Design of Effective Amplification Signal by Controlling Bandwidth Using Adaptive Learning Technique In Voice Over Internet Protocol
DOI:
https://doi.org/10.22399/ijcesen.659Keywords:
Adaptive Learning, VOIP Technology, Clock Synchronization, Multimedia TransmissionAbstract
VoIP refers to the technology that enables the transmission of audio and video in the form of data packets across an IP network, whether it be a private or public one. Voice over Internet Protocol (VOIP) enables many important benefits for both communication service providers and their customers, including reduced costs, enhanced media offerings, mobility, integration, and portability. Despite this, there are a lot of obstacles to VOIP implementation, such as complex architectures, problems with interoperability, problems with handoff management, and security concerns. In particular, the rise in voice over Internet Protocol (VOIP) call transmission is posing a severe threat to more conventional forms of data transmission, such as text messages, as these older methods simply lack up to the task. Some of the difficulties faced by the user is that packet loss, delay, security, Noise, bandwidth overhead and throughput. This research work provides the probable solution effective data transmission by employ to control the bandwidth using the Adaptive call method in clock synchronization.
References
Zhou, Q., Zhang, F., & Yang, C. (2020). AdaNN: Adaptive neural network-based equalizer via online semi-supervised learning. Journal of Lightwave Technology, 38(16), 4315-4324. doi: 10.1109/JLT.2020.2991028.
Abdullah, M. T. A., Lloret, J., Cánovas Solbes, A., & García-García, L. (2017). Survey of transportation of adaptive multimedia streaming service in internet. Network Protocols and Algorithms, 9(1-2), 85-125.
Babu, R. G., Amudha, V., & Karthika, P. (2020). Architectures and Protocols for Next‐Generation Cognitive Networking. Machine learning and cognitive computing for mobile communications and wireless networks, 155-177. https://doi.org/10.1002/9781119640554.ch7
Zhou, X., Sun, M., Li, G. Y., & Juang, B. H. F. (2018). Intelligent wireless communications enabled by cognitive radio and machine learning. China Communications, 15(12), 16-48. DOI: 10.48550/arXiv.1710.11240
Singh, R., Ghobadi, M., Foerster, K. T., Filer, M., & Gill, P. (2018, August). RADWAN: rate adaptive wide area network. In Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication (pp. 547-560).
He, J., Lee, J., Kandeepan, S., & Wang, K. (2020, November). Machine learning techniques in radio-over-fiber systems and networks. Photonics 7(4);105. https://doi.org/10.3390/photonics7040105
Salameh, H. A. B., Krunz, M., & Younis, O. (2010). Cooperative adaptive spectrum sharing in cognitive radio networks. IEEE/ACM Transactions On Networking, 18(4), 1181-1194. doi: 10.1109/TNET.2009.2039490
Raja, S. K. S., & Louis, A. B. V. (2021). A review of call admission control schemes in wireless cellular networks. Wireless Personal Communications, 120(4), 3369-3388. https://doi.org/10.1007/s11277-021-08618-6
Chincoli, M., & Liotta, A. (2018). Self-learning power control in wireless sensor networks. Sensors, 18(2), 375. https://doi.org/10.3390/s18020375
Wang, T., Li, W., Quaglia, R., & Gilabert, P. L. (2021). Machine-learning assisted optimisation of free-parameters of a dual-input power amplifier for wideband applications. Sensors, 21(8), 2831. https://doi.org/10.3390/s21082831
Shijia Liu and Askar Hamdulla (2020) The Issues on Time Synchronization Technology in Time Triggered Ethernet, Journal of Physics: Conference Series, Volume 1673, 2020 6th Annual International Conference on Computer Science and Applications 25-27 September 2020, Guangzhou, China
M. Muthumalathi, P. B. Pankajavalli and N. Priya (2019) “Efficient Clock Synchronization using Energy Based Proportional Integral and Least Common Multiple Protocol in Wireless Sensor Networks Journal engineering science and technology review, 12(4);144-151. DOI: 10.25103/jestr.124.18
Mrs.G.Saraniya,, Dr.C.Yamini (2022) effective clock synchronization using cristian’s algorithm in wireless sensor network, Semiconductor Optoelectronics 41(12);
Kaksha Thakare1, R.D.Patane (2015). Performance Improvement of QoS Routing In WSN System, International Journal of Scientific and Research Publications, 5(9);
