Improved Adaptive Neuro Fuzzy Inference System Based Congestion Control for Wireless Network
Keywords:
HMM, ANFIS, Wireless Network and congestion controlAbstract
The multi-hop wireless network has been a crucial addition to wired networks for the goal of ubiquitous networking. Quality of Service (QoS) support in multi-hop wireless networks is a topic of extensive research due to the widespread utilization of multimedia applications that demand QoS guarantees. The initial step in providing QoS assurances in multi-hop wireless networks is typically acquiring information on end-to-end available bandwidth. A wireless Congestion management Scheme based on Extended Kalman filtering and Bandwidth (CSEKB) is created in this earlier study. By constructing a noise perception factor, the CSEKB is able to discern between different types of packet loss and effectively observe the bandwidth oscillation of (WNs). The congestion management parameters are modified in accordance with the congestion factor to enhance the performance of the WNs. The CSEKB, unfortunately, is unable to resolve the issue of congestion collapse brought on by numerous packet collisions in shared media. The machine learning or soft computing methods are needed to deploy in the congestion control. In order to fix this issue, the proposed system designed a Hidden Markov Model with Improved Adaptive Neuro Fuzzy Inference System (HMM -IANFIS) for available bandwidth prediction and congestion detection in wireless network. In wireless network, to predict the available bandwidth rate Hidden Markov Model (HMM) is utilized. Additionally, the prediction outcome serves as the foundation for the subsequent step of congestion detection. Then based on the actual optimal sending rate and smoothed delay, perform congestion detection with the help of IANFIS. The experimental data demonstrates that the suggested system achieves great performance in terms of packet delivery ratio, end to end delay, and throughput when compared to the earlier techniques.
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