Internet DRAFT - draft-zhang-icnrg-icniot-requirements
draft-zhang-icnrg-icniot-requirements
ICN Research Group Y. Zhang
Internet-Draft D. Raychadhuri
Intended status: Informational WINLAB, Rutgers University
Expires: October 24, 2016 L. Grieco
Politecnico di Bari (DEI)
E. Baccelli
INRIA
J. Burke
UCLA REMAP
R. Ravindran
G. Wang
Huawei Technologies
A. Lindren
B. Ahlgren
SICS Swedish ICT
O. Schelen
Lulea University of Technology
April 22, 2016
Requirements and Challenges for IoT over ICN
draft-zhang-icnrg-icniot-requirements-01
Abstract
The Internet of Things (IoT) promises to connect billions of objects
to the Internet. After deploying many stand-alone IoT systems in
different domains, the current trend is to develop a common, "thin
waist" of protocols forming a horizontal unified, defragmented IoT
platform. Such a platform will make objects accessible to
applications across organizations and domains. Towards this goal,
quite a few proposals have been made to build a unified host-centric
IoT platform as an overlay on top of today's host-centric Internet.
However, there is a fundamental mismatch between the host-centric
nature of todays Internet and the information-centric nature of the
IoT system. To address this mismatch, we propose to build a common
set of protocols and services, which form an IoT platform, based on
the Information Centric Network (ICN) architecture, which we call
ICN-IoT. ICN-IoT leverages the salient features of ICN, and thus
provides seamless mobility support, security, scalability, and
efficient content and service delivery.
This draft describes representative IoT requirements and ICN
challenges to realize a unified ICN-IoT framework. Towards this, we
first identify a list of important requirements which a unified IoT
architecture should have to support tens of billions of objects, then
we discuss how the current IP-IoT overlay fails to meet these
requirements, followed by discussion on suitability of ICN for IoT.
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Though we see most of the IoT requirements can be met by ICN, we
discuss specific challenges ICN has to address to satisfy them. Then
we provide discussion of popular IoT scenarios including the "smart"
home, campus, grid, transportation infrastructure, healthcare,
Education, and Entertainment for completeness, as specific scenarios
requires appropriate design choices and architectural considerations
towards developing an ICN-IoT solution.
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This Internet-Draft will expire on October 24, 2016.
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Table of Contents
1. IoT Motivation . . . . . . . . . . . . . . . . . . . . . . . 3
2. IoT Architectural Requirements . . . . . . . . . . . . . . . 4
2.1. Naming . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2. Scalability . . . . . . . . . . . . . . . . . . . . . . . 4
2.3. Resource Constraints . . . . . . . . . . . . . . . . . . 5
2.4. Traffic Characteristics . . . . . . . . . . . . . . . . . 5
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2.5. Contextual Communication . . . . . . . . . . . . . . . . 6
2.6. Handling Mobility . . . . . . . . . . . . . . . . . . . . 6
2.7. Storage and Caching . . . . . . . . . . . . . . . . . . . 7
2.8. Security and Privacy . . . . . . . . . . . . . . . . . . 7
2.9. Communication Reliability . . . . . . . . . . . . . . . . 8
2.10. Self-Organization . . . . . . . . . . . . . . . . . . . . 8
2.11. Ad hoc and Infrastructure Mode . . . . . . . . . . . . . 8
2.12. Open API . . . . . . . . . . . . . . . . . . . . . . . . 9
2.13. IoT Platform Management . . . . . . . . . . . . . . . . . 9
3. State of the Art . . . . . . . . . . . . . . . . . . . . . . 9
3.1. Silo IoT Architecture . . . . . . . . . . . . . . . . . . 10
3.2. Overlay Based Unified IoT Solutions . . . . . . . . . . . 10
3.2.1. Weaknesses of the Overlay-based Approach . . . . . . 11
4. Advantages of using ICN for IoT . . . . . . . . . . . . . . . 13
5. ICN Challenges for IoT . . . . . . . . . . . . . . . . . . . 14
5.1. Naming Devices, Data, and Services . . . . . . . . . . . 14
5.2. Name Resolution . . . . . . . . . . . . . . . . . . . . . 16
5.3. Caching/Storage . . . . . . . . . . . . . . . . . . . . . 17
5.4. Routing and Forwarding . . . . . . . . . . . . . . . . . 18
5.5. Contextual Communication . . . . . . . . . . . . . . . . 19
5.6. In-network Computing . . . . . . . . . . . . . . . . . . 20
5.7. Security and Privacy . . . . . . . . . . . . . . . . . . 21
5.8. Self-Orgnization . . . . . . . . . . . . . . . . . . . . 22
5.9. Communications Reliability . . . . . . . . . . . . . . . 23
5.10. Energy Efficiency . . . . . . . . . . . . . . . . . . . . 23
6. Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . 23
6.1. Homes . . . . . . . . . . . . . . . . . . . . . . . . . . 23
6.2. Enterprise . . . . . . . . . . . . . . . . . . . . . . . 25
6.3. Smart Grid . . . . . . . . . . . . . . . . . . . . . . . 26
6.4. Transportation . . . . . . . . . . . . . . . . . . . . . 27
