{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T15:41:54Z","timestamp":1785858114356,"version":"3.56.0"},"reference-count":97,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T00:00:00Z","timestamp":1685232000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In IoT environments, voluminous amounts of data are produced every single second. Due to multiple factors, these data are prone to various imperfections, they could be uncertain, conflicting, or even incorrect leading to wrong decisions. Multisensor data fusion has proved to be powerful for managing data coming from heterogeneous sources and moving towards effective decision-making. Dempster\u2013Shafer (D\u2013S) theory is a robust and flexible mathematical tool for modeling and merging uncertain, imprecise, and incomplete data, and is widely used in multisensor data fusion applications such as decision-making, fault diagnosis, pattern recognition, etc. However, the combination of contradictory data has always been challenging in D\u2013S theory, unreasonable results may arise when dealing with highly conflicting sources. In this paper, an improved evidence combination approach is proposed to represent and manage both conflict and uncertainty in IoT environments in order to improve decision-making accuracy. It mainly relies on an improved evidence distance based on Hellinger distance and Deng entropy. To demonstrate the effectiveness of the proposed method, a benchmark example for target recognition and two real application cases in fault diagnosis and IoT decision-making have been provided. Fusion results were compared with several similar methods, and simulation analyses have shown the superiority of the proposed method in terms of conflict management, convergence speed, fusion results reliability, and decision accuracy. In fact, our approach achieved remarkable accuracy rates of 99.32% in target recognition example, 96.14% in fault diagnosis problem, and 99.54% in IoT decision-making application.<\/jats:p>","DOI":"10.3390\/s23115141","type":"journal-article","created":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T15:29:52Z","timestamp":1685287792000},"page":"5141","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Multisensor Data Fusion in IoT Environments in Dempster\u2013Shafer Theory Setting: An Improved Evidence Distance-Based Approach"],"prefix":"10.3390","volume":"23","author":[{"given":"Nour El Imane","family":"Hamda","sequence":"first","affiliation":[{"name":"ASL, Aeronautics and Spatial Studies Institute, Blida 1 University, Blida 09000, Algeria"},{"name":"LIAS, National Engineering School for Mechanics and Aerotechnics, 86961 Futuroscope Chasseneuil, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4452-1647","authenticated-orcid":false,"given":"Allel","family":"Hadjali","sequence":"additional","affiliation":[{"name":"LIAS, National Engineering School for Mechanics and Aerotechnics, 86961 Futuroscope Chasseneuil, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohand","family":"Lagha","sequence":"additional","affiliation":[{"name":"ASL, Aeronautics and Spatial Studies Institute, Blida 1 University, Blida 09000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1007\/978-3-030-90639-9_88","article-title":"Mathematical Methods for Data Fusion in IoT: A Survey","volume":"Volume 1418","author":"Kacprzyk","year":"2022","journal-title":"Advanced Intelligent Systems for Sustainable Development (AI2SD\u20192020)"},{"key":"ref_2","unstructured":"Hall, D.L., and McMullen, S.A.H. (2004). Mathematical Techniques in Multisensor Data Fusion, Artech House Information Warfare Library. [2nd ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1111\/j.2517-6161.1968.tb00722.x","article-title":"A generalization of Bayesian inference","volume":"30","author":"Dempster","year":"1968","journal-title":"J. R. Stat. Soc."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shafer, G. (1976). A Mathematical Theory of Evidence, Princeton University Press.","DOI":"10.1515\/9780691214696"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1964","DOI":"10.1016\/j.eswa.2013.08.092","article-title":"Conjunctive combination of belief functions from dependent sources using positive and negative weight functions","volume":"41","author":"Fu","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"84701","DOI":"10.1109\/ACCESS.2019.2924945","article-title":"An ELECTRE-Based Multiple Criteria Decision Making Method for Supplier Selection Using Dempster-Shafer Theory","volume":"7","author":"Fei","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3077","DOI":"10.1002\/int.22175","article-title":"A Dempster Shafer theory and uninorm-based framework of rea-soning and multiattribute decision-making for surveillance system","volume":"11","author":"Ma","year":"2019","journal-title":"Int. J. Intell. