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Mathematical Methods for Data Fusion in IoT: A Survey

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Advanced Intelligent Systems for Sustainable Development (AI2SD’2020) (AI2SD 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1418))

Abstract

IoT (Internet of Things) is a paradigm that connects multiple and diverse smart objects via internet, it is currently widely used in a number of real-life fields. Smart interconnected devices generate tremendous amounts of raw data using their sensors. Such data need to be processed, analyzed, and mined efficiently to obtain improved information that help to take corrective actions and provide advanced services. This paper provides a critical review of existing methods on data fusion in IoT context with a particular focus on the most applicable and mature mathematical methods. Inherent characteristics of data fusion theories are restated, then a comparative study is conducted by pointing out the advantages and limitations of each method taking into account data imperfections (uncertainty, inaccuracy, etc.) and IoT environments constraints (real-time, etc.). This work constitutes a big step towards building a guide for researchers working in the field of data fusion in IoT.

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References

  1. Evans, D.: The Internet of Things - how the next evolution of the internet is changing everything. Cisco white Pap. 1, 1–11 (2011)

    Google Scholar 

  2. Tanwar, S., Tyagi, S., Kumar, S.: The role of internet of things and smart grid for the development of a smart city. In: Hu, Y.-C., Tiwari, S., Mishra, K.K., Trivedi, M.C. (eds.) Intelligent Communication and Computational Technologies. LNNS, vol. 19, pp. 23–33. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-5523-2_3

    Chapter  Google Scholar 

  3. Qin, X., Gu, Y.: Data fusion in the Internet of Things. Procedia Eng. 15, 3023–3026 (2011). https://doi.org/10.1016/j.proeng.2011.08.567

    Article  Google Scholar 

  4. El Faouzi, N., Leung, H., Kurian, A.: Data fusion in intelligent transportation systems : progress and challenges – a survey. Inf. Fusion 12, 4 (2011). https://doi.org/10.1016/j.inffus.2010.06.001

    Article  Google Scholar 

  5. Khaleghi, B., Khamis, A., Karray, F.O., Razavi, S.N.: Multisensor data fusion: a review of the state-of-the-art. Inf. Fusion 14, 28–44 (2013). https://doi.org/10.1016/j.inffus.2011.08.001

    Article  Google Scholar 

  6. Castanedo, F.: A review of data fusion techniques. Sci. World J. 2013, 1–19 (2013). https://doi.org/10.1155/2013/704504

    Article  Google Scholar 

  7. Gite, S., Agrawal, H.: On Context-Awareness for Multisensor Data Fusion in IoT. Adv. Intell. Syst. Comput. \textbf{380}, 73–82 (2016). https://doi.org/10.1007/978-81-322-2526-3

  8. Alam, F., Mehmood, R., Katib, I., Albogami, N.N., Albeshri, A.: Data fusion and IoT for smart ubiquitous environments: a survey. IEEE Access 5, 9533–9554 (2017). https://doi.org/10.1109/ACCESS.2017.2697839

    Article  Google Scholar 

  9. Pires, I.M., Garcia, N., Pombo, N.: From Data Acquisition to Data Fusion : A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using. Sensors 16, 184 (2016). https://doi.org/10.3390/s16020184

    Article  Google Scholar 

  10. Wang, M., et al.: City data fusion: sensor data fusion in the Internet of Things. Int. J. Distrib. Syst. Technol. 7, 15–36 (2016). https://doi.org/10.4018/IJDST.2016010102

    Article  Google Scholar 

  11. Alofi, A., Alghamdi, A., Alahmadi, R., Aljuaid, N., Hemalatha, M.: A review of data fusion techniques. Int. J. Comput. Appl. 167, 37–41 (2017)

    Google Scholar 

  12. Rashinkar, P., Krushnasamy, V.S.: An overview of data fusion techniques. In: IEEE International Conference on Innovative Mechanisms for Industry Applications, pp. 694–697. Bangalore, India (2017). https://doi.org/10.1109/ICIMIA.2017.7975553

  13. Ding, W., Jing, X., Yan, Z., Yang, L.T.: A survey on data fusion in internet of things: towards secure and privacy-preserving fusion. Inf. Fusion 51, 129–144 (2019). https://doi.org/10.1016/j.inffus.2018.12.001

    Article  Google Scholar 

  14. Lau, B.P.L., et al.: A survey of data fusion in smart city applications. Inf. Fusion 52, 357–374 (2019). https://doi.org/10.1016/j.inffus.2019.05.004