6.5. Healthcare . . . . . . . . . . . . . . . . . . . . . . . 28
6.6. Education . . . . . . . . . . . . . . . . . . . . . . . . 29
6.7. Entertainment, arts, and culture . . . . . . . . . . . . 30
7. Informative References . . . . . . . . . . . . . . . . . . . 31
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 37
1. IoT Motivation
During the past decade, many standalone Internet of Things (IoT)
systems have been developed and deployed in different domains. The
recent trend, however, is to evolve towards a globally unified IoT
platform, in which billions of objects connect to the Internet,
available for interactions among themselves, as well as interactions
with many different applications across boundaries of administration
and domains. Building a unified IoT platform, however, poses great
challenges on the underlying network and systems. To name a few, it
needs to support 50-100 Billion networked objects [1], many of which
are mobile. The objects will have extremely heterogeneous means of
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connecting to the Internet, often with severe resource constraints.
Interactions between the applications and objects are often real-time
and dynamic, requiring strong security and privacy protections. In
addition, IoT applications are inherently information centric (e.g.,
data consumers usually need data sensed from the environment without
any reference to the sub-set of motes that will provide the asked
information). Taking a general IoT perspective, we first discuss the
IoT requirements generally applicable to many well known scenarios.
We then discuss how the current IP overlay models fail to meet these
requirements. We follow this by key ICN features that makes it a
better candidate to realize an unified IoT framework. We then
discuss IoT challenges from an ICN perspective and requirements posed
towards its design. Final discussion focuses on IoT scenarios and
their unique challenges.
2. IoT Architectural Requirements
A unified IoT platform has to support interactions among a large
number of mobile devices across the boundaries of organizations and
domains. As a result, it naturally poses stringent requirements in
every aspect of the system design. Below, we outline a few important
requirements that a unified IoT platform has to address.
2.1. Naming
The first step towards realizing a unified IoT platform is the
ability to assign names that are unique within the scope and lifetime
of each device, data items generated by these devices, or a group of
devices towards a common objective. Naming has the following
requirements: first, names need to be persistent (within one or more
contexts) against dynamic features that are common in IoT systems,
such as lifetime, mobility or migration; second, names need to be
secure based on application requirements; third, names should provide
advantages to application authors in comparison with traditional host
address based schemes.
2.2. Scalability
Cisco predicts there will be around 50 Billion IoT devices such as
sensors, RFID tags, and actuators, on the Internet by 2020 [1]. As
mentioned above, a unified IoT platform needs to name every entity
such as data, device, service etc. Scalability has to be addressed
at multiple levels of the IoT architecture including naming,
security, name resolution, routing and forwarding level. In
addition, mobility adds further challenge in terms of scalability.
Particularly with respect to name resolution the system should be
able to register/update/resolve a name within a short latency.
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2.3. Resource Constraints
IoT devices can be broadly classified into two groups: resource-
sufficient and resource-constrained. In general, there are the
following types of resources: power, computing, storage, bandwidth,
and user interface.
Power constraints of IoT devices limit how much data these devices
can communicate, as it has been shown that communications consume
more power than other activities for embedded devices. Flexible
techniques to collect the relevant information are required, and
uploading every single produced data to a central server is
undesirable. Computing constraints limit the type and amount of
processing these devices can perform. As a result, more complex
processing needs to be conducted in cloud servers or at opportunistic
points, example at the network edge, hence it is important to balance
local computation versus communication cost.
Storage constraints of the IoT devices limit the amount of data that
can be stored on the devices. This constraint means that unused
sensor data may need to be discarded or stored in aggregated compact
form time to time. Bandwidth constraints of the IoT devices limit
the amount of communication. Such devices will have the same
implication on the system architecture as with the power constraints;
namely, we cannot afford to collect single sensor data generated by
the device and/or use complex signaling protocols.
