Abstract
With its strong coverage, small power consumption, low cost, and large connectivity, narrow-band Internet of Things technology has become the key technology in Internet of Things communication. However, in the face of a large number of terminals, the rational allocation of limited resources and the heterogeneous data fusion in the system become an important topic in the research of narrow-band Internet of Things. So a multi-source heterogeneous data fusion based on perceptual semantics in NB-IoT is proposed in this paper. Firstly, we introduce the advantages and key technologies of NB-IoT, which includes the key techniques of physical layer and media access control layer. Then, in order to eliminate data redundancy and extend the network lifetime, we analyze the centralized mode and distributed mode in NB-IoT network, and proposed a multi-source heterogeneous data fusion based on semantic perception to form a uniform format. Finally, an improved D-S evidence theory is adopted to proceed data fusion, obtaining the final fusion result. The experiment has shown that our proposed algorithm has faster convergence rate, higher stability, and its judgment to fusion results are more suitable to actual conditions.
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References
Giusto D, Iera A, Morabito G, Atzori L (eds) (2010) The Internet of Things: 20th Tyrrhenian workshop on digital communications. Springer Science & Business Media
Miorandi D, Sicari S, De Pellegrini F, Chlamtac I (2012) Internet of Things: vision, applications and research challenges. Ad Hoc Netw 10(7):1497–1516
Nolan KE, Guibene W, Kelly MY (2016) An evaluation of low power wide area network technologies for the Internet of Things. In: Wireless communications and mobile computing conference (IWCMC), 2016 international. IEEE, pp 439–444
Mikhaylov K, Petaejaejaervi J, Haenninen T (2016) Analysis of capacity and scalability of the LoRa low power wide area network technology. In: Proceedings of European wireless 2016; 22th European wireless conference. VDE, pp 1–6
Suo H, Wan J, Zou C, Liu J (2012) Security in the Internet of Things: a review. In: 2012 international conference on computer science and electronics engineering (ICCSEE), vol 3 IEEE, pp 648–651
Gou Q, Yan L, Liu Y, Li Y (2013) Construction and strategies in IoT security system. In: Green computing and communications (GreenCom), 2013 IEEE and Internet of Things (iThings/CPSCom), IEEE international conference on and IEEE cyber, physical and social computing IEEE, pp 1129–1132
Amendola S, Lodato R, Manzari S, Occhiuzzi C, Marrocco G (2014) RFID technology for IoT-based personal healthcare in smart spaces. IEEE Internet Things J 1(2):144–152
Lo B, Yang GZ (2005) Key technical challenges and current implementations of body sensor networks. In: Proceedings of the 2nd international workshop on body sensor networks (BSN 2005)
Gravina R, Alinia P, Ghasemzadeh H, Fortino G (2017) Multi-sensor fusion in body sensor networks: state-of-the-art and research challenges. Inf Fusion 35:68–80
White FE Jr (1987) Data fusion lexicon, joint directors of laboratories, technical panel for C3, data fusion sub-panel. Naval Ocean Systems Center, San Diego
Elmenreich W (2002) An introduction to sensor fusion Vienna University of Technology, AustriaMATH
Veloso M, Bento C, Pereira FC (2009) Transportation systems: multi-sensor data fusion on intelligent transport systems. University of Coimbra, Coimbra
Abowd GD, Dey AK, Brown PJ, Davies N, Smith M, Steggles P (1999) Towards a better understanding of context and context-awareness. In: International symposium on handheld and ubiquitous computing. Springer, Berlin, pp 304–307
De Paola A, Ferraro P, Gaglio S, Re GL, Das SK (2017) An adaptive bayesian system for context-aware data fusion in smart environments. IEEE Trans Mob Comput 16(6):1502–1515
Hong X, Nugent C, Mulvenna M, McClean S, Scotney B, Devlin S (2009) Evidential fusion of sensor data for activity recognition in smart homes. Pervasive Mob Comput 5(3):236–252
Chetty G, Yamin M (2017) A distributed smart fusion framework based on hard and soft sensors. Int J Inf Technol 9(1):19–31
Jain V (2017) Perspective analysis of telecommunication fraud detection using data stream analytics and neural network classification based data mining. Int J Inf Technol 9(3):303–310
Baloch Z, Shaikh FK, Unar MA (2016) Interfacing physical and cyber worlds: a big data perspective. In: Mahmood ZH (ed) Data science and big data computing. Springer, Switzerland, pp 117–138
Blasch EP, Plano S (2002) JDL level 5 fusion model: user refinement issues and applications in group tracking. In: Signal processing, sensor fusion, and target recognition XI, vol 4729. International Society for Optics and Photonics, pp 270–280
Faouzi NE, Klein LA (2016) Data fusion for ITS: techniques and research needs. Transp Res Proc 15:495–512
Funding
This work was financially supported by Key Natural Science Research Project of Anhui Education Department (KJ2017A894); Key Project of Supporting Excellent Young Talents in Colleges and Universities of Anhui Education Department (gxyqZD2016584).
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Liu, Y. Multi-source heterogeneous data fusion based on perceptual semantics in narrow-band Internet of Things. Pers Ubiquit Comput 23, 413–420 (2019). https://doi.org/10.1007/s00779-019-01202-7
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DOI: https://doi.org/10.1007/s00779-019-01202-7