Opening Session

A New Way to Support Security

Lixia Zhang (University of California, Los Angeles)

Alex Afanasyev (Florida International University) 

Abstract: It is a commonly accepted notion that today's Internet security protections, mainly IPsec, (D)TLS, 

certificate authorities, and DNSSEC, are patch-on's to an already deployed Internet, and any future 

internet architecture should have the security designed-in. This talk aims to share with the community 

NDN's approach to achieve this goal, by providing an overview of the NDN security model, explaining the 

major components in this model and how they function together. The talk will also illustrate the basic 

difference between NDN security and the existing TCP/IP security, and identify new research challenges.

Panel 1

Security and Privacy in NDN: An Edge Computing Perspective

Chair: Jay Misra (New Mexico State University)

Alex Afanasyev (Florida International University)

Ken Calvert (National Science Foundation)

Tamer Refaei (MITRE Corporation)

Murugiah Souppaya (NIST)

Edge computing can support new applications, such as augmented reality and autonomous driving. It will 

also allow the interactions between multiple application and data stakeholders and tenants. This creates 

new security and privacy challenges at the edge. Several questions arise: How does an end-user trust the 

edge devices? Who owns the context generated by the edge device from user data? How can data sharing 

happen between the stakeholders and tenants? How are the data and user privacy issues handled? NDN 

may provide unique opportunities to address these challenges. In this panel, NDN experts and experts 

from outside the NDN community will present their thoughts on these and other challenges and discuss 

how NDN positions itself as a solution in the edge computing perspective. 

Panel 2

Edge Computing: Shaping the Named Data Edge

Chair: Christian Tschudin (University of Basel)

Jeff Burke (University of California, Los Angeles)

Asit Chakraborti (Huawei Technologies)

Martial Michel (Data Machines Corp.)

Lan Wang (University of Memphis)

The edge highlights locality and diversity while the cloud wants to hide them. How can NDN support this 

difference? Where do computing resources play a role? Is NDN "edge-ready", has it the potential and is it 

already actively shaping the edge? In this panel, experts from inside as well as outside the NDN core 

community will reflect on these questions. The goal is to review the edge's use cases (IoT, reverse CDN) 

and requirements (security, low-latency) and to identify NDN's opportunities and challenges for building 

a post-IP named data edge.
 

Session 1: NDN support for computing 

Towards an Augmented Reality Browser using NDN

Jeff Burke (UCLA REMAP) <jburke@remap.ucla.edu>

Peter Gusev (UCLA REMAP) <peter@remap.ucla.edu> 

Jeff Thompson (UCLA REMAP) <jefft0@remap.ucla.edu>

Augmented reality is typically discussed as being achieved by individual "apps" that follow existing mobile 

application models. We propose that NDN enables a different and potentially more appropriate way of 

looking this application domain that is less oriented around the current model of vertically integrated, 

silo'd apps. AR overlays digital content on the physical world, and it is reasonable to assume that more 

than one such overlay might be active at any given time. Inspired by this requirement and the success of 

the world wide web, we propose a model of an ecosystem of named, signed, linkable NDN content that is 

retrieved and displayed by sandboxed code running in one of many layers within an AR "browser". NDN 

provides the necessary capabilities for low-latency, granular content access and efficient multicast 

distribution of user context to enable this approach. In this presentation, we present the application 

model, an initial NDN design and prototype focusing on edge support for mobile AR, and plans for future 

development.

Edge Computing Over Named Data Networking 

Abderrahmen Mtibaa (New Mexico State University) <amtibaa@cs.nmsu.edu>

Satyajayant Misra (New Mexico State University) <misra@cs.nmsu.edu> 

Reza Tourani (New Mexico State University) <rtourani@cs.nmsu.edu> 

Jeff Burke (UCLA) <jburke@remap.ucla.edu> 

Lixia Zhang (UCLA) <lixia@cs.nmsu.edu>

While edge computing research is rapidly growing, most of the proposed IP-based edge computing 

systems are centered around optimizing applications and services and are agnostic of the underlying 

networking problems. Such problems include complex mapping between application names and IP 

addresses without leveraging the available network conditions. Named Data Networking (NDN), however, 

solves the above problem by marrying app names to network forwarding. We discuss leveraging the NDN 

in-network features that can be used for seamless and efficient edge computing. We discuss how NDN 

can enable seamless computing resource discovery, task forwarding, and compute re-use in a distributed 

fashion without relying on a centralized entity while incurring a minimum overhead. Our approach can 

aid the optimization envisaged today with proxies and SDN controllers and may also be strong enough to 

be leveraged alone. We discuss the feasibility of a generic NDN edge computing paradigm that leverages 