    Article  Google Scholar 

  15. Ullah, I., Yong, H.: Intelligent data fusion for smart IoT environment: a survey. Wireless Pers. Commun. 114(1), 409–430 (2020). https://doi.org/10.1007/s11277-020-07369-0

    Article  Google Scholar 

  16. Meng, T., Jing, X., Yan, Z., Pedrycz, W.: A survey on machine learning for data fusion. Inf. Fusion 57, 115–129 (2020). https://doi.org/10.1016/j.inffus.2019.12.001

    Article  Google Scholar 

  17. Llinas, J. Hall, D.L.: Introduction to multi-sensor data fusion. In: Proceedings of - IEEE International Symposium Circuits System 6, 537–540 (1998). https://doi.org/10.1109/iscas.1998.705329

  18. Goodman, I.R., Nguyen, H.T.: Uncertainty Models for Knowledge-Based Systems. North-Holland, Amsterdam (1985)

    MATH  Google Scholar 

  19. Babu, M.S.I., Ping, P.: Fusion based on wavelet transform. Int. J. Eng. Dev. Res. 4, 764–768 (2016). https://doi.org/10.1109/SNPD.2007.307

    Article  Google Scholar 

  20. Nagla, K., Uddin, M., Singh, D.: Multisensor data fusion and integration for mobile robots: a review. IAES Int. J. Robot. Autom. 3, 131–138 (2014). https://doi.org/10.11591/ijra.v3i2.4075

    Article  Google Scholar 

  21. Vechet, S., Krejsa, J.: Sensors data fusion via Bayesian Network. In: Recent Advances in Mechatronics, pp. 221–226 (2010)

    Google Scholar 

  22. Cabria, I., Gondra, I.: MRI segmentation fusion for brain tumor detection. Inf. Fusion 36, 1–9 (2017). https://doi.org/10.1016/j.inffus.2016.10.003

    Article  Google Scholar 

  23. Taleb-ahmed, A., Gautier, L., Rombaut, M.: Structure of data fusion based on the theory of evidence for the reconstruction of vertebra. Trait. du Signal. 19, 267–283 (2002)

    MATH  Google Scholar 

  24. Sukkarieh, S., Nettleton, E., Kim, J.H., Ridley, M., Goktogan, A., Durrant-Whyte, H.: The ANSER project: data fusion across multiple uninhabited air vehicles. Int. J. Rob. Res. 22, 505–539 (2003). https://doi.org/10.1177/02783649030227005

    Article  Google Scholar 

  25. Schettini, F., Di Rito, G., Galatolo, R., Denti, E.: Sensor fusion approach for aircraft state estimation using inertial and air-data systems. In: 3rd IEEE International Workshop on Metrology for Aerospace. pp. 624–629, U.S.A (2016). https://doi.org/10.1109/MetroAeroSpace.2016.7573289

  26. Guo, K., Tang, Y., Zhang, P.: CSF: Crowdsourcing semantic fusion for heterogeneous media big data in the internet of things. Inf. Fusion. 37, 77–85 (2017). https://doi.org/10.1016/j.inffus.2017.01.008

    Article  Google Scholar 

  27. Shen, B., Rho, S., Zhou, X., Wang, R.: A delay-aware schedule method for distributed information fusion with elastic and inelastic traffic. Inf. Fusion 36, 68–79 (2017). https://doi.org/10.1016/j.inffus.2016.11.008

    Article  Google Scholar 

  28. Huang, M., Liu, Z., Tao, Y.: Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion. Simul. Model. Pract. Theor. 102, 101981. https://doi.org/10.1016/j.simpat.2019.101981

  29. Coyle, L., Neely, S., Stevenson, G., Sullivan, M., Dobson, S., Nixon, P.: Sensor fusion-based middleware for smart homes. Int. J. Robot. Mechatron. 8, 53–60 (2007). http://ecite.utas.edu.au/69488

  30. Zimmermann, L., Weigel, R., Fischer, G.: Fusion of nonintrusive environmental sensors for occupancy detection in smart homes. IEEE Internet Things J. 5, 2343–2352 (2018). https://doi.org/10.1109/jiot.2017.2752134

    Article  Google Scholar 

  31. Zhang, L., Leung, H., Chan, K.: Information fusion based smart home control system and its application. IEEE Trans. Consum. Electron. 54, 1157–1165 (2008). https://doi.org/10.1109/tce.2008.4637601

