Named Data Networking Projects examples using omnet++

Named Data Networking (NDN) is a paradigm shift from old-style IP-based networking, concentrating on content-centric networking instead of host-centric. It is used to retrieve the data by name instead of by location (IP address) that can optimize security, efficiency and resilience in different networking scenarios. Below, we provide some examples of Named Data Networking (NDN) projects that can be implemented using OMNeT++:

1. Basic NDN Architecture Simulation

• Objective: Based on the content names instead of IP addresses, we can understand how the data is requested and retrieved by simulating a simple Named Data Networking (NDN).
• Implementation: Generate a network where nodes request data by sending Interest packets and receive Data packets in response. Execute the basic elements of NDN encompassing Content Store (CS), Pending Interest Table (PIT) and Forwarding Information Base (FIB).
• Extension: Evaluate the performance of NDN in terms of data retrieval times, cache hit ratios, and network overhead. Compare the efficiency of NDN with traditional IP-based communication in different network topologies.

2. NDN Caching Strategies

• Objective: Mimic various caching strategies in NDN to emhance data retrieval and decrease network congestion.
• Implementation: Execute different caching strategies lilke Least Recently Used (LRU), Least Frequently Used (LFU), and Random Replacement, in the Content Store (CS) of NDN nodes. Nodes should cache Data packets to serve future requests efficiently.
• Extension: Analyze the influence of various caching strategies on cache hit ratios, latency, and network load in changing traffic conditions. Simulate scenarios with popular and less popular content to assess the effectiveness of each strategy.

3. Security and Privacy in NDN

• Objective: Guard data consistency, authenticity and user confidentiality by simulating security mechanisms in NDN.
• Implementation: Implement content-based security features like digital signatures and encryption for Data packets. Discover techniques for securing Interest packets from potential attacks like Interest flooding.
• Extension: Simulate different attack scenarios such as Interest flooding, cache poisoning, and replay attacks. Assess the effectiveness of the executed security measures in mitigating these threats while upholding network performance.

4. NDN in IoT Networks

• Objective: Simulate Named Data Networking in an Internet of Things (IoT) environment to explore its advantages in data-centric IoT applications.
• Implementation: Set up an IoT network where sensors and devices communicate using NDN principles. Deploy features for efficient data spreading, accumulation, and retrieval in the network.
• Extension: Measure the performance of NDN based on energy consumption, data retrieval latency, and network scalability. Compare the effectiveness of NDN in IoT networks with old-fashioned IP-based approaches.

5. NDN-Based Content Distribution

• Objective: Enhance the delivery of multimedia content like video or live streams by simulating a content distribution network (CDN).
• Implementation: Build a network where content is distributed from several sources using NDN. Implement strategies for efficient content imitation, caching, and retrieval as per the user demand.
• Extension: Analyze the performance of the NDN-based CDN according to the content delivery latency, bandwidth usage, and user experience. Act out scenarios with changing content popularity and network conditions to assess the CDN’s scalability.

6. NDN-Based Vehicular Networks

• Objective: Optimize the data distribution and recovery in a dynamic and mobile environment by simulating NDN in Vehicular Ad Hoc Networks (VANETs).
• Implementation: Develop a VANET where vehicles transmit data using NDN. Execute strategies for efficient data forwarding, caching, and retrieval considering vehicle mobility and intermittent connectivity.
• Extension: Estimate the performance of NDN in VANETs in terms of data delivery success rates, latency, and network overhead. Simulate various traffic scenarios like urban intersections, highways, and rural roads, to assess the sturdiness of NDN in vehicular environments.

7. NDN-Based Disaster Recovery Networks

• Objective: Mimic the use of NDN in disaster recovery scenarios where outdated communication structure may be compromised.
• Implementation: Desing a network where nodes include drones or mobile devices, use NDN to share vital information in a disaster-stricken area. Implement strategies for efficient data dissemination and retrieval in the absence of stable infrastructure.
• Extension: Simulate scenarios with changing degrees of network damage, node mobility, and data priorities. Evaluate the effectiveness of NDN in upholding communication and information sharing in disaster recovery efforts.

8. NDN in Smart City Applications

• Objective: Imitate the use of NDN in smart city applications to enforce efficient data sharing and communication amongst different city services and infrastructures.
• Implementation: Set up a smart city network where services like traffic management, public safety, and environmental observing use NDN for data exchange. Implement strategies for data aggregation, caching, and retrieval through various services.
• Extension: Assess the performance of NDN in terms of data retrieval efficiency, scalability, and network resilience in a smart city environment. Simulate scenarios with high data traffic and changing service demands to analyze the system’s flexibility.

