In general, Named Data Networking (NDN) is popularly known as a data-intensive network model. Specifically, NDN incorporates Information-centric and Content-centric Networks for better performance. NDN model is usually constructed based on real-time events like in what way people will search, query, and develop content. To make their tasks simplified, NDN replaces the long addresses with a short data name.

From this article, you can gain more awareness on Named Data Networking using MATLAB with current research areas and ideas!!!

From the code execution point of view, we recommend the MATLAB tool for NDN-related projects. Since it enables object-oriented programming to effectively simulate differently. Also, it supports various cache replacement strategies.

How does Named Data Networking works?

Compare to other networks, NDN has unique characteristics to deliver requested user data. Here, it uses two types of packets as Data Packets and Interest Packets.

• On using interest packets, the user/client can request content retrieval.
• On using data packets, the provider can deliver the content to the request user/client.

To efficiently forward the data through optimal route, the network component should meet the following structures,

• Content Store (CS)
• Pending Interest Table (PIT)
• Forwarding Information Base (FIB)

In recent days, NDN creates its imprints in many real-time research areas such as video conferencing, live broadcast, and data dissemination due to its versatile capability. Further, it also largely prefers interest storage more than forwarding. Below, we have given you some interesting research notions in NDN.

Research Ideas in NDN

• Detection of Flooding attack
• Active Pending Interest / Data Lifespan
• Optimal Route Selection for data forwarding
• Discovery of Data Caching
• Emergency or Traffic Message Distribution

Further, we have also given you the various types of NDN techniques that are widely used in many applications. Our research team has long-lasting experience in handling the NDN research domain. So, we are familiarized with all possible techniques and algorithms regardless of type to implement Named Data Networking using MATLAB. Further, we also suggest apt solutions based on your handpicked application’s needs.

Different Types of NDN Methodologies

• Neighbour based
  • Utilized Parameters – Caching and Speed
  • For instance – Density based delay tolerant data forwarding
• Distribution based
  • Utilized Parameters – Interest Packet Acceptable rate and Automobile Speed
  • For example: Bio-inspired based scattered data forwarding
• Geolocation based
  • Utilized Parameters – Number of Transferred Packets and Locality
  • For instance – Last Encounter Content
• Priority based
  • Utilized Parameters – QoS metrics
  • For example: Communication link stability-aware data forwarding
• Push Traffic based
  • Utilized Parameters – Commercial Messages and Traffic
  • For example: Push-aware critical interest forwarding

Now, we can see few hybrid technologies of named data networks that support in MATLAB tool. Since the current research scholars are moving towards hybrid innovations to elevate the status of research work from others. Here, we have listed hybrid NDN areas that we currently working for our handhold scholars.

Integrated Research Areas in Named Data Networking

• NDN-based Internet of Things (IoT)
  • Improvisation of QoS in Push Pull Traffic Routing
  • NDN-based Edge Device Manipulation for IIoT
  • 5G Network Coding in Enhanced IP forwarding
• NDN-based Space-Grounded Integrated Network (SGIN)
  • Delay Tolerable-Aware SGI-NDN Model Designing
  • Energy-Aware Resource Distribution
  • Various System Architecture Modeling for SGI-NDN
• NDN-based Vehicular Ad-hoc Network (VANET)
  • NDN based Video Search and Retrieval in Urban Mobility Scenario
  • VNDN-based Directional Antenna for Geo-based Routing
  • SDN-based Interest Packet Monitoring, Forwarding and Storing

Beyond the above set of areas, NDN holds the hands of other emerging research areas such as WSN, Heterogeneous Networks, MANET, 5G, SDN, etc. We support you not only on hybrid technologies but also in other independent NDN projects. Overall, we have a motive to achieve novel research topics for unique research contributions. In addition, we have highlighted two significant trends for Named Data Networking using MATLAB projects

Research Trends in Named Data Networking

LTE-based NDN

• Share data between eNodeB and UE through unicast protocol (uplink / downlink)
• When the UE are need of resources, then UE can request eNodeN through dynamic scheduling process
• For that, UE firstly send buffer status report for informing buffered data with priority and then secondly transmit scheduling request message
• In addition, NDN enables V2X, V2V, M2M, D2D communications through M-MIMO and MIMO techniques

5G-based NDN

• IP over ICN Protocol (ICoP) is a protocol used to establish connection between NDN and 5G
• Similar to VPN, ICoP users are addressed as user equipment (UE)
• UE produced traffic (minimum interest packets) are transmitted to the local ICoP client
• Mid-way routers again transmit the interest packets to IPoC gateway for content retrieval

We hope you are clear with the current research update of the NDN research field. Now, we can briefly see about the MATLAB simulation tool. Since NDN-related projects are largely implemented in MATLAB due to its high capabilities and sophisticated in-built tolls for NDN.

