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Composing WoT services with uncertain and correlated data

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Abstract

The Web of Things (WoT) aims to connect everyday objects to the Web. With the data provided by these connected objects, we can build several interesting applications by composing WoT services which collect and process WoT data and give orders to objects. Often, WoT data are uncertain and correlated due to various reasons. In this paper, we propose a probabilistic approach based on Bayesian networks to model and evaluate the composition of WoT services with uncertain and correlated data. Our approach is implemented and evaluated and the obtained results are promising.

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Notes

  1. https://www.ambius.com/indoor-plants/why-plants-turn-yellow.

  2. https://www.thespruce.com/houseplants-leaves-turning-brown-1902675.

  3. https://www.gardenmyths.com/water-plant-leaves-wilt.

  4. The exponents \(^S, ^P, ^A\) are used to designate sensing, processing and actuating services, respectively.

References

  1. Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805

    Article  Google Scholar 

  2. Awad S, Malki A, Malki M, Barhamgi M, Benslimane D (2019) Composing WoT services with uncertain data. Future Gener Comput Syst 101:940–950

    Article  Google Scholar 

  3. Cassar G, Barnaghi P, Wang W, De S, Moessner K (2013) Composition of services in pervasive environments: a divide and conquer approach. In: 2013 IEEE symposium on computers and communications (ISCC), pp 000226–000232

  4. Chen Y, Ying S, Jia X (2012) Bayesian network-based exception handling for web service composition. In: Proceedings of 2012 2nd international conference on computer science and network technology, pp 2197–2200

  5. Cooper GF (1990) The computational complexity of probabilistic inference using Bayesian belief networks. Artif Intell 42(2):393–405

    Article  MathSciNet  Google Scholar 

  6. Cowell RG, Dawid AP, Lauritzen SL, Spiegelhalter DJ (2007) Probabilistic networks and expert systems: exact computational methods for Bayesian networks, 1st edn. Springer, Berlin

    MATH  Google Scholar 

  7. Dalvi N, Ré C, Suciu D (2009) Probabilistic databases: diamonds in the dirt. Commun ACM 52(7):86–94

    Article  Google Scholar 

  8. Darwiche A (2009) Modeling and reasoning with Bayesian networks, 1st edn. Cambridge University Press, New York

    Book  Google Scholar 

  9. Dechter R (1996) Bucket elimination: a unifying framework for probabilistic inference. In: Proceedings of the twelfth international conference on uncertainty in artificial intelligence, pp 211–219

  10. Georgievski I, Aiello M (2016) Automated Planning for ubiquitous computing. ACM Comput Surv 49(4): 63:1–63:46

  11. Geyik SC, Szymanski BK, Zerfos P (2013) Robust dynamic service composition in sensor networks. IEEE Trans Serv Comput 6(4):560–572

    Article  Google Scholar 

  12. Guinard D (2011) A web of things application architecture: integrating the real-world into the web. Ph.D. thesis, ETH Zurich

  13. Guinard D, Trifa V (2009) Towards the Web of Things: Web Mashups for embedded devices. In: MEM 2009 in proceedings of WWW 2009. ACM

  14. Guofeng C (2013) Using Bayesian networks to measure web service QoS. In: Proceedings of the 2012 international conference on communication, electronics and automation engineering, pp. 1233–1238

  15. Han SN, Khan I, Lee GM, Crespi N, Glitho RH (2016) Service composition for IP smart object using realtime Web protocols: concept and research challenges. Comput Stand Interfaces 43:79–90

    Article  Google Scholar 

  16. He J, Zhang Y, Huang G, Cao J (2012) A smart web service based on the context of things. ACM Trans Internet Technol 11(3): 13:1–13:23

  17. Jensen FV, Nielsen TD (2007) Bayesian networks and decision graphs, 2nd edn. Springer, Berlin

    Book  Google Scholar 

  18. Kalasapur S, Kumar M, Shirazi BA (2007) Dynamic service composition in pervasive computing. IEEE Trans Parallel Distrib Syst 18(7):907–918

    Article  Google Scholar 

  19. Karakostas B (2016) Event prediction in an IoT environment using Naïve Bayesian models. Procedia Comput Sci 83: 11–17. The 7th international conference on ambient systems, networks and technologies (ANT 2016)/The 6th international conference on sustainable energy information technology (SEIT-2016)/affiliated workshops

  20. Lemos AL, Daniel F, Benatallah B (2015) Web service composition: a survey of techniques and tools. ACM Comput Surv 48(3):33:1–33:41

  21. Liu C, Cao J, Wang J (2017) A reliable and efficient distributed service composition approach in pervasive environments. IEEE Trans Mobile Comput 16(5):1231–1245

    Article  Google Scholar 

  22. Malki A (2015) Semantic modeling for cloud computing: toward Daas service composition with uncertain semantics. Ph.D. thesis, Claude Bernard University - Lyon I, France

  23. Malki A, Barhamgi M, Benslimane SM, Benslimane D, Malki M (2015) Composing data services with uncertain semantics. IEEE Trans Knowl Data Eng 27(4):936–949

    Article  Google Scholar 

  24. Malki A, Benslimane D, Benslimane SM, Barhamgi M, Malki M, Ghodous P, Drira K (2016) Data Services with uncertain and correlated semantics. World Wide Web 19(1):157–175

    Article  Google Scholar 

  25. Mokhtar SB, Georgantas N, Issarny V (2007) COCOA: conversation-based service composition in pervasive computing environments with QoS support. J Syst Softw 80(12):1941–1955

