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Fusion: Practice and Applications

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Fusion: Practice and Applications

Volume 10 , Issue 1 , PP: 116-127, 2023 | Cite this article as | XML | Html | PDF | Full Length Article

Intelligent Multilevel Fusion System for Wireless Sensor Network Virtualization Using Deep Reinforcement Learning in Education

Shahad Al-yousif 1 * , Aws Nabeel 2 , Waleed K. Ibrahim 3 , Mustafa Musa Jaber 4 , Mohammed Hasan Ali 5 , M. jaber 6 , Asaad Shakir Hameed 7 , Ahmed Hussein Al-khayyat 8 , Ahmed F. Omer 9 , Nuridawati Mustafa 10 , Kadim A. Jabbar 11 , A. Abd Ali Abbood 12

  • 1 Department, College of Electrical and Electronic Engineering Department, College of Engineering, Gulf University, Sanad 26489, Kingdom of Bahrain; university of northampton, faculty of engineering, Department of Electronics and Computer Engineering,University Drive, NORTHAMPTON, Northamptonshire NN1 5PH, London, UK. - (Dr.shahad.alyousif@gulfuniversity.edu.bh)
  • 2 Dijlah University College,Baghdad, Iraq - (aws.nabeel@duc.edu.iq)
  • 3 Department of Medical Instrumentation Techniques Engineering, al-farahidi University, Baghdad, Iraq - (waleed.khalid@alfarahidiuc.edu.iq)
  • 4 Institute of Informatics and Computing in Energy, Univerity tenaga nasional, Kajang, Malaysia - (Mjaber87@gmail.com )
  • 5 Computer Techniques Engineering Department, Faculty of Information Technology, Imam Ja’afar Al-Sadiq University, Najaf 10023, Iraq - (mh180250@gmail.com)
  • 6 Department of Computer Science, Al-turath University College, Baghdad, Iraq - (Mustafa.jaber@turath.edu.iq)
  • 7 Quality Assurance and Academic Performance Unit, Mazaya University College Thi Qar Department of Quality Assurance, The Islamic University, Najaf, Iraq - (asaad.shakir@mpu.edu.iq)
  • 8 Medical instruments engineering techniques, National University of science and technology, Thi Qar,Iraq - (Ahmed.Hussein@iunajaf.edu.iq)
  • 9 Computer Technology Engineering, College of Engineering Technology, Al-Kitab University, Iraq - (dilnea89@gmail.com)
  • 10 Faculty of Information & Communication Technology, Universiti Teknikal Malaysia Melaka, 75450 Durian Tunggal, Melaka, Malaysia - (nuridawati@utem.edu.my)
  • 11 Department of Business Administration, Al-Mustaqbal University College, Babylon, Hilla, 51001, Iraq - (kadim.jabber@nust.edu.iq)
  • 12 Department of Business Administration, Al-Mustaqbal University College, Babylon, Hilla, 51001, Iraq - (abbas.abdali@mustaqbal-college.edu.iq)

    • Doi: https://doi.org/10.54216/FPA.100107

    Received: June 02, 2022 Accepted: November 08, 2022
    Abstract

    wireless sensor networks (WSN) in ubiquitous learning environments to enhance teaching and learning quality. WSNs can serve as a learner-to-context interface, enabling learners to interact with the learning environment while collecting contextual information. With the help of WSN virtualization technology, learners can leverage different virtualized characteristics of the state-of-the-art WSN and engage with the ubiquitous learning paradigm to gain knowledge and skills. The report examines the current state of WSN virtualization and its potential for sharing in this context. Research concerns are discussed in-depth, and an in-depth overview of the current state of the art is provided. This paper presents the fundamentals of WSN virtualization and argues for its usefulness. By allowing learners to learn while on the go in an environment that interests them, gadgets and embedded computers work together to keep students connected to their learning environment. Recent years have seen an increase in interest in deep reinforcement learning technologies. Despite the availability of several internet resources for researching this field, it might be challenging for those just getting started to design effective teaching systems for autonomous vehicles. This article offers a model for a highly effective and interactive ubiquitous learning environment system based on ubiquitous computing technology. An educational system based on deep reinforcement learning and system development is developed in this project using the WSNV-ES method. The web-based system that has been designed can do the following: settings for reinforcing student success, learning scripts to run, and the learning state to monitor are described.

    Keywords :

    Education , Intelligent Multilevel Fusion System , WSN, DRL , Students , Technology.

    References

    [1]  She, M. (2021). Deep Reinforcement Learning-Based Smart Manufacturing Plants with a Novel Digital Twin Training Model. Wireless Personal Communications, 1-20.

    [2]  Cao, Z., Zhou, P., Li, R., Huang, S., & Wu, D. (2020). Multiagent deep reinforcement learning for joint multichannel  access  and  task  offloading  of  mobile-edge  computing  in  industry  4.0. IEEE  Internet  of Things Journal, 7(7), 6201-6213.

    [3]  Saravanan, V., Alagan, A., & Naik, K. (2020). Computational biology as a compelling pedagogical tool in computer science education. J. Comput. Sci, 11(1), 45-52.