User interface constraints refer to whether the device is itself
capable of directly interacting with a user should the need arise
(e.g., via a display and keypad or LED indicators) or requires the
network connectivity, either global or local, to interact with
humans.
The above discussed device constraints also affect application
performance with respect to latency and jitter. This in particular
applies to satellite or other space based devices.
2.4. Traffic Characteristics
IoT traffic can be broadly classified into local area traffic and
wide area traffic. Local area traffic is between nearby devices.
For example, neighboring cars may work together to detect potential
hazards on the highway, sensors deployed in the same room may
collaborate to determine how to adjust the heating level in the room.
These local area communications often involve data aggregation and
filtering, have real time constraints, and require fast device/data/
service discovery and association. At the same time, the IoT
platform has to also support wide area communications. For example,
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in Intelligent Transportation Systems, re-routing operations may
require a broad knowledge of the status of the system, traffic load,
availability of freights, whether forecasts and so on. Wide area
communications require efficient data/service discovery and
resolution services.
While traffic characteristics for different IoT systems are expected
to be different, certain IoT systems have been analyzed and shown to
have comparable uplink and downlink traffic volume in some
applications such as [2], which means that we have to optimize the
bandwidth/energy consumption in both directions. Further, IoT
traffic demonstrates certain periodicity and burstiness [2]. As a
result, when provisioning the system, the shape of the traffic volume
has to be properly accounted for.
2.5. Contextual Communication
Many IoT applications shall rely on dynamic contexts in the IoT
system to initiate communication between IoT devices. Here, we refer
to a context as attributes applicable to a group of devices that
share some common features, such as their owners may have a certain
social relationship or belong to the same administrative group, or
the devices may be present in the same location. For example, cars
traveling on the highway may form a "cluster" based upon their
temporal physical proximity as well as the detection of the same
event. These temporary groups are referred to as contexts. IoT
applications need to support interactions among the members of a
context, as well as interactions across contexts.
Temporal context can be broadly categorized into two classes, long-
term contexts such as those that are based upon social contacts as
well as stationary physical locations (e.g., sensors in a car/
building), and short-term contexts such as those that are based upon
temporary proximity (e.g., all taxicabs within half a mile of the
Time Square at noon on Oct 1, 2013). Between these two classes,
short-term contexts are more challenging to support, requiring fast
formation, update, lookup and association.
2.6. Handling Mobility
There are several degrees of mobility in a unified IoT platform,
ranging from static as in fixed assets to highly dynamic in vehicle-
to-vehicle environments.
Mobility in the IoT platform can mean 1) the data producer mobility
(i.e., location change), 2) the data consumer mobility, 3) IoT
Network mobility (e.g., a body-area network in motion as a person is
walking); and 4) disconnection between the data source and
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destination pair (e.g., due to unreliable wireless links). The
requirement on mobility support is to be able to deliver IoT data
below an application's acceptable delay constraint in all of the
above cases, and and if necessary to negotiate different connectivity
or security constraints specific to each mobile context.
2.7. Storage and Caching
Storage and caching plays a very significant role depending on the
type of IoT ecosystem, also a function subjected to privacy and
security guidelines. In a unified IoT platform, depending on
application requirements, content caching may or may not be policy
driven. If caching is pervasive, intermediate nodes don't need to
always forward a content request to its original creator; rather,
locating and receiving a cached copy is sufficient for IoT
applications. This optimization can greatly reduce the content
access latencies.
Furthermore considering hierarchical nature of IoT systems, ICN
architectures enable a more flexible, heterogeneous and potentially
fault-tolerant approach to storage providing persistence at multiple
levels.
In network storage and caching, however, has the following
requirements on the IoT platform. The platform needs to support the
efficient resolution of cached copies. Further the platform should
strive for the balance between caching, content security/privacy, and
regulations.
2.8. Security and Privacy
In addition to the fundamental challenge of trust management, a
variety of security and privacy concerns also exist in ICNs.
The unified IoT platform makes physical objects accessible to
applications across organizations and domains. Further, it often
integrates with critical infrastructure and industrial systems with
life safety implications, bringing with it significant security
challenges and regulatory requirements [11].
Security and privacy thus become a serious concern, as does the
flexibility and usability of the design approaches. Beyond the
overarching trust management challenge, security includes data
integrity, authentication, and access control at different layers of
the IoT platform. Privacy means that both the content and the
context around IoT data need to be protected. These requirements
will be driven by various stake holders such as industry, government,
consumers etc.
