1
Department of Information Technology , Georgia Gwinnett College, Georgia, USA
(Mnasir1@ggc.edu)
2
College of Computer Information Technology, American University in the Emirates, Dubai, UAE
(ahmed.almasri@aue.ae)
Abstract :
In recent years, with the rapid development of the domestic economy, the concept of sustainable development has been paid more and more attention. Ecological environment protection is more and more important, and the ecological environment is closely related to economic development. How to measure the relationship between the two is very important. Whether it is to build ecological environment protection or to ensure sustainable development of the economy, we should take the green development concept as a guiding concept, promote ecological economic development, and study the integration of ecological data is of great significance for solving these problems. The research of this thesis studies the multi-source heterogeneous (MSH) ecological big data (BD)adaptive fusion based (FM) based on symmetric encryption. This paper sets up a comparative experiment, multi-sensor (MS) data fusion based (DFM) based on Rough set theory, MSH data fusion based on data information conversion. The method is compared with the symmetric fusion MSH BD adaptive FM proposed in this paper. The results show that the MSH DFM based on Rough set theory has the highest confidence of 0.812; the MSH DFM based on data information conversion has the highest confidence of 0.68; based on symmetric encryption MSH BD The fusion confidence of the adaptive FM is up to 0.965, and the MSH ecological BD adaptive FM based on symmetric encryption is superior.
Keywords :
Sustainable Development , Symmetric Encryption , Multi-source Heterogeneity , BD Fusion
References :
[1] Ashish Kothari, Federico Demaria, Alberto Acosta. Buen Vivir, Degrowth, and Ecological Swaraj: Alternatives to Sustainable Development and Green Economy[J]. Development, 2015, 57(3-4):57-53.
[2] ZHANG, HUIYUAN. China's Ecological Progress and Global Sustainable Development[J]. Beijing Review, 2017, 60(48):46-51.
[3] K. Yu, L. Tan, L. Lin, X. Cheng, Z. Yi and T. Sato, "Deep-Learning-Empowered Breast Cancer Auxiliary Diagnosis for 5GB Remote E-Health," IEEE Wireless Communications, vol. 28, no. 3, pp. 54-61, June 2021
[4] K. Yu, L. Tan, S. Mumtaz, S. Al-Rubaye, A. Al-Dulaimi, A. K. Bashir, F. A. Khan, “Securing Critical Infrastructures: Deep Learning-based Threat Detection in the IIoT”, IEEE Communications Magazine, 2021.
[5] C.H. Xiong, D.G. Yang, X.H. Zhang. Research on the spatial patterns of ecological and economic sustainable development capacities in the Xinjiang Region[J]. Acta Ecologica Sinica, 2015, 35(10):3428-3436.
[6] T. Ren, X.-C. Chen. Sustainable development of regional ecological economic system based on the DEAHP model[J]. Hunan Daxue Xuebao/journal of Hunan University Natural Sciences, 2015, 42(3):132-139.
[7] Wang Qiang, Qi Xiao-jie. Strategy research of harbin city green transport and sustainable development from low carbon ecological perspective[J]. Iop Conference, 2017, 61(1):012153.
[8] Zhiou Xu, Huiyuan Jiang. Evaluation System for the Sustainable Development of Urban Traffic and Ecological Environment Based on Support Vector Machine[J]. Journal of Computational & Theoretical Nanoscience, 2016, 13(10):6978-6981.
[9] K. Yu, Z. Guo, Y. Shen, W. Wang, J. C. Lin, T. Sato, “Secure Artificial Intelligence of Things for Implicit Group Recommendations”, IEEE Internet of Things Journal, 2021
[10] H. Li, K. Yu, B. Liu, C. Feng, Z. Qin and G. Srivastava, "An Efficient Ciphertext-Policy Weighted Attribute-Based Encryption for the Internet of Health Things," IEEE Journal of Biomedical and Health Informatics, 2021
[11] Chenyu Lu, Chunjuan Wang, Weili Zhu. GIS-Based Synthetic Measurement of Sustainable Development in Loess Plateau Ecologically Fragile Area—Case of Qingyang, China[J]. Sustainability, 2015(7):1576-1593.
[12] Eugenia Rosca, Jack Reedy, Julia C. Bendul. Does Frugal Innovation Enable Sustainable Development? A Systematic Literature Review[J]. European Journal of Development Research, 2018, 30(1):136-157.
[13] E. Mieszajkina. Ecological entrepreneurship and sustainable development[J]. Social Science Electronic Publishing, 2016, 11(1):163-171.
[14] L. Zhen, A. K. Bashir, K. Yu, Y. D. Al-Otaibi, C. H. Foh, and P. Xiao, “Energy-Efficient Random Access for LEO Satellite-Assisted 6G Internet of Remote Things”, IEEE Internet of Things Journal
[15] L. Zhen, Y. Zhang, K. Yu, N. Kumar, A. Barnawi and Y. Xie, "Early Collision Detection for Massive Random Access in Satellite-Based Internet of Things," IEEE Transactions on Vehicular Technology, vol. 70, no. 5, pp. 5184-5189, May 2021
[16] Y.-R. Lu, F.-E. Zhang, Q. Liu. The construction of EC for the environmental security and sustainable development of new urbanization[J]. Acta Geoscientica Sinica, 2015, 36(4):403-412.
[17] A. V. Nikitina, A. I. Sukhinov, G. A. Ugolnitsky. Optimal control of sustainable development in the biological rehabilitation of the Azov Sea[J]. Mathematical Models & Computer Simulations, 2017, 9(1):101-107.
[18] Czuba, Michał. Prosumption as a factor of sustainable development[J]. Social Science Electronic Publishing, 2017, 12(1):55-61.