NDN networking and forwarding features to seamlessly offload computation efficiently. We focus on innetwork compute execution in NDN and we highlight the open issues for enabling secure, efficient and 

seamless edge computing functionalities over NDN.

Compute-First-Network for NDN 

Asit Chakraborti (Huawei Technologies) <asit.chakraborti@huawei.com>

Dirk Kutscher (Huawei Technologies) <dirk.kutscher@huawei.com>

He Jianfei (Huawei Technologies) <jeffrey.he@huawei.com>

Cedric Westphal (Huawei Technologies) <cedric.westphal@huawei.com>
Syed Obaid Amin (Huawei Technologies) <obaid.amin@huawei.com>

Modern applications often expect the network to deliver it processed data. As a result, in-network 

compute capability has often been considered as a complementary feature for network architectures like 

Named Data Network (NDN). The desire for the computation to be performed in the network instead of 

at the data source, destination or a remote server might stem from reasons related to computing resource 

availability, access latency, network bandwidth optimization, geographical or other application context, 

etc. Compute-First-Network (CFN) is a generalized framework that will allow application vendors to 

provide functions that can be discovered and invoked by applications in a location agnostic way, with the 

network selecting the most optimal location for the function execution. CFN expands on the abstractions 

provided by NDN: it allows named access to functions and receiver driven data flow, the function body 

being treated essentially as data. A Request For Execution is a primary protocol entity of CFN. Functions 

can be short lived or long lived, stateless or stateful; and on-demand function instantiation is a key feature. 

It is expected that initially functions will be loaded at service points at the cloud and edge locations, but 

mechanisms will be available for a CFN node to download a function and offer execution of the same. A 

CFN capable network node will feature a Function Store to cache function bodies, and a Function 

Controller capable of managing the execution environment that uses Unikernels and Linux containers. The 

Function Controller maintains a state for every active function, and is capable of responding to new 

function instantiation requests from the NDN forwarder. CFN will accommodate protocols that facilitate 

parameter and result passing between the client and the function, as well as their mutual authentication. 

The discovery of function execution points can be aided by centralized entities, for example, a cloud based 

service; or it could be performed in a distributed way that takes care of resource congestion locally. One 

of the use-cases for CFN is performing resource heavy computation for AR/VR applications at a 

dynamically determined edge location. At the same time, the platform isintended to be equally applicable 

for ephemeral computations for IoT data. Other approaches to in-network computing like NFN and NFaaS 

lack the generality needed to be suitable for a large set of applications. NFN lacks a standard virtualization 

environment, and incorporating stateful functions and high-volume parameter passing using the lambda 

expressions could be difficult. While NFaaS offers Unikernel based execution, it does not focus on aspects 

like function discovery, parameter passing and congestion control.

Session 2: IoT and security 

NDNoT: A Framework for Named Data of Things 

Zhiyi Zhang (UCLA) <zhiyi@cs.ucla.edu>

Yanbiao Li (UCLA) <lybmath@cs.ucla.edu>

Tianyuan Yu (Sichuan University) <royu9710@outlook.com>

Alex Afanasyev (Florida International University) <aa@cs.fiu.edu> 

Lixia Zhang (UCLA) <lixia@cs.ucla.edu> 

The Named Data Networking (NDN) architecture provides simple solutions to the communication needs 

of Internet of Things (IoT) in terms of ease-of-use, security, and content delivery. To utilize the desirable 

properties of NDN architecture in IoT scenarios, we are working to provide an integrated framework, 

dubbed NDNoT, to support IoT over NDN. NDNoT provides solutions to auto configuration, service 

discovery, data-centric security, content delivery, and other needs of IoT application developers. Utilizing 

