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arxiv: 2605.13104 · v1 · submitted 2026-05-13 · 💻 cs.NI

Recognition: unknown

Content Caching Methods in Named Data Networks

Pankaj Chaudhary , Neminath Hubballi , Sameer G. Kulkarni
Authors on Pith no claims yet

Pith reviewed 2026-05-14 18:21 UTC · model grok-4.3

classification 💻 cs.NI
keywords Named Data NetworkingInformation Centric Networkingcontent cachingcaching algorithmsnetwork cachingcontent deliverysurvey
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The pith

This survey presents a taxonomy of caching techniques for Named Data Networking along with their principles, advantages, and disadvantages.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper reviews caching algorithms developed for Information Centric Networking with a focus on Named Data Networking implementations. It organizes these methods into a taxonomy and examines how each stores content in routers to serve future requests from nearby locations. The review explains the operational details of the techniques and notes their respective benefits such as lower latency and drawbacks such as higher storage demands. It also identifies standard performance measures used to compare the methods and indicates directions for additional investigation.

Core claim

The paper establishes a classification of NDN caching methods that groups them by placement policies, replacement strategies, and cooperation approaches, supplying a systematic way to compare how each method reduces redundant data transfers and improves access times in the network.

What carries the argument

A taxonomy of caching techniques that classifies methods according to where content is stored, what content is chosen for caching, and how decisions are coordinated across routers.

If this is right

  • Caching at routers meets repeated requests locally and thereby shortens delivery paths.
  • Different techniques trade storage space against improvements in hit rate and retrieval speed.
  • Metrics such as cache hit ratio and average retrieval delay allow consistent comparisons among methods.
  • Identified gaps point to opportunities for refining existing approaches or creating hybrids.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The taxonomy could be tested by applying it to caching designs in other information-centric architectures.
  • Live network measurements might expose differences in behavior not visible in the simulation-based evaluations reviewed.
  • The classification offers a starting point for automated selection of caching rules based on traffic patterns.

Load-bearing premise

That the papers selected for review are representative of NDN caching research and that the summarized advantages and disadvantages match actual performance.

What would settle it

A new NDN caching method published after the survey that does not fit any category in the taxonomy or that shows performance results contradicting the listed advantages and disadvantages.

Figures

Figures reproduced from arXiv: 2605.13104 by Neminath Hubballi, Pankaj Chaudhary, Sameer G. Kulkarni.

Figure 1
Figure 1. Figure 1: NDN Packet Structure [2] requested data exists, then they send it to the consumer in the reverse direction. But, when the requested data is not found in their CS, the routers proceed with a PIT lookup. Pending Interest Table: The PIT entries include names of all the interface(s) which have pending Interest(s). These interest requests have been forwarded but have not yet been serviced. When the lookup in CS… view at source ↗
Figure 2
Figure 2. Figure 2: NDN Packet Processing through NDN Router [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Hourglass Models of TCP/IP and NDN Protocol [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Taxonomy of ICN/NDN Caching 1) Centralized and Decentralized Caching: This approach addresses how routers in the network engage in the decision-making process to cache or not to cache the content. It explores how routers actively manage caches, searching for content, deciding on content caching based on various factors, and making room for new content when cache becomes full. The categorization encompasses… view at source ↗
Figure 5
Figure 5. Figure 5: Reference Network Topology Another concern with LCD is that if consumers request different content each time, for example, in a round-robin fashion, caching and eviction operations only occur at a specific router (e.g., R4). This can result in a reduced cache hit ratio and increased content access time. Move Copy Down (MCD) [62]: MCD is another strategy that caches content at a single router along the deli… view at source ↗
read the original abstract

