247 162
Full Length Article
Fusion: Practice and Applications
Volume 14 , Issue 2, PP: 08-25 , 2024 | Cite this article as | XML | Html |PDF

Title

A Novel Approach for Minimizing Response Time in IoT using Adaptive Algorithm

  Hitesh Kumar Sharma 1 * ,   Samta Jain Goyal 2 ,   Sumit Kumar 3 ,   Abhishek Kumar 4

1  Amity University, Gwalior, M.P, India
    (hitesh.sharma2@s.amity.edu)

2  Amity University, Gwalior, M.P, India
    (sjgoyal@gwa.amity.edu)

3   G.N.S University, Sasaram, Bihar, India
    (sumit170787@gmail.com)

4  Amity University, Gwalior, M.P, India
    (kabhishek.mcse@gmail.com)

Full Text Download


Doi   :   https://doi.org/10.54216/FPA.140201

Received: July 23, 2023 Revised: October 26, 2023 Accepted: January 04, 2024

Abstract :

This research offers four work and computer tool setups. The dynamic Resource Allocation Algorithm is crucial to the system.  This lets you manage changing supply. Once the PWMA knows how much work is coming up, it may divide resources and plan. The Load Balancing Algorithm (LBA) distributes work evenly to avoid over- or under-utilization and it also provides access content faster via the Adaptive Caching Algorithm (ACA). The proposed system surpasses the top alternative in several domains, such as data transmission, reaction time, energy conservation, load distribution effectiveness, and recovery time from failures. This is because the suggested solution incorporates many disparate approaches. Graphs and charts are visual representations that effectively illustrate the similarities and differences between the two methodologies. The hybrid technique is especially beneficial when the workload is unpredictable and prone to fluctuations. To do this, it instructs you on the fundamentals of efficient and adaptable computer resource management.

Keywords :

Adaptive Caching; Computational Resource Management; Dynamic Resource Allocation; Load Balancing; Performance Optimization; Predictive Workload Management; Resource Utilization; System Efficiency; Workload Handling; Workload Prediction.

References :

 

[1]    D. Steenken, “Container terminal operation and operations research-a classification and literature review,” Spectrum, vol. 26, no. 1, pp. 3–49, 2004. [Online]. Available: Google Scholar

[2]    R. Stahlbock and S. Voß, “Operations research at container terminals: a literature update,” Spectrum, vol. 30, no. 1, pp. 1–52, 2008. [Online]. Available: Google Scholar

[3]    D. Pathak and R. Kashyap, "Neural correlate-based E-learning validation and classification using convolutional and Long Short-Term Memory networks," Traitement du Signal, vol. 40, no. 4, pp. 1457-1467, 2023. [Online]. Available: https://doi.org/10.18280/ts.400414

[4]    R. Kashyap, "Stochastic Dilated Residual Ghost Model for Breast Cancer Detection," J Digit Imaging, vol. 36, pp. 562–573, 2023. [Online]. Available: https://doi.org/10.1007/s10278-022-00739-z

[5]    D. Bavkar, R. Kashyap, and V. Khairnar, "Deep Hybrid Model with Trained Weights for Multimodal Sarcasm Detection," in Inventive Communication and Computational Technologies, G. Ranganathan, G. A. Papakostas, and Á. Rocha, Eds. Singapore: Springer, 2023, vol. 757, Lecture Notes in Networks and Systems. [Online]. Available: https://doi.org/10.1007/978-981-99-5166-6_13

[6]    H. J. Carlo, “Transport operations in container terminals: literature overview, trends, research directions and classification scheme,” European Journal of Operational Research, vol. 236, no. 1, pp. 1–13, 2014. [Online]. Available: Google Scholar

[7]    C. Zhou, “Challenges and Opportunities in Integration of Simulation and Optimization in Maritime Logistics,” in Proceedings of the 2018 Winter Simulation Conference, pp. 2897–2908, Gothenburg, Sweden, December 2018. [Online]. Available: Google Scholar

[8]    E. VanDerHorn and S. Mahadevan, “Digital twin: generalization, characterization and implementation,” Decision Support Systems, vol. 145, Article ID 113524, 2021. [Online]. Available: Publisher Site | Google Scholar

[9]    Y. Zhou, Z. Fu, J. Zhang, W. Li, and C. Gao, “A digital twin-based operation status monitoring system for port cranes,” Sensors, vol. 22, p. 3216, 2022. [Online]. Available: Publisher Site | Google Scholar

[10] C. Zhou, “Analytics with digital-twinning: a decision support system for maintaining a resilient port,” Decision Support Systems, vol. 143, 2021. [Online]. Available: Google Scholar

[11] D. Raba, R. D. Tordecilla, P. Copado, A. A. Juan, and D. Mount, “A digital twin for decision making on livestock feeding,” INFORMS Journal on Applied Analytics, vol. 52, no. 3, pp. 267–282, 2022. [Online]. Available: Publisher Site | Google Scholar

[12] J. G. Kotwal, R. Kashyap, and P. M. Shafi, "Artificial Driving based EfficientNet for Automatic Plant Leaf Disease Classification," Multimed Tools Appl, 2023. [Online]. Available: https://doi.org/10.1007/s11042-023-16882-w

[13] V. Roy et al., “Detection of sleep apnea through heart rate signal using Convolutional Neural Network,” International Journal of Pharmaceutical Research, vol. 12, no. 4, pp. 4829-4836, Oct-Dec 2020.

