Congestion Control Techniques to Improve the Performance of Wireless Networks Using Dynamic Routing and Load Balancing Techniques

Abstract

The proliferation of wireless networks has revolutionized our communication landscape, enabling ubiquitous connectivity and empowering various applications and services. However, new difficulties arise as wireless networks continue to develop and grow, necessitating novel strategies for effectively reducing congestion. In this paper, we explore the arising congestion control issue in remote organizations and propose novel procedures to address it. Customary congestion control components were fundamentally intended for wired networks and may not completely line up with the special attributes and limitations of remote conditions. Congested wireless networks have resulted in decreased performance, increased latency, and reduced throughput as a result of the rapid growth in the number of wireless devices and the rising demand for high-bandwidth applications. Moreover, the heterogeneity of remote connections, portability examples, and impedance acquaint extra intricacies with blockage control. We propose a multifaceted approach to the new wireless network congestion control issue to address these issues. Right off the bat, we advocate for the combination of cutting edge traffic separation methods. We can allocate network resources more effectively and prioritize critical traffic during congestion events by categorizing traffic according to priority, requirements for quality of service, and application characteristics. Second, we stress the significance of channel access mechanisms that are adaptable. Existing conflict based admittance conventions like CSMA/CA are restricted in their capacity to deal with clog in remote organizations. We propose improved channel access instruments that powerfully change access probabilities, ease off boundaries, or conflict window sizes in light of the noticed clog levels and organization conditions. This adaptive strategy makes sure that channels are used fairly and effectively, preventing congestion hotspots and maximizing network performance overall. Thirdly, we investigate how artificial intelligence and machine learning can be used to improve congestion control in wireless networks.