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ICN Research Group                                              Y. Zhang
Internet-Draft                                            D. Raychadhuri
Intended status: Informational                WINLAB, Rutgers University
Expires: March 26, 2015                                        L. Grieco
                                               Politecnico di Bari (DEI)
                                                             E. Baccelli
                                                                   INRIA
                                                                J. Burke
                                                              UCLA REMAP
                                                       R. Ravindran (Ed)
                                                                 G. Wang
                                                     Huawei Technologies
                                                      September 22, 2014


      ICN based Architecture for IoT - Requirements and Challenges
                   draft-zhang-iot-icn-challenges-00

Abstract

   The Internet of Things (IoT) promises to connect billions of objects
   to Internet.  After deploying many stand-alone IoT systems in
   different domains, the current trend is to develop a common, "thin
   waist" of protocols forming a unified, defragmented IoT platform.
   Such a platform will make objects accessible to applications across
   organizations and domains.  Towards this goal, quite a few proposals
   have been made to build a unified IoT platform as an overlay on top
   of today's Internet.  Such overlay solutions, however, are inadequate
   to address the important challenges posed by a heterogeneous, global
   scale deployment of IoT, especially in terms of mobility,
   scalability, and communication reliability, due to the inherent
   inefficiencies of the current Internet.  To address this problem, we
   propose to build a common set of protocols and services, which form
   an IoT platform, based on the Information Centric Network (ICN)
   architecture, which we call ICN-IoT.  ICN-IoT leverages the salient
   features of ICN, and thus provides seamless mobility support,
   scalability, and efficient content and service delivery.

   This draft sets the IoT requirements and ICN challenges to realize a
   unified ICN-IoT framework.  Towards this, we first identify a list of
   important requirements which a unified IoT architecture should have
   to support tens of billions of objects.  Then we analyze the current
   state of art deployment model and discuss important and popular IoT
   scenarios including the "smart" home, campus, grid, transportation
   infrastructure, healthcare, Education, and Entertainment.  Though we
   see most of these requirements are met by ICN, we discuss specific
   challenges ICN has to address to satisfy them considering
   heterogeneity in IoT environments and scenarios.



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Status of This Memo

   This Internet-Draft is submitted in full conformance with the
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   Internet-Drafts are working documents of the Internet Engineering
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   This Internet-Draft will expire on March 26, 2015.

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Table of Contents

   1.  IoT Motivation  . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  IoT Architectural Requirements  . . . . . . . . . . . . . . .   4
     2.1.  Naming  . . . . . . . . . . . . . . . . . . . . . . . . .   4
     2.2.  Scalability . . . . . . . . . . . . . . . . . . . . . . .   4
     2.3.  Resource Constraints  . . . . . . . . . . . . . . . . . .   4
     2.4.  Traffic Characteristics . . . . . . . . . . . . . . . . .   5
     2.5.  Contextual Communication  . . . . . . . . . . . . . . . .   5
     2.6.  Handling Mobility . . . . . . . . . . . . . . . . . . . .   6
     2.7.  Storage and Caching . . . . . . . . . . . . . . . . . . .   6
     2.8.  Security and Privacy  . . . . . . . . . . . . . . . . . .   7
     2.9.  Communication Reliability . . . . . . . . . . . . . . . .   7
     2.10. Self-Organization . . . . . . . . . . . . . . . . . . . .   7
     2.11. Ad hoc and Infrastructure Mode  . . . . . . . . . . . . .   8
     2.12. Open API  . . . . . . . . . . . . . . . . . . . . . . . .   8



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   3.  State of the Art  . . . . . . . . . . . . . . . . . . . . . .   8
     3.1.  Silo IoT Architecture . . . . . . . . . . . . . . . . . .   9
     3.2.  Overlay Based Unified IoT Solutions . . . . . . . . . . .   9
       3.2.1.  Weaknesses of the Overlay-based Approach  . . . . . .  10
   4.  Popular Scenarios . . . . . . . . . . . . . . . . . . . . . .  11
     4.1.  Homes . . . . . . . . . . . . . . . . . . . . . . . . . .  12
     4.2.  Enterprise  . . . . . . . . . . . . . . . . . . . . . . .  12
     4.3.  Smart Grid  . . . . . . . . . . . . . . . . . . . . . . .  13
     4.4.  Transportation  . . . . . . . . . . . . . . . . . . . . .  13
     4.5.  Healthcare  . . . . . . . . . . . . . . . . . . . . . . .  14
     4.6.  Education . . . . . . . . . . . . . . . . . . . . . . . .  15
     4.7.  Entertainment, arts, and culture  . . . . . . . . . . . .  15
   5.  ICN Challenges for IoT  . . . . . . . . . . . . . . . . . . .  16
     5.1.  Naming  . . . . . . . . . . . . . . . . . . . . . . . . .  16
     5.2.  Caching/Storage . . . . . . . . . . . . . . . . . . . . .  17
     5.3.  Name Resolution . . . . . . . . . . . . . . . . . . . . .  17
     5.4.  Contextual Communication  . . . . . . . . . . . . . . . .  18
     5.5.  Routing and Forwarding  . . . . . . . . . . . . . . . . .  18
     5.6.  In-network Computing  . . . . . . . . . . . . . . . . . .  19
     5.7.  Security and Privacy  . . . . . . . . . . . . . . . . . .  20
     5.8.  Energy Efficiency . . . . . . . . . . . . . . . . . . . .  21
   6.  Informative References  . . . . . . . . . . . . . . . . . . .  21
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  25

1.  IoT Motivation

   During the past decade, many standalone Internet of Things (IoT)
   systems have been developed and deployed in different domains.  The
   recent trend, however, is to evolve towards a globally unified IoT
   platform, in which billions of objects connect to the Internet,
   available for interactions among themselves, as well as interactions
   with many different applications across boundaries of administration
   and domains.  Building a unified IoT platform, however, poses great
   challenges on the underlying network and systems.  To name a few, it
   needs to support 50-100 Billion networked objects [1], many of which
   are mobile.  The objects will have extremely heterogeneous means of
   connecting to the Internet, often with severe resource constraints.
   Interactions between the applications and objects are often real-time
   and dynamic, requiring strong security and privacy protections.  In
   addition, IoT applications are inherently information centric (e.g.,
   data consumers usually need data sensed from the environment without
   any reference to the sub-set of motes that will provide the asked
   information).  Taking a general IoT perspective, we begin by
   presenting IoT architectural requirements, then summarize how state-
   of-art approaches address these requirements.  We then discuss well
   known IoT scenarios focusing on their unique challenges.  The final
   discussion focuses on IoT challenges from an ICN perspective and
   requirements posed towards its design.



