A walkthrough of name data networking: Architecture, functionalities, operations and open issues

Singh, Vishwa Pratap; Ujjwal, R. L.

doi:10.1016/j.suscom.2020.100419

Cited by 15 publications

(6 citation statements)

References 49 publications

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“…In recent years, the named data network (NDN) is one of the most widely used information-centric networking (ICN) architectures through which vehicular communications are realized [20]. In this architecture, hierarchical names are used to identify content generated by vehicular applications instead of location-based internet protocol (IP) addresses.…”

Section: Distance-based Methods In Vehicular Named Data Networkmentioning

confidence: 99%

The impact of channel model on the performance of distance-based schemes in vehicular named data networks

Ahed

Benamar²,

Ouazzani³

2022

IJECE

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<span lang="EN-US">Distance-based schemes present one of the methods to avoid the broadcast problem in vehicular named data networks. However, such schemes overlook the most factor in performance evaluation which is the variation in received signal strength caused by the propagation model choice. Thus, they are evaluated under one propagation model while neglecting the effect of the others. This paper evaluates the impact of the propagation variation model over three distance-based schemes, namely rapid named data networking (RNDN), enhanced vehicle on named data networking (EVNDN) and opportunistic interest forwarding protocol (OIFP). Simulation experiments are performed over three propagation models. Simulation results show that Nakagami significantly degrades network performance. However, it has a noticeable positive effect over higher distance resulting in a higher interest satisfaction ratio as compared to the other models. The RNDN exhibits a higher number of retransmissions across the Nakagami. In contrast, a higher number of retransmissions is exhibited by EVNDN when compared to the other schemes over the Friis and random. The OIFP show a higher interest satisfaction ratio when compared to EVNDN and RNDN under all models. OIFP shows robustness towards the adverse fading effects resulting from the Nakagami and exhibits lower end to end delays.</span>

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Section: Distance-based Methods In Vehicular Named Data Networkmentioning

confidence: 99%

The impact of channel model on the performance of distance-based schemes in vehicular named data networks

Ahed

Benamar²,

Ouazzani³

2022

IJECE

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“…In NDN, information retrieval is performed using both the interest and data packets [9]. The content is segmented by the content provider, unique names are assigned to each of these segments (data chunks), and information retrieval is performed according to this name.…”

Section: A Brief Overview Of Named Data Networkmentioning

confidence: 99%

A New Cache Replacement Policy in Named Data Network Based on FIB Table Information

Hosseinzadeh,

Moghim,

Taheri

et al. 2023

Preprint

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Named Data Network (NDN) is proposed for the Internet as an information-centric architecture. Content storing in the router’s cache plays a significant role in NDN. When a router’s cache becomes full, a cache replacement policy determines which content should be discarded for the new content storage. This paper proposes a new cache replacement policy called Discard of Fast Retrievable Content (DFRC). In DFRC, the retrieval time of the content is evaluated using the FIB table information, and the content with less retrieval time receives more discard priority. An impact weight is also used to involve both the grade of retrieval and stale parameters in the discard priority calculation. This weight adjusts the relative impact of these two parameters on the discard priority. The grade of retrieval reflects the content’s retrieval time, and the stale parameter indicates its popularity. The reinforcement Learning (RL) method is used to calculate the suitable weight dynamically and based on the network condition. Simulation results show that DFRC improves hit rate and round-trip time compared to Least Recently Used (LRU), Optimized Cache Replacement algorithm for Information-Centric Networks (OCRICN), Efficient Popularity-aware Probabilistic Caching (EPPC), and Shortest Round-Trip Time (SRTT).

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“…Instead of sending packets between a source and destination devices identified by their numeric IP addresses, NDN disseminates named data at the network level, forwarding names directly that carry application semantics. Moreover, each packet in NDN is secured at the time of production, allowing data replication anywhere in the network and preserving the security properties of the data over its lifetime [48]. The ICN architecture building blocks enabling the shift from a host-centric to a content-centric paradigm are elaborated in the following subsections.…”

Section: Information Centric Networking (Icn)mentioning

confidence: 99%

Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions

et al. 2022

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Recently the Internet of Vehicles (IoV) has become a promising research area in the field of the Internet of Things (IoT), which enables vehicles to communicate and exchange real-time information with each other, as well as with infrastructure, people, and other sensors and actuators through various communication interfaces. The realization of IoV networks faces various communication and networking challenges to meet stringent requirements of low latency, dynamic topology, high data-rate connectivity, resource allocation, multiple access, and QoS. Advances in information-centric networks (ICN), edge computing (EC), and artificial intelligence (AI) will transform and help to realize the Intelligent Internet of Vehicles (IIoV). Information-centric networks have emerged as a paradigm promising to cope with the limitations of the current host-based network architecture (TCP/IP-based networks) by providing mobility support, efficient content distribution, scalability and security based on content names, regardless of their location. Edge computing (EC), on the other hand, is a key paradigm to provide computation, storage and other cloud services in close proximity to where they are requested, thus enabling the support of real-time services. It is promising for computation-intensive applications, such as autonomous and cooperative driving, and to alleviate storage burdens (by caching). AI has recently emerged as a powerful tool to break through obstacles in various research areas including that of intelligent transport systems (ITS). ITS are smart enough to make decisions based on the status of a great variety of inputs. The convergence of ICN and EC with AI empowerment will bring new opportunities while also raising not-yet-explored obstacles to realize Intelligent IoV. In this paper, we discuss the applicability of AI techniques in solving challenging vehicular problems and enhancing the learning capacity of edge devices and ICN networks. A comprehensive review is provided of utilizing intelligence in EC and ICN to address current challenges in their application to IIoV. In particular, we focus on intelligent edge computing and networking, offloading, intelligent mobility-aware caching and forwarding and overall network performance. Furthermore, we discuss potential solutions to the presented issues. Finally, we highlight potential research directions which may illuminate efforts to develop new intelligent IoV applications.

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A walkthrough of name data networking: Architecture, functionalities, operations and open issues

Cited by 15 publications

References 49 publications

The impact of channel model on the performance of distance-based schemes in vehicular named data networks

The impact of channel model on the performance of distance-based schemes in vehicular named data networks

A New Cache Replacement Policy in Named Data Network Based on FIB Table Information

Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions

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