically annotated resource, SDP server stores additional attributes • OUUID • Semantic annotation expressed in DIG • Context-aware numerical attributes (post processing filtering)‏ • New data types and PDUs are introduced for client-server interaction, exploiting identifiers reserved for future purposes in the original standard • Ontology support through browsing of managed ontologies and ontology transfer between client and server via Bluetooth 13 of 43

  13. Semantic-enhanced Bluetooth SDP [Ruta et al., IJWGS, 2006] (2/2)‏ MatchMaking SDP_OntologySearchResp hotspot (server)‏ SDP_SemanticServiceSearchResp SDP_SemanticServiceSearchReq SDP_OntologySearchReq mobile host (client)‏ t0 t1 t2 t3 t4 • Client searches for a OUUIDR • Server selects OUUIDs matching each OUUIDRand replies to the client • Client sends a service request (R) to server • Server extracts descriptions of managed resources classified with OUUIDR • Server performs the matchmaking between R and selected resources. All the resources are ranked w.r.t. R • Server replies to the user with best matching resources 14 of 43

  14. EPCglobal RFID protocol evolution • Requirements • Support for both object identification and semantic discovery • Backward compatibility (RFID infrastructures for supply chain – readers and middleware)‏ • Integration with matchmaking and ontology support facilities provided by semantic-enhanced Bluetooth SDP • Implementation • Slight extensions to tag memory organization • Introduction of Semantic Resource Discovery functionalities into the reader-tag protocol, by exploiting standard commands in innovative ways 15 of 43

  15. Tag memory extension (1/4)‏ • Optional bank, standard leaves freedom in data organization • Stores: • context-aware numerical attributes • compressed DIG annotation of the object w.r.t. a reference ontology R 16 of 43

  16. Tag memory extension (2/4)‏ • Standard allows adding data to indicate optional features or manufacturer/model specific extensions • Stores the 128-bit OUUIDR of the reference ontology • One-to-one match with OUUIDs used in semantic-enhanced Bluetooth SDP 17 of 43

  17. Tag memory extension (3/4)‏ • Two reserved bits in Protocol Control field used to: • indicate whether tag has user memory or not (bit at 15h)‏ • mark semantic-enabled tags (bit at 16h)‏ 18 of 43

  18. Tag memory extension (4/4)‏ Unmodified 19 of 43

  19. Semantic-based object discovery • An RFID reader can: • preselect only semantic-enabled tags • inventory preselected tags (scanning their EPC codes for item identification) in the standard way • accessa semantic-enabled tag to • read OUUID of the reference ontology • read/write context-aware attributes and semantic-based description No new protocol commands are required 20 of 43

  20. Parameter Target Action MemBank Pointer Length Mask Value 1002 0002 012 000101012 000000102 112 Preselection of semantic-enabled tags • SELECT command is exploited If bits match, tag will set its SL flag to 1, otherwise to 0 Identify the two bits at address 15-16h in the EPC tag memory bank Tag will compare their value with 112; bits will match only for semantic enabled tags • Inventory step proceeds in the standard way, scanning only tags with SL = 1 21 of 43

  21. Parameter MemBank WordPtr WordCount Value 102 000000102 000010002 Extracting reference OUUID • READ command is exploited Start reading from address 20h (third 16-bit word)‏ Read 128 bits (eight 16-bit words)‏ Read from TID memory bank 22 of 43

  22. Parameter MemBank WordPtr WordCount Value 112 000000002 000000002 Extracting semantic annotations • READ command is exploited to access both context-aware attributes and semantic annotation Start reading from the beginning of the bank Read up to the end of the bank Read from User memory bank • Similarly, object description can be updated by means of one or more WRITE commands 23 of 43

  23. Annotation encoding • XML-based metadata languages: RDF, OWL, DIG • Approaches to compression • General purpose • static: fixed or no source statistics • semi-adaptive: 1st step statistics collection, 2nd step compression • adaptive: statistics collection during compression • XML-specific • homomorphic: preserves document structure • non-homomorphic • Homomorphic compression allows direct query evaluation on encoded RDF annotations [Min et al., ACM TOIT, 6(3), 2006] 24 of 43

