The improvement of uncertainty measurements accuracy in sensor networks based on fuzzy dempster-shafer theory

(1) * Ehsan Azimirad Mail (University of Torbat Heydarieh, Torbat Heydarieh, Iran, Islamic Republic of)
(2) Seyyed Reza Movahhed Ghodsinya Mail (University of Torbat Heydarieh, Torbat Heydarieh, Iran, Islamic Republic of)
*corresponding author


Threat Assessment is one of the most important components in combat management systems. However, uncertainty is one of the problems that occur in the input data of these systems that have been provided using several sensors in sensor networks. In literature, there are some theories that state and model uncertainty in the information. One of the new methods is the Fuzzy Dempster-Shafer Theory. In this paper, a model-based uncertainty is presented in the air defense system based on the Fuzzy Dempster-Shafer Theory to measure uncertainty and its accuracy. This model uses the two concepts naming of the Fuzzy Sets Theory, and the Dempster-Shafer Theory. The input parameters to sensors are fuzzy membership functions, and the basic probability assignment values are earned from the Dempster-Shafer Theory. Therefore, in this paper, the combination of two methods has been used to calculate uncertainty in the air defense system. By using these methods and the output of the Dempster-Shafer theory are calculated and presented the uncertainty diagrams. The advantage of the combination of two theories is the better modeling of uncertainties. This makes that the output of the air defense system is more reliable and accurate. In this method, the air defense system’s total uncertainty is measured using the best uncertainty measure based on the Fuzzy Dempster-Shafer Theory. The simulation results show that this new method has increased the accuracy to 97% that is more computational toward other theories. This matter significantly increases the computational accuracy of the air defense system in targets threat assessment.


Sensor Networks; Fuzzy Dempster-Shafer Theory; Model-Based Uncertainty; Fuzzy Sets Theory; Combat Management System



Article metrics

Abstract views : 694 | PDF views : 155




Full Text



[1] Aselsan, “C4I System Solutions,” 2016, available at:

[2] M. Azak and A. E. Bayrak, “A new approach for Threat Evaluation and Weapon Assignment problem, hybrid learning with multi-agent coordination,” in 2008 23rd International Symposium on Computer and Information Sciences, 2008, pp. 1–6, doi: 10.1109/ISCIS.2008.4717866.

[3] Y. Zhang, S. Huang, S. Guo, and J. Zhu, “Multi-sensor Data Fusion for Cyber Security Situation Awareness,” Procedia Environ. Sci., vol. 10, pp. 1029–1034, 2011, doi: 10.1016/j.proenv.2011.09.165.

[4] B. Li, “Navigation risk assessment scheme based on fuzzy Dempster–Shafer evidence theory,” Int. J. Adv. Robot. Syst., vol. 15, no. 5, p. 172988141879957, Sep. 2018, doi: 10.1177/1729881418799572.

[5] N. Sadeghi, A. R. Fayek, and W. Pedrycz, “Fuzzy Monte Carlo Simulation and Risk Assessment in Construction,” Comput. Civ. Infrastruct. Eng., vol. 25, no. 4, pp. 238–252, May 2010, doi: 10.1111/j.1467-8667.2009.00632.x.

[6] M. Raikhan, K. Bolat, Z. Meiram, and O. Altynay, “Assessing information security risk with the fuzzy set theory,” J. Theor. Appl. Inf. Technol., 2018, available at: Google Scholar.

[7] Y. Pan, L. Zhang, Z. Li, and L. Ding, “Improved Fuzzy Bayesian Network-Based Risk Analysis With Interval-Valued Fuzzy Sets and D-S Evidence Theory,” IEEE Trans. Fuzzy Syst., pp. 1–1, 2019, doi: 10.1109/TFUZZ.2019.2929024.

[8] H. M. Feng, X. F. Li, and J. F. Chen, “A comparative study of four Fuzzy Integrals for classifier fusion,” in 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010, 2010, doi: 10.1109/ICMLC.2010.5581040.

[9] K. Zhang, W. Kong, P. Liu, J. Shi, Y. Lei, and J. Zou, “Assessment and sequencing of air target threat based on intuitionistic fuzzy entropy and dynamic VIKOR,” J. Syst. Eng. Electron., vol. 29, no. 2, pp. 305–310, Apr. 2018, doi: 10.21629/JSEE.2018.02.11.

[10] M. R. Delavar and M. Sadrykia, “Assessment of Enhanced Dempster-Shafer Theory for Uncertainty Modeling in a GIS-Based Seismic Vulnerability Assessment Model, Case Study—Tabriz City,” ISPRS Int. J. Geo-Information, vol. 9, no. 4, p. 195, Mar. 2020, doi: 10.3390/ijgi9040195.

[11] M. Tavana, D. A. Trevisani, and D. T. Kennedy, “A Fuzzy cyber-risk analysis model for assessing attacks on the availability and integrity of the military command and control systems,” 2015, doi: 10.4018/978-1-4666-7456-1.ch053.

[12] M. Yazdi and S. Kabir, “Fuzzy evidence theory and Bayesian networks for process systems risk analysis,” Hum. Ecol. Risk Assess. An Int. J., vol. 26, no. 1, pp. 57–86, Jan. 2020, doi: 10.1080/10807039.2018.1493679.

[13] T. Lampinen, J. Ropponen, and T. Laitinen, “Joint threat assessment with asset profiling and entity bayes net,” in 2009 12th International Conference on Information Fusion, FUSION 2009, 2009, available at:

[14] Y. Liang, “A Fuzzy Knowledge Based System in Situation and Threat Assessment.,” J. Syst. Sci. Inf., vol. 4, no. 4, 2006, available at : Google Scholar.

