ICN Research Group Y. Zhang
Internet-Draft D. Raychadhuri
Intended status: Informational WINLAB, Rutgers University
Expires: February 29, 2016 L. Grieco
Politecnico di Bari (DEI)
E. Baccelli
J. Burke
R. Ravindran (Ed)
G. Wang
Huawei Technologies
August 28, 2015

ICN based Architecture for IoT - Requirements and Challenges


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

This draft 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. 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 discuss important and popular IoT scenarios including the "smart" home, campus, grid, transportation infrastructure, healthcare, Education, and Entertainment.

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 Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet-Drafts is at http://datatracker.ietf.org/drafts/current/.

Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress."

This Internet-Draft will expire on February 29, 2016.

Copyright Notice

Copyright (c) 2015 IETF Trust and the persons identified as the document authors. All rights reserved.

This document is subject to BCP 78 and the IETF Trust's Legal Provisions Relating to IETF Documents (http://trustee.ietf.org/license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Simplified BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Simplified BSD License.

Table of Contents

1. IoT Motivation

During the past decade, many standalone Internet of Things (IoT) systems have been developed and deployed in different domains. The recent trend, however, is to evolve towards a globally unified IoT platform, in which billions of objects connect to the Internet, available for interactions among themselves, as well as interactions with many different applications across boundaries of administration and domains. Building a unified IoT platform, however, poses great challenges on the underlying network and systems. To name a few, it needs to support 50-100 Billion networked objects [1], many of which are mobile. The objects will have extremely heterogeneous means of connecting to the Internet, often with severe resource constraints. Interactions between the applications and objects are often real-time and dynamic, requiring strong security and privacy protections. In addition, IoT applications are inherently information centric (e.g., data consumers usually need data sensed from the environment without any reference to the sub-set of motes that will provide the asked information). Taking a general IoT perspective, we begin by presenting IoT architectural requirements, then summarize how state-of-art approaches address these requirements. We then discuss IoT challenges from an ICN perspective and requirements posed towards its design. Final discussion focusses 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 spanning naming, security, name resolution, routing and forwarding level. In addition, mobility adds further challenge in terms of scalability. Particularly with respect to name resolution the system should be able to register/update/resolve up a name within a short latency. To satisfy this requirement, decentralization of the name resolution can be the key.

2.3. Resource Constraints

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

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, 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 contextual information such as social, relationships of owners, administrative groupings, location, type of ecosystem (home, grid, transport etc.) of devices and data (which are referred to as contexts in this document) to initiate dynamic relationship and communication. For 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 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.

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.).

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.

2.11. Ad hoc and Infrastructure Mode

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

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

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

2.12. Open API

General IoT applications involve sensing, processing, and secure content distribution 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.

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 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 standardization efforts including ETSI[3], oneM2M[4],and CORE[5]. Network operators can use standard API to build common IOT gateways and servers for their customers. Figure 2 shows the architecture adopted in this approach.

              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:

4. ICN Challenges for IoT

ICN integrates content/service/host abstraction, name-based routing, compute, caching/storage as part of the network infrastructure connecting consumers and services which meets most of the requirements discussed above; however IoT requires special considerations given heterogeneity of devices and interfaces such as for constrained networking [31], data processing, and content distribution models to meet specific application requirements which we identify as challenges in this section. We also discuss scenario specific challenges discussed in Section 5.

4.1. Naming and 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 [30] [33] [34] [47]. 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 [26] [27]:

In addition to the above general requirements, we identify the following specific requirements for different IoT applications:

4.2. Caching/Storage

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

Caching in ICN-IoT faces several challenges:

Next we use specific IoT systems to explain the caching challenge:

4.3. 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).

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:

Finally, in addition to the above requirements, specific IoT applications may impose specific challenges on routing and forwarding:

4.4. Contextual Communication

Contextualization through metadata in ICN control or application payload allows IoT applications to adapt to different environments. This enables intelligent networks which are self-configurable and enable intelligent networking among consumers and producers [29]. For example, let us look at the following smart transportation scenario: "James walks on NYC streets and wants to find an empty cab closest to his location." In this example, the context is the relative locations of James and taxi drivers. A context service, as an IoT middleware, processes the contextual information and bridges the gap between raw sensor information and application requirements. 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:

Next, in addition to the above requirements, specific IoT services may impose specific challenges on contextual communication:

4.5. In-network Computing

In-network computing enables ICN routers to host heterogenous 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 [29]. Another major purpose of in-network computing is to filter and cleanse sensed data in IoT applications is critical as the data is noisy as is [37]. Named Function Networking [54] 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.