  24. DIGCompressor [Di Noia et al., IJSWIS, 4(1), 2008] • Compression of DIG documents for pervasive computing applications • Separation of structure (XML tags and attributes) from data (XML attribute values)‏ • Three-step compression process: • Main properties • Adaptive (step 1 is static, 2 is semi-adaptive, 3 is adaptive)‏ • Non-homomorphic (zlib mangles document structure)‏ • High compression rates even for very small documents (such as RFID tag annotations)‏ Header Attribute values packing zlib Data structure encoding DIG XML Schema Recurrence check Ziv-Lempel algorithm 25 of 43

  25. COX [Scioscia, Ruta, SWIM2009] • Compressor for Ontological XML-based languages • Designed to support queries directly on compressed data (homomorphism)‏ • Compression approaches • Structure: Reverse Arithmetic Encoding [Min et al.] • Data: like in DIGCompressor • Two-step compression process • statistic collection for tag and attribute value occurrencies • encoding 26 of 43

  26. RAE: first step • Document parsing • Tag frequency and intervals calculation <tells xmlns="http://dl.kr.org/dig/2003/02/lang"> <defindividual name="milk_delivery_M2"/> <instanceof> <individual name="milk_delivery_M2"/> <and> <catom name="cow_milk"/> <all> <ratom name="processed_with"/> <catom name="refrigeration"/> </all> </and> </instanceof> </tells> adjusted tag frequency number of different tag names 27 of 43

  27. RAE: second step simple path @name > defindividual > tells leaf root • Build document header • Correspondence between each tag and the INF of its interval • All values are between 1 and 2, so that in 32-bit floating point representation the first byte can be discarded • Encode tags/attributes • The whole simple path is encoded with a real number in ]1,2[ • Only the two central bytes are written into the compressed file <tells xmlns="http://dl.kr.org/dig/2003/02/lang"> <defindividual name="milk_delivery_M2"/> . . . 28 of 43

  28. COX: attribute values encodingand output • COX encodes only attribute value strings with • length > 3 • occurrencies > 4 • Encoded strings • substituted with 1-byte code between 01h and FDh • FEh delimiter byte is appended • Non-encoded strings • string is copied to output • FFh delimiter byte is appended • A second header in the output file contains the string decoding table • Tags, attributes and values are encoded in the same order as in the original document homomorphic compression 29 of 43

  29. Performance evaluation – Compression rate 30 of 43

  30. Performance evaluation – Turnaround time Instance annotations (smaller)‏ Domain ontologies (larger)‏ 31 of 43

  31. Performance evaluation – Main memory usage peak 32 of 43

  32. Final outcomes • COX allows queries directly on compressed semantic annotations • Homomorphic compression • Reverse arithmetic encoding for tag paths • Encoding of frequent attribute values • Compression rates are lower than algorithms designed only for efficient encoding • Tool performance (time, memory) is adequate to mobile computing environments • Future developments • Assess efficiency of query evaluation on compressed annotations • Design and implement a SPARQL query compiler to interrogate compressed RDF documents on RFID tags exploiting EPCglobal UHF Gen. 2 protocol 33 of 43

  33. Ontologysupport ISSUES • Resource retrieval session starts after the ontology agreement procedure • The ontology identifier (OUUID) to be used is read from the tag attached to the object • What happens if a client does not support the ontology or even the hotspot does not manage it? SOLUTIONS • By using the same OUUIDs as in semantic-enhanced SDP, ontology support facilities at Bluetooth level are preserved • If no host in the Bluetooth piconet manages the ontology but an Internet connection is available, Object Naming Service can be exploited to retrieve the ontology files 34 of 43

