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1. A Review of Research Works in Named Data
Networking and Information Centric Networking
Egil Olsen
University of Bergen
Abstract—In this paper, we present a review of existing works
in Named Data Networking (NDN). These works cover areas such
as edge computing, mobility, caching, peer-to-peer data sharing,
security, video streaming and others. In the future, we will review
more fields.
I. INTRODUCTION
Information-Centric Networking (ICN) and Named Data
Networking (NDN) [47] is a future network architecture that
makes data the first class citizen of the communication model.
In this paper, we review existing works in NDN in several
areas that include data sharing and synchronization, in-network
caching, edge computing, security, mobility and others. Our
paper has following structure: Section 2 discusses data sharing
and synchronization, Section 3 discusses mobile networks,
Section 4 presents edge computing, Section 5 describes video
streaming, Section 6 evaluation and simulation tools, Section 7
NDN mobility, Section 8 security in NDN, Section 9 machine
and deep learning in NDN. Finally, Section 10 presents the
conclusions of the paper.
II. DATA SHARING AND SYNCHRONIZATION
People have described several approaches to design dis-
tributed dataset synchronization protocols. iSync [11] is a high
performance synchronization protocol for NDN. iSync sup-
ports efficient data reconciliation by representing the synchro-
nized datasets using a two-level invertible Bloom filter (IBF)
structure. A set of differences can be found by subtracting a re-
mote IBF from a local IBF. Several differences can be inferred
through a single round data exchanges. RoundSync [7] splits
data publications into “rounds” and uses two separate Interest
types for state inconsistency detection and update retrieval.
iRoundSync, that exchanges fewer messages in the multiple-
change case and is more resilient to packet losses. [12].
Under intermittent connectivity, sync protocols have been
also explored, mproving the recovery process of ChronoSync
in order to enhance its adaptability to intermittent network
scenarios [24]. DDSN [22] has a unique feature of letting
individual entities exchange their dataset states directly, instead
of using some compressed form of the states. Other people
performed surveys of synchronization and publish-subscribe
protocols [35]. Authors also used SDN which can be combined
with NDN and ICN to share data [2].
Peer-to-peer file and data sharing has been proposed in
NDN [26]. nTorrent is a BitTorrent-like application that is
based on the natively data-centric NDN network architec-
ture [28]. People also presented Matryoshka, a pure peer-to-
peer multiplayer online game using the NDN future internet
architecture [45]. Authors also developed a distributed service
based on NDN with two-tier hierarchical ID-based encryption
(HIDE) for authentication [36]. DAPES runs on NDN and
extends NDN to achieve communication over multiple wireless
hops through an adaptive hop-by-hop forwarding/suppression
mechanism [32].
III. MOBILE NETWORKS
To distribute large volumes of data in IoT, the authors
applied network coding into NDN to improve IoT network
throughput and efficiency of content delivery for 5G. A
probability-based multipath forwarding strategy was designed
for network coding to make full use of its potential. [21].
A request-based handover strategy (RBHS) was presented to
improve the user experience in performance and obtain the
optimal allocation of resources, and a caching mechanism
based on the users’ requests is introduced for it [15]. Authors
also proposed an ICN-capable RAN architecture for 5G edge
computing environments that offers device to device communi-
cation and ICN application layer support at base stations [43].
A novel FIB named B-MaFIB was proposed to enhance the
performance of NDN router that well supports heterogeneous
network toward 5G, which was designed based on an improved
index called a bitmap-mapping bloom filter (B-MBF) [23].
Authors considered best effort hop-by-hop link layer reli-
ability protocol (BELRP) for point-to-point communication
links [44].
IV. EDGE COMPUTING AND NDN
Edge computing nodes selection strategy for NDN was
proposed [19]. A computation request of location data is
forwarded to the right edge routers. ICedge was published
to build a general-purpose networking framework that stream-
lines service invocation and improves the reuse of redundant
computation at the edge [34], [20]. Authors extend NDN to
turn the network edge into a dynamic computing environment
for running user applications relying on IoT data streams
processing and analytics. Novel naming and forwarding mech-
anisms were suggested [1]. Authors also considered a scenario,
where applications need to discover the services running in the
edge network and demonstrated the design and implementation
of a distributed service discovery mechanism over NDN [33].