Matlab Tools and Toolboxes for NDN Projects

Here, we have listed few top toolboxes that are specially provisioned for the NDN research field.

• Spline Toolbox
• Fuzzy Logic Toolbox
• Control System Toolbox
• Robust Control Toolbox
• Wavelet Toolbox
• Optimization Toolbox
• Vehicle Network Toolbox
• Database Toolbox
• Signal Processing Toolbox
• Statistic Toolbox
• Neural Network Toolbox
• Simulink
• Robust Control Toolbox
• Communication Toolbox
• System Identification Toolbox
• Symbolic Math Toolbox

For your reference, our development team has shared one sample on Named Data Networking using MATLAB. In this, we have specified the different input parameters (detecting DDoS attacks and backpropagation) and simulation parameters. Since the simulation results are needed to be evaluated to know the performance of the utilized techniques and methods. The below parameters are specific for this project. The parameters will vary from project to project based on requirements. Our developer surely suggests you best-fitting parameters for achieving expected results.

Best Matlab Projects in Named Data Networking

Project title – Backpropagation Technique for DDoS Attack Detection in NDN using MATLAB

Input Parameters for Detecting DDOS Attack through back propagation

• In_Data assessment of Data for incoming
• In_Interests assessment of Interests for incoming
• SatisfiedInterests calculation of acceptable Interests (all faces)
• In_Nacks valuation of NACKs for incoming
• TimedOutInterests calculation of session out Interests (all faces)
• Out_Nacks valuation of NACKs for outgoing
• Out_SatisfiedInterests estimation of outgoing acceptable Interests (for each outgoing face)
• Out_Interests assessment of Interests for outgoing
• Out_Data assessment of Data for outgoing
• In_TimedOutInterests computation of incoming session out Interests (for each incoming face)
• Out_TimedOutInterests estimation of outgoing session Interests (for each outgoing face)
• In_SatisfiedInterests computation of incoming acceptable Interests (for each incoming face)

Parameters for Backpropagation

• Activation Function
  • SIGMOID and SOFTMAX
• Hidden Layers Counts – 2
• Output Layers – 2
  • “01” (No-Attack)
  • “10” (Attack)
• Total node count in the Hidden Layers – 20

Matlab Simulation Parameters for NDN

• Data Freshness Time
• Sampling Time
• Good Provider Content
• User’s Interest Rate (Default)
• Good Provider Link Delay
• Experiment Time
• Bad Provider Link Delay
• Repetition per Attack Rate
• Bad Provider Content

One most important factor in the development phase is the performance assessment. Through this, we can analyze the overall network behaviour and employed techniques achievements. Further, we can also enhance the performance by adjusting the performance metrics in the designing phase itself using Matlab.

Performance Metrics for NDN

• Delay
• Completion Rate
• Throughput
• Response Duration
• Accumulation Time
• Number of Messages
• Number of Satisfied Interests
• Content Delivery Rate
• Number of Locations
• Total Number of Transmitted Packets
• CDF of Interest Satisfaction Time
• Provider Delay (Certificate Sending and Data Signing)
• Client Delay (Certificate Fetching, Data Validation and Data Fetching)

Beyond MATLAB, we also support you in other tools that provide the accurate result on Named Data Networking projects. Depends on the key requirement of the project, we will select the appropriate tool. For your information, here we have given few add-on tools.

Simulation Tools for NDN projects

• NS2
• OMNeT++ with SUMO
• Qualnet with VanetMobiSim
• NS3 ndnSIM

From the above list, we have handpicked “NS3 ndnSIM” as an example. To support NDN, the NS-3 tool offered the ndn-cxx library which encloses ndnSIM. In this, it uses the revised ndn::Face method to transmit both Data Packets and Interest Packets. Further, it includes NFD instances for uplink and downlink transmission. Overall, the revised version with ndnSIM allows you to any kind of real-time NDN application. To the end, efficiently allocate the workload in the logical processor to increase the parallelization.