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ICN Research Group                                              Y. Zhang
Internet-Draft                                            D. Raychadhuri
Intended status: Informational                WINLAB, Rutgers University
Expires: October 24, 2016                                      L. Grieco
                                               Politecnico di Bari (DEI)
                                                             E. Baccelli
                                                                   INRIA
                                                                J. Burke
                                                              UCLA REMAP
                                                            R. Ravindran
                                                                 G. Wang
                                                     Huawei Technologies
                                                              A. Lindren
                                                              B. Ahlgren
                                                        SICS Swedish ICT
                                                              O. Schelen
                                          Lulea University of Technology
                                                          April 22, 2016


              Requirements and Challenges for IoT over ICN
                draft-zhang-icnrg-icniot-requirements-01

Abstract

   The Internet of Things (IoT) promises to connect billions of objects
   to the 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 horizontal 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 host-centric
   IoT platform as an overlay on top of today's host-centric Internet.
   However, there is a fundamental mismatch between the host-centric
   nature of todays Internet and the information-centric nature of the
   IoT system.  To address this mismatch, 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, security, scalability, and
   efficient content and service delivery.

   This draft describes representative 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 discuss how the current IP-IoT overlay fails to meet these
   requirements, followed by discussion on suitability of ICN for IoT.



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   Though we see most of the IoT requirements can be met by ICN, we
   discuss specific challenges ICN has to address to satisfy them.  Then
   we provide discussion of popular IoT scenarios including the "smart"
   home, campus, grid, transportation infrastructure, healthcare,
   Education, and Entertainment for completeness, as specific scenarios
   requires appropriate design choices and architectural considerations
   towards developing an ICN-IoT solution.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
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   This Internet-Draft will expire on October 24, 2016.

Copyright Notice

   Copyright (c) 2016 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

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   described in the Simplified BSD License.

Table of Contents

   1.  IoT Motivation  . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  IoT Architectural Requirements  . . . . . . . . . . . . . . .   4
     2.1.  Naming  . . . . . . . . . . . . . . . . . . . . . . . . .   4
     2.2.  Scalability . . . . . . . . . . . . . . . . . . . . . . .   4
     2.3.  Resource Constraints  . . . . . . . . . . . . . . . . . .   5
     2.4.  Traffic Characteristics . . . . . . . . . . . . . . . . .   5



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     2.5.  Contextual Communication  . . . . . . . . . . . . . . . .   6
     2.6.  Handling Mobility . . . . . . . . . . . . . . . . . . . .   6
     2.7.  Storage and Caching . . . . . . . . . . . . . . . . . . .   7
     2.8.  Security and Privacy  . . . . . . . . . . . . . . . . . .   7
     2.9.  Communication Reliability . . . . . . . . . . . . . . . .   8
     2.10. Self-Organization . . . . . . . . . . . . . . . . . . . .   8
     2.11. Ad hoc and Infrastructure Mode  . . . . . . . . . . . . .   8
     2.12. Open API  . . . . . . . . . . . . . . . . . . . . . . . .   9
     2.13. IoT Platform Management . . . . . . . . . . . . . . . . .   9
   3.  State of the Art  . . . . . . . . . . . . . . . . . . . . . .   9
     3.1.  Silo IoT Architecture . . . . . . . . . . . . . . . . . .  10
     3.2.  Overlay Based Unified IoT Solutions . . . . . . . . . . .  10
       3.2.1.  Weaknesses of the Overlay-based Approach  . . . . . .  11
   4.  Advantages of using ICN for IoT . . . . . . . . . . . . . . .  13
   5.  ICN Challenges for IoT  . . . . . . . . . . . . . . . . . . .  14
     5.1.  Naming Devices, Data, and Services  . . . . . . . . . . .  14
     5.2.  Name Resolution . . . . . . . . . . . . . . . . . . . . .  16
     5.3.  Caching/Storage . . . . . . . . . . . . . . . . . . . . .  17
     5.4.  Routing and Forwarding  . . . . . . . . . . . . . . . . .  18
     5.5.  Contextual Communication  . . . . . . . . . . . . . . . .  19
     5.6.  In-network Computing  . . . . . . . . . . . . . . . . . .  20
     5.7.  Security and Privacy  . . . . . . . . . . . . . . . . . .  21
     5.8.  Self-Orgnization  . . . . . . . . . . . . . . . . . . . .  22
     5.9.  Communications Reliability  . . . . . . . . . . . . . . .  23
     5.10. Energy Efficiency . . . . . . . . . . . . . . . . . . . .  23
   6.  Appendix  . . . . . . . . . . . . . . . . . . . . . . . . . .  23
     6.1.  Homes . . . . . . . . . . . . . . . . . . . . . . . . . .  23
     6.2.  Enterprise  . . . . . . . . . . . . . . . . . . . . . . .  25
     6.3.  Smart Grid  . . . . . . . . . . . . . . . . . . . . . . .  26
     6.4.  Transportation  . . . . . . . . . . . . . . . . . . . . .  27
     6.5.  Healthcare  . . . . . . . . . . . . . . . . . . . . . . .  28
     6.6.  Education . . . . . . . . . . . . . . . . . . . . . . . .  29
     6.7.  Entertainment, arts, and culture  . . . . . . . . . . . .  30
   7.  Informative References  . . . . . . . . . . . . . . . . . . .  31
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  37