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2.  IoT Architectural Requirements

   A unified IoT platform has to support interactions among a large
   number of mobile devices across the boundaries of organizations and
   domains.  As a result, it naturally poses stringent requirements in
   every aspect of the system design.  Below, we outline a few important
   requirements that a unified IoT platform has to address.

2.1.  Naming

   The first step towards realizing a unified IoT platform is the
   ability to assign names that are unique within the scope and lifetime
   of each device, data items generated by these devices, or a group of
   devices towards a common objective.  Naming has the following
   requirements.  First, names need to be persistent (within one or more
   contexts) against dynamic features that are common in IoT systems,
   such as mobility or migration; Second, names need to be secure based
   on application requirements.

2.2.  Scalability

   Cisco predicts there will be around 50 Billion IoT devices such as
   sensors, RFID tags, and actuators, on the Internet by 2020 [1].  As
   mentioned above, a unified IoT platform needs to name every entity
   such as data, device, service etc.  Scalability has to be addressed
   at multiple levels of the IoT architecture spanning naming, security,
   name resolution, routing and forwarding level.  In addition, mobility
   adds further challenge in terms of scalability.  Particularly with
   respect to name resolution the system should be able to
   insert/update/look up a name within a short latency.  To satisfy this
   requirement, decentralization of the name resolution can be the the
   key.

2.3.  Resource Constraints

   IoT devices can be broadly classified into two groups: resource-
   sufficient and resource-constrained.  In general, there are the
   following types of resources: power, computing, storage, and
   bandwidth.

   Power constraints of IoT devices limit how much data these devices
   can communicate, as it has been shown that communications consume
   more power than other activities for embedded devices.  Flexible
   techniques to collect the relevant information are required, and
   uploading every single produced data to a central server is
   undesirable.  Computing constraints limit the type and amount of
   processing these devices can perform.  As a result, more complex
   processing needs to be conducted at opportunistic points, example at



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   the network edge, hence it is important to balance local computation
   versus communication cost.

   Storage constraints of the IoT devices limit the amount of data that
   can be stored on the devices.  This constraint means that unused
   sensor data may need to be discarded from time to time.  Bandwidth
   constraints of the IoT devices limit the amount of communication.
   Such devices will have the same implication on the system
   architecture as with the power constraints; namely, we cannot afford
   to communicate with every single sensor data generated by the device
   and/or use complex signaling protocols.

   User interface constraints refer to whether the device is itself
   capable of directly interacting with a user should the need arise
   (e.g., via a display and keypad or LED indicators) or requires the
   network connectivity, either global or local, to interact with
   humans.

2.4.  Traffic Characteristics

   IoT traffic can be broadly classified into local area traffic and
   wide area traffic.  Local area traffic is between nearby devices.
   For example, neighboring cars may work together to detect potential
   hazards on the highway, sensors deployed in the same room may
   collaborate to determine how to adjust the heating level in the room.
   These local area communications often involve data aggregation and
   filtering, have real time constraints, and require fast device/data/
   service discovery and association.  At the same time, the IoT
   platform has to also support wide area communications.  For example,
   commuters can find out real-time traffic and road information and
   then decide which commuting route to take.  Wide area communications
   require efficient data/service discovery and resolution services.

   While traffic characteristics for different IoT systems are expected
   to be different, certain IoT systems have been analyzed and shown to
   have comparable uplink and downlink traffic volume in some
   applications such as [2], which means that we have to optimize the
   bandwidth/energy consumption in both directions.  Further, IoT
   traffic demonstrates certain periodicity and burstiness [2].  As a
   result, when provisioning the system, the shape of the traffic volume
   has to be properly accounted for.

2.5.  Contextual Communication

   Many IoT applications shall rely on contextual information such as
   social, grouping, location, type of ecosystem (home, grid, transport
   etc.) of devices and data (which are referred to as contexts in this
   document) to initiate dynamic relationship and communication.  For



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   example, cars traveling on the highway may form a "cluster" based
   upon their temporal physical proximity as well as the detection of
   the same event.  These temporary groups are referred to as contexts.
   IoT applications need to support interactions among the members of a
   context, as well as interactions across contexts.

   Temporal context can be broadly categorized into two classes, long-
   term contexts such as those that are based upon social contacts as
   well as stationary physical locations (e.g., sensors in a car/
   building), and short-term contexts such as those that are based upon
   temporary proximity (e.g., all taxicabs within half a mile of the
   Time Square at noon on Oct 1, 2013).  Between these two classes,
   short-term contexts are more challenging to support, requiring fast
   formation, update, lookup and association.

2.6.  Handling Mobility

   There are varying degrees of mobility in a unified IoT platform,
   ranging from static as in fixed assets to highly dynamic in vehicle-
   to-vehicle environments.

   Mobility in the IoT platform can mean 1) the data producer mobility
   (i.e., location change), 2) the data consumer mobility, 3) IoT
   Network mobility; and 4) disconnection between the data source and
   destination pair (e.g., due to unreliable wireless links).  The
   requirement on mobility support is to be able to deliver IoT data
   below an application acceptable delay constraint in all of the above
   cases.

