Design Considerations for Applying ICN to IoT
draft-zhang-icnrg-icniot-00

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Document Type Active Internet-Draft (individual)
Authors Yanyong Zhang  , Dipankar Raychadhuri  , Luigi Grieco  , Emmanuel Baccelli  , Jeff Burke  , Ravi Ravindran  , Guoqiang Wang  , Bengt Ahlgren  , Olov Schelen 
Last updated 2017-03-28
Replaced by draft-irtf-icnrg-icniot, draft-irtf-icnrg-icniot, draft-irtf-icnrg-icniot, draft-irtf-icnrg-icniot, draft-irtf-icnrg-icniot, draft-irtf-icnrg-icniot, draft-irtf-icnrg-icniot, draft-irtf-icnrg-icniot
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ICN Research Group                                              Y. Zhang
Internet-Draft                                            D. Raychadhuri
Intended status: Informational                WINLAB, Rutgers University
Expires: September 29, 2017                                    L. Grieco
                                               Politecnico di Bari (DEI)
                                                             E. Baccelli
                                                                   INRIA
                                                                J. Burke
                                                              UCLA REMAP
                                                            R. Ravindran
                                                                 G. Wang
                                                     Huawei Technologies
                                                              A. Lindren
                                                              B. Ahlgren
                                                               RISE SICS
                                                              O. Schelen
                                          Lulea University of Technology
                                                          March 28, 2017

             Design Considerations for Applying ICN to IoT
                      draft-zhang-icnrg-icniot-00

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 an application-layer
   based unified IoT platform 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 naming, security, seamless mobility support, scalability,
   and efficient content and service delivery.

   This draft describes representative IoT requirements, ICN challenges
   and design considerations to realize a unified ICN-IoT framework.
   Towards this, we first motivate ICN for IoT using specific use case
   scenarios.  Then taking a general IoT perspective, we discuss the IoT
   requirements generally applicable to many well known scenarios.  We
   then discuss how the current application layer unified IoT

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   architecture fails to meet these requirements, followed by discussion
   on suitability of ICN for IoT.  Though we see most of the IoT
   requirements can be met by ICN, we discuss specific design challenges
   ICN has to address to satisfy them.

Status of This Memo

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   This Internet-Draft will expire on September 29, 2017.

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Table of Contents

   1.  IoT Motivation  . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Motivating ICN for IoT  . . . . . . . . . . . . . . . . . . .   4
   3.  IoT Architectural Requirements  . . . . . . . . . . . . . . .   8
     3.1.  Naming  . . . . . . . . . . . . . . . . . . . . . . . . .   8
     3.2.  Security and Privacy  . . . . . . . . . . . . . . . . . .   8
     3.3.  Scalability . . . . . . . . . . . . . . . . . . . . . . .   9
     3.4.  Resource Constraints  . . . . . . . . . . . . . . . . . .   9
     3.5.  Traffic Characteristics . . . . . . . . . . . . . . . . .  10
     3.6.  Contextual Communication  . . . . . . . . . . . . . . . .  11

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     3.7.  Handling Mobility . . . . . . . . . . . . . . . . . . . .  11
     3.8.  Storage and Caching . . . . . . . . . . . . . . . . . . .  11
     3.9.  Communication Reliability . . . . . . . . . . . . . . . .  12
     3.10. Self-Organization . . . . . . . . . . . . . . . . . . . .  12
     3.11. Ad hoc and Infrastructure Mode  . . . . . . . . . . . . .  13
     3.12. Unified Architecture  . . . . . . . . . . . . . . . . . .  13
     3.13. IoT Platform Management . . . . . . . . . . . . . . . . .  13
   4.  State of the Art  . . . . . . . . . . . . . . . . . . . . . .  14
     4.1.  Silo IoT Architecture . . . . . . . . . . . . . . . . . .  14
     4.2.  Application-Layer Unified IoT Solutions . . . . . . . . .  15
       4.2.1.  Weaknesses of the Application-Layer Approach  . . . .  16
       4.2.2.  Suitability of Delay Tolerant Networking(DTN) . . . .  18
   5.  Advantages of using ICN for IoT . . . . . . . . . . . . . . .  18
   6.  ICN Design Considerations for IoT . . . . . . . . . . . . . .  20
     6.1.  Naming Devices, Data, and Services  . . . . . . . . . . .  20
     6.2.  Name Resolution . . . . . . . . . . . . . . . . . . . . .  22
     6.3.  Security and Privacy  . . . . . . . . . . . . . . . . . .  23
     6.4.  Caching/Storage . . . . . . . . . . . . . . . . . . . . .  25
     6.5.  Routing and Forwarding  . . . . . . . . . . . . . . . . .  26
     6.6.  Mobility Management . . . . . . . . . . . . . . . . . . .  27
     6.7.  Contextual Communication  . . . . . . . . . . . . . . . .  27
     6.8.  In-network Computing  . . . . . . . . . . . . . . . . . .  28
     6.9.  Self-Orgnization  . . . . . . . . . . . . . . . . . . . .  29
     6.10. Communications Reliability  . . . . . . . . . . . . . . .  29
     6.11. Resource Constraints and Heterogeneity  . . . . . . . . .  30
   7.  Informative References  . . . . . . . . . . . . . . . . . . .  30
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  39