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2.9. Communication Reliability
IoT applications can be broadly categorized into mission critical and
non-mission critical. For mission critical applications, reliable
communication is one of the most important features as these
applications have strong QoS requirements. Reliable communication
requires the following capabilities for the underlying system: (1)
seamless mobility support in the face of extreme disruptions (DTN),
(2) efficient routing in the presence of intermittent disconnection,
(3) QoS aware routing, (4) support for redundancy at all levels of a
system (device, service, network, storage etc.), and (5) support for
rich communication patterns (unlike the tree-like routing structure
supported by RPL developed by ROLL WG).
2.10. Self-Organization
The unified IoT platform should be able to self-organize to meet
various application requirements, especially the capability to
quickly discover heterogeneous and relevant (local or global)
devices/data/services based on the context. This discovery can be
achieved through an efficient platform-wide publish-subscribe
service, or through private community grouping/clustering based upon
trust and other security requirements. In the former case, the
publish-subscribe service must be efficiently implemented, able to
support seamless mobility, in- network caching, name-based routing,
etc. In the latter case, the IoT platform needs to discover the
private community groups/clusters efficiently.
Another aspect of self-organization is decoupling the sensing
Infrastructure from applications. In a unified IoT platform, various
applications run on top of a vast number of IoT devices. Upgrading
the firmware of the IoT devices is a difficult work. It is also not
practical to reprogram the IoT devices to accommodate every change of
the applications. The infrastructure and the application specific
logics need to be decoupled. A common interface is required to
dynamically configure the interactions between the IoT devices and
easily modify the application logics on top of the sensing
infrastructure [23] [24].
2.11. Ad hoc and Infrastructure Mode
Depending upon whether there is communication infrastructure, an IoT
system can operate either in ad-hoc or infrastructure mode.
For example, a vehicle may determine to report its location and
status information to a server periodically through cellular
connection, or, a group of vehicles may form an ad-hoc network that
collectively detect road conditions around them. In the cases where
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infrastructure is unavailable, one of the participating nodes may
choose to become the temporary gateway.
The unified IoT platform needs to design a common protocol that
serves both modes. Such a protocol should be able to provide: (1)
energy-efficient topology discovery and data forwarding in the ad-hoc
mode, and (2) scalable name resolution in the infrastructure mode.
2.12. Open API
General IoT applications involve sensing, processing, and secure
content distribution occurring at various timescales and at multiple
levels of hierarchy depending on the application requirements. This
requires open APIs to be generic enough to support commonly used
interactions between consumers, content producer, and IoT services,
as opposed to proprietary APIs that are common in today's systems.
Examples include pull, push, and publish/subscribe mechanisms using
common naming, payload, encryption and signature schemes.
2.13. IoT Platform Management
An IoT platforms' service, control, and data plane will be governed
by its own management infrastructure which includes distributed and
centralized middleware, discovery, naming, self-configuring, analytic
functions, and information dissemination to achieve specific IoT
system objectives [18][19][20]. Towards this new IoT management
mechanisms and service metrics need to be developed to measure the
success of an IoTdeployment. Considering an IoT systems' defining
characteristics such as, its potential large number of IoT devices,
ephemeral nature to save power, mobility, and ad hoc communication,
autonomic self-management mechanisms become very critical. Further
considering its hierarchical information processing deployment model,
the platform needs to orchestrate computational tasks according to
the involved sensors and the available computation resources which
may change over time. An efficient computation resource discovery
and management protocol is required to facilitate this process. The
trade-off between information transmission and processing is another
challenge.
3. State of the Art
Over the years, many stand-alone IoT systems have been deployed in
various domains. These systems usually adopt a vertical silo
architecture and support a small set of pre-designated applications.
A recent trend, however, is to move away from this approach, towards
a unified IoT platform in which the existing silo IoT systems, as
well as new systems that are rapidly deployed. This will make their
data and services accessible to general Internet applications (as in
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ETSI- M2M and oneM2M standards). In such a unified platform,
resources can be accessed over Internet and shared across the
physical boundaries of the enterprise. However, current approaches
to achieve this objective are based upon Internet overlays, whose
inherent inefficiencies due to IP protocol [8] hinders the platform
from satisfying the IoT requirements outlined earlier (particularly
in terms of scalability, security, mobility, and self-organization)
3.1. Silo IoT Architecture
[IoT Server]
|
|
______|_______
_______ { }
{ } { }
{IoT Dev}\ { Internet }---[IoT Application]
{_______} [IoTGW]---{ }
{ }
{______________}
Figure 1:Silo architecture of standalone IoT systems
A typical standalone IoT system is illustrated in Figure 1, which
includes devices, a gateway, a server and applications. Many IoT
devices have limited power and computing resources, unable to
directly run normal IP access network (Ethernet, WIFI, 3G/LTE etc.)
protocols. Therefore they use the IoT gateway to the server.