Information Centric Networking (ICN) is a new network architecture (Internet) that focuses on content rather than the end-hosts. Named Data Networking (NDN) is a specific implementation of ICN, which relies on the use of named data and a request-response model for content distribution. These Internet architectures are known for their ability to cache content at the network level. Many caching techniques have been designed as part of various ICN/NDN projects. Caching techniques help improve the content delivery performance by storing content in the router to meet future demand. In this survey, we provide a structured review of caching algorithms designed for ICN, with a particular emphasis on NDN. We first present a taxonomy of caching techniques, followed by a detailed discussion of the various methods. Alongside their working principles, we also summarize their advantages and disadvantages. Finally, we discuss the performance metrics commonly used in the literature to evaluate caching methods and outline directions for future research in this area.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript surveys caching techniques in Information-Centric Networking (ICN) with emphasis on Named Data Networking (NDN). It introduces a taxonomy of caching methods, discusses selected algorithms along with their operating principles, advantages, and disadvantages, reviews commonly used performance metrics, and identifies directions for future work.

Significance. If the taxonomy is comprehensive and the summaries of advantages/disadvantages accurately reflect the cited literature, the survey could provide a useful organizing framework for NDN caching research. The work contains no original derivations, experiments, or quantitative claims, so its value rests entirely on coverage and clarity of presentation rather than novel technical results.

major comments (1)
  1. The manuscript does not describe the paper-selection methodology (search strings, databases, time window, or inclusion/exclusion criteria). This omission is load-bearing for the central claim of providing a 'structured review' and 'detailed discussion of the various methods,' because readers cannot evaluate whether the taxonomy is representative or whether important classes of caching schemes have been omitted.
minor comments (1)
  1. Ensure that every cited work in the taxonomy is accompanied by at least one explicit reference so that the advantages/disadvantages statements can be traced to primary sources.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback on our survey manuscript. We address the single major comment below and will revise the manuscript accordingly to improve transparency.

read point-by-point responses

  1. Referee: The manuscript does not describe the paper-selection methodology (search strings, databases, time window, or inclusion/exclusion criteria). This omission is load-bearing for the central claim of providing a 'structured review' and 'detailed discussion of the various methods,' because readers cannot evaluate whether the taxonomy is representative or whether important classes of caching schemes have been omitted.

    Authors: We agree that a clear description of the review methodology is necessary for readers to assess the scope and representativeness of the taxonomy. In the revised manuscript we will add a dedicated subsection (placed early in the introduction or as a new 'Review Methodology' section) that specifies the databases searched (IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Google Scholar), the search strings used (combinations of terms such as 'NDN caching', 'ICN cache replacement policy', 'named data networking caching algorithms', and 'content caching in information-centric networks'), the time window (papers published from 2010 through 2024), and the inclusion/exclusion criteria (peer-reviewed journal and conference papers focused on caching mechanisms in NDN/ICN with performance evaluation; exclusion of non-English works, purely theoretical papers without implementation details, and duplicate or superseded studies). This addition will directly address the concern and strengthen the credibility of the structured review. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

This is a survey paper whose central contribution is a taxonomy and structured review of existing NDN/ICN caching methods drawn from prior published literature. No original derivations, equations, fitted parameters, predictions, or uniqueness theorems are presented. All summarized advantages, disadvantages, and metrics are attributed to the reviewed external works rather than derived internally. The selection of papers is a standard survey limitation and does not create a self-referential reduction. No load-bearing step reduces to the paper's own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a literature survey paper. It does not introduce new parameters, axioms, or entities; it reviews existing work in the field.

pith-pipeline@v0.9.0 · 5471 in / 1032 out tokens · 23101 ms · 2026-05-14T18:21:19.662240+00:00 · methodology

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Reference graph

Works this paper leans on

123 extracted references · 1 canonical work pages · 1 internal anchor

  1. [1]

    Mini-NDN. Online. A vailable: https://github.com/ named-data/mini-ndn. Last accessed on May 14, 2026

    2026

  2. [2]

    NDN packet format specification. Online. A vailable: https:// docs.named-data.net/NDN-packet-spec/current/index.html. Last accessed on May 14, 2026

    2026

  3. [3]

    NDN Testbed. Online. A vailable: https://named-data.net/ ndn-testbed/. Last accessed on May 14, 2026