NDN naming conventions, NDNoT aims to create an open environment where IoT applications and 

different services can easily cooperate and work together. This presentation will help to introduce the 

basic components of our framework and explains how these components function together. 
Blockchain-based Decentralized Public Key Management for Named Data Networking 

Kan Yang (University of Memphis) <kan.yang@memphis.edu>

Jobin J. Sunny (St. Jude Children's Research Hospital) <jjsunny@memphis.edu>

Lan Wang (University of Memphis) <lanwang@memphis.edu>

Named Data Networking (NDN) uses public-key based identities and trust models to achieve data-centric 

security. Each NDN data packet is signed by its producer, and any data consumer can check the data 

integrity and authenticity by following a chain of trust to verify that the data is signed by a public key 

associated with the data producer. Such trust chains typically end at an application-specific trust anchor 

whose public key is either preconfigured into the software package or can be verified through some means 

outside the application. As these trust anchors play a critical role in ensuring the security of NDN 

applications, it is highly desirable to develop a public key management system to register, query, update, 

validate, and revoke their public keys. However, traditional public key management system such as Public 

Key Infrastructure (PKI) and Web-of-Trust (WoT) suffer from various problems. In this paper, we propose 

BC-PKM, a public key management system for NDN that takes advantage of the decentralized and tamperproof design features of Blockchains. We further prove that BC-PKM can resist a variety of attacks from 

adversaries that compromise less than half of the public key miners. Moreover, we demonstrate a 

prototype that implements the proposed API of BC-PKM.

Distributed Ledger over NDN for A Real-world Solar System 

Zhiyi Zhang (UCLA) <zhiyi@cs.ucla.edu>

Vishrant Vasavada (UCLA) <vishrantvasavada@gmail.com>

Randy King (Operant Solar) <randy.king@operantsolar.com>

Lixia Zhang (UCLA) <lixia@cs.ucla.edu> 

Distributed blockchain system has great potentials in cybersecurity systems for its built-in robustness, 

incorruptibility, and publicity. Named Data Networking (NDN) secures communication at the network 

layer by requiring all data packets to be signed when produced, ensuring data authentication and integrity. 

Therefore, an NDN Data packet naturally serves as an unforgeable and undeniable record of an entity's 

behavior. We are now working to build a distributed ledger over NDN for a real-world solar system 

(Operant Solar) that requires a distributed ledger system to record the energy production and 

consumption. In our system, we directly use NDN Data packets as the blocks and link Data packets 

together through names, thus each block can directly be fetched from the Internet by the name without 

knowing where to fetch. We use signature signing/verification to place proof-of-work, and make the 

record in a question-answer mode to prevent compromised attackers from keeping adding fake records 

to the system.

Session 3: NDN for Vehicular Networks 

Vehicular Named Data Networking 

Dennis Grewe (Robert Bosch GmbH) <dennis.grewe@de.bosch.com>

Claudio Marxer (University of Basel) <claudio.marxer@unibas.ch>

Christopher Scherb (University of Basel) <christopher.scherb@unibas.ch>

Marco Wagner (Robert Bosch GmbH) <marco.wagner3@de.bosch.com> 

Christian Tschudin (University of Basel) <christian.tschudin@unibas.ch>
Named Data Networks (NDN) oer the service to deliver information based on the content’s name. This 

means, clients are released from holding server locations and request content in a connection oriented 

fashion. As an extension of NDN, Named Function Networking (NFN) allows users to request for derived, 

i.e. dynamically produced, data. In NFN, named functions are published, which can be applied to any 

available content on demand of a user. For this purpose, a user composes a data name which encodes the 

application of named function(s) to certain data. It is the task of the network to compute and deliver the 

result. Behind the scenes, the network implements a forwarding strategy which decides where a 

computation is executed. Recently, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure(V2I) 

connectivity transitioned from a vision of the future to reality. Applications in such environments vary

from large-scale traffic flow control systems to local propagation of road conditions. We present a 

network stack for the data exchange in the automotive IoT. Based on the Named Function Networking 

(NFN) principles, the communication model is not restricted to propagation of static data but natively 

supports computation-offloading to other nodes. We present solutions and report on experiments with 

real cars on a test course.