Through the IoT server, applications can subscribe to data collected
by devices, or interact with devices.
There have been quite a few popular protocols for standalone IoT
systems, such as DF-1, MelsecNet, Honeywell SDS, BACnet, etc.
However, these protocols are operating at the device-level
abstraction, instead of information driven, leading to a highly
fragmented protocol space with limited interoperability.
3.2. Overlay Based Unified IoT Solutions
The current approach to a unified IoT platform is to make IoT
gateways and servers adopt standard APIs. IoT devices connect to the
Internet through the standard APIs and IoT applications subscribe and
receive data through standard control and data APIs. Building on top
of today's Internet as an overlay, this is the most practical
approach towards a unified IoT platform. There are ongoing
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standardization efforts including ETSI[3], oneM2M[4]. Network
operators can use frameworks to build common IOT gateways and servers
for their customers. In addition, IETF's CORE working group [5] is
developing a set of protocols like CoAP (Constrained Application
Protocol) [49], that is a lightweight protocol modeled after HTTP
[50] and adapted specifically for the Internet of Things (IoT). CoAP
adopts the Representational State Transfer (REST) architecture with
Client-Server interactions. It uses UDP as the underlying transport
protocol with reliability and multicast support. Both CoAP and HTTP
are considered as the suitable application level protocols for
Machine-to-Machine communications, as well as IoT. For example,
oneM2M (which is one of leading standards for unified M2M platform)
has both the protocol bindings to HTTP and CoAP for its primitives.
Figure 2 shows the architecture adopted in this approach.
Publishing----[IoT Server]----Subscribing--
| / | \ |
| / | \ |
| /______|_______ \ |
___________ | /{ } publishing |
{ } | | { } | |
{Smart Homes}\ | | { Internet }---------[IoT Application]
{___________} [IoTGW]---{ }\ | ________________
| { } \ | { }
| {______________} [IoTGW]-{Smart Healthcare}
| | {________________}
Publishing [IoTGW]
| ____|_____
| { }
---{Smart Grid}
{__________}
Figure 2: Implementing an open IoT platform through standarized APIs
on the IoT gateways and the server
3.2.1. Weaknesses of the Overlay-based Approach
The above overlay-based approach can work with many different
protocols, but the system is built upon today's IP network, which has
inherent weaknesses towards supporting a unified IoT system. As a
result, it cannot satisfy some of the requirements we outlined in
Section 2:
o Naming. In current overlays for IoT systems the naming scheme is
host centric, i.e., the name of a given resource/service is linked
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to the one of device that can provide it. In turn, device names
are coupled to IP addresses, which are not persistent in mobile
scenarios. On the other side, in IoT systems the same service/
resource could be provided by many different devices thus
requiring a different design rationale.
o Trust. Trust management schemes are still relatively weak,
focusing on securing communication channels rather than managing
the data that needs to be secured directly.
o Mobility. The overlay-based approach uses IP addresses as names
at the network layer, which hinders the support for device/service
mobility or flexible name resolution. Further the Layer 2/3
management, and application-layer addressing and forwarding
required to deploy current IoT solutions limit the scalability and
management of these systems.
o Resource constraints. The overlay-based approach requires every
device to send data to an aggregator or to the IoT server.
Resource constraints of the IoT devices, especially in power and
bandwidth, could seriously limit the performance of this approach.
o Traffic Characteristics. In this approach, applications are
written in a host-centric manner suitable for point-to-point
communication. IoT requires multicast support that is challenging
in overlay systems today.
o Contextual Communications. This overlay-based approach cannot
react to dynamic contextual changes in a timely fashion. The main
reason is that context lists are kept at the IoT server in this
approach, and they cannot help efficiently route requests
information at the network layer.
o Storage and Caching. The overlay-based approach supports
application-centric storage and caching but not what ICN envisions
at the network layer, or flexible storage enabled via name-based
routing or name-based lookup.
o Self-Organization. The overlay-based approach is topology-based
as it is bound to IP semantics, and thus does not sufficiently
satisfy the self-organization requirement. In addition to
topological self-organization, IoT also requires data- and
service-level self-organization [59], which is not supported by
the overlay approach.
o Ad-hoc and infrastructure mode. As mentioned above, the overlay-
based approach lacks self-organization, and thus does not provide
efficient support for the ad-hoc mode.