2.7.  Storage and Caching

   Storage and caching plays a very significant role depending on the
   type of IoT ecosystem with the fact that data generated is also
   subjected to privacy and security guidelines.  In a unified IoT
   platform, depending on application requirements, content caching may
   or may not be policy driven though the latter would be a more common
   scenario.  If caching is pervasive, intermediate nodes don't need to
   always forward a content request to its original creator; rather,
   locating and receiving a cached copy is sufficient for IoT
   applications.  This optimization can greatly reduce the content
   access latencies.

   Further, ICN architectures enable a more flexible, heterogeneous and
   potentially fault-tolerant approach to storage, that provides
   persistence at a variety of levels in a hierarchical network of
   devices.





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   In network storage and caching, however, has the following
   requirements on the IoT platform.  The platform needs to support the
   efficient resolution of cached copies.  Further the platform should
   strive for the balance between caching, content security/privacy, and
   regulations.

2.8.  Security and Privacy

   In addition to the fundamental challenge of trust management, a
   variety of security and privacy concerns also exist in ICNs.

   The unified IoT platform makes physical objects accessible to
   applications across organizations and domains.  Further, it often
   integrates with critical infrastructure and industrial systems with
   life safety implications, bringing with it significant security
   challenges and regulatory requirements [12].

   Security and privacy thus become a serious concern, as does the
   flexibility and usability of the design approaches.  Beyond the
   overarching trust management challenge, security includes data
   integrity, authentication, and access control at different layers of
   the IoT platform.  Privacy means that both the content and the
   context around IoT data need to be protected.  These requirements
   will be driven by various stake holders such as industry, government,
   consumers etc.

2.9.  Communication Reliability

   IoT applications can be broadly categorized into mission critical and
   non-mission critical.  For mission critical applications, reliable
   communication is one of the most important features as these
   applications have strong QoS requirements.  Reliable communication
   requires the following capabilities for the underlying system: (1)
   seamless mobility support in the face of extreme disruptions (DTN),
   (2) efficient routing in the presence of intermittent disconnection,
   (3) QoS aware routing, (4) support for redundancy at all levels of a
   system (device, service, network, etc.).

2.10.  Self-Organization

   The unified IoT platform should be able to self-organize to meet
   various application requirements, especially the capability to
   quickly discover heterogeneous and relevant devices/data/services
   based on the context.  This discovery can be achieved through an
   efficient platform-wide publish-subscribe service, or through private
   community grouping/clustering based upon trust and other security
   requirements.  In the former case, the publish-subscribe service must
   be efficiently implemented, able to support seamless mobility, in-



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   network caching, name-based routing, etc.  In the latter case, the
   IoT platform needs to discover the private community groups/clusters
   efficiently.

2.11.  Ad hoc and Infrastructure Mode

   Depending upon whether there is communication infrastructure, an IoT
   system can operate either in ad-hoc or infrastructure mode.

   For example, a vehicle may determine to report its location and
   status information to a server periodically through cellular
   connection, or, a group of vehicles may form an ad-hoc network that
   collectively detect road conditions around them.  In the cases where
   infrastructure is unavailable, one of the participating nodes may
   choose to become the temporary gateway.

   The unified IoT platform needs to design a common protocol that
   serves both modes.  Such a protocol should be able to provide: (1)
   energy-efficient topology discovery and data forwarding in the ad-hoc
   mode, and (2) scalable name resolution in the infrastructure mode.

2.12.  Open API

   General IoT applications involve sensing, processing, and secure
   content distribution occuring at various timescales depending on the
   application requirements.  This requires open APIs to be generic
   enough to support Pull, Push, and support Pub/Sub mode of interaction
   between consumers, content producer, and IoT services, as opposed to
   proprietary APIs that are common in today's systems.

3.  State of the Art

   Over the years, many stand-alone IoT systems have been deployed in
   various domains.  These systems usually adopt a vertical silo
   architecture and support a small set of pre-designated applications.
   A recent trend, however, is to move away from this approach, towards
   a unified IoT platform in which the existing silo IoT systems, as
   well as new systems that are rapidly deployed, will make their data
   and services accessible to general Internet applications (as in ETSI-
   M2M and oneM2M standards).  In such a unified platform, resources can
   be accessed over Internet and shared across the physical boundaries
   of the enterprise.  However, current approaches to achieve this
   objective are based upon Internet overlays, whose inherent
   inefficiencies due to IP protocol [9] hinders the platform from
   satisfying the IoT requirements outlined earlier (particularly in
   terms of scalability, security, mobility, and self-organization)





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3.1.  Silo IoT Architecture


                          [IoT Server]
                               |
                               |
                         ______|_______
    _______             {              }
   {       }            {              }
   {IoT Dev}\           {   Internet   }---[IoT Application]
   {_______}  [IoTGW]---{              }
                        {              }
                        {______________}


        Figure 1:Silo architecture of standalone IoT systems


   A typical standalone IoT system is illustrated in Figure 1, which
   includes devices, a gateway, a server and applications.  Many IoT
   devices have limited power and computing resources, unable to
   directly run normal IP access network (Ethernet, WIFI, 3G/LTE etc.)
   protocols.  Therefore they use the IoT gateway to the server.
   Through the IoT server, applications can subscribe to data collected
   by devices, or interact with devices.

   There have been quite a few popular protocols for standalone IoT
   systems, such as DF-1, MelsecNet, Honeywell SDS, BACnet, etc.
   However, these protocols are operating at the device-level
   abstraction, instead of information driven, leading to a highly
   fragmented protocol space with limited interoperability.

3.2.  Overlay Based Unified IoT Solutions

   The current approach to a unified IoT platform is to make IoT
   gateways and servers adopt standard APIs.  IoT devices connect to the
   Internet through the standard APIs and IoT applications subscribe and
   receive data through standard control and data APIs.  Building on top
   of today's Internet as an overlay, this is the most practical
   approach towards a unified IoT platform.  There are ongoing
   standardization efforts including ETSI[3], oneM2M[4],and CORE[5].
   Network operators can use standard API to build common IOT gateways
   and servers for their customers.  Figure 2 shows the architecture
   adopted in this approach.