4.6. Security and Privacy

Security and privacy is crucial to all the IoT 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]. 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.

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

Finally, in addition to the above requirements, specific IoT applications may impose specific challenges on privacy that impact both applications and the ICN-IoT network:

4.7. 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 4.1-4.6 for challenges associated with energy efficiency for ICN-IoT.

5. Popular Scenarios

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

5.1. 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 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.

5.2. Enterprise

Enterprise building deployments, from university campuses [15] [55] [56] [57] 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.

5.3. 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 reach more significant optimizations. The challenges for smart grid include reliability, real-time control, secure communications, and data privacy.

Deployment of the smart grid [17] [18] faces the following issues that are hard to address with IP-based overlay solutions: (1) scalability: tomorrow's electrical grids must be able to scale gracefully to manage a large number of heterogeneous devices; (2) real time: grids must be able to perform real-time data collection, data processing and control; (3) reliability: grids must be resilient to hardware/software/networking failures; (4) security: grids and associated systems are often considered critical infrastructure -- they must be able to defend against malicious attacks, detect intrusion, and route around disruption.

5.4. Transportation

We are currently witnessing the increasing integration of sensors into cars, other vehicles transportation systems [19]. Current production cars already carry many sensors ranging from rain gauges and accelerometers over wheel rotation/traction sensors, to cameras. While intended for internal vehicle functions, these could also be networked and leveraged for applications such as monitoring external 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 [19][20] 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 on real-time and remain resilient in the presence of failures; (3) in-network computing/filtering: transportation systems will benefit from in-network computing/ filtering as such operations can reduce the end-to-end latency; (4) inter-operatibility: transportation systems must operate with heterogeneous device and protocols; (5) security: transportation systems must be resilient to malicious physical and cyber attacks.

5.5. 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 [21]. 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 [49].

Healthcare [21][22] 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.

5.6. 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 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.

