Investigating the Impact of Network Congestion on the Quality of Service in Wireless Networks Interim Report Contents Literature Review............................................................................................................................ 2 Methodology ................................................................................................................................... 4 Network Topology ...................................................................................................................... 4 Congestion Control Mechanisms ................................................................................................ 4 a. Slow Start Threshold ............................................................................................................... 4 b. Congestion Avoidance ............................................................................................................ 4 c. Congestion Detection .............................................................................................................. 5 Data Collection and Analysis ...................................................................................................... 5 Experiment ...................................................................................................................................... 5 Impact on Delay .......................................................................................................................... 5 Effect on Throughput .................................................................................................................. 5 Analysis of Packet Loss .............................................................................................................. 6 Evaluation of Network Performance ........................................................................................... 6 Visualization and Analysis .......................................................................................................... 6 Conclusion and Implications ....................................................................................................... 7 Plan for completion ......................................................................................................................... 8 References ....................................................................................................................................... 9 Literature Review Previous research has shown that congestion in a network may have a negative impact on quality of service metrics including latency, throughput, and packet loss. Network congestion dramatically impairs QoS, resulting in higher delays and decreased throughput. Because of this, there is a pressing need for efficient methods of traffic control. Bursty traffic, among other traffic characteristics, has been highlighted as a cause of congestion and a detriment to quality of service. Bursty traffic was observed by Ahuja and Shore (2011). to worsen congestion, which in turn reduced quality of service. On the other hand, Kadadha et al., (2018) showed that traffic prioritisation and scheduling, two types of traffic shaping, may reduce congestion and boost QoS in wireless networks. Important to reducing congestion and raising QoS are congestion control technologies. When compared to other congestion management methods, TCP Vegas fared best in terms of keeping latency and throughput to a minimum in an evaluation conducted by Al-Qurabat et al., (2022). Flow-based congestion management algorithms are dynamically adaptable to network circumstances, which is why Chou, Shankar N and Shin, (2005) emphasised their usefulness in enhancing QoS. Delay, throughput, jitter, and packet loss are just few of the metrics that need to be taken into account while assessing QoS in wireless networks. An all-encompassing assessment of network performance in congestion situations was presented, who used a QoS evaluation framework built on fuzzy logic and fuzzy analytic hierarchy process (FAHP). To better understand and anticipate congestion, Tung et al., (2008) created a machine learning-based model to predict congestion and assess its influence on QoS. Joint optimization approaches and the use of NFV and SDN are only two examples of the methods presented by researchers to improve QoS in the face of congestion. Throughput and latency were significantly improved by the introduction of a combined optimisation strategy by Han et al. (2018) that included power control, resource allocation, and user scheduling. The possibility of NFV and SDN for dynamically distributing network resources and reducing congestion to improve quality of service. The literature study concludes that congestion in wireless networks has a major effect on quality of service. Congestion may be reduced and the user experience improved by gaining a deeper knowledge of traffic patterns, establishing efficient congestion management mechanisms, analysing quality of service measurements, and adopting improvement strategies. Network operators, service providers, and academics may all benefit from the review's described results and research contributions when it comes to developing and optimising wireless networks for excellent QoS even in crowded settings. Methodology Network Topology To simulate the wireless network under study, a topology was created. Nodes in the architecture might be hardwired into the network or wirelessly linked, depending on the needs of the investigation. To generate plausible scenarios and evaluate the effect of congestion on quality of service, careful consideration of the network topology was required. Congestion Control Mechanisms Three primary operations, slow start threshold, congestion avoidance, and congestion detection, were implemented in the seventh.cc file to model network congestion and examine its effect on quality of service. a. Slow Start Threshold The slow start threshold method was added to the seventh.cc file. With this technique, the rate at which data is first conveyed increases exponentially. The slow start threshold technique helps the network determine the available bandwidth