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4. Advantages of using ICN for IoT
A key concept of ICN is the ability to name data independently from
the current location at which it is stored, which simplifies caching
and enables decoupling of sender and receiver. Using ICN to design
an architecture for IoT data potentially provides such advantages
compared to using traditional host-centric networks. This section
highlights general benefits that ICN could provide to IoT networks.
o Naming of Devices, Data and Services. The heterogeneity of both
network equipment deployed and services offered by IoT networks
leads to a large variety of data, services and devices. While
using a traditional host-centric architecture, only devices or
their network interfaces are named at the network level, leaving
to the application layer the task to name data and services. In
many common applications of IoT networks, data and services are
the main goal, and specific communication between two devices is
secondary. The network distributes content and provides a
service, instead of establishing a communication link between two
devices. In this context, data content and services can be
provided by several devices, or group of devices, hence naming
data and services is often more important than naming the devices.
This naming mechanism also enables self-configuration of the IoT
system.
o Distributed Caching and Processing. While caching mechanisms are
already used by other types of overlay networks, IoT networks can
potentially benefit even more from caching and in-network
processing systems, because of their resource constraints.
Wireless bandwidth and power supply can be limited for multiple
devices sharing a communication channel, and for small mobile
devices powered by batteries. In this case, avoiding unnecessary
transmissions with IoT devices to retrieve and distribute IoT data
to multiple places is important, hence processing and storing such
content in the network can save wireless bandwidth and battery
power. Moreover, as for other types of networks, applications for
IoT networks requiring shorter delays can benefit from local
caches and services to reduce delays between content request and
delivery.
o Decoupling between Sender and Receiver. IoT devices may be mobile
and face intermittent network connectivity. When specific data is
requested, such data can often be delivered by ICN without any
consistent direct connectivity between devices. Apart from using
structured caching systems as described previously, information
can also be spread by forwarding data opportunistically.
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5. ICN Challenges for IoT
This section outlines some of the ICN specific challenges [71] that
must be considered when defining an IoT framework over ICN, and
describes some of the trade offs that will be involved.
ICN integrates content/service/host abstraction, name-based routing,
compute, caching/storage as part of the network infrastructure
connecting consumers and services which meets most of the
requirements discussed above; however IoT requires special
considerations given heterogeneity of devices and interfaces such as
for constrained networking [38][70], data processing, and content
distribution models to meet specific application requirements which
we identify as challenges in this section.
5.1. Naming Devices, Data, and Services
The ICN approach of named data and services (i.e., device independent
naming) is typically desirable when retrieving IoT data. However,
data centric naming may also pose challenges.
o Naming of devices: Naming devices is often important in an IoT
network. The presence of actuators requires clients to act
specifically on a device, e.g. to switch it on or off. Also,
managing and monitoring the devices for administration purposes
requires devices to have a specific name allowing to identify them
uniquely. There are multiple ways to achieve device naming, even
in systems that are data centric by nature. For example, in
systems that are addressable or searchable based on metadata or
sensor content, the device identifier can be included as a special
kind of metadata or sensor reading.
o Size of data/service name: In information centric applications,
the size of the data is typically larger than its name. For the
IoT, sensors and actuators are very common, and they can generate
or use data as small as a short integer containing a temperature
value, or a one-byte instruction to switch off an actuator. The
name of the content for each of these pieces of data has to
uniquely identify the content. For this reason, many existing
naming schemes have long names that are likely to be longer than
the actual data content for many types of IoT applications.
Furthermore, naming schemes that have self certifying properties
(e.g., by creating the name based on a hash of the content),
suffer from the problem that the object can only be requested when
the object has been created and the content is already known, thus
requiring some form of indexing service. While this is an
acceptable overhead for larger data objects, it is infeasible for
use when the object size is on the order of a few bytes.
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o Hash-based content name: Hash algorithms are commonly used to name
content in order to verify that the content is the one requested.
