FSR Protocol Projects Examples Using NS2

FSR Protocol Projects Examples Using NS2 tool that we worked are listed here if you want novel project ideas and topics let our team assure you with best project guidance. Below we present numerous project ideas containing the FSR (Fisheye State Routing) protocol that can be implemented in NS2:

  1. Performance Evaluation of FSR in MANETs
  • Objective: Examine the performance of Fisheye State Routing (FSR) in Mobile Ad-Hoc Networks (MANETs) under differing mobility and traffic loads.
  • Method: Replicate a MANET within NS2 using FSR as the routing protocol. Differ the node mobility and traffic patterns, and calculate the performance parameters like packet delivery ratio, end-to-end delay, routing overhead, and control message overhead.
  • Outcome: A comprehensive estimation of FSR’s scalability and effectiveness in highly dynamic environments, concentrating on how its minimized routing overhead influences the exactness of routing information across long distances.
  1. Comparison of FSR and AODV in Mobile Ad-Hoc Networks
  • Objective: Compare the performance of FSR including AODV (Ad-hoc On-demand Distance Vector) routing protocol within MANETs to discover which protocol is better under particular conditions.
  • Method: Replicate a MANET using NS2 with two scenarios: one using FSR and the other using AODV. Compute the parameters like packet delivery ratio, route discovery time, routing overhead, and latency under various network sizes and mobility patterns.
  • Outcome: A comparative analysis which emphasises the strengths and weaknesses of FSR’s proactive, distance-based routing and AODV’s reactive routing mechanism, concentrating on its adaptability in mobile networks.
  1. Energy-Efficient FSR in Wireless Sensor Networks (WSNs)
  • Objective: Alter FSR to make an energy-efficient version suitable for Wireless Sensor Networks (WSNs) that energy conservation is crucial.
  • Method: Mimic a WSN within NS2 using altered version of FSR, which minimizes the frequency of routing updates according to the node energy levels. Estimate the performance metrics like network lifetime, energy consumption, packet delivery ratio, and routing overhead compared to the standard FSR protocol.
  • Outcome: An energy-optimized FSR protocol, which conserves battery life even though maintaining efficient routing, improving the network’s longevity in resource-constrained environments.
  1. FSR Protocol in Vehicular Ad-Hoc Networks (VANETs)
  • Objective: Compute the performance of FSR in Vehicular Ad-Hoc Networks (VANETs) that high mobility and fast topology changes are usual.
  • Method: Mimic a VANET within NS2 using FSR as the routing protocol. Examine the network under various vehicle speeds and traffic conditions, and calculate the performance metrics like route stability, packet delivery ratio, and end-to-end delay.
  • Outcome: An analysis of FSR’s suitability for VANET environments, concentrating on its ability to adjust to frequent topology changes and maintain exact routing data despite high mobility.
  1. FSR with QoS Support for Real-Time Applications
  • Objective: Alter FSR to deliver Quality of Service (QoS) support for real-time applications, like VoIP and video streaming, in MANETs.
  • Method: Replicate a MANET in NS2 with a changed version of FSR, which prioritizes routes depending on QoS metrics such as bandwidth, delay, and jitter. Calculate performance metrics like packet delivery ratio, end-to-end delay, and jitter for both real-time and non-real-time traffic.
  • Outcome: A QoS-enhanced FSR protocol, which delivers better support for time-sensitive applications, make sure low latency and minimal jitter for critical information streams.
  1. FSR with Adaptive Route Maintenance for Dynamic Networks
  • Objective: Change the FSR to adapt its route maintenance frequency rely on the network conditions like node mobility and link quality.
  • Method: Mimic a dynamic ad-hoc network within NS2 using a changed version of FSR, which maximizes the frequency of route updates once node mobility or link failure rates are high. Assess the performance parameters such as packet delivery ratio, routing overhead, and end-to-end delay under differing network conditions.
  • Outcome: An adaptive version of FSR, which balances routing overhead and route stability, enhancing performance in dynamic networks in which topology modifies often.
  1. FSR for Large-Scale MANETs: Scalability Analysis
  • Objective: Estimate the scalability of FSR in large-scale Mobile Ad-Hoc Networks (MANETs) including hundreds or thousands of nodes.
  • Method: Mimic a large-scale MANET within NS2 using FSR as the routing protocol. Change the number of nodes and calculate the performance metrics like packet delivery ratio, routing overhead, and convergence time as the network size maximizes.
