Multimedia Tools and Applications

, Volume 78, Issue 3, pp 2983–3007 | Cite as

Enhancing dependability through profiling in the collaborative internet of things

  • Imad BelkacemEmail author
  • Safia Nait-Bahloul
  • Damien SauveronEmail author


The future of the Internet of Things (IoT) is the Collaborative Internet of Things (C-IoT) in which different IoT deployments collaborate to provide better services. For instance, in smart city scenarios, C-IoT will have the potential to provide immersive multimedia user-experiences based on content and context fusion, immersive multi-sensory environments, location-based and media internet technologies, and augmented reality. However, this future paradigm will only be possible if the right decisions can be made based on the analysis of huge volumes of collected data: i.e. if the dependability of C-IoT is ensured. To address this challenge, we studied a simplified view of a C-IoT architecture composed of devices using three different technologies that have enabled the existence of IoT (RFID, NFC and Beacons). However, our proposal could be extended to any other devices in the context of C-IoT. To enhance the dependability of C-IoT, we deploy statistical data analysis techniques to improve the quality of the data obtained from identification and sensing devices and to select the most reliable devices that provide trusted (i.e. non-faulty) data in order to support accurate decision-making.


IoT C-IoT Collaboration Dependability Identification Sensing Data analysis 


  1. 1.
    Beacon A ASensor. Visited on 2017-10-20
  2. 2.
    Beacon A April Beacon website. Visited on 2017-10-20
  3. 3.
    Behmann F, Wu K (2015) Collaborative internet of things (C-IoT): for future smart connected life and business. WileyGoogle Scholar
  4. 4.
    Belkacem I, Bahloul SN, Aktouf OEK (2014) Data analysis of an RFID system for its dependability. Int J Embedded Real-Time Commun Syst (IJERTCS) 5(3):1–22CrossRefGoogle Scholar
  5. 5.
    Boano CA, Römer K, Voigt T (2015) RELYonIT: dependability for the internet of things. IEEE IoT Newsl 13Google Scholar
  6. 6.
    Borges Neto JB, Silva TH, Assunċão RM, Mini RA, Loureiro AA (2015) Sensing in the collaborative internet of things. Sensors 15(3):6607–6632CrossRefGoogle Scholar
  7. 7.
    Chavira G, Nava SW, Hervas R, Bravo J, Sanchez C (2007) Combining RFID and NFC technologies in an AmI conference scenario. In: Eighth Mexican International conference on current trends in computer science, 2007. ENC 2007. IEEE, pp 165–172Google Scholar
  8. 8.
    Dar KS, Taherkordi A, Eliassen F (2016) Enhancing dependability of cloud-based IoT services through virtualization. In: 2016 IEEE First international conference on internet-of-things design and implementation (IoTDI). IEEE, pp 106–116Google Scholar
  9. 9.
    Dean RB, Dixon W (1951) Simplified statistics for small numbers of observations. Anal Chem 23(4):636–638CrossRefGoogle Scholar
  10. 10.
    Fritz G, Beroulle V, Nguyen M, Aktouf OEK, Parissis I (2010) Read-error-rate evaluation for RFID system on-line testing. In: 2010 IEEE 16th international mixed-signals, sensors and systems test workshop (IMS3TW). IEEE, pp 1–6Google Scholar
  11. 11.
  12. 12.
    Grubbs FE (1950) Sample criteria for testing outlying observations. Ann Math Stat 27–58Google Scholar
  13. 13.
    Grubbs FE (1969) Procedures for detecting outlying observations in samples. Technometrics 11(1):1–21CrossRefGoogle Scholar
  14. 14.
    Grubbs FE, Beck G (1972) Extension of sample sizes and percentage points for significance tests of outlying observations. Technometrics 14(4):847–854MathSciNetCrossRefGoogle Scholar
  15. 15.
    Hamdan D (2013) Détection et diagnostic des fautes dans des systèmes à base de réseaux de capteurs sans fils. Ph.D. thesis, Université de GrenobleGoogle Scholar
  16. 16.