[19] Yanbo Han, Chen Liu, Shen Su. A Proactive Service Model Facilitating Stream Data Fusion and Correlation[J]. International Journal of Web Services Research, 2017, 14(3):1-16.
[20] L. Tan, K. Yu, N. Shi, C. Yang, W. Wei and H. Lu, "Towards Secure and Privacy-Preserving Data Sharing for COVID-19 Medical Records: A Blockchain-Empowered Approach," IEEE Transactions on Network Science and Engineering
[21] L. Tan, K. Yu, F. Ming, X. Cheng, G. Srivastava, “Secure and Resilient Artificial Intelligence of Things: a HoneyNet Approach for Threat Detection and Situational Awareness”, IEEE Consumer Electronics Magazine, 2021
[22] Z．Guo, K. Yu, Y. Li, G. Srivastava, and J. C. -W. Lin, “Deep Learning-Embedded Social Internet of Things for Ambiguity-Aware Social Recommendations”, IEEE Transactions on Network Science and Engineering.
[23] Ambra R. Di Rosa, Francesco Leone, Carmelo Scattareggia. Botanical origin identification of Sicilian honeys based on artificial senses and multi-sensor data fusion[J]. European Food Research & Technology, 2018, 244(2):1-9.
[24] Wafa M. Elmannai, Khaled M. Elleithy. A Highly Accurate and Reliable Data Fusion Framework for Guiding the Visually Impaired[J]. IEEE Access, 2018, PP(99):1-1.
[25] Wenliang YONG. Identification Algorithm of Longitudinal Road Slope Based on Multi-sensor Data Fusion Filtering[J]. Journal of Mechanical Engineering, 2018, 54(1):116.
[26] F. Wang, J. Fu, Y. Zhu. Coarse and Fine Data Fusion of Absolute Round Inductosyn[J]. Chinese Journal of Sensors & Actuators, 2018, 31(2):213-217.
[27] L. Tan, K. Yu, A. K. Bashir, X. Cheng, F. Ming, L. Zhao, X. Zhou, “Towards Real-time and Efficient Cardiovascular Monitoring for COVID-19 Patients by 5G-Enabled Wearable Medical Devices: A Deep Learning Approach”, Neural Computing and Applications, 2021
[28] Z. Guo, K. Yu, A. Jolfaei, A. K. Bashir, A. O. Almagrabi, and N. Kumar, “A Fuzzy Detection System for Rumors through Explainable Adaptive Learning”, IEEE Transactions on Fuzzy Systems
[29] Qiang Sun. Research on the influencing factors of reverse logistics carbon footprint under sustainable development[J]. Environmental Science & Pollution Research, 2016, 24(29):1-9.
[30] Patrick Bogaert, Sarah Gengler. Bayesian maximum entropy and data fusion for processing qualitative data: theory and application for crowdsourced cropland occurrences in Ethiopia[J]. Stochastic Environmental Research & Risk Assessment, 2017, 32(2):1-17.
[31] L. Tan, N. Shi, K. Yu, M. Aloqaily, Y. Jararweh, “A Blockchain-Empowered Access Control Framework for Smart Devices in Green Internet of Things”, ACM Transactions on Internet Technology, vol. 21, no. 3, pp. 1-20, 2021
[32] Z. Guo, A. K. Bashir, K. Yu, J. C. Lin, Y. Shen, “Graph Embedding-based Intelligent Industrial Decision for Complex Sewage Treatment Processes”, International Journal of Intelligent Systems，2021
[33] Xi D, Li L, Zhang J, et al. Improvement of Mammographic Mass Classification Performance Using an Intelligent Data Fusion Method[J]. Journal of Medical Imaging & Health Informatics,2018, 8(2):275-283.
| Style | # |
|---|---|
| MLA | Manal Nasir , Ahmed N. Al-Masri. "Multi-source Heterogeneous Ecological Big Data Adaptive Fusion Method Based on Symmetric Encryption." Fusion: Practice and Applications, Vol. 5, No. 1, 2021 ,PP. 08-20 (Doi : https://doi.org/10.54216/FPA.050101) |
| APA | Manal Nasir , Ahmed N. Al-Masri. (2021). Multi-source Heterogeneous Ecological Big Data Adaptive Fusion Method Based on Symmetric Encryption. Journal of Fusion: Practice and Applications, 5 ( 1 ), 08-20 (Doi : https://doi.org/10.54216/FPA.050101) |
| Chicago | Manal Nasir , Ahmed N. Al-Masri. "Multi-source Heterogeneous Ecological Big Data Adaptive Fusion Method Based on Symmetric Encryption." Journal of Fusion: Practice and Applications, 5 no. 1 (2021): 08-20 (Doi : https://doi.org/10.54216/FPA.050101) |
| Harvard | Manal Nasir , Ahmed N. Al-Masri. (2021). Multi-source Heterogeneous Ecological Big Data Adaptive Fusion Method Based on Symmetric Encryption. Journal of Fusion: Practice and Applications, 5 ( 1 ), 08-20 (Doi : https://doi.org/10.54216/FPA.050101) |
| Vancouver | Manal Nasir , Ahmed N. Al-Masri. Multi-source Heterogeneous Ecological Big Data Adaptive Fusion Method Based on Symmetric Encryption. Journal of Fusion: Practice and Applications, (2021); 5 ( 1 ): 08-20 (Doi : https://doi.org/10.54216/FPA.050101) |
| IEEE | Manal Nasir, Ahmed N. Al-Masri, Multi-source Heterogeneous Ecological Big Data Adaptive Fusion Method Based on Symmetric Encryption, Journal of Fusion: Practice and Applications, Vol. 5 , No. 1 , (2021) : 08-20 (Doi : https://doi.org/10.54216/FPA.050101) |