2. Two major obstacles were found toward achieving the
benefit of network-edge computing. Efficient algorithms for
data reduction in time series (one of the most common types
of data in IoT) need to be developed to work posteriori upon
big datasets, but they cannot make decisions for each incoming
data item. Also the state of the art lacks systems that can apply
any of the possible data reduction methods without adding sig-
nificant delays or major reconfigurations. [14]. Other authors
collaborated to talk about the current networking challenges
both quantitatively (by analyzing AR/VR network interactions
of head-mounted displays) and quantitatively (by distributing
a targeted community survey among AR/VR researchers) [40].
Common authors also designed CFN for distributed applica-
tions both in edge computing and data centers [17]. Authors
enabled AR applications and combined NDN and Edge Com-
puting (EC) to do fast information response time [42].
V. VIDEO STREAMING
The best works do not consider the multipath capabilities of
NDN and the potential improvements that this communication
brings, such as increased throughput and reliability, which
are fundamental for video streaming systems. Authors thought
of a novel architecture for dynamic adaptive streaming over
network coding enabled NDN [38]. A dynamic layer switching
strategy using Lyapunov Optimization is proposed for dy-
namic adaptive scalable video streaming in the NDN future
Internet [46]. Researchers compared ICN and transmission
control protocol/internet protocol (TCP/IP) with an experimen-
tal approach through DASH controllers (PANDA, AdapTech,
and BOLA) on an ICN versus TCP/IP network stack [39].
The authors present Realtime Data Retrieval (RDR), a simple
protocol that enables applications to discover the latest data in
NDN future Internet [31].
VI. EVALUATION PLATFORMS
ndnSIM is most popular simulation platform for NDN [29],
[27]. The authors gave overview of ndnSIM design, the
ndnSIM development process, the design tradeoffs, and the
reasons behind the design decisions. SocialCCNSim allows
the performance of caching systems. In fact, all available
simulators are either designed for a particular architecture
or unable to execute simulations within a reasonable time-
frame and at the required criterion [8]. Icarus [37] allows
users to evaluate caching replacement strategies for ICN and
provides modeling tools useful for caching research in NDN
and ICN. Authors reviewed related research papers to find the
ICN literature [41].
VII. NDN MOBILITY
Consumer mobility in NDN is supported, but content
provider mobility is challenge. Authors handle mobility effec-
tively while frequently changing the Access Points as well as
staying at same Access Point for long-time for both producers
and consumers [13]. KITE [48] follows soft-state approach to
create hop-by-hop path between reachable rendezvous server
and mobile producer through authenticated Interest-Data ex-
changes. KITE does not have locators and it is transparent
to data retrieval and routing. KITE handles producer mobility
in NDN. Authors designed an anchor-less solution to manage
micro mobility of content producer via NDN and ICN name-
based data plane, supporting sensitive applications [3]. Authors
proposed an optimal scheme to support the content providers
in NDN. The solution predicts the movement of the provider
and uses state information from the NDN forwarding plane to
set up an optimal routing path for mobile providers [9].
VIII. SECURITY IN NDN
Researchers have done review of NDN security mech-
anisms [49]. People have done analysis of NDN security
using basic BAN logic [10] where under several security
goals with a set of logical postulates, the idealized procedure
is analyzed. Researchers studied the practical vulnerabilities
exposed by NDN Forwarding Daemon (NFD), the current
implementation of NDN, and especially its Pending Interest
Table. An attack scenario, based on the Interest Flooding
Attack, is implemented on NFDs [25].
IX. MACHINE LEARNING IN NDN
Since NDN has different architecture than TCP/IP, it is
prone to new types of attack. These attacks are Interest
Flooding Attack (IFA), Cache Privacy Attack, Cache Pollution
Attack, Content Poisoning Attack, etc. The authors applied
machine learning for the detection of IFA [18]. The authors
study and discuss the applicability of deep learning (DL), i.e.,
convolutional neural network (CNN), recurrent neural network
(RNN), and reinforcement learning (RL), with IoT and ICN
combined all together with edge computing [16]. Others have
proposed FIF to do fuzzy forwarding of Interest packets in
NDN by modifying the processing of the NDN forwarding
daemon [6], [30].
The authors proposed a new approach for intelligent for-
warding of data and HVAC control based on deep learning
in Building Management Systems (BMS) that could improve
forwarding performance and energy needs [4]. Authors present
a review of existing deep learning approaches that applies to
networking. They proposed an approach and an algorithm that
leverage existing machine learning approaches for network
forwarder. They used proposed forwarder for IoT. [5].
