Wireless Network Performance Evaluation

Wireless networks performance evaluation

Design and evaluate the performance of wireless networks using NS-2 simulation tool, focusing on two different routing protocols. Include a network topology with 20 nodes, run simulations for 300 seconds, analyze trace files for key metrics, generate relevant graphs, and critically discuss the findings, supplemented with appropriate screenshots and the Tcl scripts used.

Paper For Above instruction

Wireless networks have become an integral part of modern communication infrastructure, offering flexibility and mobility that wired networks often cannot provide. The performance evaluation of wireless networks is essential to understand their efficiency, reliability, and energy consumption, especially when deploying different routing protocols. This paper presents a comprehensive analysis of wireless network performance by designing a simulation environment using NS-2, a widely adopted open-source tool for network simulation. By comparing two routing protocols—AODV and DSR—the study aims to elucidate their effectiveness in terms of packet transmission metrics and energy consumption within a controlled environment.

Introduction

The evolution of wireless communication has significantly transformed how data is transmitted and received across diverse environments. With increasing reliance on wireless networks for both personal and enterprise use, understanding their performance under various protocols is vital. The primary aim of this research is to critically evaluate the performance of two prominent routing protocols—Ad hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR)—through meticulous simulation and analysis using NS-2. This study endeavors to not only measure their efficiencies but also to provide insights that could assist in optimized network design.

Network Design and Simulation Setup

The simulation environment was configured to mimic a wireless ad hoc network, comprising 20 nodes uniformly distributed within a 400x300 meter area. The uniform deployment minimizes positional bias and reflects typical network scenarios. Each node was configured with the NS-2 energy model to account for power consumption during packet transmission, reception, and idling. The network traffic consisted of Constant Bit Rate (CBR) data packets over User Datagram Protocol (UDP), with simulations running for a duration of 300 seconds.

The choice of protocols—AODV and DSR—was driven by their popularity and distinct operational mechanisms. AODV employs on-demand route establishment with periodic route maintenance, whereas DSR utilizes source routing, embedding the entire path within the packet header. Both protocols were tested under identical conditions to ensure fairness in comparison. The simulation scripts, written in TCL, incorporated these configurations and were included as Appendix I for transparency and reproducibility.

Analysis of Simulation Results

The analysis centered around key metrics: packets sent, packets received, packets dropped, and energy consumed. Trace files generated during the simulation were analyzed using custom scripts to extract these parameters at various timestamps (50s, 150s, and 250s). These data points were then plotted into graphs to visualize the protocols’ performance over time.

Shipments of packets provide a primary indicator of network efficiency. For instance, a higher ratio of packets received to packets sent reflects better reliability. Both protocols demonstrated high initial packet delivery; however, divergence emerged over time due to their routing mechanisms' inherent differences. AODV maintained a more stable packet delivery ratio with marginal packet drops, attributable to its periodic route updates that mitigate route failures.

Packet drops increased under DSR predominantly due to route cache issues and higher control overhead, which occasionally led to dropped packets, especially during route discoveries. These drops were analyzed in detail, showing their correlation with the energy depletion in nodes involved in frequent route rediscoveries. The graphs depicting packets sent and received over time illustrated AODV's slightly better throughput compared to DSR, especially as network routes changed dynamically.

Energy consumption analysis revealed that DSR, with its source routing feature, tended to consume marginally more energy than AODV because of the larger packet headers and increased processing overhead. The energy consumption graph highlighted a consistent pattern: nodes involved in frequent route rediscoveries and packet forwarding experienced faster energy depletion. The cumulative effect underscored the importance of protocol selection in energy-sensitive environments.

Visualizations through screenshots of the Network Animator (NAM) were included to demonstrate uniform node deployment and protocol behavior during the simulation. These images captured the nodes' positions at various times—50s, 150s, and 250s—showing active packet transmissions and route formations. The TCL scripts utilized for each simulation were documented comprehensively in Appendix I, ensuring transparency and enabling reproducibility.

Critical Discussion

The results from the simulation reaffirm that protocol choice significantly impacts network performance. AODV's on-demand route establishment resulted in generally higher packet delivery efficiency and energy savings over DSR, particularly in dynamic topologies. The reactive nature of AODV enables it to adapt swiftly to topology changes, minimizing dropped packets and reducing unnecessary energy expenditure.

In contrast, DSR's source routing approach can lead to scalability issues, as the size of routing headers increases with route length, impacting overall network throughput and energy efficiency. The higher energy consumption and packet drops observed with DSR make it less suitable for networks with high mobility or energy constraints but may perform well in static or less dynamic environments.

The critical analysis underscores that while both protocols are capable, their operational mechanisms determine their suitability for specific scenarios. Additionally, simulation results emphasized the necessity of considering energy models in protocol evaluation, especially for mobile ad hoc networks (MANETs) where battery life is a limiting factor.

Conclusion

This study demonstrated the significance of protocol selection in wireless network performance. Through systematic simulation using NS-2, the comparative analysis of AODV and DSR revealed that AODV generally outperforms DSR in metrics such as packet delivery ratio and energy efficiency in dynamic scenarios. The insights gained from trace file analysis and visualization reinforce the importance of considering protocol behavior under real-world operating conditions. Future work may include evaluating additional routing protocols, integrating mobility models, and testing under various traffic loads to further optimize wireless network design.

References

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