Abstract
In the process of sensor target recognition, attitude estimation and information decision-making, most of the current sensor information decisions require probability conversion or weight calculation of sensor data. The calculation process is complex and requires a large amount of computation. In addition, the decision result is greatly affected by the probability value. This paper proposes a multi-sensor information decision algorithm with high-discrimination based on distance vectors. At the same time, the support function, dominance function and discrimination function for the algorithm are presented. The dominance function is obtained through the normalization processing of the support matrix, and then the dominance function after normalization is sorted. The maximum value is taken as the optimal solution. The discrimination function mainly provides the basis for the evaluation of the algorithm. The simulation results show that the discrimination degree of this method in sensor information decision-making reaches more than 0.5, and the decision-making effect is good. Compared with the classic D-S evidence theory, this algorithm can effectively avoid the phenomenon that D-S evidence theory contradicts with the actual situation when making a decision. It is less affected by a single sensor and the decision result is stable. Compared with the probabilistic transformation of the initial data of the sensor in the decision-making process, it has obvious advantages.
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References
Onasanya, A., Lakkia, S., Elshakankiri, M.: Implementing IoT/SWN based smart Saskatchewan healthcare system. Wirel. Netw. 25, 3999–4020 (2019)
Quinn, N.W.T., Ortega, R., Rahilly, P.J.A., et al.: Use of environmental sensors and sensor networks to develop water and salinity budgets for seasonal wetland real-time water quality management. Environ. Model. Softw. 25, 1045–1058 (2010)
Karthika, P., Ganesh Babu, R., Jayaram, K.: Biometric based on steganography image security in wireless sensor networks. Procedia Comput. Sci. 167, 1291–1299 (2020)
RodrÃguez, S., De Paz, J.F., Villarrubia, G., et al.: Multi-agent information fusion system to manage data from a WSN in a residential home. Inf. Fusion 23(5), 43–57 (2015)
Zhou, X., Peng, T.: Application of multi-sensor fuzzy information fusion algorithm in industrial safety monitoring system. Saf. Sci. 122, 1–5 (2020)
Pan, Y., Zhang, L., Wu, X., et al.: Multi-classifier information fusion in risk analysis. Inf. Fusion 60(8), 121–136 (2020)
Xiao, F.: A new divergence measure for belief functions in D-S evidence theory for multisensor data fusion. Inf. Sci. 514(4), 462–483 (2020)
Wang, J., Qiao, K., Zhang, Z.: An improvement for combination rule in evidence theory. Future Gener. Comput. Syst. 91(2), 1–9 (2019)
Yang, K., Liu, S., Shen, J.: Trust model based on D-S evidence theory in wireless sensor networks. In: China Conference on Wireless Sensor Networks, Advances in Wireless Sensor Networks, pp. 293–301 (2014)
Leung, Y., Ji, N.-N., Ma, J.-H.: An integrated information fusion approach based on the theory of evidence and group decision-making. Inf. Fusion 14, 410–422 (2013)
Si, L., Wang, Z., Tan, C., et al.: A novel approach for coal seam terrain prediction through information fusion of improved D-S evidence theory and neural network. Measurement 54, 140–151 (2014)
Zhao, G., Chen, A., Guangxi, L., et al.: Data fusion algorithm based on fuzzy sets and D-S theory of evidence. Tsinghua Sci. Technol. 25(1), 12–19 (2018)
Chuanqi, L., Wang, S., Wang, X.: A multi-source information fusion fault diagnosis for aviation hydraulic pump based on the new evidence similarity distance. Aerosp. Sci. Technol. 71(12), 392–401 (2017)
Si, L., Wang, Z., Tan, C., et al.: An approach to testability evaluation based on improved D-S evidence theory. In: ACM International Conference Proceeding Series, pp. 155–159 (2019)
Mi, J., Wang, X., Cheng, Y., et al.: Multi-source uncertain information fusion method for fault diagnosis based on evidence theory. In: Prognostics and System Health Management Conference, Qingdao, China (2020)
Gao, X., et al.: Collaborative fault diagnosis decision fusion algorithm based on improved DS evidence theory. In: Wang, Y., Martinsen, K., Yu, T., Wang, K. (eds.) IWAMA 2019. LNEE, vol. 634, pp. 379–387. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-2341-0_47
Han, C., Zhu, H., Duan, Z., et al.: Multi-Source Information Fusion, 2nd edn. Tsinghua University Press, Beijing (2010)
Ma, L., Xu, C., He, Z.: System detection technology based on multi-sensor information fusion. In: Third International Conference on Measuring Technology and Mechatronics Automation, Shanghai, China, pp. 625–628 (2011)
Acknowledgments
The authors acknowledge the financial support of the Key Projects of R&D and Achievement Transformation in Qinghai Province (Grant: 2018-NN-151), the National Natural Science Foundation of China (Grant: 61761040), and the Basic Research Program of Qinghai Province (Grant: 2020-ZJ-709).
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Zhang, L., Chen, B. (2020). High-Discrimination Multi-sensor Information Decision Algorithm Based on Distance Vector. In: Wang, X., Leung, V.C.M., Li, K., Zhang, H., Hu, X., Liu, Q. (eds) 6GN for Future Wireless Networks. 6GN 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-030-63941-9_4
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DOI: https://doi.org/10.1007/978-3-030-63941-9_4
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