  34. Order Pref Flags Service RegExp Replacement 0 0 u EPC+xxx !^.*$!yyy! . DNS resolver LOCAL System DNS ONS ontology support (1/2)‏ • Standard EPCglobal Object Naming Service is based on the Domain Naming Service (DNS Internet addresses resolution)‏ Suffix identifies service type URL of service endpoint NAPTR record EPCIS server EPC-URI translation Web page EPC code 35 of 43

  35. ONS ontology support (2/2)‏ each value pair identifies a class of homogeneous products Described w.r.t. the same ontology (same OUUID)‏ No ambiguity in ONS ontology service records • EPCglobal Network Protocol Parameter Registry maintains valid service suffixes • By adding dig suffix, a new ontology provisioning service can be introduced • EPC code on tag will be used to query ONS and obtain URL of compressed DIG ontology files • EPC fields user for URI translation identify: • manufacturer • merchandise class Notes • Vice-versa is not true in general • The same approach can be adopted for OWL ontologies 36 of 43

  36. Additional features (1/2)‏ • In RFID-enabled m-commerce scenarios, effective user decisionsupport should be provided • When user chooses a product P, its semanticallyannotateddescription DP can drive the discovery of further resources • Discovery of two kinds of resources is highly desirable from a buyer’s perspective after selection of P: • products most similar to P • best products to be used in combination with P • Similar products have to be found among supplies whose description is logicallycompatible with DP • Product suitable for combination typically are of different kind from P, so they are to be found among supplies whose description is logicallyincompatible with DP (i.e., with conflicting characteristics)‏ TWO-STEP MATCHMAKING 37 of 43

  37. Additional features (2/2)‏ • In e-commerce scenarios, match between demand and supply should involve not only the description of the good but also data-oriented properties as: • price • availability • quantity • In m-commerce applications, also context-aware variables could influence matching results. For example: • physical distance between requester and seller • battery duration • required computational capabilities • A good overall matching function should combine contextualvalues and datareferredproperties to give a global match degree value UTILITY FUNCTIONS 38 of 43

  38. Prototype: matchmaking & C. SHOP rankPotential algorithm [Di Noia et al., IJEC 2004] rankPartial algorithm [ibidem] request compatible supplyscore incompatiblesupplies incompatible supplyscore hotspot supplies Delivery Time Delivery Time Price ProductCategory combination utility function similarity utility function combination results similarity results 39 of 43

  39. Prototype: implementation 40 of 43

  40. Conclusion • We devised a semantic-based things discovery framework • EPCGlobal RFID standard has been exploited in order to allow an object retrieval in a pervasive scenario • Backward compatibility has been granted toward legacy identification applications • The approach has been implemented and tested over the IBM WebSphere simulation environment • The approach embeds a compression algorithm especially designed for encoding semantically annotated descriptions of resources • The proposed framework integrates semantic-enhanced discovery layer of most common wireless standards • Bluetooth (presented here)‏ • IEEE 802.11 • ZigBee (WSSNs applications)‏ 41 of 43

  41. Work in progress (1/2)‏ • “On-product” discovery combined to a supply chain data aggregation • To enable extensive data analyses involving products and supply chain nodes • Analysts can perform stand-alone massive business logic elaborations • Product and process information can be queried, updated and integrated during manufacturing, packaging and supply chain management • Full traceability up to sales • Intelligent and de-localized querying of product data • A distributed database will play the role of storing data • Real-time elaborations along the supply chain • Massive multidimensional analysis on huge lapse 42 of 43

  42. Work in progress (2/2)‏ • A tensor model allows to trace products behavior in each node of the supply chain • EPC is exploited to unambiguously identify each good • Node analyses along with product ones are enabled • Mathematical operations on the supply chain tensor allow to extract relevant information to aggregate (<product, time, location>)‏ • Sub-matrixes represent views of supply chain pieces • Rank calculation allows to determine the correlation level among nodes and/or products to detect chain anomalies • For each node or product, time analyses are allowed • Arriving/departure instants are monitored • Chain schedules and timetables can be verified and tested • Product life-cycle can be monitored • Failures in chain behavior are quickly discovered 43 of 43

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