Connectivity and Location-aware Routing Scheme (CLRS) for Connected and Autonomous 

Vehicles (CAVs) 

Muktadir Chowdhury (University of Memphis) <mrchwdhr@memphis.edu> 

Junaid Ahmed Khan (University of Memphis) <junaid.khan@memphis.edu>

Lan Wang (University of Memphis) <lan.wang@memphis.edu>

Routing or forwarding data packet in Connected Vehicles is a challenging task and data retrieval rate can 

be very low due to highly dynamic topology and intermittent connectivity. Most of the data forwarding 

strategies in the literature are location-based accompanied with limited flooding when location 

information is not available. For efficient communication and data retrieval in the vehicular network, we 

propose a hybrid forwarding solution, called CLR, that will enable vehicles to choose next-hop based on 

both the vehicle’s centrality score as well as its location. Rather than flooding when location information 

is not available, CLR uses centrality score to forward packet. To overcome the shortcomings of IP in mobile 

environment, CLR is based on a data-centric network called Named Data Network (NDN). CLR will be 

compared against both location-aware forwarding strategy (GPSR and NAVIGO) and centrality-based 

forwarding strategy (STRIVE) in the literature.

Data-Centric MAC for Robust Multicast in Vehicular Networks

Mohammed Elbadry (Stony Brook University) <mohammed.salah@stonybrook.edu>

Bing Zhou (Stony Brook University) <bing.zhou@stonybrook.edu>

Fan Ye (Stony Brook University) <fan.ye@stonybrook.edu>

Peter Milder (Stony Brook University) <peter.milder@stonybrook.edu>

YuanYuan Yang (Stony Brook University) <yuanyuan.yang@stonybrook.edu>

Data-centric networks provide content instead of address (what vs. where) based communication 

primitives, and have been argued to be the proper candidate for data dissemination in high mobility 

vehicular networks (e.g., delivering road side accident video clips to affected drivers in both directions). 

However, current Medium Access Control (MAC) layers filter incoming frames based on destination 

addresses, not content. The data-centric network community has resorted to MAC broadcast, with high 

and greatly varying frame loss rates. We propose V-MAC, a data-centric MAC layer that filters frames by 

content. It supports one to many multicast at MAC level, and ensures a uniform and controllable small 

frame loss rate across all receivers, despite their varying reception qualities. We have created a V-MAC 

prototype using Raspberry Pis and WiFi dongles. Experiments under extremely noisy environment show 
that it reduces frame loss from 50% (broadcast) to less than 10%, and consistently among multiple 

receivers.

Session 4: NDN in Mobile AdHoc Networks 

Peer-to-Peer File Sharing in Mobile Ad hoc Networks over NDN 

Spyridon Mastorakis (UCLA) <mastorakis@cs.ucla.edu>

Tianxiang Li (UCLA) <tianxiang@cs.ucla.edu> 

Lixia Zhang (UCLA) <lixia@cs.ucla.edu>

Mobile Ad hoc NETworks (MANETs) are network environments, where intermittent connectivity and 

unpredictable node mobility are the norms. In such environments, sharing files among the nodes is 

challenging. In this presentation, we discuss the design of a peer-to-peer file sharing application in 

MANETs over NDN. We use our previous work, nTorrent, which is a peer-to- peer file sharing application 

in wired NDN networks, as the starting point of our design. However, our design fundamentally departs 

from nTorrent to efficiently address the communication challenges in MANETs, such as network 

partitions, short-lived ad hoc node encounters, and lossy links. Our design proposes a space-efficient 

approach for nodes to let others know about the torrent data they are missing, and leverages NDN’s datacentric security to achieve data authentication and integrity. We study a number of different design 

decisions to achieve efficient data dissemination with the fewest possible packet transmissions to 

minimize energy consumption, and we evaluate the effect of each design decision through simulations.

Distributed Dataset Synchronization in Mobile Ad Hoc Networks over NDN 

Tianxiang Li (UCLA) <tianxiang@cs.ucla.edu> 

Spyridon Mastorakis (UCLA) <mastorakis@cs.ucla.edu> 

Xin Xu (UCLA) <xinxu129@cs.ucla.edu>

Haitao Zhang (UCLA) <haitao@cs.ucla.edu>

Lixia Zhang (UCLA) <lixia@cs.ucla.edu>

Mobile Ad-hoc Networking (MANETs) is characterized by dynamic movements of mobile nodes and 

intermittent connectivity among them, creating unique challenges to running applications over MANET. 

