A CROSS LAYER FRAMEWORK FOR CONGESTION CONTROL USING RECEIVER SIDE FEEDBACK SCHEMES OVER MANETS

January 2017
Vol-3, Issue-1
Paper ID: 3731
ISSN: 2395-4396
Downloads: 0

Abstract & Details

Research Area
CSE
Keywords
TCP DSDV RRED MANETs.
Abstract
Wireless Ad hoc networks are short-lived wireless networks because they are composed to fulfil a certain goal and refrain to exist after fulfilling this goal. Wireless Mobile stations might promptly join or leave the Ad hoc network at any instance, thus Ad hoc networks have a dynamic framework. In most Wireless networking applications, the protocols are categories into distinct modules to compose a protocol stack. Each layer accomplishes benefit of the services bring by the layer directly below it, and also grant service to the layer exactly above it. The source destination transmission is restraint between neighbouring layers with a least possible set of primitives. The layering mechanism facilitates the construction and deployment and contributes the possibility of alternative layer implementations. The characteristics of wireless Ad hoc networks contradict from infrastructure based networks in many ways. Wireless networks have minimum medium scope and maximum bit error rates. Because of the forthright coupling among the physical layer and the upper layers, the conventional protocol stack is not satisfactory for infra-structureless networks. Cross-layer scheme is an effective research pace to enhance infra-structureless or wireless network performance, where information is transferred automatically between different protocol layers. in this paper, the effect of Robust Random Early Detection (RRED) active queue management technique with different TCP variant - TCP-LP, TCP-Cubic, TCP-Westwood and TCP-Compound under varying congested network density is examined on to check the improvement and performance of DSDV under the FTP traffic. The effect of network size on the TCP variants with and without RRED was studied. Experimental studies show that TCP LP and TCP Westwood along with RRED technique perform much better than the others.

Author Information

# Name Institute / Affiliation
1 Priya GNIT,Mullana, ambala
2 Kamal Gupta GNIT,Mullana, ambala

How to Cite

Use the following formats to cite this article in your research.

APA Style
Priya & Gupta, Kamal (2017). A CROSS LAYER FRAMEWORK FOR CONGESTION CONTROL USING RECEIVER SIDE FEEDBACK SCHEMES OVER MANETS. International Journal of Advance Research and Innovative Ideas In Education, 3(1), 722-732.
MLA Style
Priya, and Kamal Gupta. "A CROSS LAYER FRAMEWORK FOR CONGESTION CONTROL USING RECEIVER SIDE FEEDBACK SCHEMES OVER MANETS." International Journal of Advance Research and Innovative Ideas In Education, vol. 3, no. 1, 2017, pp. 722-732.
IEEE Style
Priya and Kamal Gupta, "A CROSS LAYER FRAMEWORK FOR CONGESTION CONTROL USING RECEIVER SIDE FEEDBACK SCHEMES OVER MANETS," International Journal of Advance Research and Innovative Ideas In Education, vol. 3, no. 1, pp. 722-732, 2017.
Vancouver Style
Priya, Gupta Kamal. A CROSS LAYER FRAMEWORK FOR CONGESTION CONTROL USING RECEIVER SIDE FEEDBACK SCHEMES OVER MANETS. International Journal of Advance Research and Innovative Ideas In Education. 2017;3(1):722-732.
Harvard Style
Priya & Gupta, Kamal (2017) 'A CROSS LAYER FRAMEWORK FOR CONGESTION CONTROL USING RECEIVER SIDE FEEDBACK SCHEMES OVER MANETS', International Journal of Advance Research and Innovative Ideas In Education, 3(1), pp. 722-732.
Chicago Style
Priya and Kamal Gupta. "A CROSS LAYER FRAMEWORK FOR CONGESTION CONTROL USING RECEIVER SIDE FEEDBACK SCHEMES OVER MANETS." International Journal of Advance Research and Innovative Ideas In Education 3, no. 1 (2017): 722-732.
Turabian Style
Priya and Kamal Gupta. "A CROSS LAYER FRAMEWORK FOR CONGESTION CONTROL USING RECEIVER SIDE FEEDBACK SCHEMES OVER MANETS." International Journal of Advance Research and Innovative Ideas In Education 3, no. 1 (2017): 722-732.

Export Citation

Related Research

A Deep Learning-Based Framework for Mood-Oriented Music Recommendation Using Facial Expression Analysis
Vaibhav Ashok Bhangare et al. 2026 Computer Engineering
PDF Unavailable
Survey On : Intelligent Payroll and Human Resource Management Systems: A Systematic Review of Automation, Security, and Analytics
Vishakha Jadhav et al. 2026 Human Resource Management, Artificial Intelligence, Machine Learning, Payroll Systems, Cybersecurity, Business Intelligence, Robotic Process Automation, Employee Analytics, Digital Transforma
PDF Unavailable
Civic Engagement & Empowerment Platform
Supriya Dadaso Bankar et al. 2026 Computer engineering
PDF Unavailable
RAG System Development with Pydantic AI ChromaDB & Groq
Prof. Priyanka P. Kakade et al. 2026 Computer Engineering
PDF Unavailable
Machine Learning Based Early Stage Diabetes Detection System
Rohan Mulik et al. 2026 Computer Engineering
PDF Unavailable
A Survey on Skillsense:AI Career Analyzer App
Kirti Datir et al. 2026 Computer Engineering
PDF Unavailable
Employee Performance Portal
P.Harika et al. 2026 Computer science and engineering
PDF Unavailable