9. NDN-Based Mobile Edge Computing

• Objective: In the network edge, we can discover its advantages in decreasing latency and enhancing data accessibility by simulating NDN in mobile edge computing (MEC) environment.
• Implementation: Use NDN to create a network where edge servers and mobile devices communicate. Implement strategies for data caching, processing, and retrieval at the edge of the network to minimize latency and bandwidth usage.
• Extension: Assess the performance of NDN in MEC in terms of data access latency, network load, and resource utilization. Simulate scenarios with changing edge server abilities and user mobility patterns to assess the system’s efficiency.

10. NDN for Real-Time Streaming Applications

• Objective: Mimic the use of NDN for real-time streaming applications like live video or audio streaming, to optimize QoS and low the latency.
• Implementation: Execute NDN-based mechanisms for real-time content delivery, containing Interest packet aggregation, adaptive bitrate streaming, and in-network caching. The network should help dynamic content retrieval to meet real-time demands.
• Extension: Analyze the effect of NDN on streaming quality, latency, and user experience in various network conditions like changing bandwidth, user density, and mobility. Compare the performance of NDN-based streaming with old-fashioned IP-based streaming approaches.

11. NDN-Based Network Function Virtualization (NFV)

• Objective: Enhance the implementation and management of virtualized network functions by simulating NDN in the context of Network Function Virtualization (NFV).
• Implementation: Configure a network where virtualized network functions (VNFs) are deployed and handled using NDN. Execute techniques for efficient function discovery, varying, and data forwarding in an NDN-enabled NFV environment.
• Extension: Measure the performance of NDN-based NFV in terms of function discovery time, resource utilization, and scalability. Assess the efficiency of the system by simulating situations that has varying network loads and function chaning requirements.

12. NDN for Privacy-Preserving Data Sharing

• Objective: Imitate privacy-preserving data sharing features in NDN, concentrates on guarding user identities and data privacy.
• Implementation: Execute privacy-enhancing methods like content-based encryption, anonymous Interest packet forwarding, and access control mechanisms in an NDN network. The system should make sure that data is only accessible to accredited users while shielding user privacy.
• Extension: Analyze the effectiveness of the privacy-preserving mechanisms in terms of data privacy, access control, and user anonymity. Evaluate the rigidity of the system by simulating scenarios with changing privacy requirements.

13. NDN in Wireless Sensor Networks (WSNs)

• Objective: Mimic the use of NDN in Wireless Sensor Networks (WSNs) to enhance data aggregation, dissemination, and energy utilization.
• Implementation: Use NDN principles to configure the WSN where sensor nodes communicate. Implement strategies for better data accumulation, caching, and forwarding, considering the restricted energy and processing abilities of sensor nodes.
• Extension: Analyze the performance of NDN in WSNs in terms of data delivery latency, energy efficiency, and network lifetime. Simulate scenarios with changing sensor densities, data generation rates, and energy constraints to evaluate the system’s efficiency.

14. NDN-Based Content Delivery for Augmented Reality (AR)

• Objective: Imitate NDN for content delivery in Augmented Reality (AR) applications, focusing on minimizing latency and making sure timely data retrieval.
• Implementation: Execute NDN-based mechanisms for delivering AR content, as well as Interest packet prioritization, in-network caching, and dynamic content recovery based on user location and context.
• Extension: Evaluate the influence of NDN on AR experience quality that has latency, frame rate, and content precision. Simulate scenarios with changing user mobility, content difficulty, and network conditions to assess the system’s performance.

15. Scalability Analysis of NDN in Large-Scale Networks

• Objective: Simulate and evaluate the scalability of NDN in large-scale networks like the Internet or large enterprise networks.
• Implementation: Generate a large-scale network topology with a vital number of nodes and execute NDN routing, caching, and data recovery features. The simulation should focus on managing large volumes of Interest and Data packets efficiently.
• Extension: Analyze the scalability of NDN in terms of routing table size, cache management, and network overhead. Simulate scenarios with differing network sizes, traffic patterns

In conclusion, we comprehensively provided the brief demonstration on how to approach the projects using Named Data Networking (NDN) and their sample examples which is implemented in OMNeT++ environment. We plan to offer additional examples through another manual, if needed. We request you to connect with us at omnet-manual.com, where we specialize in Named Data Networking Projects utilizing omnet++. Our team of skilled developers is dedicated to ensuring your projects are completed on time and efficiently