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 first motivate
   the discussion of ICN for IoT using well known scenarios.  Then we

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   discuss the IoT requirements generally applicable to many well known
   IoT scenarios.  We then discuss how the current application-layer
   unified IoT architectures 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 design challenges from
   an ICN perspective and requirements posed towards its design.

2.  Motivating ICN for IoT

   ICN offers many features including name-based networking,content
   object level security, caching, computing and storage, mobility,
   contextual networking and support for ad hoc networking features, all
   of which have to be realized in an application-specific means in the
   context of IP-IoT.  These compelling features enable a distributed
   and intelligent data distribution platform to support heterogeneous
   IoT services with features like device bootstrapping with minimal
   configuration, simpler protocols to aid self-organizing among the IoT
   elements, natural support for compute and caching logic at strategic
   points in the network.  We discuss these features through the
   following scenarios that are difficult to realize over IP today, and
   whose requirements match the features offered by ICN.

   o  Smart Mobility: It is well known that IoT technologies can play a
      pivotal role in Intelligent Transportation Systems (ITS) [33][34].
      In particular, Traffic Management Systems (TMS) aided by IoT
      technologies are creating novel approaches to traffic modeling
      [37].  Moreover, such features enable advanced design paradigms
      (e.g., Mobility as a Service (MaaS) [35]) with huge implications
      in systems architectures [38].  It is worth to note that ITS
      services are information centric by design [88].  As a
      consequence, smart mobility support can be a killer domain of ICN-
      IoT [37][38][35].  Moreover, the experimental evidence
      demonstrates that ICN can significantly magnify the effectiveness
      of IoT deployments, in terms of: energy efficiency and bandwidth
      usage [48] [70][71]; scalability [72]; and flexibility of the name
      scheme [82][83].  In the ITS use case, we expect multi-modal
      transportation means that embrace public and private municipal,
      regional, national, trans-national fleets.  This rich eco-system
      of transportation means is made available to users and citizens
      through advanced services that are able to fulfill user
      requirements while pursuing system level objectives, including the
      reduction of the CO2 footprint, the real-time delivery of some
      goods, the reduction of traffic within urban areas, the
      provisioning of pleasant journeys to tourists, and the general
      commitment of satisfactory travel time and experience [88].  From
      a technological perspective, the challenges to face are: (i)
      interoperability across different IoT technologies; (ii) design of
      a namespace that is able to harmonize ITS standards; (iii)

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      provisioning of a scalable data-sharing model across real-time
      (and non real-time) traffic sources; (iv) definition of travel-
      centric services based on ICN-IoT; (v) seamless support to
      mobility; (vi) content authentication and cryptography.