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                 Publishing----[IoT Server]----Subscribing--
                     |        /    |       \                |
                     |       /     |        \               |
                     |      /______|_______  \              |
    ___________      |     /{              }  publishing    |
   {           }     |    | {              }     |          |
   {Smart Homes}\    |    | {   Internet   }---------[IoT Application]
   {___________}  [IoTGW]---{              }\    |     ________________
                          | {              } \   |    {                }
                          | {______________}  [IoTGW]-{Smart Healthcare}
                          |        |                  {________________}
                     Publishing [IoTGW]
                          |    ____|_____
                          |   {          }
                           ---{Smart Grid}
                              {__________}


   Figure 2: Implementing an open IoT platform through standarized APIs
                on the IoT gateways and the server


3.2.1.  Weaknesses of the Overlay-based Approach

   The above overlay-based approach can work with many different
   protocols, but the system is not designed in a holistic manner to
   inter-connect heterogeneous devices, services and infrastructure.
   Another limiting factor is that it is built upon today's IP network,
   which has inherent weaknesses towards supporting a unified IoT
   system.  As a result, it cannot satisfy some of the requirements we
   outlined in Section 2:

   o  Naming.  In current overlays for IoT systems the naming scheme is
      host centric, i.e., the name of a given resource/service is linked
      to the one of device that can provide it.  In turn, device names
      are coupled to IP addresses, which are not persistent in mobile
      scenarios.  On the other side, in IoT systems the same service/
      resource could be provided by many different devices thus
      requiring a different design rationale.

   o  Trust.  Trust management schemes are still relatively weak,
      focusing on securing communication channels rather than managing
      the data that needs to be secured directly.

   o  Scalability.  The overlay-based approach uses IP addresses as
      names at the network layer, which hinders the support for device/
      service mobility or flexible name resolution.  Further the Layer
      2/3 management, and application-layer addressing and forwarding



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      required to deploy current IoT solutions limit the scalability and
      management of these systems.

   o  Resource constraints.  The overlay-based approach requires every
      device to send data to an aggregator or to the IoT server.
      Resource constraints of the IoT devices, especially in power and
      bandwidth, will seriously limit the performance of this approach.

   o  Traffic Characteristics.  In this approach, applications are
      written in a host-centric manner suitable for point-to-point
      communication.  IoT requires support for multicasting that is
      challenging the underlying for overlay systems today.

   o  Contextual Communications.  This overlay-based approach cannot
      react to dynamic contextual changes in a timely fashion.  The main
      reason is that context lists are kept at the IoT server in this
      approach, and they cannot help efficiently route requests/
      information at the network layer.

   o  Mobility.  The overlay-based approach cannot seamlessly support
      device mobility in terms of maintaining the session between data
      producers and consumers.  In this approach, lower-level
      communications are typically IP driven, which is inefficient for
      mobility support.

   o  Storage and Caching.  The overlay-based approach supports
      application-centric storage and caching but not what ICN envisions
      at the network layer, or flexible storage enabled via name-based
      routing.

   o  Self-Organization.  The overlay-based approach is topology-based
      as it is bound to IP semantics, and thus does not sufficiently
      satisfy the self-organization requirement.

   o  Ad-hoc and infrastructure mode.  As mentioned above, the overlay-
      based approach lacks self-organization, and thus does not provide
      efficient support for the ad-hoc mode.

4.  Popular Scenarios

   Several types of IoT applications exists, where the goal is efficient
   and secure management and communication among objects in the system
   and with the physical world through sensors, RFIDs and other devices.
   Below we list a few popular IoT applications.  We omit the often used
   term "smart", though it applies to each IoT scenario below, and posit
   that IoT-style interconnection of devices to make these environments
   "smart" in today's terms will simply be the future norm.




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4.1.  Homes

   The home [11] is a complex ecosystem of IoT devices and applications
   including climate control, home security monitoring, smoke detection,
   electrical metering, health/wellness, and entertainment systems.  In
   a unified IoT platform, we would inter-connect these systems through
   the Internet, such that they can interact with each other and make
   decisions at an aggregated level.  Also, the systems can be accessed
   and manipulated remotely.  Challenges in the home include topology
   independent service discovery, common protocol for heterogeneous
   device/application/service interaction, policy based routing/
   forwarding, service mobility as well as privacy protection.  Notably,
   the ease-of-use expectations and training of both users and
   installers also presents challenges in user interface and user
   experience design that are impacted by the complexity of network
   configuration, brittleness to change, configuration of trust
   management, etc.  Finally, it is unlikely that there will be a single
   "home system", but rather a collection of moderately inter-operable
   collaborating devices.

   Homes [13][14] faces the following challenges that are hard to
   address with IP-based overlay solutions: (1) context-aware control:
   home systems must make decisions (e.g., on how to control, when to
   collect data, where to carry out computation, when to interact with
   end-users, etc.) based upon the contextual information [15]; (2)
   inter-operatibility: home systems must operate with devices that
   adopt heterogeneous naming, trust, communication, and control
   systems; (3) mobility: home systems must deal with mobility caused by
   the movement of sensors or data receivers; (4) security: a home
   systems must be able to deal with foreign devices, handle a variety
   of user permissions (occupants of various types, guests, device
   manufacturers, installers and integrators, utility and infrastructure
   providers) and involve users in important security decisions without
   overwhelming them.

4.2.  Enterprise

   Enterprise building deployments, from university campuses [16] to
   industrial facilities and retail complexes, drive an additional set
   of scalability, security, and integration requirements beyond the
   home, while requiring much of its ease of use and flexibility.
   Additionally, they bring requirements for integration with business
   IT systems, though often with the additional support of in-house
   engineering support.

   Increasing number of enterprises are equipped with sensing and
   communication devices inside buildings, laboratories, and plants, at
   stadiums, in parking lots, on school buses, etc.  A unified IoT



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   platform must integrate many aspects of human interaction, H2M and
   M2M communication, within the enterprise, and thus enable many IoT
   applications that can benefit a large body of enterprise affiliates.
   The challenges in smart enterprise include efficient and secure
   device/data/resource discovery, inter-operability between different
   control systems, throughput scaling with number of devices, and
   unreliable communication due to mobility and telepresence.