    Article  Google Scholar 

  32. Dautov, R., Distefano, S., Buyya, R.: Hierarchical data fusion for smart healthcare. J. Big Data 6(1), 1–23 (2019). https://doi.org/10.1186/s40537-019-0183-6

    Article  Google Scholar 

  33. Pansiot, J., Stoyanov, D., McIlwraith, D., Lo, B.P., Yang, G.Z.: Ambient and wearable sensor fusion for activity recognition in healthcare monitoring systems. In: Leonhardt, S., Falck, T., Mähönen, P. (eds.) 4th International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2007). IFMBE Proceedings, vol. 13. Springer, Berlin, Heidelberg (2007). https://doi.org/10.1007/978-3-540-70994-7_36

  34. Torres, A.B.B., Rocha, A.R., Coelho Da Silva, T.L., De Souza, J.N., Gondim, R.S.: Multilevel data fusion for the internet of things in smart agriculture. Comput. Electron. Agric. 171, 105309 (2020). https://doi.org/10.1016/j.compag.2020.105309

  35. Maini, E., Ajith Kumar, S.: Role of data fusion in intelligent transportation system: a survey. Int. J. Comput. Sci. Trends Technol. 5, 49–53 (2013)

    Article  Google Scholar 

  36. Mitchell, H.B.: Data fusion: Concepts and ideas. (2012)

    Google Scholar 

  37. Boström, H., et al.: On the definition of information fusion as a field of research. Institutionen för Kommunikation och Information (2007)

    Google Scholar 

  38. Jitendra, R.R.: Multi-Sensor Data Fusion With Matlab. CRC Press, New York (2010)

    Google Scholar 

  39. White, F.E.: Data Fusion Lexicon. The Data Fusion Subpanel of the Joint Directors of Laboratories, Technical Panel for C3, Naval Ocean Systems Center, San Diego, Calif, USA (1991)

    Google Scholar 

  40. Farah, I.R., Boulila, W., Ettabaâ, K.S., Solaiman, B., Ben Ahmed, M.: Interpretation of multisensor remote sensing images: Multiapproach fusion of uncertain information. IEEE Trans. Geosci. Remote Sens. 46, 4142–4152 (2008). https://doi.org/10.1109/TGRS.2008.2000817

    Article  Google Scholar 

  41. Mongolini, M.: Apport de la fusion d’images satellitaires multicapteurs au niveau pixel en télédétection et photo-interprétation. Ph.D. Thesis. Université de nice, Sophia Antipolis (1994)

    Google Scholar 

  42. Hall, D.L., McMullen, S.A.H.: Mathematical techniques in multisensor data fusion. Library (Lond). (2004). https://doi.org/10.1186/1475-925X-4-23

    Article  MATH  Google Scholar 

  43. Dromigny-Badin, A., Zhu, Y.M., Gimenez, G., Goutte, R. Image segmentation through using the evidence theory based data fusion technique. In: 2nd International Conference on Signal Processing Proceedings, pp. 994–997. Beijing, China (1998). https://doi.org/10.1109/icosp.1998.770781

  44. Chen, X., Li, X.: Virtual temperature measurement for smart buildings via Bayesian model fusion. In: Proceedings - IEEE International Symposium Circuits System, pp. 950–953 (2016). https://doi.org/10.1109/ISCAS.2016.7527399

  45. Abdulhafiz, W.A., Khamis, A.: Bayesian approach to multisensor data fusion with Pre- and Post-Filtering. In: 10th IEEE International Conference on Networking, Sensing and Control. pp. 373–378, Evry, France (2013). https://doi.org/10.1109/ICNSC.2013.6548766

  46. Cheng, N., Wu, Q.: A decision-making method for fire detection data fusion based on Bayesian approach. In: 4th International Conference Digital Manufacturing Automation, pp. 21–23. Shandong, China (2013). https://doi.org/10.1109/ICDMA.2013.6

  47. Dempster, A.P.: A generalization of Bayesian inference. J. R. Stat. Soc. 30(2), 205-232 (1968)

    Google Scholar 

  48. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, NJ (1976)

    Book  Google Scholar 

  49. Osswald, C., Martin, A. Understanding the large family of Dempster-Shafer theory’s fusion operators - a decision-based measure. In: 9th International Conference on Information Fusion, Florence, Italy (2006). https://doi.org/10.1109/ICIF.2006.301631