X. CONCLUSIONS
This paper discussed the review of research work in ICN
and NDN. The subjects discussed included security, mobility,
machine learning and artificial intelligence, data sharing, edge
computing, video streaming, mobile networks, mobility, and
others. In future, we will review more subjects in NDN and
ICN and submit the paper to a journal for publication. NDN is
promising direction for future Internet and we will investigate
and do more research on it.

3. REFERENCES
[1] Marica Amadeo, Claudia Campolo, Antonella Molinaro, and Giuseppe
Ruggeri. Iot data processing at the edge with named data networking.
In European Wireless 2018; 24th European Wireless Conference, pages
1–6. VDE, 2018.
[2] Christos Argyropoulos, Spyridon Mastorakis, Kostas Giotis, Georgios
Androulidakis, Dimitrios Kalogeras, and Vasilis Maglaris. Control-plane
slicing methods in multi-tenant software defined networks. In 2015
IFIP/IEEE International Symposium on Integrated Network Manage-
ment (IM), pages 612–618. IEEE, 2015.
[3] Jordan Augé, Giovanna Carofiglio, Giulio Grassi, Luca Muscariello,
Giovanni Pau, and Xuan Zeng. Map-me: Managing anchor-less
producer mobility in information-centric networks. arXiv preprint
arXiv:1611.06785, 2016.
[4] Mohamed Issam Ayadi, Abderrahim Maizate, Mohamed Ouzzif, and
Charif Mahmoudi. Deep learning in building management systems over
ndn: Use case of forwarding and hvac control. In 2019 International
Conference on Internet of Things (iThings) and IEEE Green Computing
and Communications (GreenCom) and IEEE Cyber, Physical and Social
Computing (CPSCom) and IEEE Smart Data (SmartData), pages 1192–
1198. IEEE, 2019.
[5] Mohamed Issam Ayadi, Fatima Zahra Saadaoui, Abderrahim Maizatc,
Mohamed Ouzzif, and Charif Mahmoudi. Deep learning for packet
forwarding with an application for real time iot. In 2018 International
Conference on Selected Topics in Mobile and Wireless Networking
(MoWNeT), pages 142–148. IEEE, 2018.
[6] Kevin Chan, Bongjun Ko, Spyridon Mastorakis, Alexander Afanasyev,
and Lixia Zhang. Fuzzy Interest Forwarding. In Proceedings of the
Asian Internet Engineering Conference, pages 31–37. ACM, 2017.
[7] Pedro de-las Heras-Quirós, Eva M Castro, Wentao Shang, Yingdi Yu,
Spyridon Mastorakis, Alexander Afanasyev, and Lixia Zhang. The
design of RoundSync protocol. Technical report, Technical Report NDN-
0048, NDN, 2017.
[8] Ikram Ud Din, Suhaidi Hassan, and Adib Habbal. Socialccnsim:
A simulator for caching strategies in information-centric networking.
Advanced Science Letters, 21(11):3505–3509, 2015.
[9] Truong-Xuan Do and Younghan Kim. Optimal provider mobility in
large-scale named-data networking. KSII Transactions on Internet &
Information Systems, 9(10), 2015.
[10] Yuan Fei, Huibiao Zhu, and Phan Cong Vinh. Security analysis of the
access control solution of ndn using ban logic. Mobile Networks and
Applications, pages 1–12, 2020.
[11] Wenliang Fu, Hila Ben Abraham, and Patrick Crowley. Synchronizing
namespaces with invertible bloom filters. In 2015 ACM/IEEE Symposium
on Architectures for Networking and Communications Systems (ANCS),
pages 123–134. IEEE, 2015.
[12] Ayat Zaki Hindi, Michel Kieffer, Cédric Adjih, and Claudio Weidmann.
Ndn synchronization: iroundsync, an improved roundsync. In Proceed-
ings of the 4th ACM Conference on Information-Centric Networking,
pages 194–195, 2017.
[13] Aaditya Jain, Vipra Pardikar, and Sumit Ranjan Pratihast. Tracing based
solution for ubiquitous connectivity of mobile nodes for ndn: A ra kite.
In 2017 8th International Conference on Computing, Communication
and Networking Technologies (ICCCNT), pages 1–7. IEEE, 2017.
[14] Kusumlata Jain and Smaranika Mohapatra. Taxonomy of edge com-
puting: Challenges, opportunities, and data reduction methods. In Edge
Computing, pages 51–69. Springer, 2019.
[15] Fan Jia and Xiaolin Zheng. A request-based handover strategy using
ndn for 5g. Wireless Communications and Mobile Computing, 2018,
2018.