   Enterprises face the following challenges that are hard to address
   with IP-based overlay solutions: (1) efficient device/data/ resource
   discovery: enterprise devices must be able to quickly and securely
   discover requested device, data, or resources; (2) scalability: a
   enterprise system must be able to scale efficiently with the number
   and type of sensors and devices across not only a single building but
   multi-national corporations (for example); (3) mobility: a enterprise
   system must be able to deal with mobility caused by movement of
   devices.

4.3.  Smart Grid

   Central to the so-called "smart grid"[17]  is data flow and
   information management, achieved by using sensors and actuators,
   which enables important capabilities such as substation and
   distribution automation.  In a unified IoT platform, data collected
   from different smart grids can be integrated to reach more
   significant optimizations.  The challenges for smart grid include
   reliability, real-time control, secure communications, and data
   privacy.

   Deployment of the smart grid [18] [19] faces the following issues
   that are hard to address with IP-based overlay solutions: (1)
   scalability: tomorrow's electrical grids must be able to scale
   gracefully to manage a large number of heterogeneous devices; (2)
   real time: grids must be able to perform real-time data collection,
   data processing and control; (3) reliability: grids must be resilient
   to hardware/software/networking failures; (4) security: grids and
   associated systems are often considered critical infrastructure --
   they must be able to defend against malicious attacks, detect
   intrusion, and route around disruption.

4.4.  Transportation

   We are currently witnessing the increasing integration of sensors
   into cars, other vehicles transportation systems [20].  Current
   production cars already carry many sensors ranging from rain gauges
   and accelerometers over wheel rotation/traction sensors, to cameras.
   While intended for internal vehicle functions, these could also be
   networked and leveraged for applications such as monitoring external



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   traffic/road conditions.  Further, we can build vehicle-to-
   infrastructure (V2I) and vehicle-to-vehicle (V2V) communications that
   enable many more applications for safety, convenience, entertainment,
   etc.  The challenges for transportation include fast data/device/
   service discovery and association, efficient communications with
   mobility, trustworthy data collection and exchange.

   Transportation [20][21] faces the following challenges that are hard
   to address with IP-based overlay solutions: (1) mobility: a
   transportation system must deal with a large number of mobile nodes
   interacting through a combination of infrastructure and ad hoc
   communication methods; (2) real-time and reliability: transportation
   systems must be able to operate on real-time and remain resilient in
   the presence of failures; (3) in-network computing/filtering:
   transportation systems will benefit from in-network computing/
   filtering as such operations can reduce the end-to-end latency; (4)
   inter-operatibility: transportation systems must operate with
   heterogeneous device and protocols; (5) security: transportation
   systems must be resilient to malicious physical and cyber attacks.

4.5.  Healthcare

   As more embedded medical devices, or devices that can monitor human
   health become increasingly deployed, healthcare is becoming a viable
   alternative to traditional healthcare solutions [15][22].  Further,
   consumer applications for managing and interacting with health data
   are a burgeoning area of research and commercial applications.  For
   future health applications, a unified IoT platform is critical for
   improved patient care and consumer health support by sharing data
   across systems, enabling timely actuations, and lowering the time to
   innovation by simplifying interaction across devices from many
   manufacturers.  Challenges in healthcare include real-time
   interactions, high reliability, short communication latencies,
   trustworthy, security and privacy, and well as defining and meeting
   the regulatory requirements that should impact new devices and their
   interconnection.

   Healthcare [22][23]  faces the following challenges that are hard to
   address with IP-based overlay solutions: (1) real-time and
   reliability: healthcare systems must be able to operate on real-time
   and remain resilient in the presence of failures; (2) inter-
   operatibility: healthcare systems must operate with heterogeneous
   device and protocols; (3) security: healthcare systems must be
   resilient to malicious physical and cyber attacks and meet the
   regulatory requirement for data security and interoperability.






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4.6.  Education

   IoT technologies enable the instrumentation of a variety of
   environments (from greenhouses to industrial plants, homes and
   vehicles) to support not only their everyday operation but an
   understanding of how they operate -- a fundamental contribution to
   education.  The diverse uses of hobbyist-oriented microcontroller
   platforms (e.g., the Arduino) and embedded systems (e.g., the
   Raspberry PI) point to a burgeoning community that should be
   supported by the next generation IoT platform because of its
   fundamental importance to formal and informal education.

   Educational uses of IoT deployments include both learning about the
   operation of the system itself as well as the systems being observed
   and controlled.  Such deployments face the following challenges that
   are hard to address with IP-based overlay solutions: (1) relatively
   simple communications patterns are obscured by many layers of
   translation from the host-based addressing of IP (and layer 2
   configuration below) to the name-oriented interfaces provided by
   developers; (2) security considerations with overlay deployments and
   channel-based limit access to systems where read-only use of data is
   not a security risk; (3) real-time communication helps make the
   relationship between physical phenomena and network messages easier
   to understand in many simple cases; (4) integration of devices from a
   variety of sources and manufacturers is currently quite difficult
   because of varying standards for basic communication, and limits
   experimentation.

4.7.  Entertainment, arts, and culture

   IoT technologies can contribute uniquely to both the worldwide
   entertainment market and the fundamental human activity of creating
   and sharing art and culture.  By supporting new types of human-
   computer interaction, IoT can enable new gaming, film/video, and
   other "content" experiences, integrating them with, for example, the
   lighting control of the smart home, presentation systems of the smart
   enterprise, or even the incentive mechanisms of smart healthcare
   systems (to, say, encourage and measure physical activity).