5.7. Entertainment, arts, and culture

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

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

6. Informative References

[1] Cisco System Inc., CISCO., "Cisco visual networking index: Global mobile data traffic forecast update.", 2009-2014.
[2] Shafig, M., Ji, L., Liu, A., Pang, J. and J. Wang, "A first look at cellular machine-to-machine traffic: large scale measurement and characterization.", Proceedings of the ACM Sigmetrics , 2012.
[3] The European Telecommunications Standards Institute, ETSI., "http://www.etsi.org/.", 1988.
[4] Global Intiative for M2M Standardization, oneM2M., "http://www.onem2m.org/.", 2012.
[5] Constrained RESTful Environments, CoRE., "https://datatracker.ietf.org/wg/core/charter/.", 2013.
[6] Ghodsi, A., Shenker, S., Koponen, T., Singla, A., Raghavan, B. and J. Wilcox, "Information-Centric Networking: Seeing the Forest of the Trees.", Hot Topics in Networking , 2011.
[7] Dong, L., Zhang, Y. and D. Raychaudhuri, "Enhance Content Broadcast Efficiency in Routers with Integrated Caching.", Proceedings of the IEEE Symposium on Computers and Communications (ISCC) , 2011.
[8] NSF FIA project, MobilityFirst., "http://www.nets-fia.net/", 2010.
[9] Kim, B., Lee, S., Lee, Y., Hwang, I. and Y. Rhee, "Mobiiscape: Middleware Support for Scalable Mobility Pattern Monitoring of Moving Objects in a Large-Scale City.", Journal of Systems and Software, Elsevier, 2011.
[10] Dietrich, D., Bruckne, D., Zucker, G. and P. Palensky, "Communication and Computation in Buildings: A Short Introduction and Overview", IEEE Transactions on Industrial Electronics, 2010.
[11] Keith, K., Falco, F. and K. Scarfone, "Guide to Industrial Control Systems (ICS) Security", NIST, Technical Report 800-82 Revision 1, 2013.
[12] Darianian, M. and Martin. Michael, "Smart home mobile RFID-based Internet-of-Things systems and services.", IEEE, ICACTE, 2008.
[13] Zhu, Q., Wang, R., Chen, Q., Chen, Y. and W. Qin, "IOT Gateway: Bridging Wireless Sensor Networks into Internet of Things", IEEE/IFIP, EUC, 2010.
[14] Biswas, T., Chakrabort, A., Ravindran, R., Zhang, X. and G. Wang, "Contextualized information-centric home network", ACM, Siggcomm, 2013.
[15] Huang, R., Zhang, J., Hu, Y. and J. Yang, "Smart Campus: The Developing Trends of Digital Campus", 2012.
[16] Yan, Y., Qian, Y., Hu, Y. and J. Yang, "A Survey on Smart Grid Communication Infrastructures: Motivations, Requirements and Challenges", IEEE Communications Survey and Tutorials, 2013.
[17] Miao, Y. and Y. Bu, "Research on the Architecture and Key Technology of Internet of Things (loT) Applied on Smart Grid", IEEE, ICAEE, 2010.
[18] Zhang, Y., Yu, R., Nekovee, M., Liu, Y., Xie, S. and S. Gjessing, "Cognitive Machine-to-Machine Communications: Visions and Potentials for the Smart Grid", IEEE, Network, 2012.
[19] Zhou, H., Liu, B. and D. Wang, "Design and Research of Urban Intelligent Transportation System Based on the Internet of Things", Springer Link, 2012.
[20] Zhang, M., Yu, T. and G. Zhai, "Smart Transport System Based on the Internet of Things", Applied Mechanics and Materials, 2012.
[21] Zhang, A., Yu, R., Nekovee, M. and S. Xie, "The Internet of Things for Ambient Assisted Living", IEEE, ITNG, 2010.
[22] Savola, R., Abie, H. and M. Sihvonen, "Towards metrics-driven adaptive security management in E-health IoT applications.", ACM, BodyNets, 2012.
[23] Jacobson, V., Smetters, D., Plass, M., Stewart, P., Thornton, J. and R. Braynard, "VoCCN: Voice-over Content-Centric Networks", ACM, ReArch, 2009.
[24] Piro, G., Cianci, I., Grieco, L., Boggia, G. and P. Camarda, "Information Centric Services in Smart Cities", ACM, Journal of Systems and Software, 2014.
[25] Ravindran, R., Biswas, T., Zhang, X., Chakrabort, A. and G. Wang, "Information-centric Networking based Homenet", IEEE/IFIP, 2013.
[26] Dannewitz, C., D' Ambrosio, M. and V. Vercellone, "Hierarchical DHT-based name resolution for information-centric networks", 2013.
[27] Chai, W., He, D. and I. Psaras, "Cache "less for more" in information-centric networks", ACM, IFIP, 2012.
[28] Eum, S., Nakauchi, K., Murata, M., Shoji, Yozo. and N. Nishinaga, "Catt: potential based routing with content caching for icn", IEEE Communication Magazine, 2012.
[29] Eum, S., Shvartzshnaider, Y., Francisco, J., Martini, R. and D. Raychaudhuri, "Enabling internet-of-things services in the mobilityfirst future internet architecture", IEEE, WoWMoM, 2012.