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.patrec.2005.08.025","article-title":"Fault diagnosis of machines based on D\u2013S evidence theory. Part 1: D\u2013S evidence theory and its improvement","volume":"27","author":"Fan","year":"2006","journal-title":"Pattern. Recognit. Lett."},{"key":"ref_9","first-page":"1580","article-title":"A fault diagnosis approach by D\u2013S fusion theory and hybrid expert knowledge system","volume":"9","author":"Yuan","year":"2017","journal-title":"Acta Autom. Sin."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1007\/s12239-019-0093-9","article-title":"Weighted Evidential Fusion Method for Fault Diagnosis of Mechanical Transmission Based on Oil Analysis Data","volume":"20","author":"Yan","year":"2019","journal-title":"Int. J. Automot. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"329","DOI":"10.15837\/ijccc.2019.3.3589","article-title":"Combination of Evidential Sensor Reports with Distance Function and Belief Entropy in Fault Diagnosis","volume":"14","author":"Dong","year":"2019","journal-title":"Int. J. Comput. Commun. Control."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1109\/LGRS.2015.2390914","article-title":"Target Recognition via Information Aggregation Through Dempster\u2013Shafer\u2019s Evidence Theory","volume":"12","author":"Dong","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.inffus.2015.10.001","article-title":"Multihypotheses tracking using the Dempster\u2013Shafer theory, application to ambiguous road context","volume":"29","author":"Gruyer","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1007\/s10044-021-00966-0","article-title":"A new approach for generation of generalized basic probability assignment in the evidence theory","volume":"24","author":"Tang","year":"2021","journal-title":"Pattern Anal. Appl."},{"key":"ref_15","first-page":"85","article-title":"A Simple View of the Dempster-Shafer Theory of Evidence and Its Implication for the Rule of Combination","volume":"7","author":"Zadeh","year":"1986","journal-title":"AI Mag."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yager, R.R., and Liu, L. (2008). Classic Works of the Dempster-Shafer Theory of Belief Functions, Springer. [2nd ed.].","DOI":"10.1007\/978-3-540-44792-4"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1111\/j.1467-8640.1988.tb00279.x","article-title":"Representation and combination of uncertainty with belief functions and possibility measures","volume":"4","author":"Dubois","year":"1988","journal-title":"Comput. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/34.55104","article-title":"The combination of evidence in the transferable belief model","volume":"12","author":"Smets","year":"1990","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/S1566-2535(02)00053-2","article-title":"Belief function combination and conflict management","volume":"3","author":"Lefevre","year":"2002","journal-title":"Inf. Fusion"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.dss.2013.06.012","article-title":"How to preserve the conflict as an alarm in the combination of belief functions?","volume":"56","author":"Lefevre","year":"2013","journal-title":"Decis. Support Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0167-9236(99)00084-6","article-title":"Combining belief functions when evidence conflicts","volume":"29","author":"Murphy","year":"2000","journal-title":"Decis. Support Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.dss.2004.04.015","article-title":"Combining belief functions based on distance of evidence","volume":"38","author":"Yong","year":"2004","journal-title":"Decis. Support Syst."},{"key":"ref_23","unstructured":"Martin, A., Jousselme, A.L., and Osswald, C. (July, January 30). Conflict measure for the discounting operation on belief functions. Proceedings of the 11th International Conference on Information Fusion, Cologne, Germany."