   o  Smart Building: Smart Building is a complex ecosystem in which
      many different IoT devices manufacturers, protocols and
      applications are collaborating to manage domestic environment in
      an efficient and safe way [74], minimizing the number of
      intervention requested.  Buildings account for a very relevant
      quota of energy consumption; if not optimized, such a quantity
      might become even higher than that used in industrial field.  The
      use of renewable energy sources can only mitigate the problem.
      Efficient resources management is a rising task for Industrial IoT
      applications.  Many industrial companies have been working on the
      topic.  Anyway, this is leading to a highly fragmented market,
      with many vertical solutions designed independently for different
      applications, which unavoidably hinder a large-scale M2M
      deployment [75].  Both academic and industrial research has
      produced some standards (namely, ETSI M2M [76] and oneM2M [77])
      built as an overlay and application-layer networks of the current
      Internet, exploiting the functionalities offered by M2M
      technologies intensively.  The current version of these
      architectures is strongly centralized [73], thus requiring new
      enhancements to grant its scalability, fault tolerance, and
      flexibility.  This results in overhead due to impaired and network
      inefficiencies.  A unified ICN-IoT platform would inter-connect
      these systems through the Internet, thus enabling interaction with
      and between each other and taking decisions at an aggregated level
      efficiently, thanks to the embedded capability of ICN paradigm,
      such as native multicast, data-centric security, and data in-
      network aggregation and caching.  The main research challenges aim
      at defining a generic and incremental ICN-based platform
      supporting data collection and edge-cloud services.  From a
      technological perspective, in fact, this requires some
      intermediate steps: (i) design of a scalable namespace for
      uniquely identifying the information of interest, (ii) data-
      sharing model across heterogeneous systems, (iii) self-organizing
      functionalities for improving network connections between end-
      nodes, utilities and the control center, (iv) authentication
      procedures to grant data confidentiality and integrity.

   o  Smart Grid: Smart Grids are increasingly deployed cyber-physical
      systems [16]  that have the capabilities such as substation and
      distribution automation.  Data flow and information management is
      also very important to smart grids.  In a unified IoT platform,
      data collected from different smart grids can be integrated to
      achieve more optimizations that include reliability, real-time

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      control, secure communications, and data privacy.  Deployment of
      the smart grid [20] [24] 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:

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

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

      *  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.

      *  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.

      *  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.

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

   o  Smart Industry: In a Smart Industry/Industry 4.0 environment,
      there is a multitude of equipment with sensors that generate large
      volumes of data during normal operation.  This range from highly
      time-critical data for real-time control of production processes,
      to less time-critical data that is collected to central cloud
      environment for control room monitoring, to pure log data without
      latency requirements that is mainly kept for a posteriori
      analysis.  Industrial wireless networks are harsh environments
      with lots of potential interference at the same time as hard

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      reliability and real-time requirements are placed by many
      applications.  This means that available network capacity is not
      always high, so congestion is likely to be experienced by traffic
      with less stringent timing requirements.  The network need to
      support a mobile workforce in a smart industry automation scenario
      where users get access to data flows based on their physical
      location and task requirements.  Usually in an automation scenario
      the mobile workforce will locally perform diagnostics or
      maintenance and they rely on the information from the production
      system both for safety and to solve any issues in the plant.  The
      mobile workforce relies on both historical data in order to
      pinpoint the root cause of the problems, as well as the current
      data flows in order to assess the present state of the equipment
      under control.  High resolution measurements are generated close
      to the mobile workforce while the historic data has to be
      retrieved from the historian servers.  Furthermore, even if the
      mobile workforce is located next to the equipment under control,
      the data generated is usually transmitted to different control
      systems due to availability reasons as well as for capacity
      reasons due to the high traffic demands close to the processes.
      To realize this in current IP based systems require an excessive
      amount of traffic to and from the central control system for both
      data and coordination messages, which can have an adverse effect
      on the operational requirements of the factory.  Most importantly
      the additional traffic introduced by the mobile workforce should
      not interfere with the control traffic making the situation worse.
      Introducing ICN functionality into the system can introduce
      several benefits that will enhance the working experience and
      productivity for the mobile workforce.

      *  When using ICN, naming of data is simpler than knowing the
         address of the node that stores that data.

      *  ICN provides the possibility to get newest data without knowing
         the location of the caches or whether a particular piece of
         data is available locally or in a central repository.

      *  Possibility to get either local high-resolution data or remote
         low-resolution data (no need to store all data centrally, which
         is maybe not even possible due to large data volumes).

      *  Workforce mobility between different access points in the
         factory is inherently supported without the need to maintain
         connection state.

      *  Removing tedious configurations in clients since that is
         provided by the infrastructure.

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      *  Allow sharing of large data volumes between users that are in
         physical proximity without introducing additional traffic on
         the backbone.

      *  Caching of data means avoiding database accesses to a
         distributed redundant database in the central infrastructure
         with consistency requirements.

3.  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.

3.1.  Naming

   The first step towards realizing a unified IoT platform is the
   ability to assign names that are unique to 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 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
   offer semantic meanings to applications in comparison with
   traditional host address based schemes; finally, names should be able
   to help realize scalable IoT system architecture and high performance
   network platform.

3.2.  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

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   will be driven by various stake holders such as industry, government,
   consumers etc.