    2026

  4. [4]

    Abdullahi, S

    I. Abdullahi, S. Arif, and S. Hassan. Survey on caching approaches in information centric networking. J. Netw. Comput. Appl., 56:48–59, 2015

    2015

  5. [5]

    Afanasyev, J

    A. Afanasyev, J. Burke, T. Refaei, L. Wang, B. Zhang, and L. Zhang. A brief introduction to named data networking. In Proc. IEEE MILCOM 2018, pages 1–6, 2018

    2018

  6. [6]

    Afanasyev et al

    A. Afanasyev et al. Nfd developer’s guide. Technical report, NDN-0021, 2021

    2021

  7. [7]

    Ahlgren, C

    B. Ahlgren, C. Dannewitz, C. Imbrenda, D. Kutscher, and B. Ohlman. A survey of information-centric networking. IEEE Communications Magazine, 50(7):26–36, 2012

    2012

  8. [8]

    Alduayji, A

    S. Alduayji, A. Belghith, A. Gazdar, and S. Al-Ahmadi. PF- ClusterCache: popularity and freshness-aware collaborative cache clustering for named data networking of things. Applied Sciences, 12(13):6706, 2022

    2022

  9. [9]

    Alduayji, A

    S. Alduayji, A. Belghith, A. Gazdar, and S. Al-Ahmadi. PF-EdgeCache: popularity and freshness aware edge caching scheme for ndn/iot networks. Pervasive and Mobile Comput- ing, 91:101782, 2023

    2023

  10. [10]

    Alhowaidi, D

    M. Alhowaidi, D. Nadig, B. Hu, B. Ramamurthy, and B. Bock- elman. Cache management for large data transfers and multipath forwarding strategies in named data networking. Computer Networks, 199:108437, 2021

    2021

  11. [11]

    Alubady, M

    R. Alubady, M. Salman, and A. S. Mohamed. A review of modern caching strategies in named data network: overview, classification, and research directions. Telecommunication Systems, 84(4):581–626, 2023

    2023

  12. [12]

    Amadeo, C

    M. Amadeo, C. Campolo, A. Molinaro, and G. Ruggeri. Content-centric wireless networking: A survey. Computer Networks, 72:1–13, 2014

    2014

  13. [13]

    Amadeo, C

    M. Amadeo, C. Campolo, G. Ruggeri, and A. Molinaro. Beyond edge caching: Freshness and popularity aware iot data caching via ndn at internet-scale. IEEE Transactions on Green Communications and Networking, 6(1):352–364, 2022

    2022

  14. [14]

    Amadeo, G

    M. Amadeo, G. Ruggeri, C. Campolo, and A. Molinaro. Diversity-improved caching of popular transient contents in vehicular named data networking. Computer Networks, 184:107625, 2021

    2021

  15. [15]

    Amadeo, G

    M. Amadeo, G. Ruggeri, C. Campolo, A. Molinaro, and G. Mangiullo. Caching popular and fresh iot contents at the edge via named data networking. In Proc. IEEE INFOCOM WKSHPS, pages 610–615, 2020

    2020

  16. [16]

    Arianfar, P

    S. Arianfar, P. Nikander, and J. Ott. Packet-level caching for information-centric networking. In Proc. ACM SIGCOMM ReArch Workshop, volume 4, 2010

    2010

  17. [17]

    Awais, M

    Z. Awais, M. Hussain, A. Elshenawy, A. Arsalan, M. Anwar, M. A. Habib, S. Jabbar, and M. Ahmad. ISCC: intelligent semantic caching and control for ndn-enabled industrial iot networks. IEEE Access, 2025

    2025

  18. [18]

    Badshah, M

    J. Badshah, M. Kamran, N. Shah, and S. A. Abid. An improved method to deploy cache servers in software defined network-based information centric networking for big data. Journal of Grid Computing, 17:255–277, 2019

    2019

  19. [19]

