Named Data Networking Projects Examples Using NS2

Named Data Networking projects examples using ns2 for academics are provided below. We offer prompt assistance as well as excellent research services, so don’t hesitate to contact our team to succeed in your career. With our assistance, you can complete your thesis with distinction. We offer specialized subjects and professional advice for successful simulation solutions.

Named Data Networking (NDN) is a data-centric networking architecture, which concentrates on content retrieval by name, instead of using host-based addressing such as IP networks. NDN delivers advantages such as better security, mobility support, and efficient data dissemination. Executing NDN projects using NS2 that permits to explore several features of this developing the networking paradigm. The followings are some NDN project examples, which can replicate using NS2:

  1. Caching Strategies in NDN
  • Project Focus: Compare and replicate various in-network caching strategies like Least Recently Used (LRU), Least Frequently Used (LFU), and First-In-First-Out (FIFO) in NDN.
  • Objective: Examine the effect of caching strategies on the content retrieval efficiency and cache hit ratio.
  • Metrics: Cache hit ratio, content retrieval delay, bandwidth consumption, and cache replacement overhead.
  1. Content Distribution in NDN
  • Project Focus: Mimic and execute the content distribution within an NDN environment in which data is fetched rely on content names instead of host addresses.
  • Objective: Concentrate on how NDN can be improved content distribution efficiency in networks, minimising data duplication and enhancing network utilization.
  • Metrics: Content retrieval time, bandwidth savings, cache hit ratio, and network overhead.
  1. Security Mechanisms in NDN
  • Project Focus: Execute the security mechanisms like data signing and content verification in NDN to make certain that the authenticity and integrity of content.
  • Objective: Assess the performance overhead of security mechanisms such as latency and bandwidth usage.
  • Metrics: Verification time, message integrity, encryption overhead, and delay.
  1. Interest Packet Flooding Mitigation in NDN
  • Project Focus: Replicate the Interest flooding problem within NDN and execute the mechanisms such as rate limiting, token bucket filtering, or PIT (Pending Interest Table) size limits to mitigate it.
  • Objective: Learn how Interest packet flooding can avoid to enhance network stability and minimise congestion.
  • Metrics: Interest packet drop rate, network congestion, PIT utilization, and delay.
  1. Mobility Support in NDN
  • Project Focus: Execute mobility-aware forwarding approaches within NDN to support mobile nodes and content retrieval in dynamic environments.
  • Objective: Estimate how NDN manages mobility situations compared to traditional IP networks and how sending strategies can adjust.
  • Metrics: Data retrieval success rate, handoff latency, packet delivery ratio, and delay.
  1. Quality of Service (QoS) in NDN
  • Project Focus: Replicate QoS-aware forwarding strategies within NDN in which high-priority content (e.g., video streams) is prioritized across the regular content.
  • Objective: Focus on how NDN can execute QoS parameters like bandwidth, latency, and jitter for various kinds of content.
  • Metrics: Latency, jitter, content delivery success rate, and priority content bandwidth utilization.
  1. Routing and Forwarding in NDN
  • Project Focus: Execute and compare various forwarding strategies in NDN, like best-route, multicast, and flooding, to estimate their behaviour in various network situations.
  • Objective: Examine how several NDN routing and forwarding strategies are impact content recovery performance and network overhead.
  • Metrics: Packet delivery ratio, routing overhead, content retrieval time, and network load.
  1. Content-Based Load Balancing in NDN
  • Project Focus: Replicate the load balancing mechanisms within NDN according to content requests, distributing traffic between several ways to prevent the congestion.
  • Objective: Concentrate on how NDN can be delivered content requests over numerous nodes to balance the network load and enhance overall performance.
  • Metrics: Load distribution efficiency, content retrieval delay, packet loss rate, and bandwidth usage.
  1. NDN in IoT (Internet of Things) Networks
  • Project Focus: Execute NDN in an IoT environment in which sensor nodes are generate data, which is named and requested by users or applications.
  • Objective: Learn how NDN’s data-centric method can be enhanced data retrieval, caching, and security in IoT networks.
  • Metrics: Data retrieval time, cache hit ratio, energy consumption, and network traffic.
  1. NDN-Based Video Streaming
  • Project Focus: Mimic video streaming applications across NDN that video segments are requested by name and cached along the path.
  • Objective: Estimate the performance of NDN within delivering high-quality video streams such as buffering time and streaming quality.
  • Metrics: Buffering time, video quality (resolution), content retrieval delay, and bandwidth utilization.
  1. Content Prefetching in NDN
  • Project Focus: Execute content prefetching methods within NDN in which content is proactively cached at intermediate nodes rely on the predicted future requests.
  • Objective: Focus on how prefetching enhances the content availability and then minimizes content retrieval latency.
  • Metrics: Content retrieval time, prefetching overhead, cache hit ratio, and bandwidth consumption.
  1. Congestion Control in NDN
  • Project Focus: Execute the congestion control mechanisms within NDN to handle the data flow and avoid congestion during high traffic periods.
  • Objective: Analyse how congestion control approaches such as window-based or rate-based control can be enhanced NDN performance.
  • Metrics: Congestion occurrence, content retrieval time, packet drop rate, and throughput.
  1. Interest Aggregation in NDN
  • Project Focus: Replicate an Interest aggregation mechanisms in which several identical Interest packets are combined to minimize redundant requests in NDN.
  • Objective: Examine how Interest aggregation can be minimized traffic and also enhance the overall network performance.
  • Metrics: Interest packet overhead, bandwidth savings, delay, and PIT utilization.
  1. NDN for Smart City Applications
  • Project Focus: Execute NDN-based smart city applications (e.g., smart traffic management, smart energy grids) in which data is requested and distributed rely on named content.
  • Objective: Estimate how NDN can be enhanced the effectiveness and scalability of data recovery in smart city applications.
  • Metrics: Data retrieval time, network scalability, content delivery success rate, and resource usage.
  1. NDN for Disaster Recovery Networks
  • Project Focus: Mimic NDN in disaster recovery situations where structure is damaged, and then communication depend on content-based retrieval.
  • Objective: Learn how NDN can use to enable the efficient communication in the absence of traditional IP-based infrastructure.
  • Metrics: Delivery success rate, data retrieval latency, network survivability, and cache hit ratio.
  1. NDN in Vehicular Ad-hoc Networks (VANETs)
  • Project Focus: Replicate the NDN in a VANET situation in which vehicles are communicate with each other by requesting data according to the named content.
  • Objective: Assess how NDN can be enhanced the data sharing and recovery in highly dynamic environments such as vehicular networks.
  • Metrics: Content retrieval time, delivery ratio, handoff delay, and network overhead.
  1. Multi-Access Edge Computing (MEC) in NDN
  • Project Focus: Mimic and execute the MEC in an NDN environment that edge nodes cache and serve content near to the users.
  • Objective: Concentrate on how edge computing enhances NDN performance by minimizing the latency and offloading traffic from the core network.
  • Metrics: Content retrieval latency, cache hit ratio at the edge, and core network bandwidth savings.
  1. NDN-Based Peer-to-Peer (P2P) Networks
  • Project Focus: Replicate P2P file sharing in NDN that content is delivered over the peers and retrieved using content names.
  • Objective: Focus on how NDN can be improved the P2P networks by minimizing data duplication and then enhancing data retrieval efficiency.
  • Metrics: File retrieval time, network overhead, bandwidth usage, and peer participation rate.

As above illustrated manual, we presented the some example projects concerning the Named data networking projects containing the execution, strategies and their estimation process within the ns2 environment. We will also be provided another set of examples in another manual, if needed.