   In order to ensure the trustworthiness of the names, a name
   certificate service (NCS) needs to be considered.  Such a service
   acts as a certificate authority in assigning names, which are
   themselves public keys or appropriately bound to the name for
   verification at the consumer's end.  In short, the NCS must provide
   services analogous to those provided by a Public Key Infrastructure
   (PKI).  In ICN, users may either generate their own public keys and
   submit them to the NCS for registration, or may contact the NCS to
   acquire public keys.  Consequently, the NCS publishes approved
   cryptographic suites, object categories and object description
   formats, as well as allows users to self-certify themselves
   themselves when public keys are used as names.

3.3.  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.

3.4.  Resource Constraints

   IoT devices can be broadly classified as type 1, type 2, and type 3
   devices, with type 1 the most resource-constrained and type 3 the
   most resource-rich [36].  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.

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   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.

3.5.  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,
   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.

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3.6.  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.

3.7.  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
   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 if necessary to negotiate different connectivity or
   security constraints specific to each mobile context.

3.8.  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

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   applications.  This optimization can greatly reduce the content
   access latencies.

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

   Hence in the context of IoT while ICN allows resolution to replicated
   cached copies, it should also strive for the balance between content
   security/privacy and regulations considering application
   requirements.

3.9.  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 such as low latency and
   probability of error during information transfer.  To summarize,
   reliable communication requires the following capabilities for the
   underlying system: (1) seamless mobility support in the face of
   extreme disruptions, (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 and diverse communication
   patterns, including both communications within an IoT domain and
   communications cross different domains.

3.10.  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

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   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 [26] [27].

3.11.  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 address the challenges
   that arise in these two modes: (1) scalability and low latency for
   the infrastructure mode and (2) efficient neighbor discovery and ad-
   hoc communication for the ad-hoc mode.  Finally we note that hybrid
   modes are very common in realistic IoT systems.

3.12.  Unified Architecture

   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 the system to adopt a unified architecture providing pull,
   push and publish/subscribe mechanisms using application abstractions,
   common naming, payload, encryption and signature schemes.  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.

3.13.  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 [21][22][23].  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,

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   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.

4.  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
   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)

4.1.  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.)

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   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.

4.2.  Application-Layer 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 this application-layer unified IoT architecture
   is the most practical approach towards a unified IoT platform.
   Towards this, there are ongoing 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) [59], that is a lightweight
   protocol modeled after HTTP [60] 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.

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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

4.2.1.  Weaknesses of the Application-Layer Approach

   The above application-layer 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 application-layer IoT systems the naming
      scheme is host centric, i.e., the name of a given resource/service
      is linked to the 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  Security and Trust.  In IP, the security and trust model is based
      on session established between two hosts.  Session-based protocols
      rely on the exchange of several messages before a secure session
      is established.  Use of such protocols in constrained IoT devices
      can have serious consequences in terms of energy efficiency
      because transmission and reception of messages is often more
      costly than the cryptographic operations.  The problem is
      amplified with the number of nodes the constrained device has to
      interact with because a secure session would have to be
      established with every node.  Also the trust management schemes

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

   o  Mobility.  The application-layer 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 application-layer approach requires
      every device to send data to an aggregator, gateway 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
      the application-layer based IoT systems today.

   o  Contextual Communications.  This application-layer based IoT
      approach may not react to dynamic contextual changes in a timely
      fashion.  The main reason is that context lists are usually 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 application-layer 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 application-layer 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 [74], which is not supported
      by this 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 of communication.

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4.2.2.  Suitability of Delay Tolerant Networking(DTN)

   In [17][18], delay-tolerant networking (DTN) has been considered to
   support future IoT architecture.  DTN was created to support
   information delivery in the presence of network disruptions and
   disconnections, which has been extended to support heterogeneous
   networks and name-based routing.  The DTN Bundle Protocol is able to
   achieve some of these same advantages and could be beneficially used
   in an IoT network to, for example, decouple sender and receiver.  The
   DTN architecture is however still host centric and is mainly a way to
   transport data, while ICN provides a different paradigm centered
   around named data that addresses additional issues for IoT
   applications [19] through features such as information naming,
   information discovery, information request and dissemination.  Hence,
   in the rest of this draft, we focus on an ICN-based network-layer
   unified IoT architecture for IoT, i.e., ICN-IoT.