    Bernardini, T

    C. Bernardini, T. Silverston, and O. Festor. MPC: popularity- based caching strategy for content centric networks. In Proc. IEEE Int. Conf. Commun. (ICC), pages 3619–3623, 2013

    2013

  20. [20]

    Caching Strategies for Information Centric Networking: Opportunities and Challenges

    C. Bernardini, T. Silverston, and A. Vasilakos. Caching strategies for information centric networking: Opportunities and challenges. arXiv preprint arXiv:1606.07630, 2016

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  21. [21]

    Breslau, P

    L. Breslau, P. Cao, L. Fan, G. Phillips, and S. Shenker. Web caching and zipf-like distributions: Evidence and implications. In Proc. IEEE INFOCOM’99, volume 1, pages 126–134, 1999

    1999

  22. [22]

    less for more

    W. K. Chai, D. He, I. Psaras, and G. Pavlou. Cache “less for more” in information-centric networks (extended version). Computer Communications, 36(7):758–770, 2013

    2013

  23. [23]

    Chaudhary and N

    P. Chaudhary and N. Hubballi. Eencache: Neighborhood coop- erative content caching and delivery in named data networks. In 2025 International Conference on Computing, Networking and Communications (ICNC), pages 557–561, 2025

    2025

  24. [24]

    Chaudhary and N

    P. Chaudhary and N. Hubballi. PeNCache: popularity based cooperative caching in named data networks. Computer Networks, 257:110995, 2025

    2025

  25. [25]

    Chaudhary, N

    P. Chaudhary, N. Hubballi, and S. G. Kulkarni. NCache: neighborhood cooperative caching in named data networking. In Proc. 5th HotICN’22, pages 36–41, 2022

    2022

  26. [26]

    Chaudhary, N

    P. Chaudhary, N. Hubballi, and S. G. Kulkarni. eNCache: improving content delivery with cooperative caching in named data networking. Computer Networks, page 110104, 2023

    2023

  27. [27]

    K. Cho, M. Lee, K. Park, T. T. Kwon, Y. Choi, and S. Pack. W A VE: popularity-based and collaborative in-network caching for content-oriented networks. In Proc. IEEE INFOCOM Workshops, pages 316–321, 2012

    2012

  28. [28]

    Conti, A

    M. Conti, A. Gangwal, M. Hassan, C. Lal, and E. Losiouk. The road ahead for networking: A survey on icn-ip coexis- tence solutions. IEEE Communications Surveys & Tutorials, 22(3):2104–2129, 2020

    2020

  29. [29]

    Dannewitz, D

    C. Dannewitz, D. Kutscher, B. Ohlman, S. Farrell, B. Ahlgren, and H. Karl. Network of information (netinf)–an information- centric networking architecture. Computer Communications, 36(7):721–735, 2013

    2013

  30. [30]

    Detti, N

    A. Detti, N. Blefari Melazzi, S. Salsano, and M. Pomposini. 21 CONET: a content centric inter-networking architecture. In Proc. ACM SIGCOMM Workshop Inf. Centric Netw, pages 50–55, 2011

    2011

  31. [31]

    Dhara, A

    S. Dhara, A. Majidi, and S. Clarke. Revving up vndn: Efficient caching and forwarding by expanding content popularity per- spective and mobility. Computer Communications, 212:342– 352, 2023

    2023

  32. [32]

    I. U. Din, S. Hassan, M. K. Khan, M. Guizani, O. Ghazali, and A. Habbal. Caching in information-centric networking: Strategies, challenges, and future research directions. IEEE Communications Surveys & Tutorials, 20(2):1443–1474, 2018

    2018

  33. [33]

    Dräxler and H

    M. Dräxler and H. Karl. Efficiency of on-path and off-path caching strategies in information centric networks. In Proc. IEEE Int. Conf. Green Comput. Commun. (GreenCom), pages 581–587, 2012

    2012

  34. [34]