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



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   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 first discuss the
   IoT requirements generally applicable to many well known scenarios.
   We then discuss how the current IP overlay models fail to meet these
   requirements.  We follow this by key ICN features that makes it a
   better candidate to realize an unified IoT framework.  We then
   discuss IoT challenges from an ICN perspective and requirements posed
   towards its design.  Final discussion focuses on IoT scenarios and
   their unique challenges.

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 lifetime, mobility or migration; second, names need to be
   secure based on application requirements; third, names should provide
   advantages to application authors in comparison with traditional host
   address based schemes.

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 including 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 register/update/resolve a name within a short latency.




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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, bandwidth,
   and user interface.

   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 in cloud servers or at opportunistic
   points, example at 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 or stored in aggregated compact
   form 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 collect 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.

   The above discussed device constraints also affect application
   performance with respect to latency and jitter.  This in particular
   applies to satellite or other space based devices.

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,



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   in Intelligent Transportation Systems, re-routing operations may
   require a broad knowledge of the status of the system, traffic load,
   availability of freights, whether forecasts and so on.  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 dynamic contexts in the IoT
   system to initiate communication between IoT devices.  Here, we refer
   to a context as attributes applicable to a group of devices that
   share some common features, such as their owners may have a certain
   social relationship or belong to the same administrative group, or
   the devices may be present in the same location.  For 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 several 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 (e.g., a body-area network in motion as a person is
   walking); and 4) disconnection between the data source and



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   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's acceptable delay constraint in all of the
   above cases, and and if necessary to negotiate different connectivity
   or security constraints specific to each mobile context.

2.7.  Storage and Caching

   Storage and caching plays a very significant role depending on the
   type of IoT ecosystem, also a function subjected to privacy and
   security guidelines.  In a unified IoT platform, depending on
   application requirements, content caching may or may not be policy
   driven.  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.

   Furthermore considering hierarchical nature of IoT systems, ICN
   architectures enable a more flexible, heterogeneous and potentially
   fault-tolerant approach to storage providing persistence at multiple
   levels.

   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 [11].

   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.



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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, storage etc.), and (5) support for
   rich communication patterns (unlike the tree-like routing structure
   supported by RPL developed by ROLL WG).

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

   Another aspect of self-organization is decoupling the sensing
   Infrastructure from applications.  In a unified IoT platform, various
   applications run on top of a vast number of IoT devices.  Upgrading
   the firmware of the IoT devices is a difficult work.  It is also not
   practical to reprogram the IoT devices to accommodate every change of
   the applications.  The infrastructure and the application specific
   logics need to be decoupled.  A common interface is required to
   dynamically configure the interactions between the IoT devices and
   easily modify the application logics on top of the sensing
   infrastructure [23] [24].

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



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   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 occurring at various timescales and at multiple
   levels of hierarchy depending on the application requirements.  This
   requires open APIs to be generic enough to support commonly used
   interactions between consumers, content producer, and IoT services,
   as opposed to proprietary APIs that are common in today's systems.
   Examples include pull, push, and publish/subscribe mechanisms using
   common naming, payload, encryption and signature schemes.