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, F., and Wang, X. (2011, January 26\u201327). An Effective Combination Rule When Evidence Conflict. Proceedings of the 3rd International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China.","DOI":"10.1109\/IHMSC.2011.73"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9562","DOI":"10.3390\/s140609562","article-title":"Novel Algorithm for Identifying and Fusing Conflicting Data in Wireless Sensor Networks","volume":"14","author":"Zhang","year":"2014","journal-title":"Sensors"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Song, Y., Wang, X., Lei, L., and Xue, A. (2014, January 19\u201323). Evidence combination based on credibility and separability. Proceedings of the 12th IEEE International Conference on Signal Processing (ICSP), Hangzhou, China.","DOI":"10.1109\/ICOSP.2014.7015228"},{"key":"ref_27","unstructured":"Deng, Y. (2021, May 23). Deng Entropy: A Generalized Shannon Entropy to Measure Uncertainty. Available online: https:\/\/vixra.org\/pdf\/1502.0222v1.pdf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"509385","DOI":"10.1155\/2015\/509385","article-title":"Combination of Evidence with Different Weighting Factors: A Novel Probabilistic-Based Dissimilarity Measure Approach","volume":"2015","author":"Ma","year":"2015","journal-title":"J. Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5139","DOI":"10.1016\/j.eswa.2015.02.038","article-title":"An improved conflicting evidence combination approach based on a new supporting probability distance","volume":"42","author":"Yu","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_30","unstructured":"Jing, T. (2015, January 19\u201320). An evidence fusion method using generalized Mahalanobis distance in Dempster-Shafer Theory. Proceedings of the 4th International Conference on Computer Science and Network Technology, ICCSNT 2015, Harbin, China."},{"key":"ref_31","first-page":"1903792","article-title":"The Improvement of DS Evidence Theory and Its Application in IR\/MMW Target Recognition","volume":"2016","author":"Li","year":"2015","journal-title":"J. Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3218784","DOI":"10.1177\/155014773218784","article-title":"Weighted Evidence Combination Based on Distance of Evidence and Entropy Function","volume":"12","author":"Wang","year":"2016","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5769061","DOI":"10.1155\/2016\/5769061","article-title":"Sensor Data Fusion Based on a New Conflict Measure","volume":"2016","author":"Jiang","year":"2016","journal-title":"Math. Probl. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1186\/s40064-016-2205-6","article-title":"Conflict management based on belief function entropy in sensor fusion","volume":"5","author":"Yuan","year":"2016","journal-title":"Springerplus"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Shi, H., Yang, S., Cao, Z., Pan, W., and Li, W. (2016, January 4\u20136). An Improved Evidence Combination Method of D-S Theory. Proceedings of the IEEE International Symposium on Computer, Consumer and Control (IS3C 2016), Xi\u2019an, China.","DOI":"10.1109\/IS3C.2016.90"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ye, F., Chen, J., and Li, Y. (2017). Improvement of DS Evidence Theory for Multi-Sensor Conflicting Information. Symmetry, 9.","DOI":"10.3390\/sym9050069"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tang, Y., Zhou, D., Xu, S., and He, Z. (2017). A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion. Sensors, 17.","DOI":"10.3390\/s17040928"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1007\/s10489-016-0851-6","article-title":"A modified combination rule in generalized evidence theory","volume":"46","author":"Jiang","year":"2016","journal-title":"Appl. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhou, D., Tang, Y., and Jiang, W. (2017). A modified belief entropy in Dempster-Shafer framework. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0176832"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Tang, Y., Fang, X., Zhou, D., and Lv, X. (2017, January 10\u201313). Weighted deng entropy and its application in uncertainty measure. Proceedings of the 20th International Conference on Information Fusion (Fusion), Xi\u2019an, China.","DOI":"10.23919\/ICIF.2017.8009667"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.ijar.2018.09.001","article-title":"A correlation coefficient for belief functions","volume":"103","author":"Jiang","year":"2018","journal-title":"Int. J. Approx. Reason."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, H., Ma, Z., Deng, X., and Jiang, W. (2018, January 9\u201311). A new method to air target threat evaluation based on Dempster-Shafer evidence theory. Proceedings of the 2018 Chinese Control and Decision Conference (CCDC), Shenyang, China.","DOI":"10.1109\/CCDC.2018.8407546"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Pan, L., and Deng, Y. (2018). A New Belief Entropy to Measure Uncertainty of Basic Probability Assignments Based on Belief Function and Plausibility Function. Entropy, 20.","DOI":"10.3390\/e20110842"},{"key":"ref_44","first-page":"5858272","article-title":"Weighted Evidence Combination Rule Based on Evidence Distance and Uncertainty Measure: An Application in Fault Diagnosis","volume":"2018","author":"Chen","year":"2018","journal-title":"Math. Probl. