Personal and Ubiquitous Computing

, Volume 16, Issue 7, pp 799–818 | Cite as

Context provenance to enhance the dependability of ambient intelligence systems

  • Daniele RiboniEmail author
  • Claudio Bettini
Original Article


Ambient intelligence systems would benefit from the possibility of assessing quality and reliability of context information based on its derivation history, named provenance. While various provenance frameworks have been proposed in data management, context data have some peculiar features that claim for a specific support. However, no provenance model specifically targeted to context data has been proposed till the time of writing. In this paper, we report an initial investigation of this challenging research issue by proposing a provenance model for data acquired and processed in ambient intelligence systems. Our model supports representation of complex derivation processes, integrity verification, and a shared ontology to facilitate interoperability. The model also deals with uncertainty and takes into account temporal aspects related to the quality of data. We experimentally show the impact of the provenance model in terms of increased dependability of a sensor-based smart-home infrastructure. We also conducted experiments to evaluate the communication and computational overhead introduced to support our provenance model, using sensors and mobile devices currently available on the market.


Context Data Causal Dependency Provenance Information Ontological Reasoning Elliptic Curve Digital Signature Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work has been partially supported by a grant from Sun® Microsystems. The authors would like to thank Tim van Kasteren for providing the activity dataset used in our experiments, Roberto Cantini for his excellent programming work, and Linda Pareschi for her insightful comments and suggestions related to this work.