[16] Hakima Khelifi, Senlin Luo, Boubakr Nour, Akrem Sellami, Hassine
Moungla, Syed Hassan Ahmed, and Mohsen Guizani. Bringing deep
learning at the edge of information-centric internet of things. IEEE
Communications Letters, 23(1):52–55, 2018.
[17] Michał Król, Spyridon Mastorakis, David Oran, and Dirk Kutscher.
Compute first networking: Distributed computing meets icn. In Proceed-
ings of the 6th ACM Conference on Information-Centric Networking,
pages 67–77, 2019.
[18] Naveen Kumar, Ashutosh Kumar Singh, and Shashank Srivastava. Eval-
uating machine learning algorithms for detection of interest flooding at-
tack in named data networking. In Proceedings of the 10th International
Conference on Security of Information and Networks, pages 299–302,
2017.
[19] Yoshiki Kurihara, Yuki Koizumi, and Toru Hasegawa. Computing node
selection for location-based service in ndn networks. In 2019 IEEE
International Symposium on Local and Metropolitan Area Networks
(LANMAN), pages 1–6. IEEE, 2019.
[20] Jonathan Lee, Abderrahmen Mtibaa, and Spyridon Mastorakis. A case
for compute reuse in future edge systems: An empirical study. In 2019
IEEE Globecom Workshops (GC Wkshps), pages 1–6. IEEE, 2019.
[21] Kai Lei, Shangru Zhong, Fangxing Zhu, Kuai Xu, and Haijun Zhang.
An ndn iot content distribution model with network coding enhanced
forwarding strategy for 5g. IEEE Transactions on Industrial Informatics,
14(6):2725–2735, 2017.
[22] Tianxiang Li, Zhaoning Kong, Spyridon Mastorakis, and Lixia Zhang.
Distributed dataset synchronization in disruptive networks. In 2019 IEEE
16th International Conference on Mobile Ad Hoc and Sensor Systems
(MASS), pages 428–437. IEEE, 2019.
[23] Zhuo Li, Yaping Xu, Kaihua Liu, Xudong Wang, and Deliang Liu. 5g
with b-mafib based named data networking. IEEE Access, 6:30501–
30507, 2018.
[24] Chong Liu. Distributed databases synchronization in named data delay
tolerant networks. PhD thesis, 2016.
[25] Hoang Long Mai, Ngoc Tan Nguyen, Guillaume Doyen, Alain Ploix,
and Remi Cogranne. On the readiness of ndn for a secure deployment:
The case of pending interest table. In IFIP International Conference on
Autonomous Infrastructure, Management and Security, pages 98–110.
Springer, 2016.
[26] Spyridon Mastorakis. Peer-to-peer data sharing in named data network-
ing. PhD thesis, UCLA, 2019.
[27] Spyridon Mastorakis, Alexander Afanasyev, Ilya Moiseenko, and Lixia
Zhang. ndnSIM 2.0: A new version of the NDN simulator for NS-3.
NDN, Technical Report NDN-0028, 2015.
[28] Spyridon Mastorakis, Alexander Afanasyev, Yingdi Yu, and Lixia
Zhang. nTorrent: Peer-to-Peer File Sharing in Named Data Network-
ing. In Computer Communication and Networks (ICCCN), 2017 26th
International Conference on, pages 1–10. IEEE, 2017.
[29] Spyridon Mastorakis, Alexander Afanasyev, and Lixia Zhang. On the
evolution of ndnSIM: An open-source simulator for NDN experimenta-
tion. ACM SIGCOMM Computer Communication Review, 47(3):19–33,
2017.
[30] Spyridon Mastorakis, Kevin Chan, Bongjun Ko, Alexander Afanasyev,
and Lixia Zhang. Experimentation With Fuzzy Interest Forwarding in
Named Data Networking. arXiv preprint arXiv:1802.03072, 2018.
[31] Spyridon Mastorakis, Peter Gusev, Alexander Afanasyev, and Lixia
Zhang. Real-time data retrieval in named data networking. In 2018 1st
IEEE International Conference on Hot Information-Centric Networking
(HotICN), pages 61–66. IEEE, 2018.
[32] Spyridon Mastorakis, Tianxiang Li, and Lixia Zhang. Dapes: Named
data for off-the-grid file sharing with peer-to-peer interactions. arXiv
preprint arXiv:2006.01651, 2020.