Rajesh Chaudhary, Dr. Sonia Vatta (2014) Performance Optimization of WSN Using Deterministic Energy Efficient Clustering Protocol: A Review IOSR Journal of Engineering (IOSRJEN) 04(03);
S, P., & A, P. (2024). Secured Fog-Body-Torrent: A Hybrid Symmetric Cryptography with Multi-layer Feed Forward Networks Tuned Chaotic Maps for Physiological Data Transmission in Fog-BAN Environment. International Journal of Computational and Experimental Science and Engineering, 10(4);671-681. https://doi.org/10.22399/ijcesen.490
M, P., B, J., B, B., G, S., & S, P. (2024). Energy-efficient and location-aware IoT and WSN-based precision agricultural frameworks. International Journal of Computational and Experimental Science and Engineering, 10(4);585-591. https://doi.org/10.22399/ijcesen.480
M. Devika, & S. Maflin Shaby. (2024). Optimizing Wireless Sensor Networks: A Deep Reinforcement Learning-Assisted Butterfly Optimization Algorithm in MOD-LEACH Routing for Enhanced Energy Efficiency. International Journal of Computational and Experimental Science and Engineering, 10(4);1329-1336. https://doi.org/10.22399/ijcesen.708
J. Prakash, R. Swathiramya, G. Balambigai, R. Menaha, & J.S. Abhirami. (2024). AI-Driven Real-Time Feedback System for Enhanced Student Support: Leveraging Sentiment Analysis and Machine Learning Algorithms. International Journal of Computational and Experimental Science and Engineering, 10(4);1567-1574. https://doi.org/10.22399/ijcesen.780
S.D.Govardhan, Pushpavalli, R., Tatiraju.V.Rajani Kanth, & Ponmurugan Panneer Selvam. (2024). Advanced Computational Intelligence Techniques for Real-Time Decision-Making in Autonomous Systems. International Journal of Computational and Experimental Science and Engineering, 10(4);928-937. https://doi.org/10.22399/ijcesen.591
M. Venkateswarlu, K. Thilagam, R. Pushpavalli, B. Buvaneswari, Sachin Harne, & Tatiraju.V.Rajani Kanth. (2024). Exploring Deep Computational Intelligence Approaches for Enhanced Predictive Modeling in Big Data Environments. International Journal of Computational and Experimental Science and Engineering, 10(4);1140-1148. https://doi.org/10.22399/ijcesen.676
S.P. Lalitha, & A. Murugan. (2024). Performance Analysis of Priority Generation System for Multimedia Video using ANFIS Classifier. International Journal of Computational and Experimental Science and Engineering, 10(4);1320-1328. https://doi.org/10.22399/ijcesen.707
S. Praseetha, & S. Sasipriya. (2024). Adaptive Dual-Layer Resource Allocation for Maximizing Spectral Efficiency in 5G Using Hybrid NOMA-RSMA Techniques. International Journal of Computational and Experimental Science and Engineering, 10(4);1130-1139. https://doi.org/10.22399/ijcesen.665
Ponugoti Kalpana, L. Smitha, Dasari Madhavi, Shaik Abdul Nabi, G. Kalpana, & Kodati , S. (2024). A Smart Irrigation System Using the IoT and Advanced Machine Learning Model: A Systematic Literature Review. International Journal of Computational and Experimental Science and Engineering, 10(4);1158-1168. https://doi.org/10.22399/ijcesen.526
R. Dineshkumar, A. Ameelia Roseline, Tatiraju V. Rajani Kanth, J. Nirmaladevi, & G. Ravi. (2024). Adaptive Transformer-Based Multi-Modal Image Fusion for Real-Time Medical Diagnosis and Object Detection. International Journal of Computational and Experimental Science and Engineering, 10(4);890-897. https://doi.org/10.22399/ijcesen.562
Rama Lakshmi BOYAPATI, & Radhika YALAVARTHI. (2024). RESNET-53 for Extraction of Alzheimer’s Features Using Enhanced Learning Models. International Journal of Computational and Experimental Science and Engineering, 10(4);879-889. https://doi.org/10.22399/ijcesen.519
LAVUDIYA, N. S., & C.V.P.R Prasad. (2024). Enhancing Ophthalmological Diagnoses: An Adaptive Ensemble Learning Approach Using Fundus and OCT Imaging. International Journal of Computational and Experimental Science and Engineering, 10(4);1541-1550. https://doi.org/10.22399/ijcesen.678
B. Paulchamy, Vairaprakash Selvaraj, N.M. Indumathi, K. Ananthi, & V.V. Teresa. (2024). Integrating Sentiment Analysis with Learning Analytics for Improved Student. International Journal of Computational and Experimental Science and Engineering, 10(4);1575-1583. https://doi.org/10.22399/ijcesen.781
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