5.  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 many such
   advantages compared to using traditional host-centric networks and
   other new architectures.  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  Security and privacy.  ICN advocates the model of trust in content
      rather than trust in network hosts.  This brings in the concept of
      Object Security which is based on the idea of securing information
      objects unlike session-based security mechanisms which secure the
      communication channel between a pair of nodes.  ICN provides data

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      integrity through Name-Data Integrity, i.e., the guarantee that
      the given data corresponds to the name with which it was
      addressed.  Signature-based schemes can additionally provide data
      authenticity, meaning establishing the origin, or provenance, of
      the data, for example, by cryptographically linking a data object
      to the identity of a publisher.  Confidentiality can be handled on
      a per object basis based on keys established at the application
      level.  In an ICN network, an IoT client expects the network to
      deliver the requested content without concerning itself with the
      location of the content.  This could potentially mean that each
      individual object within a stream of immutable objects is
      retrieved from a different location.  Having a trust relationship
      with each of these different sources is not realistic.  Through
      Name-Data Integrity, ICN automatically guarantees data integrity
      to the requester regardless of the source from where it is
      delivered.  The Object Security model also ensures that the
      content is readily available in a secure state in the network.
      IoT devices producing data can secure it with regard to all the
      intended consumers and start transmitting it right away.  If the
      device constraints are severe enough that it is not able to
      perform the required cryptographic operations for Object Security,
      it may be possible to offload this operation to a trusted gateway
      to which only a single secure channel needs to be established.
      ICN can also derive a name from a public key; cryptographic hash
      of a public key also enables them to be self-certifying, i.e.,
      authenticating the resource object does not require an external
      authority [21][22].

   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

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      structured caching systems as described previously, information
      can also be spread by forwarding data opportunistically.

6.  ICN Design Considerations for IoT

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

   Though ICN integrates content/service/host abstraction, name-based
   routing, compute, caching/storage as part of the network
   infrastructure, IoT requires special considerations given
   heterogeneity of devices and interfaces such as for constrained
   networking [48][90][91], data processing, and content distribution
   models to meet specific application requirements which we identify as
   challenges in this section.

6.1.  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 or service 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

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      acceptable overhead for larger data objects, it is infeasible for
      use when the object size is on the order of a few bytes.

   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, as in
      [65].

   o  Flexibility: Further challenges arise for hierarchical naming
      schema: referring to requirements on "constructible names" and
      "on-demand publishing" [31][32].  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-

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      wire updates and cloud-based services, so scoping is unlikely to
      replace a need for data-centric security in ICN.

   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.

6.2.  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 [46]
   [50] [51] [69].  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 [40] [42]:

   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
      [46].  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 [64] 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

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      flat) by enforcing in-network cooperation among the different
      entities of the ICN-IoT system, when possible [73].  In addition,
      fast name lookup are necessary to ensure soft/hard real time
      services [78][79][80].  This challenge is especially important for
      applications with stringent latency requirements, such as health
      monitoring, emergency handling and smart transportation [81].

   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 [74].

   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.

6.3.  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 [55].  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

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   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.  The work by the JOSE WG
   [61] provides solution approaches to address some of these concerns
   for object security for constrained devices and should be considered
   to see what can be applied to an ICN architecture.  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 resource-constrained nodes, energy limitation is the
      biggest challenge.  Moreover, it has to deliver its data over a
      wireless link for a reasonable period of time 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 [56] [45].

   o  In the infrastructure part, we have several new threats introduced
      by ICN-IoT [63].  Below we list several possible attacks to a name
      resolution service that is critical to ICN-IoT:

      1.  Each IoT device is given an ICN name.  The name spoofing
          attack is a masquerading threat, where a malicious user A
          claims another user B's name and attempts to associate it with
          A's own network address NA-A, by announcing the mapping (ID-B,
          NA-A).  The consequence of this attack is a denial of service
          as it can cause traffic directed for B to be directed to A's
          network address.

      2.  The stale mapping attack is a message manipulation attack
          involving a malicious name resolution server.  In this attack,
          if a device moves and issues an update, the malicious name
          resolution server can purposely ignore the update and claim it
          still has the most recent mapping.  Perhaps worse, a name
          resolution server can selectively choose which (possibly
          stale) mapping to give out during queries.  The result is a
          denial of service.

      3.  The third potential attack, false announcement attack, is an
          information modification attack that results in illegitimate
          resource consumption.  User A, which is in network NA1, claims
          its ID-A binds to a different network address, (ID-A, NA2).

