NS2 Tool


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In research and educational objectives, NS-2 is widely used which is an open source software and a discrete-event simulator. Some of the noteworthy and important research topics on the subject of networking technologies that efficiently supports the extensive characteristics of NS-2 are proposed by us, which are very capable for performing a compelling research:

  1. Evaluation of Routing Protocols in Ad-Hoc Networks
  • Included Modules: The modules involved such as OLSR, DSR and AODV.
  • Project Aim: Based on several circumstances such as network size, traffic patterns and node mobility, it is required to contrast the performance of different ad-hoc routing protocols.
  • Assessment Parameters: Routing expenses, end-to-end delay and packet delivery ratio.
  1. Performance Analysis of TCP Variants over Wired and Wireless Networks
  • Included Modules: It incorporates IEEE 802.11 and TCP such as SACK, Vegas, Reno and NewReno.
  • Project Aim: Encompassing the condition like different response time or high packet loss, conduct an extensive research on various TCP variants, in what way it works in wired and wireless platforms.
  • Assessment Parameters: Packet loss rate, throughput and congestion window evolution.
  1. Quality of Service (QoS) in Integrated Services Networks
  • Included Modules: DiffServ and IntServ are the involved modules.
  • Project Aim: In assisting multimedia technologies, review the capability of QoS algorithms through simulating the networks which executes DiffServ and IntServ models.
  • Assessment Parameters: Delay variation like jitter, bandwidth allocation efficiency and packet loss.
  1. IPv6 Network Simulation and Mobility Management
  • Included Modules: This research area employed modules such as Mobile IP and IPv6.
  • Project Aim: Beyond IPv6 networks, it concentrates on mobility management for nodes and investigates the implementation issues and findings of IPv6 networks.
  • Assessment Parameters: Signaling cost, packet delivery success rate and handoff latency.
  1. Wireless Sensor Networks (WSNs) and Energy Consumption Optimization
  • Included Modules: For WSNs like S-MAC, it includes MAC protocols and routing protocols.
  • Project Aim: In WSNs (Wireless Sensor Networks), this study mainly focuses on expanding the durability of the network and involves estimating the energy efficiency of different MAC and routing protocols.
  • Assessment Parameters: Data delivery integrity, energy consumption per node and network lifetime.
  1. LTE Network Performance and Handover Strategies
  • Included Modules: LTE model extensions are the incorporated modules in this project.
  • Project Aim: On user experience and network performance, evaluate the effects of various handover tactics through simulating LTE networks.
  • Assessment Parameters: Productivity throughout the handover, end-to-end latency and handover failure rate.
  1. Network Security Protocols Simulation
  • Included Modules: By applying NS-2 extensions or third-party modules, it encircles modules like IPSec and SSL/TLS.
  • Project Aim: While executing diverse network security protocols, this project seeks to assess the implications of performance.
  • Assessment Parameters: CPU consumption, productivity degradation and encryption/decryption expenses.
  1. Simulation of VANETs (Vehicular Ad-hoc Networks)
  • Included Modules: It encompasses IEEE 802.11p and VANET extensions.
  • Project Aim: For vehicular networks, this study highlights the conditions such as emergency signals and traffic efficiency by exploring the communication protocols.
  • Assessment Parameters: Network connectivity, packet delivery ratio and message dissemination speed.
  1. Underwater Acoustic Sensor Networks
  • Included Modules: Underwater sensor Networks is the main module here.
  • Project Aim: An underwater sensor network is designed and simulated which crucially assess the main concerns of acoustic communication.
  • Assessment Parameters: Authenticity of data transmission, energy efficiency and signal propagation delay.
  1. Internet of Things (IoT) Network Scalability
  • Included Modules: Along with relevant NS-2 extensions or synthesization, the modules MQTT and CoAP are deployed.
  • Project Aim: In the process of handling a huge number of devices, this research aims to investigate the adaptability of Iot protocols.
  • Assessment Parameters: Resource allocation, message expenses and latency.

What are the four files on NS2 SIMULATOR?

