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Ravi, Sharan Pravin
- The Role Of Integrated Structured Cabling System (ISCS) For Reliable Bandwidth Optimization In High-speed Communication Network
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, IN
2 Department of Computer Science and Engineering, HKBK College of Engineering, IN
3 Department of Electronics and Communication Engineering, Vetri Vinayaha College of Engineering and Technology, IN
4 Department of Automation Control and Robotics, Sheffield Hallam University, GB
1 Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, IN
2 Department of Computer Science and Engineering, HKBK College of Engineering, IN
3 Department of Electronics and Communication Engineering, Vetri Vinayaha College of Engineering and Technology, IN
4 Department of Automation Control and Robotics, Sheffield Hallam University, GB
Source
ICTACT Journal on Communication Technology, Vol 13, No 1 (2022), Pagination: 2635-2639Abstract
In modern companies, the functions of divisions, departments and staff are provided by telecommunication transmitting analog and digital unit information via SCS. Such cable system refers to the use of copper or optical cable networks, passive and active switching devices. Structured cabling system or abbreviated SCS is a complex set of cable trunks and switching equipment that provide the transfer of various types of media data (audio, video, computer data) and is the basis for the operation and integration of telephone, local computer networks, security systems and other services. Many modern systems of security or communications today integrate a wide variety of interfaces into their arsenal, greatly expanding their capabilities and performance. In this paper a smart model based on high-speed communication network with the help of structured cabling system (SCS). Here the speed and bandwidth play the major role. The proposed system focused the highspeed communication between sender and receiver with some higher bandwidth optimization.Keywords
Optical Cable Network, Switching Device, Structured Cabling System, Communication Network, Security System
References
- E. Hossain, D. Niyato and Z. Han, “Dynamic Spectrum Access in Cognitive Radio Networks”, Cambridge University Press, 2009.
- T.D. Lagkas, D. Klonidis and I. Tomkos, “Joint Spatial and Spectral Resource Optimization over Both Wireless and Optical Fronthaul Domains of 5G Architectures”, Proceedings of 22nd International Conference on Transparent Optical Networks, pp. 1-7, 2020.
- Yuan Ai, Gang Qiu, and Yaohua Sun, “Joint Resource Allocation and Admission Control in Sliced Fog Radio Access Networks”, China Communications, Vol. 17, No. 8, pp. 14-30, 2020
- N. Khumalo, O. Oyerinde and L. Mfupe, Luzango, “Reinforcement Learning-based Computation Resource Allocation Scheme for 5G Fog-Radio Access Network”, Proceedings of 5th International Conference on Fog and Mobile Edge Computing, pp. 353-355, 2020.
- A. Kaloxylos, “A Survey and an Analysis of Network Slicing in 5G Networks”, IEEE Communications Standards Magazine, Vol. 2, No. 1, pp. 60-65, 2018.
- S.A. Syed, K. Sheela Sobana Rani and V.P. Sundramurthy, “Design of Resources Allocation in 6G Cybertwin Technology using the Fuzzy Neuro Model in Healthcare Systems”, Journal of Healthcare Engineering, Vol. 2022, pp. 1-9, 2022.
- Y. Wang, K. Wang, H. Huang, T. Miyazaki and S. Guo, “Traffic and Computation Co-Offloading with Reinforcement Learning in Fog Computing for Industrial Applications”, IEEE Transactions on Industrial Informatics, Vol. 15, No. 2, pp. 976-986, 2019.
- G. Dhiman, A.V. Kumar, R. Nirmalan and K. Srihari, “Multi-Modal Active Learning with Deep Reinforcement Learning for Target Feature Extraction in Multi-Media Image Processing Applications”, Multimedia Tools and Applications, Vol. 2022, pp. 1-25, 2022.
- L. Huang, X. Feng, C. Zhang, L. Qian and Y. Wu, ‘Deep Reinforcement Learning-Based Joint Task Offloading and Bandwidth Allocation for Multiuser Mobile Edge Computing”, Digital Communications and Networks, Vol. 5, No. 1, pp. 10-17, 2019.
- S. Hannah, A.J. Deepa, V.S. Chooralil and S. Brilly Sangeetha, “Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data”, BioMed Research International, Vol. 2022, pp. 1-7, 2022.
- L. Ze, C. Lijie and R. Bo, “Study on the Virtual Simulation Training System for SCS Maintenance”, Proceedings of International Conference on Virtual Reality and Intelligent Systems, pp. 143-146, 2020.
- J. Logeshwaran and R.N. Shanmugasundaram, “Enhancements of Resource Management for Device to Device (D2D) Communication: A Review”, Proceedings of 3 rd International Conference on IoT in Social, Mobile, Analytics and Cloud, pp. 51-55, 2019.
- K. Praghash and T. Karthikeyan, “Data Privacy Preservation and Trade-off Balance Between Privacy and Utility using Deep Adaptive Clustering and Elliptic Curve Digital Signature Algorithm”, Wireless Personal Communications, Vol. 89, pp. 1-16, 2021.
- N. Arivazhagan, K. Somasundaram, D. Vijendra Babu and V. Prabhu Sundramurthy, “Cloud-Internet of Health Things (IOHT) Task Scheduling using Hybrid Moth Flame Optimization with Deep Neural Network Algorithm for E Healthcare Systems”, Scientific Programming, Vol. 2022, pp. 1-8, 2022.
- K. Praghash and T. Karthikeyan, “Binary Flower Pollination (BFP) Approach to Handle the Dynamic Networking Conditions to Deliver Uninterrupted Connectivity”, Wireless Personal Communications, Vol. 82, No. 4, pp. 3383-3402, 2021.