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          Thus A can direct its traffic to network NA2, which causes
          NA2's network resources to be consumed.

      4.  The collusion attack is an example of an information
          modification attack in which a malicious user, its network and
          the location where the mapping is stored collude with each
          other.  The objective behind the malicious collusion is to
          allow for a fake mapping involving a false network address to
          pass the verification and become stored in the storage place.

      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.

   o  As far as the IoT application server is concerned, data privacy is
      one of the biggest concerns.  IoT data is collected and stored on
      such servers, which usually run learning algorithms to extract
      patterns from such data.  In this case, it is important to adopt a
      framework that enables privacy-preserving learning techniques.
      The framework defines how data is collected, modified (to satisfy
      the privacy requirement), and transmitted to application
      developers.

6.4.  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 ICN-IoT faces several challenges:

   o  The main challenge is to determine which nodes on the routing path
      should cache the data.  According to [42], 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 [43], selecting a
      random router to cache data is as good as caching the content
      everywhere.  In [66], 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.

   o  Another challenge in ICN-IoT caching is what to cache for IoT
      applications.  In many IoT applications, customers often access a

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      stream of sensor data, and as a result, caching a particular
      sensor data item may not be beneficial.  In [45], the authors
      suggest to cache IoT services on intermediate routers, and in
      [46], 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 [67].

   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.  In [68], it is also shown that there
      are benefits to caching in the network when edge links are lossy,
      in particular if losses occur close to the content producer, as is
      common in wireless IoT networks.  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.

6.5.  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 using a
   name resolution service (NRS).

   o  In direct name-based routing, packets are forwarded by the name of
      the data [69][48][52] or the name of the destination node [53].

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      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 due to the lack of aggregation
      capabilities.

   o  In indirect routing, packets are forwarded based upon the locater
      of the destination node, and the locater is obtained through the
      name resolution service.  In particular, the name-locater 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.

6.6.  Mobility Management

   Related to this, the 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 [69], 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) [47].  To this
      end, flooding techniques could be used, but an intra-domain level
      only, otherwise the network stability would be seriously impaired.

6.7.  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 [45].
   For example, let us look at the following smart transportation

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   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.
   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.

6.8.  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 [45].  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 [54].

   Named Function Networking [84] 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. [27]).  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.

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   [44]), and how a name is decomposed into smaller computation tasks
   and deployed in the network for a better performance.

   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 [41].

   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.

6.9.  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 [89].  These challenges are
   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.

6.10.  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 [91].  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

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   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.

6.11.  Resource Constraints and Heterogeneity

   Even though today's IoT devices are becoming increasingly more
   powerful, their resource constraints remain a big issue.  Many of the
   embedded devices still have very limited computation, memory, and
   communication capability.  Moreover, embedded devices vary greatly in
   their capability.  As a result, an IoT architecture should take into
   consideration these factors.  Having globally unique IDs is a key
   feature in ICN, which may consist of tens of bytes.  Each device
   would have a persistent and unique ID no matter when and where it
   moves.  It is also important for ICN-IoT to keep this feature.
   However, always carrying the long ID in the packet header may not be
   always feasible over a low-rate layer-2 protocol such as 802.15.4.
   To solve this issue, ICN can operate using lighter-weight packet
   header and a much shorter locally unique ID (LUID in short).  In this
   way, we map a device's long global ID to its short LUID when we reach
   the local area IoT domain.  To cope with collisions that may occur in
   this mapping process, we let each domain have its own global ID to
   LUID mapping which is managed by a gateway deployed at the edge of
   the domain.  Different from NAT and other existing domain-based or
   gateway-based solutions, ICN-IoT does not change the identity the
   application uses.  The applications, either on constrained IoT
   devices or on the infrastructure nodes, still use the long global IDs
   to identify each other, while the network performs translation which
   is transparent to these applications.  An IoT node carries its global
   ID no matter where it moves, even when it is relocated to another
   local IoT domain and is assigned with a new LUID.  This ensures the
   global reach-ability and mobility handling yet still considers
   resource constraints of embedded devices.

   In addition, 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 6.1-6.9 for challenges associated with energy efficiency for
   ICN-IoT.

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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

Zhang, et al.          Expires September 29, 2017              [Page 39]
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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

   Andres Lindgren
   RISE SICS
   Box 1263
   Kista  SE-164 29
   SE

   Email: andersl@sics.se

   Bengt Ahlgren
   RISE SICS
   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

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