[30] Sun, Y., Qiao, X., Cheng, B. and J. Chen, "A low-delay, lightweight publish/subscribe architecture for delay-sensitive IOT services", IEEE, ICWS, 2013.
[31] Baccelli, E., Mehlis, C., Hahm, O., Schmidt, T. and M. Wahlisch, "Information Centric Networking in the IoT:Experiments with NDN in the Wild", ACM, ICN Siggcomm, 2014.
[32] Gronbaek, I., "Architecture for the Internet of Things (IoT): API and interconnect", IEEE, SENSORCOMM, 2008.
[33] Tian, Y., Liu, Y., Yan, Z., Wu, S. and H. Li, "RNS-A Public Resource Name Service Platform for the Internet of Things", IEEE, GreenCom, 2012.
[34] Roussos, G. and P. Chartier, "Scalable id/locator resolution for the iot", IEEE, iThings,CPSCom, 2011.
[35] Amadeo, M. and C. Campolo, "Potential of information-centric wireless sensor and actor networking", IEEE, ComManTel, 2013.
[36] Nelson, S., Bhanage, G. and D. Raychaudhuri, "GSTAR: generalized storage-aware routing for mobilityfirst in the future mobile internet", ACM, MobiArch, 2011.
[37] Trappe, W., Zhang, Y. and B. Nath, "MIAMI: methods and infrastructure for the assurance of measurement information", ACM, DMSN, 2005.
[38] Rouf, I., Mustafa, H., Taylor, T., Oh, S., Xu, W., Gruteser, M., Trappe, W. and I. Seskar, "Security and privacy vulnerabilities of in-car wireless networks: A tire pressure monitoring system case study", USENIX, 2010.
[39] Liu, R. and W. Trappe, "Securing Wireless Communications at the Physical Layer", Springer, 2010.
[40] Xiao, L., Greenstein, L., Mandayam, N. and W. Trappe, "Using the physical layer for wireless authentication in time-variant channels", IEEE Transactions on Wireless Communications, 2008.
[41] Sun, S., Lannom, L. and B. Boesch, "Handle system overview", IETF, RFC3650, 2003.
[42] Liu, X., Trappe, W. and Y. Zhang, "Secure Name Resolution for Identifier-to-Locator Mappings in the Global Internet", IEEE, ICCCN, 2013.
[43] Boguna, M., Fragkiskos, P. and K. Dmitri, "Sustaining the internet with hyperbolic mapping", Nature Communications, 2010.
[44] Shang, W., "Securing building management systems using named data networking", IEEE Network 2014.
[45] Fayazbakhsh, S. and et. et al, "Less pain, most of the gain: Incrementally deployable icn", ACM, Siggcomm, 2013.
[46] Burke, J. and et. et al, "Securing instrumented environments over Content-Centric Networking: the case of lighting control", INFOCOM, Computer Communications Workshop, 2013.
[47] Li, S., Zhang, Y., Dipankar, R. and R. Ravindran, "A comparative study of MobilityFirst and NDN based ICN-IoT architectures", IEEE, QShine, 2014.
[48] Grieco, L., Alaya, M. and K. Drira, "Architecting Information Centric ETSI-M2M systems", IEEE, Pervasive and Computer Communications Workshop (PERCOM), 2014.
[49] Grieco, L., Rizzo, A., Colucci, R., Sicari, S., Piro, G., Di Paola, D. and G. Boggia, "IoT-aided robotics applications: technological implications, target domains and open issues", Computer Communications, Volume 54, 1 December, 2014.
[50] Quan, Wei., Xu, C., Guan, J., Zhang, H. and L. Grieco, "Scalable Name Lookup with Adaptive Prefix Bloom Filter for Named Data Networking", IEEE Communications Letters, 2014.
[51] Wang, Yi., Pan, T., Mi, Z., Dai, H., Guo, X., Zhang, T., Liu, B. and Q. Dong, "NameFilter: Achieving fast name lookup with low memory cost via applying two-stage Bloom filters", INFOCOM, 2013.
[52] So, W., Narayanan, A., Oran, D. and Y. Wang, "Toward fast NDN software forwarding lookup engine based on Hash tables", ACM, ANCS, 2012.
[53] Amadeo, M., Campolo, C., Iera, A. and A. Molinaro, "Named data networking for IoT: An architectural perspective", IEEE, EuCNC, 2014.
[54] Sifalakis, M., Kohler, B., Christopher, C. and C. Tschudin, "An information centric network for computing the distribution of computations", ACM, ICN Sigcomm, 2014.
[55] Lu, R., Lin, X., Zhu, H. and X. Shen, "SPARK: a new VANET-based smart parking scheme for large parking lots", INFOCOM, 2009.
[56] Wang, H. and W. He, "A reservation-based smart parking system", The First International Workshop on Cyber-Physical Networking Systems, 2011.
[57] Qian, L., "Constructing Smart Campus Based on the Cloud Computing and the Internet of Things", Computer Science 2011.
[58] Project, BonVoyage., "From Bilbao to Oslo, intermodal mobility solutions, interfaces and applications for people and goods, supported by an innovative communication network", Call H2020-MG-2014, 2015-2018.

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
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