GPSR Protocol Projects Examples Using NS2

GPSR Protocol projects examples that ns2project.com has assisted scholars with are listed here. We provide all types of research support and simulation results. Our team boasts over 14 years of experience, ensuring you receive the best guidance. No matter where you are, you can count on our online support. We guarantee timely delivery and 100% original work. Given below are some project instances for implementing the GPSR (Greedy Perimeter Stateless Routing) protocol using NS2:

  1. Performance Evaluation of GPSR in MANETs
  • Objective: Estimate the performance of GPSR in Mobile Ad-hoc Networks (MANETs) with differing node densities and mobility patterns.
  • Method: Configure a MANET simulation using NS2 with GPSR as the routing protocol. Examine various situations by differed the amount of nodes and their mobility speed. Compute the performance metrics such as packet delivery ratio, end-to-end delay, and routing overhead.
  • Outcome: Insights into how successfully GPSR behaves in mobile environments, especially under various mobility and density conditions.
  1. Comparison of GPSR with AODV and DSR in VANETs
  • Objective: Compare the performance of GPSR with other routing protocols like AODV (Ad hoc On-Demand Distance Vector) and DSR (Dynamic Source Routing) within a Vehicular Ad-Hoc Network (VANET).
  • Method: Replicate a VANET environment with vehicle mobility within NS2, using GPSR, AODV, and DSR protocols. Calculate performance parameters like packet delivery ratio, latency, and route discovery time.
  • Outcome: A comparative investigation of the suitability of GPSR, AODV, and DSR in VANETs, emphasizing which protocol is finest for high-mobility networks.
  1. Energy-Efficient GPSR in Wireless Sensor Networks
  • Objective: Enhance the GPSR for energy efficiency within Wireless Sensor Networks (WSNs) that sensor nodes have limited power.
  • Method: Alter the GPSR algorithm to integrate energy-aware metrics. Mimic a WSN using NS2, and compare the performance of the standard GPSR and the energy-enhanced version such as network lifetime and power consumption.
  • Outcome: An energy-efficient version of GPSR, including an analysis displaying how the modifications prolong the network lifetime and minimize an energy consumption.
  1. GPSR in Urban Environments with Obstacle-Aware Routing
  • Objective: Execute an improved version of GPSR, which carries into account obstacles in urban environments, like buildings that block line-of-sight communication.
  • Method: Mimic an urban scenario using NS2 that obstacles are launched among the nodes. Alter GPSR to contain obstacle avoidance, using methods like detour routing. Compute the influence of obstacles on routing efficiency.
  • Outcome: A GPSR variant enhanced for urban settings with developed packet delivery ratio and then minimized packet loss because of obstacles.
  1. Scalability Analysis of GPSR in Large-Scale Networks
  • Objective: Examine the scalability of GPSR in large-scale networks including thousands of nodes.
  • Method: Mimic large-scale network topologies with GPSR as the routing protocol. Estimate how the behaviour of GPSR scales with maximizing network size by evaluating routing overhead, packet delivery ratio, and convergence time.
  • Outcome: Insights into the scalability of GPSR and its ability to manage the large networks without important degradation in performance.
  1. GPSR-Based Geographic Multicast Routing
  • Objective: Prolong the GPSR protocol to support multicast routing by integrating geographic multicast tree construction.
  • Method: Alter GPSR to execute the multicast routing that nodes are grouped rely on geographic regions. Mimic multicast communication using NS2 and calculate the effectiveness of data delivery to numerous ends.
  • Outcome: A geographic multicast routing extension of GPSR with performance analysis displaying how effectively it supports group communication in wireless networks.
  1. Improved GPSR with Link-Quality Estimation
  • Objective: Improve GPSR by integrating the link-quality estimation to enhance routing decisions.
  • Method: Change GPSR to contain a mechanism, which estimates the quality of links amongst nodes, taking into account factors such as signal strength and packet loss. Replicate the network using NS2 and compare the performance of the standard GPSR and the improved version.
  • Outcome: A more reliable version of GPSR, which chooses higher-quality links, leading to less dropped packets and more stable routes.
  1. Security Enhancement of GPSR to Prevent Routing Attacks
  • Objective: Execute the security aspects in GPSR to avoid routing attacks like black hole and wormhole attacks.
  • Method: Replicate a network using NS2 in which malicious nodes are attempt to interrupt GPSR routing. Execute the security measures such as neighbour verification and packet authentication to defend versus these attacks. Assess the performance of the secure GPSR under attack situations.
  • Outcome: A secure version of GPSR, which mitigates routing attacks, including analysis displaying their efficiency in maintaining network performance under attack.
  1. GPSR in Underwater Sensor Networks (UWSNs)
  • Objective: Adjust GPSR for use in underwater sensor networks that old wireless communication is unreliable according to the water propagation features.
  • Method: Change the GPSR to account for the particular challenges of underwater communication, like slower propagation speeds and higher packet loss. Replicate the UWSN using NS2 and then estimate how successfully GPSR behaves in this environment.
  • Outcome: A changed GPSR protocol enhanced for underwater networks, including performance analysis displaying their suitability for UWSN applications.
  1. Mobility-Based GPSR Enhancement for UAV Networks
  • Objective: Change GPSR to enhance their behaviour in UAV (Unmanned Aerial Vehicle) networks with high node mobility.
  • Method: Execute improvements in GPSR to take into account the fast mobility of UAVs, like predictive routing rely on the velocity and direction of nodes. Replicate a UAV network using NS2 and compute the influence on routing performance.
  • Outcome: A mobility-optimized GPSR version for UAV networks, displaying enhancements such as packet delivery, route stability, and minimized overhead in highly mobile situations.

Above project examples will support you to discover various features of the GPSR protocol, its performance, and its adaptability to numerous network environments using NS2 simulations. As above we had explained how to execute and simulate the GPSR protocol through given sample projects. Also, we will offer more specific insights rely on your requirements.