i-manager Publications

Significance of Data Fusion Algorithms in IoT Environment-A Review

S. Shenbagavadivu*, M. Senthil Kumar **, B. Chidhambara Rajan ***

* Department of Information Technology, SRM Valliammai Engineering College, Chennai, Tamil Nadu, India.

** Department of Computer Science and Engineering, SRM Valliammai Engineering College, Chennai, Tamil Nadu, India.

*** Department of Electronics and Communication Engineering, SRM Valliammal Engineering College, Chennai, Tamil Nadu, India.

Periodicity:June - August'2019
DOI : https://doi.org/10.26634/jit.8.3.16734

Abstract

Data fusion is a multidisciplinary research area with a boundless range of applications in numerous domains including defense, robotics, automation, Intelligent Transportation Systems (ITS), IoT and machine learning. Detection and tracing of a moving object is a vital task in mobile robotics as well as in the field of ITS. Due to its critical role, data fusion has been extensively studied in the recent decades. Computational Intelligence would become the challenging factor to integrate real time data generated from various sensory nodes to implement Internet of Things (IoT) products such as Smart Agriculture, Smart City, Smart Wheel Chair, Smart Healthcare System, etc. The methods discussed under Data Fusion methods are Kalman Filter and Distributed Kalman Filter. Deep learning and neural network-based fusion at symbol level is analyzed in this paper. This paper also aims to provide high level future research directions of data fusion techniques for Internet of Things environment.

Keywords

Data fusion, Intelligent Transportation Systems (ITS), Internet of Things (IoT), Kalman Filter, Distributed Kalman Filter, Deep learning.

How to Cite this Article?

Shenbagavadivu, S., Kumar, M.S., Rajan, B.C.(2019). Significance of Data Fusion Algorithms in IOT Environment-A Review, i-manager's Journal on Information Technology, 8(3), 42-48. https://doi.org/10.26634/jit.8.3.16734

References

[1]. Abdelgawad, A. (2014, April). Distributed data fusion algorithm for Wireless Sensor Network. In Proceedings of the 11^thIEEE International Conference on Networking, Sensing and Control (pp. 334-337). IEEE. https://doi.org/ 10.1109/ICNSC.2014.6819648

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

[3]. Bernal, E. A., Yang, X., Li, Q., Kumar, J., Madhvanath, S., Ramesh, P., & Bala, R. (2017). Deep temporal multimodal fusion for medical procedure monitoring using wearable sensors. IEEE Transactions on Multimedia, 20(1), 107-118. https://doi.org/10.1109/TMM.2017. 2726187

[4]. Cho, H., Seo, Y. W., Kumar, B. V., & Rajkumar, R. R. (2014, May). A multi-sensor fusion system for moving object detection and tracking in urban driving environments. In 2014 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1836-1843). IEEE. https://doi.org/10.1109/ICRA.2014.6907100

[5]. Din, S., Ahmad, A., Paul, A., Rathore, M. M. U., & Jeon, G. (2017). A cluster-based data fusion technique to analyze big data in wireless multi-sensor system. IEEE Access, 5, 5069-5083. https://doi.org/10.1109/ACCESS. 2017.2679207

[6]. El-Faouzi, N. E., & Klein, L. A. (2016). Data fusion for ITS: Techniques and research needs. Transportation Research Procedia, 15, 495-512. https://doi.org/10. 1016/j.trpro.2016.06.042

[7]. Gartner. (2015). Smarter with Gartner. Retrieved from https://www.gartner.com/smarterwithgartner/top-tentechnology- trends-signal-the-digital-mesh

[8]. Goel, S., & Yuan, Y. (2015). Emerging research in connected vehicles. IEEE Intelligent Transportation Systems Magazine, 7(2), 6-9. https://doi.org/10.1109/ MITS.2015.2408136

[9]. Gordon, J., & Shortliffe, E. H. (1990, June). The Dempster-Shafer theory of evidence. In Readings in Uncertain Reasoning (pp. 529-539). Morgan Kaufmann Publishers Inc.

[10]. Liu, Z., Zhang, W., Quek, T. Q. S., & Lin, S. (2017). Data fusion of hetrogeneous sensor data. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5965-5969). IEEE. https://doi.org/10.1109/ ICASSP.2017.7953301

[11]. Rajeswari, K., Ishwarya, A., Vaishnavi, K. K., & Thiruvengadam, S. J. (2017, March). Performance analysis of data fusion methods for radar and IRST 3D target tracking. In 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) (pp. 2570-2574). IEEE. https:// doi.org/10.1109/WiSPNET.2017.8300227

[12]. Robinson, R. M., Lee, H., McCourt, M. J., Marathe, A. R., Kwon, H., Ton, C., & Nothwang, W. D. (2015, September). Human-autonomy sensor fusion for rapid object detection. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 305-312). IEEE. https://doi.org/10.1109/IROS.2015. 7353390

[13]. Shafer, G. (1976). A Mathematical Theory of Evidence (Vol. 42). Princeton University Press.

[14]. Vielzeuf, V., Lechervy, A., Pateux, S., & Jurie, F. (2018). Multilevel sensor fusion with deep learning. IEEE Sensors Letters, 3(1), 1-4. https://doi.org/10.1109/LSENS.2018. 2878908

[15]. Xie, F., Yang, H., Peng, Y., & Gao, H. (2012, November). Data fusion detection model based on SVM and evidence theory. In 2012 IEEE 14^th International Conference on Communication Technology (pp. 814- 818). IEEE. https://doi.org/10.1109/ICCT.2012.6511316

[16]. Xu, P., Davoine, F., Bordes, J. B., Zhao, H., & Denoeux, T. (2016). Multimodal information fusion for urban scene understanding. Machine Vision and Applications, 27(3), 331-349. https://doi.org/10.1007/s00138-014-0649-7

[17]. Zhang, L., Xie, Y., Xidao, L., Zhang, X.(2018). Multisource hetrogeneous data fusion, In 2018 International Conference on Artificial Intelligence and Big Data (ICABD) (pp.47-51) IEEE. https://doi.org/10.1109/ICAIBD. 2018.8396165

If you have access to this article please login to view the article or kindly login to purchase the article

Single Article

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Online	15	15

ADD TO CART

Options for accessing this content:

If you would like institutional access to this content, please recommend the title to your librarian.
If you already have i-manager's user account: Login above and proceed to purchase the article.
New Users: Please register, then proceed to purchase the article.