Abstract
In Internet of Things (IoT) ubiquitous environments, a high volume of heterogeneous data is produced from different devices in a quick span of time. In all IoT applications, the quality of information plays an important role in decision making. Data fusion is one of the current research trends in this arena that is considered in this paper. We particularly consider typical IoT scenarios where the sources measurements highly conflict, which makes intuitive fusions prone to wrong and misleading results. This paper proposes a taxonomy of decision fusion methods that rely on the theory of belief. It proposes a data fusion method for the Internet of Things (DFIOT) based on Dempster–Shafer (D–S) theory and an adaptive weighted fusion algorithm. It considers the reliability of each device in the network and the conflicts between devices when fusing data. This is while considering the information lifetime, the distance separating sensors and entities, and reducing computation. The proposed method uses a combination of rules based on the Basic Probability Assignment (BPA) to represent uncertain information or to quantify the similarity between two bodies of evidence. To investigate the effectiveness of the proposed method in comparison with D–S, Murphy, Deng and Yuan, a comprehensive analysis is provided using both benchmark data simulation and real dataset from a smart building testbed. Results show that DFIOT outperforms all the above mentioned methods in terms of reliability, accuracy and conflict management. The accuracy of the system reached up to \(99.18\%\) on benchmark artificial datasets and \(98.87\%\) on real datasets with a conflict of \(0.58 \%\). We also examine the impact of this improvement from the application perspective (energy saving), and the results show a gain of up to \(90\%\) when using DFIOT.
Similar content being viewed by others
References
Atzori, L., Iera, A.M.G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)
Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013)
Mitchell, H.: Multi-sensor data fusion: an introduction. https://www.amazon.com/Multi-Sensor-Data-Fusion-H-B-Mitchell-ebook/dp/B000SHOM3G. Springer Verlag, (2007)
Niu, W., Lei, J., Tong, E., Li, G., Chang, L., Shi, Z., Ci, S.: A survey of fault management in wireless sensor networks. J. Netw. Syst. Manag. 22(1), 50–74 (2007)
Data fusion. https://algo-data.quora.com/Data-Fusion-an-overview-of-some-relevant-works
Orchestrator, J. E. E. O. I. O. T.: https://docs.oracle.com/en/middleware/index.html Jd Edwards enterprise one internet of things orchestrator, (2015)
Abu-Elkheir, M., Hayajneh, M., Ali, N.A.: Data management for the internet of things: design primitives and solution. Sensors 13(11), 15582–15612 (2013)
Wang, M., Perera, C., Jayaraman, P.P., Zhang, M., Strazdins, P., Shyamsundar, R.K., Ranjan, R.: City data fusion: Sensor data fusion in the internet of things. Int. J. Distrib. Syst. Technol. 7(1), 15–36 (2016)
Shen, G., Liu, B.: Information resources management association. Breakthroughs in Research and Practice. In The Internet of Things. p. 530, (2017)
Santos, J., Wauters, T., Volckaert, B., De Turck, F.: Resource provisioning for iot application services in smart cities. In: 2017 13th International Conference on Network and Service Management (CNSM), pp. 1–9 (2017)
Guan, J.W., Bell, D.A.: Evidence theory and its applications. In: Studies in Computer Science and Artificial Intelligence 7, Elsevier, vol. 1 (1991)
Shafer, D.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
Dempster, A.: Upper and lower probabilities induced by a multivalued mapping. In Classic Works of the Dempster-Shafer Theory of Belief Functions. pp. 57–72 (2008)
Tazid, A., D, P., Boruah, H.: A new combination rule for conflict problem of dempster-shafer evidence theory. Int. J. Energy Inf. Commun. 3(1), 35 (2012)
Le Hegarat-Mascle, S., Bloch, I., Vidal-Madjar, D.: Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing. IEEE Trans. Geosci. Remote Sens. 35(4), 1018–1031 (1997)
Boston, J.: A signal detection system based on Dempster–Shafer theory and comparison to fuzzy detection. IEEE Trans. Syst. Man Cybern. Part C 30(1), 45–51 (2000)
Li, Y., C, J., Lin, Y.: An efficient combination method of conflict evidence. Int. J. Hybrid Inf. Technol. 8(12), 299–306 (2015)
Yager, R.: Decision making using minimization of regret. Int. J. Approx. Reason. 36(2), 109–128 (2004)
Yager, R., Filev, D.: Including probabilistic uncertainty in fuzzy logic controller modeling using Dempster-Shafer theory. IEEE Trans. Syst. Man Cybern. 25, 1221–1230 (1995)
Smets, P.: The combination of evidence in the transferable belief model. IEEE Trans. Pattern Anal. Mach. Intell. 12, 447–458 (1990)
Dubois, D., Prade, H.: Representation and combination of uncertainty with belief functions and possibility measures. Comput. Intell. 4, 3 (1988)
Murphy, C.: Combining belief functions when evidence conflicts. Decis. Support Syst. 29(1), 1–9 (2000)
Jousselme, A.L., Grenier, D., Bossé, É.: A new distance between two bodies of evidence. Inf. Fusion 2(2), 91–101 (2001)
Yong, D., WenKang, S., Z, Z., Qi, L.: Combining belief functions based on distance of evidence. Decis. Support Syst. 38(3), 489–493 (2004)
Zhang, Z., Liu, T., C, D., Zhang, W.: Novel algorithm for identifying and fusing conflicting data in wireless sensor networks. Sensors 14(6), 95629581 (2014)
Zhu, P., Xu, B., Xu, B.: An Improved Particle Swarm Optimization for Uncertain Information Fusion, pp. 494–501. Springer, Berlin (2011)
Gite, S., Agrawal, H.: On context awareness for multisensor data fusion in IoT. In Proceedings of the Second International Conference on Computer and Communication Technologies, Springer, pp. 85–93 (2016)
Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D.: Context aware computing for the Internet of things: a survey. IEEE Commun. Surv. Tutor. 16(1), 414–454 (2014)
Baloch, Z., Shaikh, F.K., Unar, M.A.: A context-aware data fusion approach for health-IoT. Int. J. Inf. Technol. 10(3), 241–245 (2018)
Deng, Y.: Deng entropy: a generalized Shannon entropy to measure uncertainty, (2015)
Shannon, C.E.: A mathematical theory of communication. SIGMOBILE Mob. Comput. Commun. Rev. 5(1), 3–55 (2001)
Lin, T.: Improving D–S evidence theory for data fusion system. (2015)
Yuan, K., Xiao, F., F, L., K, B., Yong, D.: Conflict management based on belief function entropy in sensor fusion. SpringerPlus 5(1), 638 (2016)
Judea, P.: Reasoning with belief functions: an analysis of compatibility. Int. J. Approx. Reason. 6(3), 425–443 (1992)
Moore, H.: MATLAB for Engineers. Prentice Hall Press, Upper Saddle River (2014)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Boulkaboul, S., Djenouri, D. DFIOT: Data Fusion for Internet of Things. J Netw Syst Manage 28, 1136–1160 (2020). https://doi.org/10.1007/s10922-020-09519-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10922-020-09519-y