[14] R. Kashyap, "Machine Learning, Data Mining for IoT-Based Systems," in Research Anthology on Machine Learning Techniques, Methods, and Applications, Information Resources Management Association, Ed. IGI Global, 2022, pp. 447-471. [Online]. Available: https://doi.org/10.4018/978-1-6684-6291-1.ch025

[15] L. Heilig, S. Schwarze, and S. Voß, “An Analysis of Digital Transformation in the History and Future of Modern Ports,” in Proceedings of the 50th Hawaii International Conference on System Sciences, Waikōloa Village, HI, USA, January 2017. [Online]. Available: Google Scholar

[16] L. Heilig, “Digital transformation in maritime ports: analysis and a game theoretic framework,” Netnomics: Economic Research and Electronic Networking, vol. 18, no. 2, Springer, 2017. [Online]. Available: Google Scholar

[17] H. P. Sahu and R. Kashyap, "FINE_DENSEIGANET: Automatic medical image classification in chest CT scan using Hybrid Deep Learning Framework," International Journal of Image and Graphics [Preprint], 2023. [Online]. Available: https://doi.org/10.1142/s0219467825500044

[18] S. Stalin, V. Roy, P. K. Shukla, A. Zaguia, M. M. Khan, P. K. Shukla, A. Jain, "A Machine Learning-Based Big EEG Data Artifact Detection and Wavelet-Based Removal: An Empirical Approach," Mathematical Problems in Engineering, vol. 2021, Article ID 2942808, 11 pages, 2021. [Online]. Available: https://doi.org/10.1155/2021/2942808

[19] I. Errandonea, “Digital twin for maintenance: a literature review,” Computers in Industry, vol. 123, Article ID 103316, 2020. [Online]. Available: Google Scholar

[20] S. Y. Barykin, A. A. Bochkarev, O. V. Kalinina, and V. K. Yadykin, “Concept for a supply chain digital twin,” International Journal of Mathematical, Engineering and Management Sciences, vol. 5, pp. 1498–1515, 2020. [Online]. Available: Publisher Site

[21] E. Negri, L. Fumagalli, and M. Macchi, “A review of the roles of digital twin in CPS-based production systems,” Procedia Manufacturing, vol. 11, pp. 939–948, 2017. [Online]. Available: Publisher Site | Google Scholar

[22] T. Greif, N. Stein, and C. M. Flath, “Peeking into the void: digital twins for construction site logistics,” Computers in Industry, vol. 121, Article ID 103264, 2020. [Online]. Available: Publisher Site

Cite this Article as :
Style #
MLA Hitesh Kumar Sharma , Samta Jain Goyal , Sumit Kumar, Abhishek Kumar. "A Novel Approach for Minimizing Response Time in IoT using Adaptive Algorithm." Fusion: Practice and Applications, Vol. 14, No. 2, 2024 ,PP. 08-25 (Doi   :  https://doi.org/10.54216/FPA.140201)
APA Hitesh Kumar Sharma , Samta Jain Goyal , Sumit Kumar, Abhishek Kumar. (2024). A Novel Approach for Minimizing Response Time in IoT using Adaptive Algorithm. Journal of Fusion: Practice and Applications, 14 ( 2 ), 08-25 (Doi   :  https://doi.org/10.54216/FPA.140201)
Chicago Hitesh Kumar Sharma , Samta Jain Goyal , Sumit Kumar, Abhishek Kumar. "A Novel Approach for Minimizing Response Time in IoT using Adaptive Algorithm." Journal of Fusion: Practice and Applications, 14 no. 2 (2024): 08-25 (Doi   :  https://doi.org/10.54216/FPA.140201)
Harvard Hitesh Kumar Sharma , Samta Jain Goyal , Sumit Kumar, Abhishek Kumar. (2024). A Novel Approach for Minimizing Response Time in IoT using Adaptive Algorithm. Journal of Fusion: Practice and Applications, 14 ( 2 ), 08-25 (Doi   :  https://doi.org/10.54216/FPA.140201)
Vancouver Hitesh Kumar Sharma , Samta Jain Goyal , Sumit Kumar, Abhishek Kumar. A Novel Approach for Minimizing Response Time in IoT using Adaptive Algorithm. Journal of Fusion: Practice and Applications, (2024); 14 ( 2 ): 08-25 (Doi   :  https://doi.org/10.54216/FPA.140201)
IEEE Hitesh Kumar Sharma, Samta Jain Goyal, Sumit Kumar, Abhishek Kumar, A Novel Approach for Minimizing Response Time in IoT using Adaptive Algorithm, Journal of Fusion: Practice and Applications, Vol. 14 , No. 2 , (2024) : 08-25 (Doi   :  https://doi.org/10.54216/FPA.140201)
↑