In response to this challenge, we developed DDSN, a Distributed Dataset Synchronization protocol over 

Named Data Networking (NDN). Taking wireless ad hoc, intermittent connectivity as the norm and utilizing 

NDN advantages, DDSN enables nodes running the same applications to synchronize their shared 

application dataset whenever they encounter each other securely and efficiently. We evaluated the DDSN 

design through extensive experiments, and the results show that DDSN can achieve resilient and timely 

dataset synchronization over lossy and intermittent connectivity. We also compared DDSN with TCP/IP 

based MANET solutions, both qualitatively through the design analysis and quantitatively via experimental 

results.

Toward Multi-Hop Long-Range D2D Communication via Information Centric Ad Hoc Networks 

Yaoqing Liu (Clarkson University) <liu@clarkson.edu>

Anthony Dowling (Clarkson University) <dowlinah@clarkson.edu>

Mobile wireless ad hoc networks (MANETs) do not rely on an infrastructure to operate and can 

disseminate information quickly without worrying about single point of failure, thus can be used for a 

variety of applications, such as disaster relief, vehicular communication, smart agriculture, etc. However, 
the challenges affecting MANETs, including ubiquitous connectivity, dynamic routing, and flexible 

mobility, span from various layers of the current TCP/IP protocol stack. A particularly daunting challenge 

is to enable adaptive communication and interoperability over multiple heterogeneous wireless links. To 

circumvent existing constraints, the poster leverages Named Data Networks (NDN), an emerging 

information-centric network architecture, to interconnect diverse wireless links at the network layer and 

implements flexible routing and forwarding strategies for efficient information dissemination. The system 

focuses on implementing an interface between NDN and LoRaWAN, and interconnecting LoRaWAN and 

WiFi via NDN Forwarding Daemon (NFD) into a ubiquitous ad hoc network, which bears very long-range 

and multi-hop capabilities for Device-to-Device (D2D) communication. Many compelling applications, 

such as disaster relief, smart cities, etc., can benefit from the technology. Field experimental results show 

that the newly built ad hoc network can easily cover a radius of several kilometers and make full use of 

NDN features to maximize utilization of heterogeneous wireless links and to enhance efficiency of 

information dissemination. In the poster, we will demonstrate the research results using NDN-enabled 

devices for a number of multi-hop long-range experiments and share the experiences we learned from 

the study.

Session 5: Routing and Forwarding 

High-Speed NDN-DPDK Forwarder 

Junxiao Shi (NIST) <junxiao.shi@nist.gov>

Lotfi Benmohamed (NIST) <lotfi.benmohamed@nist.gov>

NDN-DPDK forwarder is a high-speed NDN forwarder developed with Data Plane Development Kit (DPDK). 

This presentation explains the motivation, design, and current status of this project. NDN protocol’s 

reference implementation, the NDN Forwarding Daemon (NFD), is designed to be modular and extensible, 

but is not optimized for performance. The NDN community needs a high-speed forwarder that would be 

useful to data-intensive science applications that require the distribution of large amounts of scientific 

data, and to enable NDN adoption for general usage such as video streaming services. NDN-DPDK 

forwarder adopts four techniques to be fast: (1) processes packets in parallel on multiple CPU cores, fully 

utilizing available CPU resources on the target hardware; (2) uses pre-allocated memory pool, avoiding 

costly memory allocations during packet processing; (3) incorporates better data structures; (4) 

communicates with Network Interface Cards (NICs) directly, bypassing the kernel. As of Jul 2018, NDNDPDK has achieved a forwarding throughput in excess of one million Interest-Data exchanges per second. 

This was measured on a server equipped with two Intel E5-2680V3 processors, 512GB memory in two 

channels, and Mellanox ConnectX-5 100Gbps network cards. NDN-DPDK codebase includes a traffic 

generator to perform this test. In the future, NDN-DPDK forwarder will be integrated into a complete 

system that includes a management and measurement framework. 