2.13.  IoT Platform Management

   An IoT platforms' service, control, and data plane will be governed
   by its own management infrastructure which includes distributed and
   centralized middleware, discovery, naming, self-configuring, analytic
   functions, and information dissemination to achieve specific IoT
   system objectives [18][19][20].  Towards this new IoT management
   mechanisms and service metrics need to be developed to measure the
   success of an IoTdeployment.  Considering an IoT systems' defining
   characteristics such as, its potential large number of IoT devices,
   ephemeral nature to save power, mobility, and ad hoc communication,
   autonomic self-management mechanisms become very critical.  Further
   considering its hierarchical information processing deployment model,
   the platform needs to orchestrate computational tasks according to
   the involved sensors and the available computation resources which
   may change over time.  An efficient computation resource discovery
   and management protocol is required to facilitate this process.  The
   trade-off between information transmission and processing is another
   challenge.

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.  This will make their
   data and services accessible to general Internet applications (as in



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   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 [8] hinders the platform
   from satisfying the IoT requirements outlined earlier (particularly
   in terms of scalability, security, mobility, and self-organization)

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



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   standardization efforts including ETSI[3], oneM2M[4].  Network
   operators can use frameworks to build common IOT gateways and servers
   for their customers.  In addition, IETF's CORE working group [5] is
   developing a set of protocols like CoAP (Constrained Application
   Protocol) [49], that is a lightweight protocol modeled after HTTP
   [50] and adapted specifically for the Internet of Things (IoT).  CoAP
   adopts the Representational State Transfer (REST) architecture with
   Client-Server interactions.  It uses UDP as the underlying transport
   protocol with reliability and multicast support.  Both CoAP and HTTP
   are considered as the suitable application level protocols for
   Machine-to-Machine communications, as well as IoT.  For example,
   oneM2M (which is one of leading standards for unified M2M platform)
   has both the protocol bindings to HTTP and CoAP for its primitives.
   Figure 2 shows the architecture adopted in this approach.


                 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 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



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      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  Mobility.  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
      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, could 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 multicast support that is challenging
      in 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  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 or name-based lookup.

   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.  In addition to
      topological self-organization, IoT also requires data- and
      service-level self-organization [59], which is not supported by
      the overlay approach.

   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.



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4.  Advantages of using ICN for IoT

   A key concept of ICN is the ability to name data independently from
   the current location at which it is stored, which simplifies caching
   and enables decoupling of sender and receiver.  Using ICN to design
   an architecture for IoT data potentially provides such advantages
   compared to using traditional host-centric networks.  This section
   highlights general benefits that ICN could provide to IoT networks.

   o  Naming of Devices, Data and Services.  The heterogeneity of both
      network equipment deployed and services offered by IoT networks
      leads to a large variety of data, services and devices.  While
      using a traditional host-centric architecture, only devices or
      their network interfaces are named at the network level, leaving
      to the application layer the task to name data and services.  In
      many common applications of IoT networks, data and services are
      the main goal, and specific communication between two devices is
      secondary.  The network distributes content and provides a
      service, instead of establishing a communication link between two
      devices.  In this context, data content and services can be
      provided by several devices, or group of devices, hence naming
      data and services is often more important than naming the devices.
      This naming mechanism also enables self-configuration of the IoT
      system.

   o  Distributed Caching and Processing.  While caching mechanisms are
      already used by other types of overlay networks, IoT networks can
      potentially benefit even more from caching and in-network
      processing systems, because of their resource constraints.
      Wireless bandwidth and power supply can be limited for multiple
      devices sharing a communication channel, and for small mobile
      devices powered by batteries.  In this case, avoiding unnecessary
      transmissions with IoT devices to retrieve and distribute IoT data
      to multiple places is important, hence processing and storing such
      content in the network can save wireless bandwidth and battery
      power.  Moreover, as for other types of networks, applications for
      IoT networks requiring shorter delays can benefit from local
      caches and services to reduce delays between content request and
      delivery.

   o  Decoupling between Sender and Receiver.  IoT devices may be mobile
      and face intermittent network connectivity.  When specific data is
      requested, such data can often be delivered by ICN without any
      consistent direct connectivity between devices.  Apart from using
      structured caching systems as described previously, information
      can also be spread by forwarding data opportunistically.





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5.  ICN Challenges for IoT

   This section outlines some of the ICN specific challenges [71] that
   must be considered when defining an IoT framework over ICN, and
   describes some of the trade offs that will be involved.

   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 [38][70], data processing, and content
   distribution models to meet specific application requirements which
   we identify as challenges in this section.

