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. We can develop intelligent algorithms that adaptively adjust congestion control parameters in real time by utilizing historical traffic patterns, link conditions, and congestion events. These intelligent algorithms are able to learn from the dynamics of the network, anticipate scenarios that are prone to congestion, and actively take preventative measures. Congestion control in wireless networks is the focus of our study, which aims to address the particular difficulties that these environments present. We hope to improve network performance, enhance user experience, and lay the groundwork for the effective implementation of future wireless technologies by integrating intelligent decision-making, traffic differentiation, and adaptive channel access. Wireless networks necessitate novel strategies for congestion control in order to guarantee optimal performance and scalability. We can effectively reduce congestion and unlock the full potential of wireless networks for supporting a wide range of applications and services by utilizing advanced traffic differentiation techniques, adaptive channel access mechanisms, and intelligent algorithms.

Congestion Control, Wireless Networks, Contention-Based Access Protocols, Machine Learning, Intelligent Algorithms, Adaptive Channel Access, Network Performance and Scalability

[1] Lakshman, T. V., and Upamanyu Madhow. "The performance of TCP/IP for networks with high bandwidth-delay products and random loss." IEEE/ACM transactions on networking, Vol.5, Issue.3, pp.336-350, 1997.
[2] Alim Al Islam, A. B. M., and Vijay Raghunathan. "iTCP: an intelligent TCP with neural network based end-to-end congestion control for ad-hoc multi-hop wireless mesh networks." Wireless Networks, Vol.2, Issue.1, pp.581-610, 2015.
[3] Neghabi, Ali Akbar, et al. "Load balancing mechanisms in the software defined networks: a systematic and comprehensive review of the literature." IEEE access, Vol.5, Issue.6, pp.14159-14178, 2018.
[4] Tomar, G.S., Shrivastava, L. and Bhadauria, S.S., "Load balanced congestion adaptive routing for randomly distributed mobile adhoc networks", Wireless personal communications, Vol.7, Issue.7, pp.2723-2733, 2014.
[5] Xu, Xiaolong, et al. "Joint optimization of resource utilization and load balance with privacy preservation for edge services in 5G networks." Mobile Networks and Applications, Vol.2, Issue.5, pp.713-724., 2020.
[6] Singh, Karishma, Karan Singh, and Ahmed Aziz. "Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm." Computer Networks, Vol.13, Issue.8, pp.90-107, 2018.
[7] Wang, Cao, et al. "Size-based congestion control using network utility maximization." IEEE Transactions on Control of Network Systems Vol.9, issue.2, pp.550-561, 2022.
[8] Zhao, Shasha, and Gan Yu. "Channel allocation optimization algorithm for hybrid wireless mesh networks for information physical fusion system." Computer Communications, Vol.17, Issue.8, pp.212-220, 2021.
[9] Srivastava, Vikas, et al. "Energy efficient optimized rate based congestion control routing in wireless sensor network." Journal of Ambient Intelligence and Humanized Computing, Vol.1, Issue.1, pp.1325-1338, 2020.
[10] Ahmed, Omar, et al. "Energy optimized congestion control-based temperature aware routing algorithm for software defined wireless body area networks." IEEE Access, Vol.8, pp.41085-41099, 2020.
[11] H. Kaur, S. Sharma, "Behaviour Analysis of EDEEC for 4-Level Heterogeneous Wireless Sensor Networks", International Journal of Computer Sciences and Engineering, Vol.8, Issue.8, pp.5-12, 2020.
[12] G. Rajeswarappa, S. Vasundra, "Energy Aware Routing Protocols for Wireless Mobile Ad hoc Networks: A Review", International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.866-872, 2019.
[13] Keerthi D S, Shobha Rani A, Basavaraju T G, "Delay-Based Routing Mechanism for Load Balanced Routing in Wireless Mesh Networks", International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.501-506, 2019.