    C. Fan, S. Shannigrahi, C. Papadopoulos, and C. Partridge. Discovering in-network caching policies in ndn networks from a measurement perspective. In Proc. of 7th ACM ICN’20, pages 106–116, 2020

    2020

  35. [35]

    C. Fang, H. Yao, Z. Wang, W. Wu, X. Jin, and F. R. Yu. A survey of mobile information-centric networking: Research issues and challenges. IEEE Communications Surveys & Tutorials, 20(3):2353–2371, 2018

    2018

  36. [36]

    Floyd and V

    S. Floyd and V. Jacobson. Random early detection gateways for congestion avoidance. IEEE/ACM Transactions on net- working, 1(4):397–413, 1993

    1993

  37. [37]

    Fotiou, P

    N. Fotiou, P. Nikander, D. Trossen, and G. C. Polyzos. Developing information networking further: From psirp to pursuit. In Proc. Conf. Broadband Commun. Netw. Syst., pages 1–13, 2012

    2012

  38. [38]

    Gui and Y

    Y. Gui and Y. Chen. A cache placement strategy based on compound popularity in named data networking. IEEE Access, 8:196002–196012, 2020

    2020

  39. [39]

    Y. Gui, P. Li, P. Wang, Z. Hang, R. Cao, and L. Zhang. A dynamic clustering caching strategy for iot-ndn based on user- preferred contents. IEEE Internet of Things Journal, 2025

    2025

  40. [40]

    Gupta, S

    D. Gupta, S. Rani, S. H. Ahmed, S. Garg, M. J. Piran, and M. Alrashoud. ICN-based enhanced cooperative caching for multimedia streaming in resource constrained vehicular en- vironment. IEEE Transactions on Intelligent Transportation Systems, 22(7):4588–4600, 2021

    2021

  41. [41]

    X. He, H. Liu, W. Li, A. Valera, and W. K. Seah. EABC: energy-aware centrality-based caching for named data net- working in the iot. In Proc. IEEE 25th Int. Symp. World Wireless, Mobile Multimedia Netw. (WoWMoM), pages 259– 268, 2024

    2024

  42. [42]

    J. Hou, T. Tao, H. Lu, and A. Nayak. Intelligent caching with graph neural network-based deep reinforcement learning on sdn-based icn. Future Internet, 15(8):251, 2023

    2023

  43. [43]

    J. Hou, T. Tao, H. Lu, and A. Nayak. An optimized gnn-based caching scheme for sdn-based information-centric networks. In Proc. IEEE GLOBECOM 2023, pages 401–406, 2023

    2023

  44. [44]

    J. Hou, H. Xia, H. Lu, and A. Nayak. A gnn-based approach to optimize cache hit ratio in ndn networks. In Proc. IEEE GLOBECOM, pages 1–6, 2021

    2021

  45. [45]

    J. Hou, H. Xia, H. Lu, and A. Nayak. A graph neural network approach for caching performance optimization in ndn networks. IEEE Access, 10:112657–112668, 2022

    2022

  46. [46]

    X. Hu, J. Yin, S. Zheng, R. Li, G. Cheng, and J. Gong. A demand and responsiveness-based caching strategy for network coding enabled ndn. In Proc. IEEE GLOBECOM, pages 1–6, 2020

    2020

  47. [47]

    X. Hu, S. Zheng, G. Zhang, L. Zhao, G. Cheng, J. Gong, and R. Li. An on-demand off-path cache exploration based mul- tipath forwarding strategy. Computer Networks, 166:107032, 2020

    2020

  48. [48]

    Huang, T

    W. Huang, T. Song, Y. Yang, and Y. Zhang. Cluster-based cooperative caching with mobility prediction in vehicular named data networking. IEEE Access, 7:23442–23458, 2019

    2019

  49. [49]

    Hubballi and P

    N. Hubballi and P. Chaudhary. CPCache: cooperative popu- larity based caching for named data networks. In Proc. IEEE Int. Conf. Inf. Netw. (ICOIN), pages 379–384, 2024

