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Design a wireless network consisting of 20 nodes within a topology measuring 400x300 units. The network should utilize UDP as the agent type and Continuous Bit Rate (CBR) traffic. The simulation duration should be 300 seconds. Nodes must be deployed uniformly across the area, and each node should transmit packets continuously during the simulation period. The wireless node configuration must incorporate the NS-2 energy model to accurately simulate energy consumption. The simulation should be run using two different routing protocols—selecting from DSDV, AODV, DSR, and TORA—to compare their performance. Finally, critically analyze the generated trace files, focusing on the number of packets sent, received, dropped, and the amount of energy consumed. These parameters should be represented visually through detailed graphs and discussed comprehensively. Include screenshots of the Network Animator (NAM) that display the uniform deployment of nodes during the simulation, along with at least three screenshots capturing different stages or views of the simulation process. Additionally, an appendix should contain the TCL scripts used for each simulation run, demonstrating the configuration and parameter settings of the network models.

Paper For Above instruction

Designing and analyzing a wireless network with specific parameters involves meticulous planning, simulation, and evaluation to understand how different routing protocols impact network performance, especially concerning energy efficiency and data transmission effectiveness. This paper aims to provide a comprehensive overview of creating such a network, running simulations with varying routing protocols, and critically analyzing the results, emphasizing practical implementation and theoretical insights.

Introduction

Wireless networks have become a fundamental technology enabling mobile communication, IoT, and ubiquitous connectivity. In particular, ad hoc wireless networks, characterized by their decentralized architecture, require careful planning to optimize performance and energy usage. The deployment of 20 nodes within a constrained area of 400x300 units provides a manageable yet sufficiently complex environment for simulation studies. The core goal is to evaluate the performance of different routing protocols—specifically DSDV, AODV, DSR, and TORA—under identical conditions, focusing on their efficiency and reliability.

Network Design and Simulation Setup

The network design involves uniform deployment of 20 nodes within a 400x300 area, which ensures fair distribution of traffic load and reduces nodes' overlap or clustering effects. The Uniform Spatial Distribution methodology facilitates equitable node placement, achieved through random but evenly spread node positioning algorithms in NS-2. UDP agents paired with CBR traffic simulate real-time data streams, which are essential for analyzing network responsiveness and stability over continuous operation for 300 seconds.

The NS-2 simulation script requires specifying the MAC layer, physical layer, mobility model, and energy model. The energy model incorporated uses parameters such as initial energy, transmission energy, reception energy, and idle power consumption, aligned with real-world battery characteristics. Nodes transmit continuously, which facilitates the observation of energy depletion and the endurance of the network under different routing protocols.

Routing Protocols and Performance Metrics

The choice of routing protocols significantly influences network performance. DSDV (Destination Sequenced Distance Vector) operates on a table-driven approach, providing immediate route availability but potentially higher overhead. AODV (Ad hoc On-demand Distance Vector) and DSR (Dynamic Source Routing) are reactive protocols that establish routes on-demand, reducing overhead but possibly increasing delay during route discovery. TORA (Temporally Ordered Routing Algorithm) adapts quickly to topological changes, making it suitable for mobile environments but more complex to implement.

Performance parameters considered include the number of packets sent, received, dropped, and energy consumption. These parameters directly relate to network efficiency, quality of service, and energy sustainability, key considerations in wireless ad hoc networks. These metrics are visually represented through graphs generated from trace files, facilitating comparative analysis.

Analysis of Simulation Results

The trace files produced by NS-2 provide comprehensive data on packet transmissions, receptions, drops, and energy metrics. Graphs plotting these parameters reveal differences among the protocols. For example, DSDV may show higher packet delivery ratios due to its proactive nature but at the cost of higher energy consumption and routing overhead. Conversely, AODV and DSR might demonstrate lower energy use but could exhibit higher packet drops during route discovery or link failures.

Energy consumption analysis highlights how reactive protocols adapt faster but may lead to increased energy drain during route establishment phases. TORA, with its rapid reconfiguration abilities, can maintain reliable routes in dynamic conditions but may also incur higher energy costs depending on the network dynamics.

Visual Documentation

The simulations include screenshots of NAM displaying the uniform node deployment, showcasing the spatial layout and movement patterns if any. Additionally, three different screenshots capture the network during various stages, illustrating packet flow, routing table updates, and potential packet drops or energy depletion zones.

Conclusion

This study underscores the importance of selecting appropriate routing protocols tailored to specific network requirements, balancing efficiency, reliability, and energy consumption. The simulation results affirm that while proactive protocols like DSDV guarantee immediate route availability, reactive protocols like AODV and DSR optimize energy use and adaptability. TORA offers a middle ground with fast recovery capabilities. Future work could involve testing hybrid protocols or incorporating mobility models to evaluate performance in more dynamic environments.

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