A New Way of Traffic Engineering in NDN 

Klaus Schneider (The University of Arizona) <klaus@cs.arizona.edu>

Beichuan Zhang (The University of Arizona) <bzhang@cs.arizona.edu> 

Lotfi Benmohamed (NIST) <lotfi.benmohamed@nist.gov>

Today’s network providers use Traffic Engineering (TE) in order to steer traffic from heavily used paths 

towards lightly used ones, aiming to use their existing net- work in the most efficient way. The most 

common approaches for IP traffic engineering are 1) tuning the link weights of a hop-by-hop routing 

protocol [1], 2) splitting traffic among pre-established end-to-end tunnels (like MPLS) [2], and 3) using 
centralized control via Software-Defined Networking [3]. However, each of these approaches comes with 

drawbacks, respectively, 1) hard-to-predict global side- effects of changing link weights, 2) manual setup 

of label-switched paths and slowly- adjusting bandwidth reservations, and 3) requiring a centralized 

control plane. In this presentation, we show that NDN can offer an additional way of traffic engineering, 

one which does not suffer these drawbacks. We show that our new way of NDN TE can a) simplify 

configuration (avoiding manual configuration or having to pre-establish paths) and b) improve 

performance (maximizing user utility and using the available network resources more efficiently), while 

also supporting important NDN features such as multi-destination routing and ubiquitous in-network 

caching. To achieve this, we make use of hop-by-hop multipath routing, NDN’s stateful forwarding plane, 

and explicit probing of the path (congestion) state. More specifically, we address the research questions 

of how to coordinate routers which make independent forwarding decisions, how to ensure stability and 

avoid oscillations, and lastly how to determine when traffic should be split among multiple paths and 

when it shouldn’t.

NDNCONF: Network Configuration Management System for NDN based on NETCONF protocol 

Rajender Kumar (Florida International University) <rkuma013@fiu.edu>

Alexander Afanasyev (Florida International University) <aa@cs.fiu.edu>

Internet is rapidly growing and a variety of services are provided by the heterogeneous network devices 

developed by different companies. For faster information processing and efficient data delivery, it is very 

important to have some tools, mechanism or protocols for configuration management of these 

heterogeneous devices. Due to recent changes in network technology, network operators do not only 

have to deal with network devices configuration, but also they have to manage various aspect of services. 

Thus, management of services and various network devices is a major factor which affects the cost of 

networks. In the past, SNMP and CLI (Command Line Interface) are used for configuration of network 

devices. But due to introduction of heterogeneous equipment and requirements of automation in 

configuration management, these solutions are no longer used for configuration management. IETF 

introduces a new standard called NETCONF (NETwork CONFiguration) for efficiently managing the 

configuration of different network devices. The Network Configuration Protocol (NETCONF) defined in 

RFC4741 provides new mechanisms to install, manipulate, and delete the configuration of network 

devices. NETCONF protocol is based on XML data encoding scheme for encoding the protocol messages 

and configuration related data. Our understanding of design and architecture of NDN states that NDN can 

be used in place of IP-based architecture with the current state-of-the-art heterogeneous types of 

equipment for accessing data over Internet. Thus, as we stated above, it becomes necessary that there 

should be some kind of protocol to efficiently configure different types of equipment over the Internet 

for NDN. To the best of our knowledge, there is no protocol (equivalent to Netconf) implemented on the 

NDN paradigm for configuration management. So, we decide to work in this direction to design a protocol, 

called NDNCONF (NDN version of Netconf), for configuration management over NDN network so that 

different network devices can easily be configured and used in the network. We design a Yang model for 

different classes of management module of NFD. We provide an architecture of NDNCONF protocol for 

NDN network configuration management. NDNCONF protocol provides new mechanisms to view, 

manipulate, and delete the configuration of network devices over the NDN network. Our NDNCONF 

protocol is based on XML data encoding scheme for encoding the protocol messages and configuration 

related data. Our NDNCONF protocol uses the client -server architecture for configuration management 

in which a network device acts as a server and an application or another device acts as client and they can 

be connected using existing secure transport protocols over the NDN. In NDNCONF, built on top of NDN, 

all response can be properly authenticated at the network packet level and commands can leverage trust 
schema to realize various control permissions at fine granularity. Client gives commands (in the form of 

interest packets) to get or edit the configuration data, which are processed by the server device and 

resultant configuration or state data is transferred to client in a data packet. Client uses the simple interest 

to retrieve state data and command interest to edit the configuration data. Thus, NDNCONF is a variant 

of the Netconf for the NDN network.