    [4]  Gao, J., Wang, H., & Shen, H. (2020, August). Machine learning based workload prediction in cloud computing. In 2020 29th international conference on computer communications and networks (ICCCN) (pp. 1-9). IEEE.

    [5]  Amudha,  G.,  &  Narayanasamy,  P.  (2018).  Distributed  location  and  trust  based  replica  detection  in wireless sensor networks. Wireless Personal Communications, 102(4), 3303-3321.

    [6]  Li,  M.,  &  Li,  H.  (2020).  Application  of  deep  neural  network  and  deep  reinforcement  learning  in wireless communication. Plos one, 15(7), e0235447.

    [7]  Amudha, G. (2021). Dilated Transaction Access and Retrieval: Improving the Information Retrieval of Blockchain-Assimilated Internet of Things Transactions. Wireless Personal Communications, 1-21.

    [8]  Chen,  T.  (2021).  Smart  campus  and  innovative  education based  on  wireless  sensor. Microprocessors and Microsystems, 81, 103678.

    [9]  Pham, V. T., Nguyen, T. N., Liu, B. H., & Lin, T. (2021, March). Minimizing latency for multiple -type data aggregation in wireless sensor networks. In 2021 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-6). IEEE.

    [10]  Gao, J., Wang, H., & Shen, H. (2020). Task failure prediction in cloud data centers using deep learning. IEEE Transactions on Services Computing. 

    [11]  Jaber, M.M., Ali, M.H., Abd, S.K., Jassim, M.M., Alkhayyat, A., Jassim, M., Alkhuwaylidee, A.R. and Nidhal,  L.,  2022.  Q-learning  based  task  scheduling  and  energy-saving  MAC  protocol  for  wireless sensor networkss. Wireless Networks, pp.1-17.

    [12]  Nguyen,  N.  T.,  Liu,  B.  H.,  Pham,  V.  T.,  &  Luo,  Y.  S.  (2016).  On  maximizing  the  lifetime  for  data aggregation in wireless sensor networks using virtual data aggregation trees. Computer Networks, 105, 99-110.

    [13]  Sharma,  S.,  &  Kaur,  A.  (2020).  Simulation  of  Cloud  Platform  Supporting  Wireless  Sensor Network. Journal of Computational and Theoretical Nanoscience, 17(6), 2613-2620.

    [14]  Chéour,  R.,  Khriji,  S.,  Götz,  M.,  Abid,  M.,  &  Kanoun,  O.  (2020).  Accurate  Dynamic  Voltage  and Frequency  Scaling  Measurement  for  Low-Power  Microcontrollors  in  Wireless  Sensor Networks. Microelectronics Journal, 105, 104874.

    [15]  Saravanan, V. (2021). Impact of intelligence methodologies on education and training process. Journal of Intelligent & Fuzzy Systems, 40(2), 3237-3238.

    [16]  Ali,  M.H.,  ,  M.M.,  Abd,  S.K.,  Alkhayyat,  A.  and  Jameel,  H.A.,  2022.  Model  for  wireless  image correlation assisted by sensors based on 3D display technology. Optik, 268, p.169794.

    [17]  Hadimani, H. C., Latte, M. V., Tejomurthy, P. H. S.,  Dhulipala, V. S., & Baskar, S. (2016, February). Optimized mathematical model for cell receivers running in spatially problematic multi path channels for  wireless  systems  in  smart  antennas.  In 2016  International  Conference  on  Emerging  Trends  in Engineering, Technology and Science (ICETETS) (pp. 1-7). IEEE.

    [18]  Kbar,  G.,  Alazab,  A.,  &  Agbinya,  J.  (2019,  February).  Multi-factor  based  enhancing  students' motivations. In 2019 IEEE International Conference on Industrial Technology (ICIT) (pp. 1054-1059). IEEE.

    [19]  Zhou, Z., Yang, C. N., Kim, C., & Cimato, S. (2020). Introduction to the special issue on deep learning for real-time information hiding and forensics. Journal of Real-Time Image Processing, 17(1), 1-5.

    [20]  20., M.M., Ali, M.H., Abd, S.K., Jassim, M.M., Alkhayyat, A., Alreda, B.A., Alkhuwaylidee, A.R. and Alyousif, S., 2022. A Machine Learning-Based Semantic Pattern Matching Model for Remote Sensing Data Registration. Journal of the Indian Society of Remote Sensing, pp.1-14.

    [21]  Zughoul, O., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alazab, M., Amomeni, U., ... & Amomeni, B. (2021). Novel triplex procedure for ranking the ability of software engineering students based on two levels  of  AHP  and  group  TOPSIS  techniques. International  Journal  of  Information  Technology  & Decision Making (IJITDM), 20(01), 67-135.

    [22]  Li, Z., & Zhong, A. (2020). Resource allocation in wireless powered virtualized sensor networks. IEEE Access, 8, 40327-40336.

    [23]  Singh,  K.  D.,  &  Sood,  S.  K.  (2020).  Optical  fog‐assisted  cyber‐physical  system  for  intelligent surveillance in the education system. Computer Applications in Engineering Education, 28(3), 692-704.