    Herrera MM, Bonastre A, Capella JV (2008) Performance study of non-beaconed and beacon-enabled modes in IEEE 802.15.4 under bluetooth interference. In: The Second international conference on mobile ubiquitous computing, systems, services and technologies, 2008. UBICOMM’08. IEEE, pp 144–149Google Scholar
  17. 17.
    Intel A guide to the internet of things: how billions of online objects are making the world Wise. Visited on 2017-10-20
  18. 18.
    IV P PHASE IV website. Visited on 2017-10-20
  19. 19.
    Kalia M, Garg S, Shorey R (2000) Efficient policies for increasing capacity in Bluetooth: an indoor pico-cellular wireless system. In: Vehicular technology conference proceedings, 2000. VTC 2000-Spring Tokyo. 2000 IEEE 51st, vol 2. IEEE, pp 907–911Google Scholar
  20. 20.
    Kajioka S, Mori T, Uchiya T, Takumi I, Matsuo H (2014) Experiment of indoor position presumption based on RSSI of Bluetooth LE beacon. In: 2014 IEEE 3rd Global conference on consumer electronics (GCCE). IEEE, pp 337–339Google Scholar
  21. 21.
    Kendall MG, Stuart A (1969) The advanced theory of statistics - v2 Inference and relationship. Griffin, LondonGoogle Scholar
  22. 22.
    Kevin A (2009) That ’Internet of Things’ thing, in the real world things matter more than ideas. RFID J 22Google Scholar
  23. 23.
    Laprie JC, Arlat J, Blanquart J, Costes A, Crouzet Y, Deswarte Y, Fabre J, Guillermain H, Kaâniche M, Kanoun K et al (1995) Guide de la sûreté de fonctionnement. Cépaduès, ToulouseGoogle Scholar
  24. 24.
    Macedo D, Guedes LA, Silva I (2014) A dependability evaluation for internet of things incorporating redundancy aspects. In: 2014 IEEE 11th International conference on networking, sensing and control (ICNSC). IEEE, pp 417–422Google Scholar
  25. 25.
    Markowski CA, Markowski EP (1990) Conditions for the effectiveness of a preliminary test of variance. Am Stat 44(4):322–326Google Scholar
  26. 26.
    Merrill RM (2012) Fundamentals of epidemiology and biostatistics. Jones & Bartlett PublishersGoogle Scholar
  27. 27.
    Mtita C (2016) Lightweight serverless protocols for the internet of things. Institut National des Télécommunications, Ph.D. thesisGoogle Scholar
  28. 28.
    NXP Freescale home health hub reference platform. Visited on 2017-10-20
  29. 29.
    Paulson DS (2003) Applied statistical designs for the researcher. CRC PressGoogle Scholar
  30. 30.
    RELYonIT Research by experimentation for dependability on the internet of things. Visited on 2017-10-20
  31. 31.
    Rosner B (1983) Percentage points for a generalized ESD many-outlier procedure. Technometrics 25(2):165–172CrossRefGoogle Scholar
  32. 32.
    Sample A, Zhao Y NFC-WISP website. Visited on 2017-10-20
  33. 33.
    Schaffers H, Komninos N, Pallot M, Trousse B, Nilsson M, Oliveira A (2011) Smart cities and the future internet: towards cooperation frameworks for open innovation. Fut Int 431–446Google Scholar
  34. 34.
    Semiconductor N nRF51822 Bluetooth Smart Beacon Kit. Visited on 2017-10-20
  35. 35.
    Semiconductor N Nordic Semiconductor website. Visited on 2017-10-20
  36. 36.
    Stuart A, Ord JK, Arnold S (1999) Kendall’s advanced theory of statistics. Vol 2A: classical inference and the linear model, vol 2. Edward Arnold, LondonGoogle Scholar
  37. 37.
    Thinfilm Thinfilm website. Visited on 2017-10-20
  38. 38.
    Thornton F, Sanghera P (2011) How to cheat at deploying and securing RFID SyngressGoogle Scholar
  39. 39.
    WISP Home Visited on 2017-10-20
  40. 40.
    Zhao Y, Smith JR, Sample A (2015) Nfc-wisp: a sensing and computationally enhanced near-field rfid platform. In: 2015 IEEE International conference on RFID (RFID), pp 174–181.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.LITIO LaboratoryUniversity of Oran1, Ahmed Ben BellaOranAlgeria
  2. 2.XLIM (UMR CNRS 7252 / Université de Limoges)Limoges CedexFrance

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