    2024

  50. [50]

    Hubballi, P

    N. Hubballi, P. Chaudhary, and S. G. Kulkarni. PePC: popularity based early predictive caching in named data networks. In Proc. IEEE Consumer Communications & Networking Conference (IEEE CCNC’24), pages 1–6, 2024

    2024

  51. [51]

    Ioannou and S

    A. Ioannou and S. Weber. Towards on-path caching alter- natives in information-centric networks. In Proc. 39th IEEE Conf. Local Comput. Netw. (LCN), pages 362–365, 2014

    2014

  52. [52]

    Ioannou and S

    A. Ioannou and S. Weber. A survey of caching policies and forwarding mechanisms in information-centric networking. IEEE Commun. Surveys & Tuts., 18(4):2847–2886, 2016

    2016

  53. [53]

    S. M. A. Iqbal et al. Cache-mab: A reinforcement learning- based hybrid caching scheme in named data networks. Future Generation Computer Systems, 147:163–178, 2023

    2023

  54. [54]

    Jacobson, D

    V. Jacobson, D. K. Smetters, J. D. Thornton, M. F. Plass, N. H. Briggs, and R. L. Braynard. Networking named content. In Proc. of ACM CoNEXT, pages 1–12, 2009

    2009

  55. [55]

    Jmal and L

    R. Jmal and L. C. Fourati. Content-centric networking management based on software defined networks: survey. IEEE Transactions on network and service management, 14(4):1128–1142, 2017

    2017

  56. [56]

    Jmal and L

    R. Jmal and L. C. Fourati. An openflow architecture for managing content-centric-network (OF AM-CCN) based on popularity caching strategy. Computer Standards & Inter- faces, 51:22–29, 2017

    2017

  57. [57]

    Khandaker, W

    F. Khandaker, W. Li, S. Oteafy, and H. Hassanein. Maxi- mizing producer-driven cache valuation in information-centric networks. In Proc. IEEE GLOBECOM, pages 1–6, 2021

    2021

  58. [58]

    Khandaker, S

    F. Khandaker, S. Oteafy, H. S. Hassanein, and H. Farahat. A functional taxonomy of caching schemes: Towards guided designs in information-centric networks. Computer Networks, 165:106937, 2019

    2019

  59. [59]

    Khelifi, S

    H. Khelifi, S. Luo, B. Nour, H. Moungla, Y. Faheem, R. Hus- sain, and A. Ksentini. Named data networking in vehicular ad hoc networks: State-of-the-art and challenges. IEEE Communications Surveys & Tutorials, 22(1):320–351, 2020

    2020

  60. [60]

    Kim and Y

    D. Kim and Y. Kim. Enhancing ndn feasibility via dedicated routing and caching. Computer networks, 126:218–228, 2017

    2017

  61. [61]

    Koponen, M

    T. Koponen, M. Chawla, B.-G. Chun, A. Ermolinskiy, K. H. Kim, S. Shenker, and I. Stoica. A data-oriented (and beyond) network architecture. In Proc. ACM SIGCOMM, pages 181– 192, 2007

    2007

  62. [62]

    Laoutaris, H

    N. Laoutaris, H. Che, and I. Stavrakakis. The LCD intercon- nection of lru caches and its analysis. Performance Evaluation, 63(7):609–634, 2006

    2006

  63. [63]

    Laoutaris, S

    N. Laoutaris, S. Syntila, and I. Stavrakakis. Meta algorithms for hierarchical web caches. In Proc. IEEE Int. Conf. Perform. Comput. Commun. (IPCCC), pages 445–452, 2004

    2004

  64. [64]

    Lee and D

    J. Lee and D. Kim. Heartbeat: effective access to off-path cached content in ndn. IEEE Transactions on Network Science and Engineering, 12(4):2974–2988, 2025

    2025

  65. [65]