   Entertainment, arts, and culture applications generate a variety of
   challenges for IoT: (1) notably, the ability to securely "repurpose"
   deployed smart systems (e.g., lighting) to create experiences; (2)
   low-latency communication to enable end-user responsiveness; (3)
   integration with infrastructure-based sensing (e.g., computer vision)
   to create comprehensive interactive environments or to provide user
   identity information; (4) time synchronization with audio/video
   playback and rendering in 3D systems (5) simplicity of development
   and experimentation, to enable the cost- and time-efficient



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   integration of IoT into experiences being designed without expert
   engineers of IoT systems; (6) security, because of integration with
   personal devices and smart environments, as well as billing systems.

5.  ICN Challenges for IoT

   ICN integrates content/service/host abstraction, name-based routing,
   compute, caching/storage as part of the network infrastructure
   connecting consumers and services which meets most of the
   requirements discussed above; however IoT requires special
   considerations given heterogeneity of devices and interfaces such as
   for constrained networking [32], data processing, and content
   distribution models to meet specific application requirements which
   we identify as challenges in this section.

5.1.  Naming

   According to [27], the main requirement of a name space (and the
   corresponding Name Resolution System) are:

   o  Scalability: with the IoT the number of data items would inflate
      the Internet scale up to 2-3 order of magnitudes, thus making
      scalability a primary requirement of the NRS.  Notice that, if
      hierarchical names are used, scalability can be also faced by
      leveraging the inherent aggregation capabilities of the hierarchy.

   o  Latency: for real-time or delay sensitive M2M application, the
      name resolution should not affect the overall QoS.

   o  Locality and network efficiency: in the name resolution process
      the data items closer to the data consumer should be accesses
      first (subject to the application requirements)

   o  Agility: some data items could disappear while some other ones are
      created so that the NRS should be able to effectively take care of
      these dynamic conditions.

   o  Control/scoping: some information could be accessible only within
      a given scope so that the NRS shold be able to not disclose all
      nodel locators to everyone.

   o  Deployability and interoperability: graceful deployability and
      interoperability with existing platforms is a must to ensure a
      naming schema to gain success on the market [7].

   Further challenges arise for hierarchical naming schema: referring to
   requirements on "constructable names" and "on-demand publishing" [24]
   [25].  The former entails that each user is able to construct the



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   name of a desired data item through specific algorithms and that it
   is possible to retrieve information also using partially specified
   names.  The latter refers the possibility to request a content that
   has not yet been published in the past, thus triggering its creation.

5.2.  Caching/Storage

   In-network caching helps bring data closer to consumers, but its
   usage differs in constrained and infrastructure part of the IoT
   network.  Caching in constrained networks is limited to small amounts
   in the order of 10KB, while caching in infrastructure part of the
   network can allow much larger chunks.

   Caching in ICN-IoT faces several challenges:

   o  The main challenge is to determine which nodes on the routing path
      should cache the data.  According to [28], caching the data on a
      subset of nodes can achieve a better gain than caching on every
      en-route routers.  In particular, the authors propose a "selective
      caching" scheme to locate those routers with better hit
      probabilities to cache data.  According to [29], selecting a
      random router to cache data is as good as caching the content
      everywhere.

   o  Another challenge in ICN-IoT caching is what to cache for IoT
      applications.  In many IoT applications, customers often access a
      stream of sensor data, and as a result, caching a particular
      sensor data item may not be beneficial.  In [30], the authors
      suggest to cache IoT services on intermediate routers, and in
      [31], the authors suggest to cache control information such as
      pub/sub lists on intermediate nodes.  In addition, it is yet
      unclear what caching means in the context of actuation in an IoT
      system.

5.3.  Name Resolution

   Inter-connecting numerous IoT entities, as well as establishing
   reachability to them, requires a scalable name resolution system
   considering several dynamic factors like mobility of end points,
   service replication, in-network caching, failure or migration
   [1-4][33][34][35][30].  The objective is to achieve scalable name
   resolution handling static and dynamic ICN entities with low
   complexity and control overhead.

   o  The first challenge faced by ICN-IoT name resolution is its
      scalability [35][30].  Firstly, the name resolution service has to
      support billions of objects and devices that are connected to the
      Internet, many of which are crossing administrative domain



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      boundaries.  Second of all, in addition to objects/devices, the
      name resolution service is also responsible for mapping IoT
      services to their network addresses.  Many of these services are
      based upon contexts, hence dynamically changing, as pointed out in
      [30].  As a result, the name resolution service should be able to
      scale gracefully to cover a large number of names/services with
      wide variations (e.g., hierarchical names, flat names, names with
      limited scope, etc.)

   o  The second challenge is fast name resolution.  This challenge is
      especially important for applications with stringent latency
      requirements, such as health monitoring, emergency handling and
      smart transportation [36].

5.4.  Contextual Communication

   Contextualization through metadata in ICN control or application
   payload allows IoT applications to adapt to different environments.
   This enables intelligent networks which are self-configurable and
   enable intelligent networking among consumers and producers [30].
   For example, let us look at the following smart transportation
   scenario: "James walks on NYC streets and wants to find an empty cab
   closest to his location."  In this example, the context is the
   relative locations of James and taxi drivers.  A context service, as
   an IoT middleware, processes the contextual information and bridges
   the gap between raw sensor information and application requirements.

   However, extracting contextual information on a real-time basis is
   very challenging:

   o  We need to have a fast context resolution service through which
      the involved IoT devices can continuously update its contextual
      information to the application (e.g., each taxi's location and
      Jame's information in the above example).

   o  The difficulty of this challenge grows rapidly when the number of
      devices involved in a context as well as the number of contexts
      increases.

5.5.  Routing and Forwarding

   Routing in ICN-IoT differs from routing in traditional IP networks in
   that ICN routing is based upon names instead of locators.  Broadly
   speaking, ICN routing can be categorized into the following two
   categories: direct name-based routing and indirect routing through
   name resolution.