This is only possible in contexts where the requested object is
already existing, and where there is a directory service to look
up names. This approach is suitable for systems with large data
objects where it is important to verify the content.
o Metadata-based content name: Relying on metadata allows to
generate a name for an object before it is created. However this
mechanism requires metadata matching semantics.
o Naming of services: Similarly to naming of devices or data,
services can be referred to with a unique identifier, provided by
a specific device or by someone assigned by a central authority as
the service provider. It can however also be a service provided
by anyone meeting some certain metadata conditions. Example of
services include content retrieval, that takes a content name/
description as input and returns the value of that content, and
actuation, that takes an actuation command as input and possibly
returns a status code afterwards.
o Trust: We need to ensure the name of a network element is issued
by a trustworthy issuer in the context of the application, such as
a trusted organization in [44]. Further the validity of each
piece of data published by an authorized entity in the namespace
should be verifiable - e.g., by following a hierarchical chain-of-
trust to a root that is acceptable for the application. See [54]s
for an example.
o Flexibility: Further challenges arise for hierarchical naming
schema: referring to requirements on "constructible names" and
"on-demand publishing" [28][29]. The former entails that each
user is able to construct the name of a desired data item through
specific algorithms and that it is possible to retrieve
information also using partially specified names. The latter
refers the possibility to request a content that has not yet been
published in the past, thus triggering its creation.
o Control/scoping : Some information could be accessible only within
a given scope. This challenge is very relevant for smart home and
health monitoring applications, where privacy issues play a key
role and the local scope of a home or healthcare environment may
be well-defined. However, perimeter- and channel-based access
control is often violated in current networks to enable over-the-
wire updates and cloud-based services, so scoping is unlikely to
replace a need for data-centric security in ICN.
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o Confidentiality: As names can reveal information about the nature
of the communication, mechanisms for name confidentiality should
be available in the ICN-IoT architecture.
5.2. Name Resolution
Inter-connecting numerous IoT entities, as well as establishing
reachability to them, requires a scalable name resolution system
considering several dynamic factors like mobility of end points,
service replication, in-network caching, failure or migration [37]
[40] [41] [57]. The objective is to achieve scalable name resolution
handling static and dynamic ICN entities with low complexity and
control overhead. In particular, the main requirements/challenges of
a name space (and the corresponding Name Resolution System where
necessary) are [31] [33]:
o Scalability: The first challenge faced by ICN-IoT name resolution
system is its scalability. Firstly, the approach has to support
billions of objects and devices that are connected to the
Internet, many of which are crossing administrative domain
boundaries. Second of all, in addition to objects/devices, the
name resolution system is also responsible for mapping IoT
services to their network addresses. Many of these services are
based upon contexts, hence dynamically changing, as pointed out in
[37]. As a result, the name resolution should be able to scale
gracefully to cover a large number of names/services with wide
variations (e.g., hierarchical names, flat names, names with
limited scope, etc.). Notice that, if hierarchical names are
used, scalability can be also supported by leveraging the inherent
aggregation capabilities of the hierarchy. Advanced techniques
such as hyperbolic routing [53] may offer further scalability and
efficiency.
o Deployability and interoperability: Graceful deployability and
interoperability with existing platforms is a must to ensure a
naming schema to gain success on the market [7]. As a matter of
fact, besides the need to ensure coexistence between IP-centric
and ICN-IoT systems, it is required to make different ICN-IoT
realms, each one based on a different ICN architecture, to
interoperate.
o Latency: For real-time or delay sensitive M2M application, the
name resolution should not affect the overall QoS. With reference
to this issue it becomes important to circumvent too centralized
resolution schema (whatever the naming style, i.e, hierarchical or
flat) by enforcing in-network cooperation among the different
entities of the ICN-IoT system, when possible [58]. In addition,
fast name lookup are necessary to ensure soft/hard real time
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services [60][61][62]. This challenge is especially important for
applications with stringent latency requirements, such as health
monitoring, emergency handling and smart transportation [63].
o Locality and network efficiency: During name resolution the named
entities closer to the consumer should be easily accessible
(subject to the application requirements). This requirement is
true in general because, whatever the network, if the edges are
able to satisfy the requests of their consumers, the load of the
core and content seek time decrease, and the overall system
scalability is improved. This facet gains further relevance in
those domains where an actuation on the environment has to be
executed, based on the feedbacks of the ICN-IoT system, such as in
robotics applications, smart grids, and industrial plants [59].
o Agility: Some data items could disappear while some other ones are
created so that the name resolution system should be able to
effectively take care of these dynamic conditions. In particular,
this challenge applies to very dynamic scenarios (e.g., VANETs) in
which data items can be tightly coupled to nodes that can appear
and disappear very frequently.