Developing and executing simulations often includes communicating with four major types of files in NS-2 (Network Simulator). In the process of specifying, implementing and network simulations, each of these files enact a significant role. In this article, we offer the four important file types with description and practical application:

  1. Tcl Scripts (.tcl)
  • Explanation: The significant central core of NS-2 simulation is Tcl (Tool Command Language) scripts. The network topology, types of traffic for simulation process, simulation parameters and node configurations are specified by these scripts. To construct the simulation settings involves development of nodes, links and for the purpose of defining the deployed network protocols, users write Tcl scripts in an effective manner.
  • Instance of practice: It is specifically implemented to get started with simulation, defining the traffic sources and formulating a basic network topology with nodes.
  1. Trace Files (.tr)
  • Explanation: In order to file the extensive scenario logs of the simulation, NS-2 efficiently produces the trace files throughout the execution of simulation. Every detail about reception, dropping and packet transmission are encompassed in this file. Moreover, in the course of time, it offers a granular view of network activity. To assess the performance of layouts and network protocols, analysts and explorers deploy trace files.
  • Instance of practice: According to the documented scenarios, trace files are helpful to evaluate network performance metrics like response time, productivity and packet loss.
  1. Network Animator (NAM) Files (.nam)
  • Explanation: Exhibit the Simulation visually by means of NAM (Network Animator) files which is modeled by NS-2 simulator. NAM is a graphical interface which aids in opening the files and to observe the behavior of network simulation, it enables the users. To detect the problems which might not be clear in the form of numerical data and for interpreting the activities of the network, observing the simulations in NAM assists the users.
  • Instance of practice: Considering the interpretation of traffic flow, node movements (if it is mobile networks) and protocol behaviors, NAM is implemented to explore the simulation in a visual manner.
  1. AWK Scripts (.awk)
  • Explanation: For processing trace files, AWK scripts are generally deployed in integration with NS-2 simulations, as it is not a unique portion of NS-2. AWK is an effective text processing tool. From the extensive trace files which are produced by NS-2, derive the particular performance metrics by executing the AWK written scripts. Evaluating the end-to-end delay, packet delivery ratio and productivity are included in this script.
  • Instance of practice: It is applicable for parsing the trace files and computing the statistics such as packet delivery ratio, delay and productivity.
NS2 Tool Services

NS2 Simulation Tool Projects

 Explore the world of network simulation with Network Simulator 2, a powerful tool that utilizes C++ and TCL programming languages. At matlabsimulation.com, we specialize in NS2 Simulation Tool Projects customized to fit your research requirements. Keep in contact with us for top results!

  1. Jointly optimized resource allocation for SDN control and forwarding planes in edge-cloud SDN-based networks
  2. Digital twin-driven SDN for smart grid: A deep learning integrated blockchain for cybersecurity
  3. Load-aware dynamic controller placement based on deep reinforcement learning in SDN-enabled mobile cloud-edge computing networks
  4. FTODefender: An efficient flow table overflow attacks defending system in SDN
  5. Intent-based network slicing for SDN vertical services with assurance: Context, design and preliminary experiments
  6. ESCALB: An effective slave controller allocation-based load balancing scheme for multi-domain SDN-enabled-IoT networks
  7. Machine learning enabled network and task management in SDN based Fog architecture
  8. Adaptive population-based multi-objective optimization in SDN controllers for cost optimization
  9. Extensive performance analysis of OpenDayLight (ODL) and Open Network Operating System (ONOS) SDN controllers
  10. Towards a blockchain-SDN-based secure architecture for cloud computing in smart industrial IoT
  11. SDN-enabled vehicular networks: Theory and practice within platooning applications
  12. Collaborative prediction and detection of DDoS attacks in edge computing: A deep learning-based approach with distributed SDN
  13. S-FoS: A secure workflow scheduling approach for performance optimization in SDN-based IoT-Fog networks
  14. AI-assisted traffic matrix prediction using GA-enabled deep ensemble learning for hybrid SDN
  15. HyPASS: Design of hybrid-SDN prevention of attacks of source spoofing with host discovery and address validation
  16. An approach towards removal of data heterogeneity in SDN-based IoT framework
  17. Deep-transfer learning framework in SDN for gateway ports security
  18. GwPFV: A novel packet forwarding verification mechanism based on gateways in SDN-based storage environment
  19. Automatic, verifiable and optimized policy-based security enforcement for SDN-aware IoT networks
  20. An authentication approach in SDN-VANET architecture with Rider-Sea Lion optimized neural network for intrusion detection

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