[33] Spyridon Mastorakis and Abderrahmen Mtibaa. Towards service dis-
covery and invocation in data-centric edge networks. In 2019 IEEE
27th International Conference on Network Protocols (ICNP), pages 1–
6. IEEE, 2019.
[34] Spyridon Mastorakis, Abderrahmen Mtibaa, Jonathan Lee, and Satya-
jayant Misra. ICedge: When Edge Computing Meets Information-
Centric Networking. IEEE Internet of Things Journal, 2020.
[35] Boubakr Nour, Kashif Sharif, Fan Li, Song Yang, Hassine Moungla, and
Yu Wang. Icn publisher-subscriber models: Challenges and group-based
communication. IEEE Network, 33(6):156–163, 2019.
[36] Takeo Ogawara, Yoshihiro Kawahara, and Tohru Asami. Information
dissemination performance of a disaster-tolerant ndn-based distributed
application in disrupted cellular networks. In Peer-to-Peer Computing
(P2P), 2013 IEEE Thirteenth International Conference on, pages 1–5.
IEEE, 2013.
[37] Lorenzo Saino, Ioannis Psaras, and George Pavlou. Icarus: a caching
simulator for information centric networking (icn). In SimuTools,
volume 7, pages 66–75. ICST, 2014.
[38] Jonnahtan Saltarin, Eirina Bourtsoulatze, Nikolaos Thomos, and Torsten
Braun. Adaptive video streaming with network coding enabled named
data networking. IEEE transactions on multimedia, 19(10):2182–2196,
2017.
[39] Jacques Samain, Giovanna Carofiglio, Luca Muscariello, Michele Pa-
palini, Mauro Sardara, Michele Tortelli, and Dario Rossi. Dynamic
adaptive video streaming: Towards a systematic comparison of icn and
tcp/ip. IEEE transactions on Multimedia, 19(10):2166–2181, 2017.

4. [40] Susmit Shannigrahi, Spyridon Mastorakis, and Francisco R Ortega.
Next-generation networking and edge computing for mixed reality real-
time interactive systems. In 2020 IEEE International Conference on
Communications Workshops (ICC Workshops), IEEE, 2020.
[41] Michele Tortelli, Dario Rossi, Gennaro Boggia, and Luigi Alfredo
Grieco. Icn software tools: survey and cross-comparison. Simulation
Modelling Practice and Theory, 63:23–46, 2016.
[42] Rehmat Ullah, Muhammad Atif Ur Rehman, and Byung Seo Kim.
Poster: A testbed implementation of ndn-based edge computing for
mobile augmented reality. In Proceedings of the 20th International
Workshop on Mobile Computing Systems and Applications, pages 181–
181, 2019.
[43] Rehmat Ullah, Muhammad Atif Ur Rehman, Muhammad Ali Naeem,
Byung-Seo Kim, and Spyridon Mastorakis. Icn with edge for 5g:
Exploiting in-network caching in icn-based edge computing for 5g
networks. Future Generation Computer Systems, 2020.
[44] Satyanarayana Vusirikala, Spyridon Mastorakis, Alexander Afanasyev,
and Lixia Zhang. Hop-by-hop best effort link layer reliability in named
data networking. Technical report, NDN, Technical Report, NDN-0041,
2016.
[45] Zhehao Wang, Zening Qu, and Jeff Burke. Demo overview-matryoshka:
design of ndn multiplayer online game. In Proceedings of the 1st ACM
Conference on Information-Centric Networking, pages 209–210, 2014.
[46] Xiangyang Wu, Xiaobin Tan, Yunfeng Shao, Jiawei Ni, and Jin Zhu.
A stochastic optimization approach for dynamic adaptive scalable video
streaming over ndn. In 2017 36th Chinese Control Conference (CCC),
pages 2326–2331. IEEE, 2017.
[47] Lixia Zhang et al. Named data networking (ndn) project. Relatório
Técnico NDN-0001, Xerox Palo Alto Research Center-PARC, 2010.
[48] Yu Zhang, Zhongda Xia, Spyridon Mastorakis, and Lixia Zhang. KITE:
Producer Mobility Support in Named Data Networking. 5th ACM
Conference on Information-Centric Networking, 2018.
[49] Zhiyi Zhang, Yingdi Yu, Haitao Zhang, Eric Newberry, Spyridon
Mastorakis, Yanbiao Li, Alexander Afanasyev, and Lixia Zhang. An
overview of security support in named data networking. Technical report,
Technical Report NDN-0057, NDN, 2018.