    J. Li, H. Wu, B. Liu, J. Lu, Y. Wang, X. Wang, Y. Zhang, and L. Dong. Popularity-driven coordinated caching in named data networking. In Proc. ACM/IEEE Symp. Architectures Netw. Commun. Syst., pages 15–26, 2012

    2012

  66. [66]

    W. Li, Y. Li, W. Wang, Y. Xin, and Y. Xu. A collaborative caching scheme with network clustering and hash-routing in ccn. In Proc. IEEE PIMRC, pages 1–7, 2016

    2016

  67. [67]

    Y. Li, T. Lin, H. Tang, and P. Sun. A chunk caching location and searching scheme in content centric networking. In Proc. IEEE Int. Conf. Commun. (ICC), pages 2655–2659, 2012

    2012

  68. [68]

    Y. Li, S. Ouyang, and J. Lv. A lightweight caching decision scheme with a caching-resource-utilization-based strategy for information-centric networking. In 2024 IEEE 49th Confer- ence on Local Computer Networks (LCN), pages 1–7, 2024

    2024

  69. [69]

    Li and G

    Z. Li and G. Simon. Time-shifted tv in content centric networks: The case for cooperative in-network caching. In Proc. IEEE Int. Conf. Commun. (ICC), pages 1–6, 2011

    2011

  70. [70]

    Liang, J

    T. Liang, J. Pan, and B. Zhang. Ndnizing existing applica- tions: Research issues and experiences. In Proc. of 5th ACM ICN’18, pages 172–183, 2018

    2018

  71. [71]

    C. Liao, X. Liu, and H. Zhou. Environment-adaptive dynamic caching for vehicular named data networks in dynamic net- work environments. IEEE Transactions on Vehicular Technol- 22 ogy, 73(4):5861–5871, 2024

    2024

  72. [72]

    W.-X. Liu, J. Zhang, Z.-W. Liang, L.-X. Peng, and J. Cai. Content popularity prediction and caching for icn: A deep learning approach with sdn. IEEE access, 6:5075–5089, 2017

    2017

  73. [73]

    Mastorakis, A

    S. Mastorakis, A. Afanasyev, and L. Zhang. On the evolution of ndnSIM: An open-source simulator for ndn experimenta- tion. ACM SIGCOMM Computer Communication Review, 47(3):19–33, 2017

    2017

  74. [74]

    Meng and A

    Y. Meng and A. B. Ahmad. Performance measurement through caching in named data networking based internet of things. IEEE Access, 11:120569–120584, 2023

    2023

  75. [75]

    T. Mick, R. Tourani, and S. Misra. MuNCC: Multi-hop neighborhood collaborative caching in information centric networks. In Proc. of 3rd ACM ICN’16, pages 93–101, 2016

    2016

  76. [76]

    M. A. Naeem, I. U. Din, Y. Meng, A. Almogren, and J. J. Rodrigues. Centrality-based on-path caching strategies in ndn- based internet of things: A survey. IEEE Communications Surveys & Tutorials, 2024

    2024

  77. [77]

    X. N. Nguyen, D. Saucez, and T. Turletti. Efficient caching in content-centric networks using openflow. In Proc. INFOCOM Workshops, pages 67–68, 2013

    2013

  78. [78]

    M. D. Ong, M. Chen, T. Taleb, X. Wang, and V. C. Leung. FGPC: fine-grained popularity-based caching design for con- tent centric networking. In Proc. 17th ACM Int. Conf. Model. Anal. Simulat. Wireless Mobile Syst., pages 295–302, 2014

    2014

  79. [79]

    Pal and K

    A. Pal and K. Kant. A neighborhood aware caching and interest dissemination scheme for content centric networks. IEEE Transactions on Network and Service Management, 18(3):3900–3917, 2021

    2021

  80. [80]

    Pavlou, N

    G. Pavlou, N. Wang, W. K. Chai, and I. Psaras. Internet-scale content mediation in information-centric networks. annals of telecommunications-annales des télécommunications, 68:167– 177, 2013

    2013

Showing first 80 references.