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Mambe, M.D., Oumtanaga, S., and Anoh, G.N. (2018, January 24\u201326). A belief entropy-based approach for conflict resolution in IoT applications. Proceedings of the 1st International Conference on Smart Cities and Communities (SCCIC), Ouagadougou, Burkina Faso.","DOI":"10.1109\/SCCIC.2018.8584552"},{"key":"ref_46","unstructured":"Mo, H., and Deng, Y. (2014). A New Combination Approach based on Improved Evidence Distance. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Awogbami, G., Agana, N., Nazmi, S., Yan, X., and Homaifar, A. (2018, January 7\u201310). An Evidence Theory Based Multi Sensor Data Fusion for Multiclass Classification. Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan.","DOI":"10.1109\/SMC.2018.00303"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Awogbami, G., Agana, N., Nazmi, S., and Homaifar, A. (2018, January 19\u201322). A New Combination Rule Based on the Average Belief Function. Proceedings of the IEEE Southeastcon, St. Petersburg, FL, USA.","DOI":"10.1109\/SECON.2018.8478815"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"62314","DOI":"10.1109\/ACCESS.2018.2876282","article-title":"Collaborative Fusion for Distributed Target Classification Using Evidence Theory in IOT Environment","volume":"6","author":"Zhang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TR.2018.2800014","article-title":"Multisensor Fault Diagnosis Modeling Based on the Evidence Theory","volume":"67","author":"Lin","year":"2018","journal-title":"IEEE Trans. Reliab."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Xiao, F., and Qin, B. (2018). A Weighted Combination Method for Conflicting Evidence in Multi-Sensor Data Fusion. Sensors, 18.","DOI":"10.3390\/s18051487"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1007\/s40815-017-0436-5","article-title":"An Improved Method for Combining Conflicting Evidences Based on the Similarity Measure and Belief Function Entropy","volume":"20","author":"Xiao","year":"2017","journal-title":"Int. J. Fuzzy Syst."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.future.2018.08.010","article-title":"An improvement for combination rule in evidence theory","volume":"91","author":"Wang","year":"2018","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3928","DOI":"10.1109\/ACCESS.2018.2889358","article-title":"An Improved Multisensor Data Fusion Method and Its Application in Fault Diagnosis","volume":"7","author":"Wang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"7481","DOI":"10.1109\/ACCESS.2018.2890419","article-title":"A Novel Fuzzy Approach for Combining Uncertain Conflict Evidences in the Dempster-Shafer Theory","volume":"7","author":"An","year":"2019","journal-title":"IEEE Access"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Khan, N., and Anwar, S. (2019). Paradox Elimination in Dempster\u2013Shafer Combination Rule with Novel Entropy Function: Application in Decision-Level Multi-Sensor Fusion. Sensors, 19.","DOI":"10.3390\/s19214810"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1550147718823990","DOI":"10.1177\/1550147718823990","article-title":"A novel weighted evidence combination rule based on improved entropy function with a diagnosis application","volume":"15","author":"Chen","year":"2019","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1550147719841295","DOI":"10.1177\/1550147719841295","article-title":"A new method to measure the divergence in evidential sensor data fusion","volume":"15","author":"Song","year":"2019","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Ji, D., Yang, X., Fei, L., and Zhai, C. (2019). An Improved Belief Entropy to Measure Uncertainty of Basic Probability Assignments Based on Deng Entropy and Belief Interval. Entropy, 21.","DOI":"10.3390\/e21111122"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Wang, D., Gao, J., and Wei, D. (2019). A New Belief Entropy Based on Deng Entropy. Entropy, 21.","DOI":"10.3390\/e21100987"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"225507","DOI":"10.1109\/ACCESS.2020.3044605","article-title":"Weighted Conflict Evidence Combination Method Based on Hellinger Distance and the Belief Entropy","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2329","DOI":"10.1007\/s00500-019-04063-7","article-title":"Weighted belief function of sensor data fusion in engine fault diagnosis","volume":"24","author":"Zhang","year":"2019","journal-title":"Soft Comput."