  1. 1.
    Agostini A, Bettini C, Riboni D (2009) Hybrid reasoning in the care middleware for context awareness. Int J Web Eng Technol 5(1):3–23CrossRefGoogle Scholar
  2. 2.
    Allen MD, Chapman A, Blaustein BT, Seligman L (2010) Capturing provenance in the wild. In: Proceedings of provenance and annotation of data and processes. Lecture notes in computer science, vol 6378. Springer, pp 98–101Google Scholar
  3. 3.
    Benjelloun O, Sarma AD, Halevy AY, Theobald M, Widom J (2008) Databases with uncertainty and lineage. VLDB J 17(2):243–264CrossRefGoogle Scholar
  4. 4.
    Bettini C, Pareschi L, Riboni D (2008) Efficient profile aggregation and policy evaluation in a middleware for adaptive mobile applications. Pervasive Mobile Comput 4(5):697–718CrossRefGoogle Scholar
  5. 5.
    Bettini C, Brdiczka O, Henricksen K, Indulska J, Nicklas D, Ranganathan A, Riboni D (2010) A survey of context modelling and reasoning techniques. Pervasive Mobile Comput 6(2):161–180CrossRefGoogle Scholar
  6. 6.
    Boyle DE, Newe T (2009) On the implementation and evaluation of an elliptic curve based cryptosystem for Java enabled wireless sensor networks. Sens Actuat A Phys 156(2):394–405CrossRefGoogle Scholar
  7. 7.
    Buchholz T, Kpper A, Schiffers M (2003) Quality of context information: what it is and why we need it. In: Proceedings of the 10th HP OpenView University WorkshopGoogle Scholar
  8. 8.
    Chapman A, Jagadish HV, Ramanan P (2008) Efficient provenance storage. In: Proceedings of the ACM SIGMOD international conference on management of data, pp 993–1006. ACMGoogle Scholar
  9. 9.
    Chen H, Finin T, Joshi A (2004) Semantic web in the context broker architecture. In: Proceedings of the second IEEE international conference on pervasive computing and communications (PerCom 2004), pp 277–286. IEEE Computer SocietyGoogle Scholar
  10. 10.
    Chowdhury AR, Falchuk B, Misra A (2010) Medially: A provenance-aware remote health monitoring middleware. In Proceedings of the eight annual IEEE international conference on pervasive computing and communications (PerCom 2010), pp 125–134. IEEE Computer SocietyGoogle Scholar
  11. 11.
    Cui Y, Widom J (2003) Lineage tracing for general data warehouse transformations. VLDB J 12(1):41–58CrossRefGoogle Scholar
  12. 12.
    Dey AK (2001) Understanding and using context. Pers Ubiquit Comput 5(1):4–7CrossRefGoogle Scholar
  13. 13.
    Goix L-W, Valla M, Cerami L, Falcarin P (2007) Situation inference for mobile users: a rule based approach. In: Proceedings of the 8th international conference on mobile data management, pp 299–303. IEEE Computer SocietyGoogle Scholar
  14. 14.
    Grau BC, Horrocks I, Motik B, Parsia B, Patel-Schneider PF, Sattler U (2008) OWL 2: the next step for OWL. J Web Semant 6(4):309–322CrossRefGoogle Scholar
  15. 15.
    Hartig O, Zhao J (2010) Publishing and consuming provenance metadata on the web of linked data. In Proceedings of IPAW 2010. Lecture notes in computer science, vol 6378. Springer, pp 78–90Google Scholar
  16. 16.
    Hasan R, Sion R, Winslett M (2009) The case of the fake picasso: preventing history forgery with secure provenance. In Proceedings of the 7th conference on file and storage technologies. USENIX Association, pp 1–14 Google Scholar
  17. 17.
    Heinis T, Alonso G (2008) Efficient lineage tracking for scientific workflows. In Proceedings of ACM SIGMOD. ACM, pp 1007–1018Google Scholar
  18. 18.
    Horrocks I, Patel-Schneider PF, van Harmelen F (2003) From SHIQ and RDF to OWL: the making of a web ontology language. J Web Semant 1(1):7–26. ISSN 1570-8268Google Scholar
  19. 19.
    Horrocks I, Patel-Schneider PF, Boley H, Tabet S, Grosof B, Dean M (2004) SWRL: a semantic web rule language combining OWL and RuleML. W3c member submission, W3C, May 2004. URL
  20. 20.
    Huang V, Chu J (2010) Sensor information decay process modeling. In: Proceedings of the fourth international conference on sensor technologies and applications. IEEE Computer Society, pp 287–292Google Scholar
  21. 21.
    International Organization for Standardization: ISO 8601 (2004) Data elements and interchange formats—information interchange—representation of dates and times. Technical report, ISO, Geneva, Switzerland, 2004. URL
  22. 22.
    Kellokumpu V, Pietikäinen M, Heikkilä J (2005) Human activity recognition using sequences of postures. In: Proceedings of the IAPR conference on machine vision applications, pp 570–573Google Scholar
  23. 23.
    Koblitz N, Menezes A, Vanstone SA (2000) The state of elliptic curve cryptography. Des Codes Crypt 19(2/3):173–193MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Lenzini G (2009) Design of architectures for proximity-aware services: experiments in context-based authentication with subjective logic. Electron Notes Theor Comput Sci 236:47–64CrossRefGoogle Scholar
  25. 25.