5.1.  Naming Devices, Data, and Services

   The ICN approach of named data and services (i.e., device independent
   naming) is typically desirable when retrieving IoT data.  However,
   data centric naming may also pose challenges.

   o  Naming of devices: Naming devices is often important in an IoT
      network.  The presence of actuators requires clients to act
      specifically on a device, e.g. to switch it on or off.  Also,
      managing and monitoring the devices for administration purposes
      requires devices to have a specific name allowing to identify them
      uniquely.  There are multiple ways to achieve device naming, even
      in systems that are data centric by nature.  For example, in
      systems that are addressable or searchable based on metadata or
      sensor content, the device identifier can be included as a special
      kind of metadata or sensor reading.

   o  Size of data/service name: In information centric applications,
      the size of the data is typically larger than its name.  For the
      IoT, sensors and actuators are very common, and they can generate
      or use data as small as a short integer containing a temperature
      value, or a one-byte instruction to switch off an actuator.  The
      name of the content for each of these pieces of data has to
      uniquely identify the content.  For this reason, many existing
      naming schemes have long names that are likely to be longer than
      the actual data content for many types of IoT applications.
      Furthermore, naming schemes that have self certifying properties
      (e.g., by creating the name based on a hash of the content),
      suffer from the problem that the object can only be requested when
      the object has been created and the content is already known, thus
      requiring some form of indexing service.  While this is an
      acceptable overhead for larger data objects, it is infeasible for
      use when the object size is on the order of a few bytes.



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   o  Hash-based content name: Hash algorithms are commonly used to name
      content in order to verify that the content is the one requested.
      This is only possible in contexts where the requested object is
      already existing, and where there is a directory service to look
      up names.  This approach is suitable for systems with large data
      objects where it is important to verify the content.

   o  Metadata-based content name: Relying on metadata allows to
      generate a name for an object before it is created.  However this
      mechanism requires metadata matching semantics.

   o  Naming of services: Similarly to naming of devices or data,
      services can be referred to with a unique identifier, provided by
      a specific device or by someone assigned by a central authority as
      the service provider.  It can however also be a service provided
      by anyone meeting some certain metadata conditions.  Example of
      services include content retrieval, that takes a content name/
      description as input and returns the value of that content, and
      actuation, that takes an actuation command as input and possibly
      returns a status code afterwards.

   o  Trust: We need to ensure the name of a network element is issued
      by a trustworthy issuer in the context of the application, such as
      a trusted organization in [44].  Further the validity of each
      piece of data published by an authorized entity in the namespace
      should be verifiable - e.g., by following a hierarchical chain-of-
      trust to a root that is acceptable for the application.  See [54]s
      for an example.

   o  Flexibility: Further challenges arise for hierarchical naming
      schema: referring to requirements on "constructible names" and
      "on-demand publishing" [28][29].  The former entails that each
      user is able to construct the 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.

   o  Control/scoping : Some information could be accessible only within
      a given scope.  This challenge is very relevant for smart home and
      health monitoring applications, where privacy issues play a key
      role and the local scope of a home or healthcare environment may
      be well-defined.  However, perimeter- and channel-based access
      control is often violated in current networks to enable over-the-
      wire updates and cloud-based services, so scoping is unlikely to
      replace a need for data-centric security in ICN.





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   o  Confidentiality: As names can reveal information about the nature
      of the communication, mechanisms for name confidentiality should
      be available in the ICN-IoT architecture.

5.2.  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 [37]
   [40] [41] [57].  The objective is to achieve scalable name resolution
   handling static and dynamic ICN entities with low complexity and
   control overhead.  In particular, the main requirements/challenges of
   a name space (and the corresponding Name Resolution System where
   necessary) are [31] [33]:

   o  Scalability: The first challenge faced by ICN-IoT name resolution
      system is its scalability.  Firstly, the approach has to support
      billions of objects and devices that are connected to the
      Internet, many of which are crossing administrative domain
      boundaries.  Second of all, in addition to objects/devices, the
      name resolution system 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
      [37].  As a result, the name resolution 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.).  Notice that, if hierarchical names are
      used, scalability can be also supported by leveraging the inherent
      aggregation capabilities of the hierarchy.  Advanced techniques
      such as hyperbolic routing [53] may offer further scalability and
      efficiency.