    [24]  Preeth,  S.  S.  L.,  Dhanalakshmi,  R.,  &  Shakeel,  P.  M.  (2020).  An  intelligent  approach  for  energy efficient trajectory design for mobile sink based IoT supported wireless sensor networks.  Peer-to-Peer networking and applications, 13(6), 2011-2022.

    [25]  Alam,  T.,  &  Benaida,  M.  (2020).  Blockchain  and  Internet  of  Things  in  Higher  Education. Tanweer Alam, Mohamed Benaida." Blockchain and Internet of Things in Higher Education." Universal Journal of Educational Research, 8, 2164-2174.

    [26]  González-Zamar,  M.  D.,  & Abad-Segura,  E.  (2020).  Implications  of  virtual  reality  in arts  education: Research analysis in the context of higher education. Education Sciences, 10(9), 225.

    [27]  UntungRahardja,  S.  K.,  &  EkaPurnamaHarahap,  Q.  (2020).  Authenticity  of  a  diploma  using  the blockchain approach. International Journal, 9(1.2).

    [28]  Priatna,  T.,  Maylawati,  D.,  Sugilar,  H.,  &  Ramdhani,  M.  (2020).  Key  success  factors  of  e-learning implementation  in  higher  education. International  Journal  of  Emerging  Technologies  in  Learning (iJET), 15(17), 101-114.

    [29]  Almigheerbi, T. S., Ramsey, D., & Lamek, A. (2020). A collaboratively-developed enterprise  resource planning  (CD-ERP)  Approach  in  Libyan  higher  education. International  Journal  of  Information  and Education Technology, 10(4), 284-298.

    [30]  Mijwil,  M.,  Omega  John  Unogwu,  Youssef  Filali,  Indu  Bala,  &  Humam  Al-Shahwani.  (2023). Exploring  the  Top  Five  Evolving  Threats  in  Cybersecurity:  An  In-Depth  Overview.  Mesopotamian Journal of CyberSecurity, 2023, 57–63. https://doi.org/10.58496/MJCS/2023/010

    Cite This Article As :
    Al-yousif, Shahad. , Nabeel, Aws. , K., Waleed. , Musa, Mustafa. , Hasan, Mohammed. , jaber, M.. , Shakir, Asaad. , Hussein, Ahmed. , F., Ahmed. , Mustafa, Nuridawati. , A., Kadim. , Abd, A.. Intelligent Multilevel Fusion System for Wireless Sensor Network Virtualization Using Deep Reinforcement Learning in Education. Fusion: Practice and Applications, vol. , no. , 2023, pp. 116-127. DOI: https://doi.org/10.54216/FPA.100107
    Al-yousif, S. Nabeel, A. K., W. Musa, M. Hasan, M. jaber, M. Shakir, A. Hussein, A. F., A. Mustafa, N. A., K. Abd, A. (2023). Intelligent Multilevel Fusion System for Wireless Sensor Network Virtualization Using Deep Reinforcement Learning in Education. Fusion: Practice and Applications, (), 116-127. DOI: https://doi.org/10.54216/FPA.100107
    Al-yousif, Shahad. Nabeel, Aws. K., Waleed. Musa, Mustafa. Hasan, Mohammed. jaber, M.. Shakir, Asaad. Hussein, Ahmed. F., Ahmed. Mustafa, Nuridawati. A., Kadim. Abd, A.. Intelligent Multilevel Fusion System for Wireless Sensor Network Virtualization Using Deep Reinforcement Learning in Education. Fusion: Practice and Applications , no. (2023): 116-127. DOI: https://doi.org/10.54216/FPA.100107
    Al-yousif, S. , Nabeel, A. , K., W. , Musa, M. , Hasan, M. , jaber, M. , Shakir, A. , Hussein, A. , F., A. , Mustafa, N. , A., K. , Abd, A. (2023) . Intelligent Multilevel Fusion System for Wireless Sensor Network Virtualization Using Deep Reinforcement Learning in Education. Fusion: Practice and Applications , () , 116-127 . DOI: https://doi.org/10.54216/FPA.100107
    Al-yousif S. , Nabeel A. , K. W. , Musa M. , Hasan M. , jaber M. , Shakir A. , Hussein A. , F. A. , Mustafa N. , A. K. , Abd A. [2023]. Intelligent Multilevel Fusion System for Wireless Sensor Network Virtualization Using Deep Reinforcement Learning in Education. Fusion: Practice and Applications. (): 116-127. DOI: https://doi.org/10.54216/FPA.100107
    Al-yousif, S. Nabeel, A. K., W. Musa, M. Hasan, M. jaber, M. Shakir, A. Hussein, A. F., A. Mustafa, N. A., K. Abd, A. "Intelligent Multilevel Fusion System for Wireless Sensor Network Virtualization Using Deep Reinforcement Learning in Education," Fusion: Practice and Applications, vol. , no. , pp. 116-127, 2023. DOI: https://doi.org/10.54216/FPA.100107

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