In simple words, Named Data Networking (NDN) is recently introduced architecture as a substitution for existing host-based IP networks to deliver the contents by IP and data packets. In the existing method, the packets are distributed over the network based on the IP address. On the contrary, the NDN method follows “data-name” in every packet for easy data identification and distribution. On using packet names, the router makes decisions over packet forwarding. It is easy for routers to cache packets and enhance network bandwidth. In this way, it substitutes the host-intensive IP networks with data-intensive networks. Overall, NDN enables data dissemination with its caching content.
This page provides an opportunity for active scholars to know the developments of named data networking for software-defined vehicular networks from research perspectives!!!
Both Software-defined Network (SDN) and NDN are common in altering inherited networking architectures. On the one hand, NDN uses data-name instead of IP address for better and fast internet architecture. On the other hand, OMNET++ SDN detaches the control plane from the data plane for service management in the absence of physical interference. One more common factor of NDN and SDN is transmission over different interfaces. And, currently, both technologies are widely developing in vehicular networks. Our technical legends are intelligent in designing and developing innovations in NDN-SDN for enhancing vehicular network performance. Here, we have given our upgraded technical skills in NDN-SDN for VN.
Our Skills and Knowledge in NDN
To ease the vehicular networks programmability, SDN based vehicular network was introduced. It specifically handles key parameters such as safety, heterogeneity, trustworthiness, routing, flexibility, etc.
Likewise, Named Data Network aims to use data name in the place of IP address to ease the data identification, requesting, forwarding, and retrieving. For this purpose, it created a new architecture that works based on any information / content-centric network. And, NDN is capable to collaborate with both vehicular networks and software-defined networks. Below, we have given some key taxonomies of software-defined vehicular networks that every scholar should be aware of before undergoing deep research on NDN.
In point of fact, here we have combined SDN, NDN and vehicular networks in one place. The primary motive of this collaboration is to give advantages and specializations of SDN on named data in vehicular network. As a result, it helps to achieve the efficient resource usage in the network. This collaborated network is made up of:
Additionally, the integration of NDN in-network entities makes effective network management. Overall, it manages the flow of data and enables fast data requests and responses in a vehicular network. Technically, it is challenging to integrate all these areas in one. So, our resource team has collected numerous solutions to overcome all possible issues in developing SDN-controlled VNDN (Vehicular Named Data Network). Here, we have given some key challenges of Named Data Networking for Software-Defined Vehicular Networks.
As mentioned earlier, our resource team is well-equipped with a strong technical foundation in both fundamental and advanced technologies for integrating NDN with SDN-based Vehicular networks. Consequently, this integration of areas yields more benefits among digital society developments. So, our experts choose the research areas which surely create a more positive contribution to digital society. And, we collected countless thought-provoking research notions for the benefit of our handhold scholars. Here, we have highlighted the top three research ideas from the latest research areas of NDN-based SDVN.
In addition, we have also given a few more current areas/topics of NDN-based SDVN. Additionally, we have given the emerging challenges with their corresponding findings. Similarly, we also support you in other areas and provide you best-fitting solutions through modern technologies. Further, we also motivate you to bring your own ideas and guide from research problem identification to experimental result analysis.
Basically, the efficiency of the computer network model is affected by several parameters. Similarly, NDN over SDVN network also has some parameters that influence the performance of the model. On experimenting with the NDN-based content delivery, the following parameters are needed to be considered. In this, we have given content parameters followed by system and workload parameters. Further, our developers also guide you to choose the parameters appropriately to boost system performance.
Open Issues and Research Roadmap
Certainly, SDN, NDN, and vehicular networks are individually advantageous in different aspects. When we integrate all three in one place, it is even advantageous in creating new achievements. Although this collaborative field is beneficial in multiple aspects, NDN is a newly developed architecture. So, it still facing various scientific issues in the travel of seamless evolution. Here, we have given you some significant open issues in NDN in SDVN.
So far, we have discussed the role of SDN and NDN in delivering named content over vehicular networks. In this, we have debated on the functionalities and advantages of SDN-NDN, NDN-based VN, SDN-based VN, and NDN in SDVN. And also, we have discussed the modern architecture of NDN with their future transition.
Then, we have discussed the current research challenges of real-time execution and parameters to elevate the performance of the system. Though NDN and SDN are moving towards the direction of maturity, it still needs research concern to create a strong groundwork for managing dynamic mobile networks like a wireless vehicular network. So, we have framed several solutions for Named Data Networking for Software-Defined Vehicular Networks applications.
On the whole, we support you in every stage of your studies like research, development, and manuscript writing (with unlimited revisions). To the great extent, we also assist in paper publication on reputed journals like Springer, IEEE, ScienceDirect, etc. We assure you that our research works are 100% plagiarism-free and 100% top-quality. So, reach us to reach your research destiny within your stipulated time.