  50. Lemeret, Y., Lefevre, E.: Evidence theory for data fusion in transportation system. In: IFAC Proc. Vol. (IFAC-PapersOnline). 37, 81–86 (2004). https://doi.org/10.1016/s1474-6670(17)30663-8

  51. Smets, P.: Constructing the pignistic probability function in a context of uncertainty. Mach. Intell. Pattern Recogn. 10, 29–39 (1990)

    MATH  Google Scholar 

  52. Ding, Q., Peng, Z., Liu, T., Tong, Q.: Multi-sensor building fire alarm system with information fusion technology based on D-S evidence theory. Algorithms. 7, 523–537 (2014). https://doi.org/10.3390/a7040523

    Article  Google Scholar 

  53. Li, J., Luo, S., Jin, J.S.: Sensor data fusion for accurate cloud presence prediction using Dempster-Shafer evidence theory. Sensors (Switzerland). 10, 9384–9396 (2010). https://doi.org/10.3390/s101009384

    Article  Google Scholar 

  54. Basir, O., Yuan, X.: Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory. Inf. Fusion. 8, 379–386 (2007). https://doi.org/10.1016/j.inffus.2005.07.003

    Article  Google Scholar 

  55. Nesa, N., Banerjee, I.: IoT-based sensor data fusion for occupancy sensing using dempster-shafer evidence theory for smart buildings. IEEE Internet Things J. 4, 1563–1570 (2017). https://doi.org/10.1109/JIOT.2017.2723424

    Article  Google Scholar 

  56. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Google Scholar 

  57. Zhu, J., Cao, H., Shen, J., Liu, H.: Data fusion for magnetic sensor based on fuzzy logic theory. In: 4th International Conference Intelligence Computation Technology Automation, pp. 87–92. U.S.A (2011). https://doi.org/10.1109/ICICTA.2011.29

  58. Manjunatha, P., Verma, A.K., Srividya, A.: Multi-sensor data fusion in cluster based wireless sensor networks using fuzzy logic method. In: 3rd International Conference Industrial Information System, pp. 6–11. Kharagpur, India (2008). https://doi.org/10.1109/ICIINFS.2008.4798453

  59. Aziz, A.M.: Effects of fuzzy membership function shapes on clustering performance in multisensor-multitarget data fusion systems. In: IEEE International Conference Fuzzy System, pp. 1839–1844. U.S.A (2009). https://doi.org/10.1109/FUZZY.2009.5277313

  60. Leekwijck, W.V., Kerre, E.E.: Defuzzification: criteria and classification. Fuzzy Sets Syst. 108(2), 159–178 (1999). https://doi.org/10.1016/s0165-0114(97)00337-0

    Article  MathSciNet  MATH  Google Scholar 

  61. Medjahed, H., Istrate, D., Boudy, J., Baldinger, J.L., Dorizzi, B.: A pervasive multi-sensor data fusion for smart home healthcare monitoring. In: IEEE International Conference Fuzzy System, pp. 1466–1473. U.S.A (2011). https://doi.org/10.1109/FUZZY.2011.6007636

  62. Cook, B., Cohen, K.: Multi-source sensor fusion for small unmanned aircraft systems using fuzzy logic. In: IEEE International Conference Fuzzy System, Italy (2017). https://doi.org/10.1109/fuzz-ieee.2017.8015593

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Authors and Affiliations

  1. Aeronautical Sciences Laboratory, Aeronautics and Spatial Studies Institute, Blida1 University, Blida, Algeria

    Nour El Imane Hamda & Mohand Lagha

  2. LIAS Laboratory, ISAE-ENSMA, Poitiers, France

    Allel Hadjali

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  1. Nour El Imane Hamda
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  2. Mohand Lagha
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  3. Allel Hadjali
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Correspondence to Nour El Imane Hamda .

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Editors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Warszawa, Poland

    Janusz Kacprzyk

  2. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina E. Balas

  3. Sciences and Techniques of Tangier, Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

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Hamda, N.E.I., Lagha, M., Hadjali, A. (2022). Mathematical Methods for Data Fusion in IoT: A Survey. In: Kacprzyk, J., Balas, V.E., Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2020). AI2SD 2020. Advances in Intelligent Systems and Computing, vol 1418. Springer, Cham. https://doi.org/10.1007/978-3-030-90639-9_88

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