A softwarized and MEC-enabled protocol architecture supporting consumer mobility in Information-Centric Networks

Benedetti, Paolo; Piro, Giuseppe; Grieco, Luigi Alfredo

doi:10.1016/j.comnet.2021.107867

Cited by 9 publications

(2 citation statements)

References 51 publications

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“…Most ICN architecture designs claim to inherently support consumer mobility; however, the challenge is when the producer node also moves. Recent studies, such as [110], argue that the existing schemes to address consumer mobility have shortcomings as they waste bandwidth and mobile consumers may miss real-time content during handover. The paper proposes a softwarized and MEC-enabled protocol architecture supporting consumer mobility in ICN and claims improvement in the communication overhead by 99.93%.…”

Section: Mobility Support For Iiovmentioning

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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Section: Mobility Support For Iiovmentioning

confidence: 99%

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

et al. 2022

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“…In recent years, the concept of caching‐enabled network (CEN) 1 has been proposed with the emerging of information‐centric network (ICN) 2 and the widespread application of content distribution network (CDN) 3 . With the integrated advantages of ICN and CDN, CEN has in‐network caching characteristic and is compatible with TCP/IP network architecture.…”

Section: Introductionmentioning

confidence: 99%

0‐1 Knapsack problem driven resource scheduling in caching‐enabled network: A case study on sports video

Xu¹

2021

Internet Technology Letters

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In caching‐enabled network (CEN), the cache resources are very limited and expensive, thus it needs to make the exact scheduling for these cache resources. The cache resources in CEN are usually stored in the buffer/segment form, that is, with integer feature. Therefore, this paper plans to convert the cache resource scheduling into a 0‐1 knapsack problem (KP01) with a given total cache budget. For the solution procedure of KP01, it includes two stages. At first, some cache segments are locked in advance, where ant colony optimization is adopted for this. Then, the rest cache segments are evaluated in turn based on expectation efficiency, and some cache segments with high expectation efficiency value are locked. For the proposed resource scheduling strategy in CEN, this paper makes a detailed case study by considering a hot application, that is, sports video transmission, including two parts. On one hand, a concrete example is established to illustrate the correctness of cache resource scheduling. On the other hand, two baselines are added to prove the efficiency of cache resource scheduling by introducing lots of sports videos. All experiments are driven over NS3, and the results are considerably satisfactory.

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Deep Reinforcement Learning Based Congestion Control Mechanism for SDN and NDN in Satellite Networks

Xing

et al. 2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A softwarized and MEC-enabled protocol architecture supporting consumer mobility in Information-Centric Networks

Cited by 9 publications

References 51 publications

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

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

0‐1 Knapsack problem driven resource scheduling in caching‐enabled network: A case study on sports video

Deep Reinforcement Learning Based Congestion Control Mechanism for SDN and NDN in Satellite Networks

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