and adapt to the current network circumstances by gradually raising the packet transmission rate. b. Congestion Avoidance Congestion avoidance is an essential part of traffic management. The strategy for avoiding congestion was implemented by adding lines of code to the seventh.cc file. The pace at which packets are sent is increased additively by this approach. The network's transmission rate may be increased gradually while congestion is being tracked. Congestion avoidance is used so that network performance is optimised without sacrificing throughput. c. Congestion Detection The seventh.cc file was changed to contain congestion detection methods in order to detect network congestion and react properly. Congestion causes a multiplication drop in the packet transfer rate. Congestion is reduced and consistent network performance is preserved thanks to this adaptive technique. Congestion detection allows the network to dynamically modify its transmission rate to prevent unnecessary packet loss and delays. Data Collection and Analysis To measure how network congestion affected QoS, many performance indicators were gathered throughout the simulation. Delay, throughput, packet loss, and other pertinent Quality of Service indicators were among those measured. The seventh.cc file included techniques for collecting data at various points throughout the simulation to record these parameters. This method of data collecting enabled a thorough evaluation of the functioning of the network under heavy load. Experimenta Impact on Delay The outcomes of the experiment conducted through simulation have provided significant perceptions regarding the influence of network congestion on the quality of service. Through the manipulation of network topology, traffic volume, and congestion levels, various scenarios were generated to examine their impact on Quality of Service (QoS). Delay was identified as a crucial performance metric that was evaluated. Effect on Throughput Throughput is a significant quality of service (QoS) metric that quantifies the quantity of data that has been effectively transmitted across a network during a specified time frame. The findings of the simulation indicate that an increase in congestion levels resulted in a decrease in the network's throughput. The decrease in throughput can be attributed to the presence of congestion, which results in the loss of packets and subsequent retransmissions, thereby diminishing the overall capacity for data transfer. Analysis of Packet Loss The analysis also encompassed the phenomenon of packet loss, which denotes the proportion of packets that do not successfully arrive at their intended endpoint. The empirical findings evinced a positive association between network congestion and packet loss. The escalation of congestion resulted in a heightened likelihood of packet loss, attributable to buffer overflow and the incapacity to manage the amplified traffic volume. Evaluation of Network Performance Moreover, the data gathered during the simulation facilitated a thorough assessment of the network's efficiency when subjected to high traffic. Through a comparative analysis of quality of service (QoS) metrics under varying levels of congestion, the efficacy of the employed congestion control mechanisms could be evaluated. Visualization and Analysis The utilization of visual aids in presenting the outcomes of the experiment enabled the discernment of recurring tendencies and configurations. The utilization of graphical representations such as charts and graphs facilitated the comprehension of the correlation between congestion and quality of service (QoS) metrics. The utilization of visual aids facilitated the process of deriving significant inferences and conveying the outcomes proficiently. Conclusion and Implications Finally, the results of the performance assessment demonstrated the noteworthy influence of network congestion on the quality of service in wireless networks. The statement underscores the significance of deploying efficient congestion control mechanisms to alleviate the adverse impacts of congestion. The results of this investigation make a valuable contribution to the continuous exploration and enhancement of approaches for managing congestion in wireless network settings. The study's methodology was utilized to systematically simulate and assess the effects of network congestion on quality of service in wireless networks, ultimately leading to the aforementioned conclusion. The comprehensive understanding of the relationship between congestion and quality of service was facilitated by the experimental results, analysis of performance indicators, and visualization of data. The results underscore the significance of deploying efficient congestion control protocols for preserving ideal network functionality and augmenting user contentment. Plan for completion The introduction includes context, aim, and objectives, and the significance of the research has been completed. In addition, the literature review is also completed. Alongside technical work and project implementation, an appropriate methodology is also completed. Within the month of July, the results and conclusion will be completed. Portion Status Introduction Completed Literature Completed Review Methodology Completed Technical Completed Work Results and Remaining Conclusion March April May June July References Ahuja, S.P. and Shore, W.R., 2011. Wireless Transport Layer Congestion Control Evaluation. International Journal of Wireless Networks and Broadband Technologies, 1(3), pp.71–81. https://doi.org/10.4018/ijwnbt.2011070105. Al-Qurabat, A.K.M., Idrees, A.K., Makhoul, A. and Jaoude, C.A., 2022. A Bi-Level Data Lowering Method to Minimize Transferring Big Data in the Sensors of IoT Applications. Karbala International Journal of Modern Science, [online] 8(2), pp.246–261. https://doi.org/10.33640/2405-609X.3228. Tung, H.Y., Tsang, K.F., Lee, L.T. and Ko, K.T., 2008. QoS for mobile WiMAX networks: Call admission control and bandwidth allocation. In: 2008 5th IEEE Consumer Communications and Networking Conference, CCNC 2008. [online] pp.576–580. https://doi.org/10.1109/ccnc08.2007.134.