Session 6: NDN support for new deployments 

NDN Supporting Ultra-Dense Networks 

Xiaoyan Hong (University of Alabama) <hxy@cs.ua.edu>

Pawan Subedi (University of Alabama) <psubedi@crimson.ua.edu>

Dense presences of IoT devices as well as high population of mobile phones are likely future scenarios. 

Under the 5G paradigm of Ultra Dense Networks (UDN), there is also a high density of access points (APs), 

or called base stations (BS), via the deployment of macro-cell, micro-cell, fermtocell, and user devices 

acting as APs. Thus in addition to small cell stations, relay APs and distributed remote radio heads are all 

components in the network architecture. One of the network paradigm to achieve the resource 

management, interference management, mobility management and security aspect in such dense 

networks is User-Centric Ultra-Dense Network (UUDN). In this architecture, each device is treated as the 

center, surrounded by a small pseudo-celled network which is created such that the network provides 

multiple access points (AP Group, or APG) for the device. The UUDN architecture has a functional unit of 

Local Access Server (LAS) at wireless edge with control plane for APG management and user plane for data 

communications. LASs connect to the core network service centers. The UUDN architecture, if 

implemented using the traditional IP network, will encounter the same problems in dealing with mobility, 

content sharing, security, etc., as those identified and motivated Named-Data Network Internet 

Architecture. As such, in this work, we present an implementation blueprint of UUDN using Named-Data 

Networking. The goal is to demonstrate the benefits of NDN in supporting UUDN model for ultra-dense 

networks. The name conventions of the control messages, the communications among the main types of 

devices in the UUDN, and various tables will be prototyped. The presentation/demo will show a threenodes scenario that is general enough to extend to multiple IoT scenarios, for examples, Intelligent 

Transportation System and smart home systems.

Automated Neighbor Discovery to Run NDN Anywhere 

Arthi Padmanabhan (UCLA) <artpad@cs.ucla.edu>

Lan Wang (University of Memphis) <lanwang@memphis.edu> 

Lixia Zhang (UCLA) <lixia@cs.ucla.edu> 

NDN is designed as a "universal overlay" that operates over existing infrastructure, which allows it to be 

deployed incrementally. To run over IP, NDN allows its packets to be sent using IP tunnels. NDN's use of 

IP tunnels can be leveraged in situations where establishing NDN connectivity through normal methods is 

not feasible. Some networks prohibit multicast, unicast, or both within the local network, leaving NDN 

nodes isolated. This presentation introduces the NDN Neighbor Discovery service (NDND), which details 

the communication required for NDN nodes in such networks to establish connectivity through the use of 

a rendezvous server.
NDN for Data Intensive Science

Susmit Shannigrahi (Colorado State University) <susmit@colostate.edu>

In this talk, we discuss the progress of the SANDIE project since the last NDNComm. The SANDIE project 

aims to integrate NDN and SDN concepts with the existing High Energy Particle Physics workflows for 

efficient data distribution, in-network strategic caching, and request redirection to the nearest replica, 

among other novel mechanisms. In addition, we expect that integrating NDN/SDN with current workflows 

will reduce the complexity of data management software, such as xtootd, that are currently used in the 

HEP domain. We discuss how we have inferred the network topology with detailed metrics such as link 

capacity, RTT delay, the available bandwidth for US-CMS. We then use this topology to simulate delay 

reduction by strategically adding in-network caches at Tier-2 sites in the US. We also analyzed real data 

from CMS and found that they display a significant locality of reference. Consequently, our simulations 

showed that we should be able to reduce the average data distribution delay by 48% using NDN and the 

VIP caching algorithm. Having simulated the improvements, we are extending the ndn-sci testbed to 

deploy our algorithms in a real environment. We are also working on integrating an NDN plugin with 

xrootd that will allow the xrootd system to redirect requests and utilize caches in the network 

transparently. In this talk, I discuss these updates as well as how we plan to study NDN's benefits using 

real HEP data requests once we integrate NDN to the existing production xrootd system.