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Deng, Z., and Wang, J. (2020). A Novel Evidence Conflict Measurement for Multi-Sensor Data Fusion Based on the Evidence Distance and Evidence Angle. Sensors, 20.","DOI":"10.3390\/s20020381"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.1007\/s10922-020-09519-y","article-title":"DFIOT: Data Fusion for Internet of Things","volume":"28","author":"Boulkaboul","year":"2020","journal-title":"J. Netw. Syst. Manag."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1814","DOI":"10.1002\/int.22273","article-title":"A method for combining conflicting evidences with improved distance function and Tsallis entropy","volume":"35","author":"Li","year":"2020","journal-title":"Int. J. Intell. Syst."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"e3320","DOI":"10.5028\/jatm.v12.1173","article-title":"A Weighted Evidence Combination Method Based on the Pignistic Probability Distance and Deng Entropy","volume":"12","author":"Sun","year":"2020","journal-title":"J. Aerosp. Technol. Manag."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"51550","DOI":"10.1109\/ACCESS.2020.2979605","article-title":"A Novel Measure of Uncertainty in the Dempster-Shafer Theory","volume":"8","author":"Wen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"57505","DOI":"10.1109\/ACCESS.2020.2982579","article-title":"An Improved Belief Entropy in Evidence Theory","volume":"8","author":"Yan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"7917512","DOI":"10.1155\/2020\/7917512","article-title":"Multisensor Fusion Method Based on the Belief Entropy and DS Evidence Theory","volume":"2020","author":"Fan","year":"2020","journal-title":"J. Sens."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Ni, S., Lei, Y., and Tang, Y. (2020). Improved Base Belief Function-Based Conflict Data Fusion Approach Considering Belief Entropy in the Evidence Theory. Entropy, 22.","DOI":"10.3390\/e22080801"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"3564365","DOI":"10.1155\/2020\/3564365","article-title":"A New Method to Handle Conflict when Combining Evidences Using Entropy Function and Evidence Angle with an Effective Application in Fault Diagnosis","volume":"2020","author":"Chen","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Qin, M., Tang, Y., and Wen, J. (2020). An Improved Total Uncertainty Measure in the Evidence Theory and Its Application in Decision Making. Entropy, 22.","DOI":"10.3390\/e22040487"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"104369","DOI":"10.1016\/j.engappai.2021.104369","article-title":"iDCR: Improved Dempster Combination Rule for multisensor fault diagnosis","volume":"104","author":"Ghosh","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"3003","DOI":"10.1007\/s00500-020-05359-9","article-title":"An evidence combination approach based on fuzzy discounting","volume":"25","author":"Xue","year":"2020","journal-title":"Soft Comput."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1109\/TFUZZ.2020.3002431","article-title":"A Novel Conflict Measurement in Decision-Making and Its Application in Fault Diagnosis","volume":"29","author":"Xiao","year":"2020","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Xiao, F. (2021). Complex Pignistic Transformation-Based Evidential Distance for Multisource Information Fusion of Medical Diagnosis in the IoT. Sensors, 21.","DOI":"10.3390\/s21030840"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Chen, Y., and Tang, Y. (2021). An Improved Approach of Incomplete Information Fusion and Its Application in Sensor Data-Based Fault Diagnosis. Mathematics, 9.","DOI":"10.3390\/math9111292"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"104452","DOI":"10.1016\/j.engappai.2021.104452","article-title":"A belief Hellinger distance for D\u2013S evidence theory and its application in pattern recognition","volume":"106","author":"Zhu","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"37813","DOI":"10.1109\/ACCESS.2021.3063242","article-title":"Multisensor Data Fusion Based on Modified Belief Entropy in Dempster\u2013Shafer Theory for Smart Environment","volume":"9","author":"Ullah","year":"2021","journal-title":"IEEE Access"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"7614","DOI":"10.1007\/s10489-021-02279-5","article-title":"An improved evidence fusion algorithm in multi-sensor systems","volume":"51","author":"Zhao","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1851","DOI":"10.1002\/int.22363","article-title":"Multisource basic probability assignment fusion based on information quality","volume":"36","author":"Li","year":"2021","journal-title":"Int. J. Intell. Syst."