  • Outcome: Insights into the scalability of FSR, concentrating on how its fisheye approach (with more frequent updates for near nodes and less frequent updates for distant nodes) behaves in large, dense networks.
  1. Security Enhancements for FSR in MANETs
  • Objective: Execute the security mechanisms in FSR to defend the routing process from attacks like blackhole, grayhole, and wormhole attacks in MANETs.
  • Method: Replicate a MANET in NS2 using FSR and launch malicious nodes, which execute routing attacks. Execute security aspects such as message authentication, encryption, and trust-based routing. Calculate the protocol’s performance such as packet delivery ratio, routing overhead, and resilience to attacks.
  • Outcome: A security-enhanced FSR protocol, which protects versus general routing attacks, maintaining high routing efficiency and then make sure secure communication within MANET environments.
  1. Energy-Efficient FSR for IoT (Internet of Things) Networks
  • Objective: Adjust FSR to manage an energy constraints in IoT networks that devices have limited power and need efficient communication.
  • Method: Replicate an IoT network within NS2 using a changed version of FSR, which enhances route updates and packet forwarding rely on node energy levels. Compute performance parameters such as energy consumption, network lifetime, and packet delivery ratio.
  • Outcome: An energy-aware FSR protocol, which extends the operational lifetime of IoT devices whereas maintaining reliable data delivery over the network.
  1. FSR with Congestion Control for High-Traffic MANETs
  • Objective: Execute the congestion control mechanisms in FSR to enhance the performance in high-traffic networks.
  • Method: Replicate a high-traffic MANET within NS2 using FSR. Launch congestion control approaches like adaptive route selection rely on traffic load or limiting the number of forwarded packets. Calculate the performance metrics such as packet loss, throughput, and end-to-end delay.
  • Outcome: A congestion-controlled version of FSR, which minimizes packet loss and enhances throughput in high-traffic networks, make sure effective communication even under heavy traffic loads.
  1. FSR in Disaster Recovery Networks
  • Objective: Assess the use of FSR for reliable communication in disaster recovery networks that infrastructure is unobtainable, and nodes must depend on ad-hoc routing.
  • Method: Replicate a disaster recovery situation within NS2 using FSR as the routing protocol. Estimate performance parameters like packet delivery ratio, route discovery time, and routing overhead under differing node mobility and network density conditions.
  • Outcome: An estimation of how FSR can be used in disaster recovery situations to deliver reliable communication although the absence of fixed infrastructure and dynamic network conditions.
  1. FSR with Mobility Prediction for Improved Route Stability
  • Objective: Execute the mobility prediction in FSR to enhance the route stability in highly dynamic networks.
  • Method: Replicate a MANET within NS2 using a changed version of FSR, which expects node mobility and proactively adapts routes before nodes are moved out of range. Estimate performance metrics such as packet delivery ratio, route discovery time, and route stability under various mobility patterns.
  • Outcome: A mobility-prediction-enabled FSR protocol, which enhances route stability and minimise packet loss by proactively maintaining routes rely on predicted mobility.
  1. Performance Analysis of FSR in Mixed Traffic Networks
  • Objective: Examine how FSR behaves in networks with mixed traffic types like real-time video, VoIP, and best-effort traffic.
  • Method: Mimic a MANET using NS2 with FSR managing both real-time and non-real-time traffic. Calculate the performance metrics like end-to-end delay, jitter, packet delivery ratio, and throughput for each traffic type under differing network loads.
  • Outcome: A performance estimation displaying how successfully FSR balances the requirements of real-time and non-real-time traffic, make sure effective routing and communication in mixed traffic networks.

Above illustrated some project examples deliver a broad range of opportunities to discover the Fisheye State Routing (FSR) utilising NS2. These projects cover vital features such as scalability, security, energy efficiency, congestion control, and QoS, permitting you to estimate how FSR behaves and can enhance for various environments, like MANETs, VANETs, WSNs, IoT, and disaster recovery networks. Also, we will be offered additional details on this protocol in another manual.