   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].  As a matter of
      fact, besides the need to ensure coexistence between IP-centric
      and ICN-IoT systems, it is required to make different ICN-IoT
      realms, each one based on a different ICN architecture, to
      interoperate.

   o  Latency: For real-time or delay sensitive M2M application, the
      name resolution should not affect the overall QoS.  With reference
      to this issue it becomes important to circumvent too centralized
      resolution schema (whatever the naming style, i.e, hierarchical or
      flat) by enforcing in-network cooperation among the different
      entities of the ICN-IoT system, when possible [58].  In addition,
      fast name lookup are necessary to ensure soft/hard real time



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      services [60][61][62].  This challenge is especially important for
      applications with stringent latency requirements, such as health
      monitoring, emergency handling and smart transportation [63].

   o  Locality and network efficiency: During name resolution the named
      entities closer to the consumer should be easily accessible
      (subject to the application requirements).  This requirement is
      true in general because, whatever the network, if the edges are
      able to satisfy the requests of their consumers, the load of the
      core and content seek time decrease, and the overall system
      scalability is improved.  This facet gains further relevance in
      those domains where an actuation on the environment has to be
      executed, based on the feedbacks of the ICN-IoT system, such as in
      robotics applications, smart grids, and industrial plants [59].

   o  Agility: Some data items could disappear while some other ones are
      created so that the name resolution system should be able to
      effectively take care of these dynamic conditions.  In particular,
      this challenge applies to very dynamic scenarios (e.g., VANETs) in
      which data items can be tightly coupled to nodes that can appear
      and disappear very frequently.

5.3.  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 [33], 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 [34], selecting a
      random router to cache data is as good as caching the content
      everywhere.  In [55], the authors suggest that edge caching
      provides most of the benefits of in-network caching typically
      discussed in NDN, with simpler deployment.  However, it and other
      papers consider workloads that are analogous to today's CDNs, not
      the IoT applications considered here.  Further work is likely
      required to understand the appropriate caching approach for IoT
      applications.





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   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 [36], the authors
      suggest to cache IoT services on intermediate routers, and in
      [37], 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.  For example, it could mean caching the result of a
      previous actuation request (using other ICN mechanisms to suppress
      repeated actuation requests within a given time period), or have
      little meaning at all if actuation uses authenticated requests as
      in [56].

   o  Another challenge is that the efficiency of Distributed Caching
      may be application dependent.  When content popularity is
      heterogeneous, some content is often requested repeatedly.  In
      that case, the network can benefit from caching.  Another case
      where caching would be beneficial is when devices with low duty
      cycle are present in the network and when access to the cloud
      infrastructure is limited.  However, using distributed caching
      mechanisms in the network is not useful when each object is only
      requested at most once, as a cache hit can only occur for the
      second request and later.  It may also be less beneficial to have
      caches distributed throughout ICN nodes in cases when there are
      overlays of distributed repositories, e.g., a cloud or a Content
      Distribution Network (CDN), from which all clients can retrieve
      the data.  Using ICN to retrieve data from such services may add
      some efficiency, but in case of dense occurrence of overlay CDN
      servers the additional benefit of caching in ICN nodes would be
      lower.  Another example is when the name refers to an object with
      variable content/state.  For example, when the last value for a
      sensor reading is requested or desired, the returned data should
      change every time the sensor reading is updated.  In that case,
      ICN caching may increase the risk that cache inconsistencies
      result in old data being returned.

5.4.  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 using a
   name resolution service (NRS).

   o  In direct name-based routing, packets are forwarded by the name of
      the data [57][38][42] or the name of the destination node [43].
      Here, the main challenge is to keep the ICN router state required



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      to route/forward data low.  This challenge becomes more serious
      when a flat naming scheme is used due to the lack of aggregation
      capabilities.

   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 the
      request for Data, the Data may get lost, which requires the
      consumer to simply resend the request, a technique used by direct
      routing approach.  Indirect routing approach doesn't differentiate
      between consumer and producer mobility [57], also network caching
      can improve data recovery for this approach.

   o  If the data producer itself has moved, the challenge is to control
      the control overhead while searching for a new data producer (or
      for the same data producer in its new position).  To this end,
      flooding techniques could be used, but an intra-domain level only,
      otherwise the network stability would be seriously impaired.  For
      handling mobility across different domains, more sophisticated
      approaches could be used, including the adoption of a SDN-based
      control plane.