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"7123","DOI":"10.1007\/s00500-022-07160-2","article-title":"An evidence combination rule based on a new weight assignment scheme","volume":"26","author":"Wang","year":"2022","journal-title":"Soft Comput."},{"key":"ref_83","unstructured":"Ma, W., Wang, S., Li, Y., Fan, X., and Jiang, Y. (2022, September 20). Essential Conflict: A Novel Conflict Measurement in Dempster-Shafer Theory for Multi-Sensor Data Fusion. Available online: https:\/\/ssrn.com\/abstract=4187083."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"553","DOI":"10.53106\/160792642022052303013","article-title":"A Weighted Evidence Combination Method for Multisensor Data Fusion","volume":"23","author":"Liu","year":"2022","journal-title":"J. Internet Technol."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Gou, L., Zhang, J., Li, N., Wang, Z., Chen, J., and Qi, L. (2022). Weighted assignment fusion algorithm of evidence conflict based on Euclidean distance and weighting strategy, and application in the wind turbine system. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0262883"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Hamda, N.E.I., Hadjali, A., and Lagha, M. (2022, January 23\u201325). An Advanced Weighted Evidence Combination Method for Multisensor Data Fusion in IoT. Proceedings of the International Conference on Decision Aid Sciences and Applications (DASA2022), Chiangrai, Thailand.","DOI":"10.1109\/DASA54658.2022.9765125"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Liang, Q., Liu, Z., and Chen, Z. (2023). A Networked Method for Multi-Evidence-Based Information Fusion. Entropy, 25.","DOI":"10.3390\/e25010069"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Tang, Y., Wu, S., Zhou, Y., Huang, Y., and Zhou, D. (2023). A New Reliability Coefficient Using Betting Commitment Evidence Distance in Dempster\u2013Shafer Evidence Theory for Uncertain Information Fusion. Entropy, 25.","DOI":"10.3390\/e25030462"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"e1307","DOI":"10.7717\/peerj-cs.1307","article-title":"A novel evidence combination method based on stochastic approach for link-structure analysis algorithm and Lance-Williams distance","volume":"9","author":"Tang","year":"2023","journal-title":"PeerJ Comput. Sci."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Ma, L., Yao, W., Dai, X., and Jia, R. (2023). A New Evidence Weight Combination and Probability Allocation Method in Multi-Sensor Data Fusion. Sensors, 23.","DOI":"10.3390\/s23020722"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Hua, Z., and Jing, X. (2023). An improved belief Hellinger divergence for Dempster-Shafer theory and its application in multi-source information fusion. Appl. Intell.","DOI":"10.1007\/s10489-022-04428-w"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1103\/RevModPhys.50.221","article-title":"General properties of entropy","volume":"50","author":"Wehrl","year":"1978","journal-title":"Rev. Mod. Phys."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1145\/584091.584093","article-title":"A mathematical theory of communication","volume":"5","author":"Shannon","year":"2001","journal-title":"ACM SIGMOBILE Mob. Comput. Commun. Rev."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"104030","DOI":"10.1016\/j.engappai.2020.104030","article-title":"A new belief divergence measure for Dempster\u2013Shafer theory based on belief and plausibility function and its application in multi-source data fusion","volume":"97","author":"Wang","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/j.ins.2019.11.022","article-title":"A new divergence measure for belief functions in D\u2013S evidence theory for multisensor data fusion","volume":"514","author":"Xiao","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1687814016641820","DOI":"10.1177\/1687814016641820","article-title":"An evidential sensor fusion method in fault diagnosis","volume":"8","author":"Jiang","year":"2016","journal-title":"Adv. Mech. Eng."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Wang, Z., and Xiao, F. (2019). An Improved Multi-Source Data Fusion Method Based on the Belief Entropy and Divergence Measure. Entropy, 21.","DOI":"10.3390\/e21060611"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5141\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:43:47Z","timestamp":1760125427000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5141"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,28]]},"references-count":97,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23115141"],"URL":"https:\/\/doi.org\/10.3390\/s23115141","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,28]]}}}