Abstract
The technological advances in low-cost sensor devices and communication technologies bring rapid increase in development of smart homes and smart environments. The developments in wireless sensor networks (WSN), body area networks (BAN), cloud computing and big data technologies trigger the use of Internet of Things (IoT) in healthcare industry. This poses many challenges such as heterogeneous data fusion, context-awareness, complex query processing, reliability and accuracy etc. Data fusion techniques are used to extract meaningful information from heterogeneous IoT data. It combines individual data from sensor sources to collectively obtain a result, which is more reliable, accurate and complete. Apart from wearable sensors, additional context sensors need to be added to build a context. Health IoT applications has potential benefits of using context-aware data fusion. By using context information, the behavior of the application can be customized according to the specific situation. This paper provides a brief concept of context-aware data fusion and includes data management approach for context-aware systems for healthcare applications. Finally, a context-aware data fusion approach for health IoT is proposed. It includes context acquisition, situation building and reasoning and inference.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
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
El Faouzi NE, Klein LA (2016) Data fusion for ITS: techniques and research needs. Transp Res Proc 15:495–512
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, Austria
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, Heidelberg, 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. Pervas Mobile 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
Acknowledgements
This work has been performed under IICT, Mehran University of Engineering and Technology Jamshoro funded by ICT Endowment Fund for sustainable development.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Baloch, Z., Shaikh, F.K. & Unar, M.A. A context-aware data fusion approach for health-IoT. Int. j. inf. tecnol. 10, 241–245 (2018). https://doi.org/10.1007/s41870-018-0116-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41870-018-0116-1