Posters and Demos

IoT+NDN+Wireless: Experimental Insights from Using 802.11 Infrastructure and Ad-hoc Modes 

Travis Machacek (New Mexico State University) 

Abderrahmen Mtibaa (New Mexico State University) 

Reza Tourani (New Mexico State University) 

Satyajayant Misra (New Mexico State University)

A Raspberry Pi Based Data-Centric MAC for Robust Multicast in Vehicular Networks 

Mohammed Elbadry (Stony Brook University) 

Bing Zhou (Stony Brook University) 

Fan Ye (Stony Brook University) 

Peter Milder (Stony Brook University) 

YuanYuan Yang (Stony Brook University)

Bootstrapping Trust in NDN-Based Vehicular Network Using SWIFT Trust 

Sanjeev Kaushik Ramani (Florida International University) 

Manusri Viswanath (Rensselaer Polytechnic Institute) 

Jalena Jones (Claflin University) 

Alex Afanasyev (Florida International University)

Named-based Access Control 

Zhiyi Zhang (UCLA) 

Yingdi Yu (UCLA) 

Alex Afanasyev (Florida International University) 

Lixia Zhang (UCLA)
Get an NDN certificate with NDNCERT protocol 

Zhiyi Zhang (UCLA) 

Alex Afanasyev (Florida International University)

Upcoming Changes in NDN Protocol 

Alex Afanasyev (Florida International University)

Optimal Cache Allocation under Network-Wide Capacity Constraint 

Van Sy Mai (NIST) 

Stratis Ioannidis (Northeastern University) 

Davide Pesavento (NIST) 

Lotfi Benmohamed (NIST)

NDN for Public Safety Deployable Networks 

Davide Pesavento (NIST) 

Junxiao Shi (NIST) 

Edward Lu (NIST) 

Lotfi Benmohamed (NIST) 

Maxwell Maurice (NIST)

ANTD NDN-IoT Testbed: Smart Building Sensing 

Edward Lu (NIST) 

Junxiao Shi (NIST) 

Davide Pesavento (NIST) 

Kerry McKay (NIST) 

David Cooper (NIST) 

Lotfi Benmohamed (NIST)

HomeCam Browser-based Home Surveillance Camera 

Junxiao Shi (NIST)

Performance evaluation of the NDN-DPDK forwarder 

Siham Khoussi (NIST), 

Junxiao Shi (NIST)

Ayoub Nouri (Grenoble University)

James Filliben (NIST)

Lotfi Benmohamed (NIST)

Saddek Bensalem (Grenoble University)

PSync deployment on IoT sensor testbed 

Ashlesh Gawande (Uinversity of Memphis) 

Lan Wang (University of Memphis)

Named Query Framework for Named Data Networking of Things 

Muhammad Atif Ur Rehman (Hongik University Sejong Campus, South Korea) 

Rehmat Ullah (Hongik University Sejong Campus, South Korea) 

Byung-Seo Kim (Hongik University Sejong Campus, South Korea)
Leveraging Named Data Networking in Edge Computing 

Rehmat Ullah (Hongik Univeristy Sejong Campus, South Korea) 

Byung-Seo Kim (Hongik Univeristy Sejong Campus, South Korea)

NDN Supporting Ultra-Dense Networks 

Xiaoyan Hong (University of Alabama) 

Pawan Subedi (University of Alabama)

ICE-AR Application Progress 

Peter Gusev (UCLA REMAP)

An Overlay NDN Architecture for Application-Driven Satellite-Terrestrial Integration 

Yating Yang (Beijing Institute of Technology) 

Tian Song (Beijing Institute of Technology)

Distributed Network Measurement Protocol (DNMP): A Secure Role-Based Approach 

Kathleen Nichols (Pollere, Inc)

Vehicular Named Data Networking 

Dennis Grewe (Robert Bosch GmbH) 

Claudio Marxer (University of Basel) 

Christopher Scherb (University of Basel) 

Marco Wagner (Robert Bosch GmbH) 

Christian Tschudin (University of Basel)