    Lester J, Choudhury T, Kern, Nicky B, Gaetan HB (2005) A hybrid discriminative/generative approach for modeling human activities. In: Leslie PK, Alessandro S (eds) IJCAI-05, proceedings of the nineteenth international joint conference on artificial intelligence. Professional Book Center, pp 766–772Google Scholar
  26. 26.
    Lutz C, Milicic M (2007) A tableau algorithm for description logics with concrete domains and general tboxes. J Autom Reason 38(1–3):227–259MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Malan DJ, Welsh M, Smith MD (2004) A public-key infrastructure for key distribution in tinyos based on elliptic curve cryptography. In: Proceedings of the first annual IEEE communications society conference on sensor and Ad Hoc communications and networks. IEEE Computer Society, pp 71–80Google Scholar
  28. 28.
    Mayrhofer R, Radi H, Ferscha A (2003) Recognizing and predicting context by learning from user behavior. In Schreiner W, Kotsis G, Ferscha A, Ibrahim K (eds) Proceedings of MoMM 2003: 1st international conference on advances in mobile multimedia. vol 171. Austrian Computer Society (OCG), pp 25–35 Google Scholar
  29. 29.
    McGuinness DL, Ding LI, da Silva, PP, Chang C (2007) Pml 2: a modular explanation interlingua. In: Proceedings of the 2007 AAAI workshop on explanation-aware computing, vol WS-07-06 of AAAI technical report. AAAI Press, pp 49–55.Google Scholar
  30. 30.
    Moreau Luc (2010) The foundations for provenance on the web. Found Trends Web Sci 2(2–3):99–241MathSciNetCrossRefGoogle Scholar
  31. 31.
    Moreau L, Clifford B, Freire J, Futrelle J, Gil Y, Groth PT, Kwasnikowska N, Miles S, Missier P, Myers J, Plale B, Simmhan Y, Stephan EG, Vanden BJ(2011) The open provenance model core specification (v1.1). Future Gener Compt Syst 27(6):743–756CrossRefGoogle Scholar
  32. 32.
    Moni N, Moti Y (1989) Universal one-way hash functions and their cryptographic applications. In Proceedings of the twenty-first annual ACM symposium on theory of computing. ACM, pp 33–43Google Scholar
  33. 33.
    Ranganathan A, Al-Muhtadi J, Chetan S, Campbell RH, Mickunas MD (2004) Middlewhere: a middleware for location awareness in ubiquitous computing applications. In Proceedings of Middleware 2004. Lecture notes in computer science, vol 3231. Springer, pp 397–416Google Scholar
  34. 34.
    Riboni D, Bettini C (2011) COSAR: hybrid reasoning for context-aware activity recognition. Person Ubiquit Comput 7(3):379–395Google Scholar
  35. 35.
    Riboni D, Pareschi L, Bettini C (2009) Privacy in georeferenced context-aware services: a survey. In Privacy in location-based applications. Lecture notes in computer science, vol 5599. Springer, pp 151–172Google Scholar
  36. 36.
    Sahoo SS, Sheth A (2009) Provenir ontology: towards a framework for escience provenance management. In: Microsoft eScience workshop. MicrosoftGoogle Scholar
  37. 37.
    Sarma AD, Theobald M, Widom J (2008) Exploiting lineage for confidence computation in uncertain and probabilistic databases. In Proceedings of ICDE 2008. IEEE Comput Soc, pp 1023–1032Google Scholar
  38. 38.
    Sheikh K, Wegdam M, van Sinderen M (2008) Quality-of-context and its use for protecting privacy in context aware systems. J Softw 3(3):83–93CrossRefGoogle Scholar
  39. 39.
    Simmhan Y, Plale B, Gannon D (2005) A survey of data provenance in e-science. SIGMOD Rec 34(3):31–36CrossRefGoogle Scholar
  40. 40.
    Simmhan YL, Plale B, Gannon D (2008) Karma2: provenance management for data-driven workflows. Int J Web Serv Res 5(2):1–22CrossRefGoogle Scholar
  41. 41.
    Sundararaman B, Buy U, Kshemkalyani AD (2005) Clock synchronization for wireless sensor networks: a survey. Ad Hoc Netw 3(3):281–323CrossRefGoogle Scholar
  42. 42.
    van K, Tim N, Athanasios K, Englebienne G, Kröse BJA (2008) Accurate activity recognition in a home setting. In: Proceedings of UbiComp 2008. ACM international conference proceeding series, vol 344. ACM, pp 1–9Google Scholar
  43. 43.
    Wang H, Li Q (2010) Achieving robust message authentication in sensor networks: a public-key based approach. Wirel Netw 16(4):999–1009CrossRefGoogle Scholar
  44. 44.
    Wessel M (2001) Obstacles on the way to qualitative spatial reasoning with description logics: some undecidability results. In International description logics workshop (DL-2001). CEUR workshop proceedings, vol 49., pp 122–131Google Scholar
  45. 45.
    Yavas G, Katsaros D, Ulusoy Ö, Manolopoulos Y (2005) A data mining approach for location prediction in mobile environments. Data Knowl Eng 54(2):121–146CrossRefGoogle Scholar
  46. 46.
    Zhang J, Chapman A, LeFevre K (2009) Do you know where your data’s been?—Tamper-evident database provenance. In Proceedings of secure data management, 6th VLDB workshop. Lecture notes in computer science, vol 5776. Springer, pp 17–32Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Università degli Studi di Milano, D.I.Co., EveryWare LabMilanoItaly

Personalised recommendations