[15] M. J. Liebhaber and B. Feher, “Air Threat Assessment: Research, Model, and Display Guidelines,” Jan. 2002, doi: 10.21236/ADA458047.

[16] H. Liu, Z. Ma, X. Deng, and W. Jiang, “A new method to air target threat evaluation based on Dempster-Shafer evidence theory,” in 2018 Chinese Control And Decision Conference (CCDC), 2018, pp. 2504–2508, doi: 10.1109/CCDC.2018.8407546.

[17] Z. Shu, “Target Ship Identification Algorithm Based on Comprehensive Correlation Discriminant and Information Entropy,” J. Comput. Commun., vol. 08, no. 03, pp. 61–71, 2020, doi: 10.4236/jcc.2020.83007.

[18] J. Roux and J. Van Vuuren, “Threat evaluation and weapon assignment decision support: A review of the state of the art,” ORiON, vol. 23, no. 2, Dec. 2007, doi: 10.5784/23-2-54.

[19] S. Kumar and A. M. Dixit, “Threat evaluation modelling for dynamic targets using fuzzy logic approach,” in International Conference on Computer Science and Engineering, 2012, pp. 143–149, available at : Google Scholar.

[20] A. Burkov, S. Paquet, G. Michaud, and P. Valin, “An empirical study of uncertainty measures in the fuzzy evidence theory,” in 14th International Conference on Information Fusion, 2011, pp. 1–8, available at :

[21] A. Sarabi-Jamab and B. N. Araabi, “An information-based approach to handle various types of uncertainty in fuzzy bodies of evidence,” PLoS One, 2020, doi: 10.1371/journal.pone.0227495.

[22] Hatefi, Basiri, and Tamošaitienė, “An Evidential Model for Environmental Risk Assessment in Projects Using Dempster–Shafer Theory of Evidence,” Sustainability, vol. 11, no. 22, p. 6329, Nov. 2019, doi: 10.3390/su11226329.

[23] J. Huang, B. C. Li, and Y. J. Zhao, “Target Threat Assessment Based on Intuitionistic Fuzzy Sets Choquet Integral,” Appl. Mech. Mater., vol. 433–435, pp. 736–743, Oct. 2013, doi: 10.4028/

[24] T. Ali and P. Dutta, “Methods to obtain basic Probability Assignment in Evidence Theory,” Int. J. Comput. Appl., vol. 38, no. 4, pp. 46–51, Jan. 2012, doi: 10.5120/4600-6802.

[25] H. Zhang, J. Xie, Y. Song, J. Ge, and Z. Zhang, “A novel ranking method for intuitionistic fuzzy set based on information fusion and application to threat assessment,” Iran. J. Fuzzy Syst., vol. 17, no. 1, pp. 91–104, 2020, available at : Google Scholar.

[26] M. Gul and A. F. Guneri, “A fuzzy multi criteria risk assessment based on decision matrix technique: A case study for aluminum industry,” J. Loss Prev. Process Ind., vol. 40, pp. 89–100, Mar. 2016, doi: 10.1016/j.jlp.2015.11.023.

[27] P. Dutta, “Fuzzy-DSS Human Health Risk Assessment Under Uncertain Environment,” 2018, pp. 316–347, doi: 10.4018/978-1-5225-5396-0.ch015.

[28] A. Maseleno, M. M. Hasan, N. Tuah, and C. R. Tabbu, “Fuzzy Logic and Mathematical Theory of Evidence to Detect the Risk of Disease Spreading of Highly Pathogenic Avian Influenza H5N1,” in Procedia Computer Science, 2015, doi: 10.1016/j.procs.2015.07.349.

[29] A. Lepskiy and A. Suevalov, “Application of the Belief Function Theory to the Development of Trading Strategies,” Procedia Comput. Sci., vol. 162, pp. 235–242, 2019, doi: 10.1016/j.procs.2019.11.280.

[30] W. Mei, “Air defense threat evaluation using fuzzy Bayesian classifier,” in IJCCI 2013 - Proceedings of the 5th International Joint Conference on Computational Intelligence, 2013, doi: 10.5220/0004512602270232.

[31] B. M. Ayyub and G. J. Klir, Uncertainty Modeling and Analysis in Engineering and the Sciences, 2006, doi: 10.1201/9781420011456.

[32] Y. Yang, D. Han, and J. Dezert, “A new non-specificity measure in evidence theory based on belief intervals,” Chinese J. Aeronaut., vol. 29, no. 3, pp. 704–713, Jun. 2016, doi: 10.1016/j.cja.2016.03.004.

[33] E. Azimirad and J. Haddadnia, “A New Data Fusion Instrument for Threat Evaluation Using of Fuzzy Sets Theory,” Int. J. Comput. Sci. Inf. Secur., vol. 13, no. 4, p. 19, 2015, available at : Google Scholar.

[34] P. Dutta and T. Ali, “Fuzzy focal elements in dempster-shafer theory of evidence: case study in risk analysis,” Int. J. Comput. Appl., vol. 34, no. 1, 2011, available at : Google Scholar.

[35] E. Azimirad and J. Haddadnia, “A new model for threat assessment in data fusion based on fuzzy evidence theory,” Int. J. Adv. Intell. Informatics, vol. 2, no. 2, p. 54, Jul. 2016, doi: 10.26555/ijain.v2i2.56.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

International Journal of Advances in Intelligent Informatics
ISSN 2442-6571  (print) | 2548-3161 (online)
Organized by Informatics Department - Universitas Ahmad Dahlan, and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
E: (paper handling issues), (publication issues)

View IJAIN Stats

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0