5.5.  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 [36].
   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



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   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.
   Alternatively, naming conventions could be used to allow applications
   to request content in namespaces related to their local context
   without requiring a specific service, such as /local/geo/
   mgrs/4QFJ/123/678 to retrieve objects published in the 100m grid area
   4QFJ 123 678 of the military grid reference system (MGRS).  In both
   cases, trust providers may emerge that can vouch for an application's
   local knowledge.

   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).  Or, in the namespace
      driven approach, mechanisms for continuous nearest neighbor
      queries in the namespace need to be developed.

   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.

5.6.  In-network Computing

   In-network computing enables ICN routers to host heterogeneous
   services catering to various network functions and applications
   needs.  Contextual services for IoT networks require in-network
   computing, in which each sensor node or ICN router implements context
   reasoning [36].  Another major purpose of in-network computing is to
   filter and cleanse sensed data in IoT applications, that is critical
   as the data is noisy as is [44].

   Named Function Networking [64] describes an extension of the ICN
   concept to named functions processed in the network, which could be
   used to generate data flow processing applications well-suited to,
   for example, time series data processing in IoT sensing applications.
   Related to this, is the need to support efficient function naming.
   Functions, input parameters, and the output result could be
   encapsulated in the packet header, the packet body, or mixture of the
   two (e.g. [24]).  If functions are encapsulated in packet headers,
   the naming scheme affects how a computation task is routed in the
   network, which IoT devices are involved in the computation task (e.g.
   [35]), and how a name is decomposed into smaller computation tasks
   and deployed in the network for a better performance.




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   Another is challenge is related to support computing-aware routing.
   Normal routing is for forwarding requests to the nearest source or
   cache and return the data to the requester, whereas the routing for
   in-network computation has a different purpose.  If the computation
   task is for aggregating sensed data, the routing strategy is to route
   the data to achieve a better aggregation performance [32].

   In-network computing also includes synchronization challenges.  Some
   computation tasks may need synchronizations between sub-tasks or IoT
   devices, e.g. a device may not send data as soon as it is available
   because waiting for data from the neighbours may lead to a better
   aggregation result; some devices may choose to sleep to save energy
   while waiting for the results from the neighbours; while aggregating
   the computation results along the path, the intermediate IoT devices
   may need to choose the results generated within a certain time
   window.

5.7.  Security and Privacy

   Security and privacy is crucial to all the IoT applications
   applications including the use cases discussed in Section 5.  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 [38].  The ICN paradigm is information-centric as opposed to
   state-of-the-art host-centric internet.  Besides aspects like naming,
   content retrieval and caching this also has security implications.
   ICN advocates the model of trust in content rather than trust in
   network hosts.  This brings in the concept of Object Security which
   is contrary to session-based security mechanisms such as TLS/DTLS
   prevalent in the current host-centric internet.  Object Security is
   based on the idea of securing information objects unlike session-
   based security mechanisms which secure the communication channel
   between a pair of nodes.  This reinforces an inherent characteristic
   of ICN networks i.e. to decouple senders and receivers.  In the
   context of IoT, the Object Security model has several concrete
   advantages.  Many IoT applications have data and services are the
   main goal and specific communication between two devices is
   secondary.  Therefore, it makes more sense to secure IoT objects
   instead of securing the session between communicating endpoints.
   Though ICN includes data-centric security features the mechanisms
   have to be generic enough to satisfy multiplicity of policy
   requirements for different applications.  Furthermore security and
   privacy concerns have to be dealt in a scenario-specific manner with
   respect to network function perspective spanning naming, name-
   resolution, routing, caching, and ICN-APIs.  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.



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

   o  In the resource-constrained nodes, 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 [46] [36].

   o  In the infrastructure part, we have several new threats introduced
      by ICN-IoT [52]:

      1.  We need to ensure the name of a network element is issued by a
          trustworthy organization entity such as in [48], or by its
          trusted delegate.  As name securely binds to data in ICN,
          security constraints of content that has not yet been
          published yet should also be taken into consideration.

      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.

      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.  Self-Orgnization

   General IoT deployments involves heterogeneous IoT systems or
   subsystems within a particular scenario.  Here scope-based self-
   organization is required to ensure logical isolation between the IoT
   subsystems, which should be enabled at different levels -- device/
   service discovery, naming, topology construction, routing over
   logical ICN topologies, and caching [69].  These challenges are



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   extended to constrained devices as well and they should be energy and
   device capability aware.  In the infrastructure part, intelligent
   name-based routing, caching, in-network computing techniques should
   be studied to meet the scope-based self-configuration needs of ICN-
   IoT.