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   o  In direct name-based routing, packets are forwarded by the name of
      the data [37][32][38] or the name of the destination node [39].
      Here, the main challenge is to keep the ICN router state required
      to route/forward data low.  This challenge becomes more serious
      when a flat naming scheme is used.

   o  In indirect routing, packets are forwarded based upon the locator
      of the destination node, and the locator is obtained through the
      name resolution service.  In particular, the name locator binding
      can be done either before routing (i.e., static binding) or during
      routing (i.e., dynamic binding).  For static binding, the router
      state is the same as that in traditional routers, and the main
      challenge is the need to have fast name resolution, especially
      when the IoT nodes are mobile.  For dynamic binding, ICN routers
      need to main a name-based routing table, hence the challenge of
      keeping the state information low.  At the same time, the need of
      fast name resolution is also critical.  Finally, another challenge
      is to quantify the cost associated with mobility management,
      especially static binding vs. dynamic binding.

   During a network transaction, either the data producer or the
   consumer may move away and thus we need to handle the mobility to
   avoid information loss.  ICN may differentiate mobility of a data
   consumer from that of a producer:

   o  When a consumer moves to a new location after sending out an
      Interest, the Data may get lost, which requires the consumer to
      simply resend the Interest.  Depending on the network topology and
      data availability, the new Interest might be forwarded to the same
      or a different data producer.

   o  If the data producer itself has moved, the challenge is to control
      the control overhead while flooding it across the network.

5.6.  In-network Computing

   Contextual services for IoT networks require in-network computing, in
   which each sensor node or ICN router implements context reasoning
   [30].  Another major purpose of in-network computing is to filer and
   cleanse sensed data in IoT applications is critical as the data is
   noisy as is [40].

   In-network computing faces different challenges in the constrained
   and infrastructure parts of the network.

   o  In the constrained part of the network, the challenge rises due to
      serious resource limitations on IoT devices or sensors.  As a




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      result, the consensus is to include very simple computing
      functionalities on constrained nodes.

   o  In the infrastructure part of the IoT network, in-network
      computing faces the challenge of breaking the computing task into
      smaller pieces and assigning each task to one or more ICN routers
      [30].

5.7.  Security and Privacy

   Security and privacy is crucial to all the IoT applications including
   the use cases discussed in Section 4.  In one recent demonstration,
   it was shown that passive tire pressure sensors in cars could be
   hacked and used as a gateway into the automotive system [41].  Though
   ICN includes data-centric security features the mechanisms have to be
   generic enough to satisfy multiplicity of policy requirements for
   different applications.  In general, we feel that security and
   privacy protection in IoT systems should mainly focus on the
   following aspects: confidentiality, integrity, authentication and
   non-repudiation, and availability.

   Implementing security and privacy methods faces different challenges
   in the constrained and infrastructure part of the network.

   o  In the constrained part, energy limitation is the biggest
      challenge.  As an example, let us look at a typical sensor tag.
      Suppose the tag has a single 16-bit processor, often running at 6
      MHz to save energy, with 512Bytes of RAM and 16KB of flash for
      program storage.  Moreover, it has to deliver its data over a
      wireless link for at least 10,000 hours on a coin cell battery.
      As a result, traditional security/privacy measures are impossible
      to be implemented in the constrained part.  In this case, one
      possible solution might be utilizing the physical wireless signals
      as security measures [42] [30].

   o  In the infrastructure part, we have several new threats introduced
      by ICN-IoT [45]:

      1.  We need to ensure the name of a network element is issued by a
          trustworthy organization such as in [44].

      2.  An intruder may gain access or gather information from a
          resource it is not entitled to.  As a consequence, an
          adversary may examine, remove or even modify confidential
          information.






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      3.  An intruder may mimic an authorized user or network process.
          As a result, the intruder may forge signatures, or impersonate
          a source address.

      4.  An adversary may manipulate the message exchange process
          between network entities.  Such manipulation may involve
          replay, rerouting, mis-routing and deletion of messages.

      5.  An intruder may insert fake/false sensor data into the
          network.  The consequence might be an increase in delay and
          performance degradation for network services and applications.

5.8.  Energy Efficiency

   All the optimizations for other components of the ICN-IoT system
   (described in earlier subsections) can lead to optimized energy
   efficiency.  As a result, we refer the readers to read sections
   5.1-5.6 for challenges associated with energy efficiency for ICN-IoT.

6.  Informative References

   [1]        Cisco System Inc., CISCO., "Cisco visual networking index:
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   [2]        Shafig, M., Ji, L., Liu, A., Pang, J., and J.  Wang, "A
              first look at cellular machine-to-machine traffic: large
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   [3]        The European Telecommunications Standards Institute,
              ETSI., "http://www.etsi.org/.", 1988.

   [4]        Global Intiative for M2M Standardization, oneM2M.,
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   [5]        Constrained RESTful Environments, CoRE.,
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   [6]        Ghodsi, A., Shenker, S., Koponen, T., Singla, A.,
              Raghavan, B., and J. Wilcox, "Information-Centric
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              in Networking , 2011.

   [7]        Melazzi, N., Detti, A., Arumaithurai, M., and K.
              Ramakrishnan, "Internames: A Name-to-Name Principle for
              the Future Internet.", International Workshop on Quality,
              Reliability, and Security in Information-Centric
              Networking (Q-ICN) , 2014.



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   [8]        Dong, L., Zhang, Y., and D. Raychaudhuri, "Enhance Content
              Broadcast Efficiency in Routers with Integrated Caching.",
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   [9]        FIA project, NSF., "http://www.nets-fia.net/", 2010.

   [10]       Kim, B., Lee, S., Lee, Y., Hwang, I., and Y. Rhee,
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   [11]       Dietrich, D., Bruckne, D., Zucker, G., and P. Palensky,
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   [12]       Keith, K., Falco, F., and K. Scarfone, "Guide to
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   [13]       Darianian, M. and Martin. Michael, "Smart home mobile
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   [14]       Zhu, Q., Wang, R., Chen, Q., Chen, Y., and W. Qin, "IOT
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   [15]       Biswas, T., Chakrabort, A., Ravindran, R., Zhang, X., and
              G. Wang, "Contextualized information-centric home
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   [16]       Huang, R., Zhang, J., Hu, Y., and J. Yang, "Smart Campus:
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   [17]       Yan, Y., Qian, Y., Hu, Y., and J. Yang, "A Survey on Smart
              Grid Communication Infrastructures: Motivations,
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   [18]       Miao, Y. and Y. Bu, "Research on the Architecture and Key
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              Grid", 2010.