    Article  Google Scholar 

  26. Motallebi MR, Ishikawa F, Honiden S (2012) Trust computation in web service compositions using Bayesian networks. In: 2012 IEEE 19th international conference on web services, pp 623–625

  27. Neapolitan RE (2003) Learning Bayesian networks. Prentice Hall series in artificial intelligence. Prentice Hall Inc, Boston

    Google Scholar 

  28. Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers Inc., San Francisco

    MATH  Google Scholar 

  29. Pourhaji Kazem AA, Pedram H, Abolhassani H (2015) BNQM: a Bayesian network based QoS model for grid service composition. Expert Syst Appl 42(20):6828–6843

    Article  Google Scholar 

  30. Qin Y, Sheng QZ, Falkner NJ, Dustdar S, Wang H, Vasilakos AV (2016) When things matter: a survey on data-centric internet of things. J Netw Comput Appl 64:137–153

    Article  Google Scholar 

  31. Richardson L, Ruby S (2007) Restful Web services, 1st edn. O’Reilly, Newton

    Google Scholar 

  32. Rodriguez-Mier P, Pedrinaci C, Lama M, Mucientes M (2016) An integrated semantic web service discovery and composition framework. IEEE Trans Serv Comput 9(4):537–550

    Article  Google Scholar 

  33. Safaei M, Ismail AS, Chizari H, Driss M, Boulila W, Asadi S, Safaei M (2020) Standalone noise and anomaly detection in wireless sensor networks: a novel time-series and adaptive Bayesian-network-based approach. Softw Pract Exp 50(4):428–446

    Article  Google Scholar 

  34. Sen P, Deshpande A (2007) Representing and querying correlated tuples in probabilistic databases. In: 2007 IEEE 23rd international conference on data engineering, pp 596–605

  35. Sen P, Deshpande A, Getoor L (2009) PrDB: managing and exploiting rich correlations in probabilistic databases. VLDB J 18(5):1065–1090

    Article  Google Scholar 

  36. Sheng QZ, Qiao X, Vasilakos AV, Szabo C, Bourne S, Xu X (2014) Web services composition: a decade’s overview. Inf Sci 280:218–238

    Article  Google Scholar 

  37. Shi Q, Kang J, Wang R, Yi H, Lin Y, Wang J (2018) A framework of intrusion detection system based on Bayesian network in IoT. Int J Perform Eng 14(10):2280–2288

    Google Scholar 

  38. Silva ALM, de Jesús Pérez Alcázar J, Kofuji ST (2019) Interoperability in semantic Web of Things: design issues and solutions. Int J Commun Syst 32(6), e3911

  39. Suciu D, Olteanu D, Ré C, Koch C (2011) Probabilistic databases. Synthesis lectures on data management. Morgan & Claypool Publishers, San Rafael

    MATH  Google Scholar 

  40. Tzortzis G, Spyrou E (2016) A semi-automatic approach for semantic IoT service composition. In: Proceedings of workshop on artificial intelligence and internet of things (AI-IoT), held at the Hellenic Conference on Artificial Intelligence (SETN), pp 1–6

  41. Tzounis A, Katsoulas N, Bartzanas T, Kittas C (2017) Internet of Things in agriculture, recent advances and future challenges. Biosyst Eng 164:31–48

    Article  Google Scholar 

  42. Wang DZ, Michelakis E, Garofalakis M, Hellerstein MJ (2008) BayesStore: managing large, uncertain data repositories with probabilistic graphical models. Proc VLDB Endow 1(1):340–351

    Article  Google Scholar 

  43. Weiser M (1991) The computer for the 21st century. Sci Am 265(3):94–104

    Article  Google Scholar 

  44. Xu W (2015) Modeling and exploiting the knowledge base of web of things. Ph.D. thesis, Pierre and Marie Curie University-Paris, France

  45. Xu W, Marsala C, Christophe B (2013) Matching objects to user’s queries in Web of Things’ applications. In: 2013 IEEE symposium on computational intelligence for communication systems and networks (CIComms), pp 31–38

  46. Yachir A, Amirat Y, Chibani A, Badache N (2012) Towards an event-aware approach for ubiquitous computing based on automatic service composition and selection. Ann Telecommun - annales des télécommunications 67(7):341–353

    Article  Google Scholar 

  47. Yang K, Cho SB (2017) A context-aware system in Internet of Things using modular Bayesian networks. Int J Distrib Sens Netw 13(5):68–75

    Google Scholar 

  48. Yang Z, Li D (2014) IoT information service composition driven by user requirement. In: 2014 IEEE 17th international conference on computational science and engineering, pp 1509–1513

  49. Yue K, Liu W, Li W (2007) Towards Web services composition based on the mining and reasoning of their causal relationships. In: Advances in data and web management, pp 777–784

  50. Zhang NL, Poole D (1994) A simple approach to Bayesian network computations. In: Proceedings of the Tenth Canadian conference on artificial intelligence, pp 171–178

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Acknowledgements

This research was supported by the Algerian Directorate General for Scientific Research and Technological Development, Algeria under Grant No. 01/ESI Sidi Bel Abbes/DGRSDT/2019.

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  1. LabRi Laboratory, Ecole Superieure en Informatique, Sidi Bel Abbes, 22000, Algeria

    Samir Awad, Abdelhamid Malki & Mimoun Malki

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  1. Samir Awad
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  2. Abdelhamid Malki
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  3. Mimoun Malki
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Correspondence to Samir Awad.

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Awad, S., Malki, A. & Malki, M. Composing WoT services with uncertain and correlated data. Computing 103, 1501–1517 (2021). https://doi.org/10.1007/s00607-020-00879-6

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