5.9.  Communications Reliability

   ICN offers many ingredients for reliable communication such as multi-
   home interest anycast over heterogeneous interfaces, caching, and
   forwarding intelligence for multi-path routing leveraging state-
   based forwarding in protocols like CCN/NDN.  However these features
   have not been analyzed from the QoS perspective when heterogeneous
   traffic patterns are mixed in a router, in general QoS for ICN is an
   open area of research [71].  In-network reliability comes at the cost
   of a complex network layer; hence the research challenges here is to
   build redundancy and reliability in the network layer to handle a
   wide range of disruption scenarios such as congestion, short or long
   term disconnection, or last mile wireless impairments.  Also an ICN
   network should allow features such as opportunistic store and forward
   mechanism to be enabled only at certain points in the network, as
   these mechanisms also entail overheads in the control and forwarding
   plane overhead which will adversely affect application throughput.

5.10.  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
   5.1-5.9 for challenges associated with energy efficiency for ICN-IoT.

6.  Appendix

   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.

6.1.  Homes

   The home [10] 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



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   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.  In addition, several IoT-enabled homes could
   form a smart district where it becomes possible to bargain resources
   and trade with utility suppliers.

   Homes [12][13] 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 [14]; (2)
   inter-operability: 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; (5) user interface / user experience: homes need
   to provide reasonable interfaces to their highly heterogeneous IoT
   networks for users with a variety of skill levels, backgrounds,
   cultures, interests, etc.

   Smart homes have the following specific requirement for the
   underlying architecture:

   o  Smart homes require names that can enable local and wide area
      interactions; Also, security, privacy, and access control is
      particularly important for smart homes.

   o  Smart homes may use in-network caching at gateway to enable
      efficient content access.

   o  In smart homes, we need local, intra-domain and inter-domain
      routing protocols.






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   o  In smart homes many control loops and actions depend heavily on
      the context, and the contexts evolve with time, e.g., temperature,
      weather, number of occupants, etc.

   o  In smart homes, local services can provide value-added
      contributions to a standardized home gateway network, through
      features such as reporting, context-based control, coordination
      with mobile devices, etc.

   o  In smart homes, the access to networked information should be
      shielded to protect the privacy of people, for example, cross-
      correlation of device activity patterns to infer higher-level
      activity information.

6.2.  Enterprise

   Enterprise building deployments, from university campuses [15] [65]
   [66] [67] 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
   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) security: security for IoT applications in the
   enterprise should integrate with other enterprise-wide security
   components.





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6.3.  Smart Grid

   Central to the so-called "smart grid"[16]  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 achieve more
   optimizations that include reliability, real-time control, secure
   communications, and data privacy.

   Deployment of the smart grid [17] [21] faces the following issues
   that are hard to address with IP-based overlay solutions: (1)
   scalability: future 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.

   Smart grids have the following specific requirements for the
   underlying IoT architecture:

   o  Smart grids require names and name resolution system that can
      enable networked control loops, real-time control, and security.

   o  Smart grids may use in-network caching to back up valuable data
      improving reliability.

   o  In smart grids, we often require very timely data delivery.
      Therefore, it is important to be able to locate the closest
      information.  In addition, routing/forwarding robustness and
      resilience is also critical.

   o  In smart grids, contextual information such as location, time,
      voltage fluctuations, depending on the specific segment of the
      grid, can be used to optimize several power distribution
      objectives.

   o  In smart grids, we often rely on in-network computing to increase
      the scalability and efficiency of the system, putting computation
      closer to the data sources.

   o  In smart grids, energy consumptions profiles should never be
      disclosed at a fine granularity as it can be used to violate user
      privacy.




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6.4.  Transportation

   We are currently witnessing the increasing integration of sensors
   into cars, other vehicles transportation systems [22].  Current
   production cars already carry many sensors ranging from rain gauges
   and accelerometers over wheel rotation/traction sensors, to cameras.
   These sensors can not only be used for internal vehicle functions,
   but they could also be networked and leveraged for applications such
   as monitoring external traffic/road conditions.  Further, we can
   build vehicle-to- infrastructure (V2I),Vehicle-to-Roadside (V2R), 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 [22][25] 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; ; also, during the journey the user might
   cross several realms, each one implementing different stacks (whether
   ICN or IP); (2) real-time and reliability: transportation systems
   must be able to operate in 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.

