This research focuses on the simulation and comparative analysis of two prominent queue management techniques, Tail Drop and Random Early Detection (RED), within the context of a mesh network. Efficient queue management is crucial for optimizing network performance, especially in scenarios with diverse traffic patterns and varying levels of congestion. The study aims to evaluate and contrast the effectiveness of Tail Drop and RED in mitigating congestion and enhancing the overall quality of service in a mesh network environment.

The simulation involves the integration of Tail Drop and RED within a simulated mesh network using established networking simulation tools. Various network parameters, such as traffic load, node density, and topology, are manipulated to assess the performance of each queue management technique. Key metrics, including packet loss, throughput, and delay, are analyzed to provide insights into the strengths and weaknesses of Tail Drop and RED in different network conditions.

The paper discusses the simulation setup, detailing the configuration of Tail Drop and RED parameters, and highlights the observed behaviors of each technique under various scenarios. Results from the simulations contribute to a comparative analysis, aiding network designers and researchers in selecting the most suitable queue management technique based on specific application requirements and network conditions.

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