   [19]       Zhang, Y., Yu, R., Nekovee, M., Liu, Y., Xie, S., and S.
              Gjessing, "Cognitive Machine-to-Machine Communications:
              Visions and Potentials for the Smart Grid", 2012.





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   [20]       Zhou, H., Liu, B., and D. Wang, "Design and Research of
              Urban Intelligent Transportation System Based on the
              Internet of Things", 2012.

   [21]       Zhang, M., Yu, T., and G. Zhai, "Smart Transport System
              Based on the Internet of Things", 2012.

   [22]       Zhang, A., Yu, R., Nekovee, M., and S. Xie, "The Internet
              of Things for Ambient Assisted Living", 2010.

   [23]       Savola, R., Abie, H., and M. Sihvonen, "Towards metrics-
              driven adaptive security management in E-health IoT
              applications.", 2012.

   [24]       Jacobson, V., Smetters, D., Plass, M., Stewart, P.,
              Thornton, J., and R. Braynard, "VoCCN: Voice-over Content-
              Centric Networks", 2009.

   [25]       Piro, G., Cianci, I., Grieco, L., Boggia, G., and P.
              Camarda, "Information Centric Services in Smart Cities",
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   [26]       Ravindran, R., Biswas, T., Zhang, X., Chakrabort, A., and
              G. Wang, "Information-centric Networking based Homenet",
              2014.

   [27]       Dannewitz, C., D' Ambrosio, M., and V. Vercellone,
              "Hierarchical DHT-based name resolution for information-
              centric networks", 2013.

   [28]       Chai, W., He, D., and I. Psaras, "Cache "less for more" in
              information-centric networks", 2012.

   [29]       Eum, S., Nakauchi, K., Murata, M., Shoji, Yozo., and N.
              Nishinaga, "Catt: potential based routing with content
              caching for icn", 2012.

   [30]       Eum, S., Shvartzshnaider, Y., Francisco, J., Martini, R.,
              and D. Raychaudhuri, "Enabling internet-of-things services
              in the mobilityfirst future internet architecture", 2012.

   [31]       Sun, Y., Qiao, X., Cheng, B., and J. Chen, "A low-delay,
              lightweight publish/subscribe architecture for delay-
              sensitive IOT services", 2013.

   [32]       Baccelli, E., Mehlis, C., Hahm, O., Schmidt, T., and M.
              Wahlisch, "Information Centric Networking in the
              IoT:Experiments with NDN in the Wild", 2013.



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   [33]       Gronbaek, I., "Architecture for the Internet of Things
              (IoT): API and interconnect", 2008.

   [34]       Tian, Y., Liu, Y., Yan, Z., Wu, S., and H. Li, "RNS-A
              Public Resource Name Service Platform for the Internet of
              Things", 2012.

   [35]       Roussos, G. and P. Chartier, "Scalable id/locator
              resolution for the iot", 2011.

   [36]       Li, S., Zhang, Y., Raychaudhuri, D., and R. Ravindran, "A
              Comparative Study of MobilityFirst and NDN based ICN-IoT
              Architectures", 2011.

   [37]       Amadeo, M., Campolo, C., Iera, A., and A. Molinaro, "Named
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   [38]       Amadeo, M. and C. Campolo, "Potential of information-
              centric wireless sensor and actor networking", 2014.

   [39]       Nelson, S., Bhanage, G., and D. Raychaudhuri, "GSTAR:
              generalized storage-aware routing for mobilityfirst in the
              future mobile internet", 2014.

   [40]       Trappe, W., Zhang, Y., and B. Nath, "MIAMI: methods and
              infrastructure for the assurance of measurement
              information", 2005.

   [41]       Rouf, I., Mustafa, H., Taylor, T., Oh, S., Xu, W.,
              Gruteser, M., Trappe, W., and I. Seskar, "Security and
              privacy vulnerabilities of in-car wireless networks: A
              tire pressure monitoring system case study", 2005.

   [42]       Liu, R. and W. Trappe, "Securing Wireless Communications
              at the Physical Layer", 2010.

   [43]       Xiao, L., Greenstein, L., Mandayam, N., and W. Trappe,
              "Using the physical layer for wireless authentication in
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   [44]       Sun, S., Lannom, L., and B. Boesch, "Handle system
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   [45]       Liu, X., Trappe, W., and Y. Zhang, "Secure Name Resolution
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              Internet", 2003.




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Authors' Addresses

   Prof.Yanyong Zhang
   WINLAB, Rutgers University
   671, U.S 1
   North Brunswick, NJ  08902
   USA

   Email: yyzhang@winlab.rutgers.edu


   Prof. Dipankar Raychadhuri
   WINLAB, Rutgers University
   671, U.S 1
   North Brunswick, NJ  08902
   USA

   Email: ray@winlab.rutgers.edu


   Prof. Luigi Alfredo Grieco
   Politecnico di Bari (DEI)
   Via Orabona 4
   Bari  70125
   Italy

   Email: alfredo.grieco@poliba.it


   Prof. Emmanuel Baccelli
   INRIA
   Room 148, Takustrasse 9
   Berlin  14195
   France

   Email: Emmanuel.Baccelli@inria.fr


   Jeff Burke
   UCLA REMAP
   102 East Melnitz Hall
   Los Angeles, CA  90095
   USA

   Email: jburke@ucla.edu






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   Ravishankar Ravindran
   Huawei Technologies
   2330 Central Expressway
   Santa Clara, CA  95050
   USA

   Email: ravi.ravindran@huawei.com


   Guoqiang Wang
   Huawei Technologies
   2330 Central Expressway
   Santa Clara, CA  95050
   USA

   Email: gq.wang@huawei.com



































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