   Smart transportation applications have the following specific
   requirements for the underlying IoT architecture:

   o  Smart transportation systems require names and name resolution
      system to be able to handle extreme mobility, short latency and
      security.  In addition, the mobility patterns of transportation
      systems increase the likelyhood that a user migrates from one
      network realm to another one during the journey.  In this case,
      names and NRS should be designed in such a way to enable
      interoperability between different heterogeneous ICN realms and/or
      ICN and IP realms [68].

   o  Smart transportation may implement in-network caching on vehicles
      for efficient information dissemination

   o  In smart transportation, vehicle-to-vehicle ad-hoc communication
      is required for efficient information dissemination.



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   o  In smart transportation, many different contexts exist,
      intertwined to each other and highly changing, which include
      location - both geographical and jurisdictional, time - absolute
      and relative to a schedule, traffic, speed, etc.

   o  In smart transportation, in-network computing is very useful to
      make vehicle become an active element of the system and to improve
      response time and scalability.

   o  In smart transportation, the habits of users can be inferred by
      looking at their movement patterns -- privacy protection is
      essential.

6.5.  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 [26].  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.  In addition to this dimension, assistive robotics
   applications are gaining momentum to provide 24/24 7/7 assistance to
   patients [59].

   Healthcare [26][27]  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-
   operability: healthcare systems must operate with heterogeneous
   devices 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; (4)
   privacy: user trust in healthcare systems is critical, and privacy
   considerations paramount to garner adoption and continued user; (5)
   user interface / user experience: the highly heterogeneous nature of
   real-world healthcare systems, which will continue to increase
   through the introduction of IoT devices, presents significant
   challenges in interface design that may have architectural
   implications.




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   Smart healthcare applications have the following specific
   requirements for the underlying IoT architecture:

   o  Smart healthcare system requires names and name resolution system
      to enable real- time interactions, dependability, and security.

   o  Smart healthcare may use in-network caching for rapid information
      dissemination.

   o  In smart healthcare, timely and dependable routing and information
      forwarding is the key.

   o  In smart healthcare several contexts can be used to delineate
      between levels of care and urgency, for example delineating
      between chronic, everyday, urgent, and emergency situations.  Such
      contexts can evolve rapidly with significant impact to individuals
      health.  Hence timely and accurate detection of contexts is
      critical.

   o  In smart healthcare, in-network computing can help resolve
      contexts and ensure security and dependability, as well as provide
      low-latency responses to urgent situations.

   o  In smart healthcare, personal medical data about patients should
      remain shielded to protect their privacy, implementing both
      regulatory requirements and current industry best practices.

6.6.  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 micro-controller
   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



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   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; (5) programming interfaces must be carefully
   developed to expose important concepts clearly and in light of
   current best practices in education.

   Smart campus systems have the following specific requirements for the
   underlying IoT architecture:

   o  Smart campus systems usually consist of heterogeneous IoT
      services, thus requiring names and name resolution system to
      enable resource/ service ownership, and be application-centric.

   o  Smart campus systems may use in-network caching to enable social
      interactions and efficient content access.

   o  In smart campus, inter-domain routing protocols are required which
      often need short latency.

   o  In smart campus, due to the existence of many services, relevant
      contextual inputs can be used to improve the quality and
      efficiency of different services.

   o  In smart campus, in-network computing services can be used to
      provide context for different applications.

   o  In smart campus, it is required to differentiate among different
      profiles and to allocate different rights and protection levels to
      them.

6.7.  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)



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   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
   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.

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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








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Internet-Draft       ICN based Architecture for IoT           April 2016


   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


   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








Zhang, et al.           Expires October 24, 2016               [Page 38]


Internet-Draft       ICN based Architecture for IoT           April 2016


   Andres Lindgren
   SICS Swedish ICT
   Box 1263
   Kista  SE-164 29
   SE

   Email: andersl@sics.se


   Bengt Ahlgren
   SICS Swedish ICT
   Box 1263
   Kista, CA  SE-164 29
   SE

   Email: bengta@sics.se


   Olov Schelen
   Lulea University of Technology
   Lulea  SE-971 87
   SE

   Email: lov.schelen@ltu.se



























Zhang, et al.           Expires October 24, 2016               [Page 39]


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