5.3. Caching/Storage
In-network caching helps bring data closer to consumers, but its
usage differs in constrained and infrastructure part of the IoT
network. Caching in constrained networks is limited to small amounts
in the order of 10KB, while caching in infrastructure part of the
network can allow much larger chunks.
Caching in ICN-IoT faces several challenges:
o The main challenge is to determine which nodes on the routing path
should cache the data. According to [33], caching the data on a
subset of nodes can achieve a better gain than caching on every
en-route routers. In particular, the authors propose a "selective
caching" scheme to locate those routers with better hit
probabilities to cache data. According to [34], selecting a
random router to cache data is as good as caching the content
everywhere. In [55], the authors suggest that edge caching
provides most of the benefits of in-network caching typically
discussed in NDN, with simpler deployment. However, it and other
papers consider workloads that are analogous to today's CDNs, not
the IoT applications considered here. Further work is likely
required to understand the appropriate caching approach for IoT
applications.
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o Another challenge in ICN-IoT caching is what to cache for IoT
applications. In many IoT applications, customers often access a
stream of sensor data, and as a result, caching a particular
sensor data item may not be beneficial. In [36], the authors
suggest to cache IoT services on intermediate routers, and in
[37], the authors suggest to cache control information such as
pub/sub lists on intermediate nodes. In addition, it is yet
unclear what caching means in the context of actuation in an IoT
system. For example, it could mean caching the result of a
previous actuation request (using other ICN mechanisms to suppress
repeated actuation requests within a given time period), or have
little meaning at all if actuation uses authenticated requests as
in [56].
o Another challenge is that the efficiency of Distributed Caching
may be application dependent. When content popularity is
heterogeneous, some content is often requested repeatedly. In
that case, the network can benefit from caching. Another case
where caching would be beneficial is when devices with low duty
cycle are present in the network and when access to the cloud
infrastructure is limited. However, using distributed caching
mechanisms in the network is not useful when each object is only
requested at most once, as a cache hit can only occur for the
second request and later. It may also be less beneficial to have
caches distributed throughout ICN nodes in cases when there are
overlays of distributed repositories, e.g., a cloud or a Content
Distribution Network (CDN), from which all clients can retrieve
the data. Using ICN to retrieve data from such services may add
some efficiency, but in case of dense occurrence of overlay CDN
servers the additional benefit of caching in ICN nodes would be
lower. Another example is when the name refers to an object with
variable content/state. For example, when the last value for a
sensor reading is requested or desired, the returned data should
change every time the sensor reading is updated. In that case,
ICN caching may increase the risk that cache inconsistencies
result in old data being returned.
5.4. Routing and Forwarding
Routing in ICN-IoT differs from routing in traditional IP networks in
that ICN routing is based upon names instead of locators. Broadly
speaking, ICN routing can be categorized into the following two
categories: direct name-based routing and indirect routing using a
name resolution service (NRS).
o In direct name-based routing, packets are forwarded by the name of
the data [57][38][42] or the name of the destination node [43].
Here, the main challenge is to keep the ICN router state required
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to route/forward data low. This challenge becomes more serious
when a flat naming scheme is used due to the lack of aggregation
capabilities.
o In indirect routing, packets are forwarded based upon the locator
of the destination node, and the locator is obtained through the
name resolution service. In particular, the name-locator binding
can be done either before routing (i.e., static binding) or during
routing (i.e., dynamic binding). For static binding, the router
state is the same as that in traditional routers, and the main
challenge is the need to have fast name resolution, especially
when the IoT nodes are mobile. For dynamic binding, ICN routers
need to main a name-based routing table, hence the challenge of
keeping the state information low. At the same time, the need of
fast name resolution is also critical. Finally, another challenge
is to quantify the cost associated with mobility management,
especially static binding vs. dynamic binding.
During a network transaction, either the data producer or the
consumer may move away and thus we need to handle the mobility to
avoid information loss. ICN may differentiate mobility of a data
consumer from that of a producer:
o When a consumer moves to a new location after sending out the
request for Data, the Data may get lost, which requires the
consumer to simply resend the request, a technique used by direct
routing approach. Indirect routing approach doesn't differentiate
between consumer and producer mobility [57], also network caching
can improve data recovery for this approach.
o If the data producer itself has moved, the challenge is to control
the control overhead while searching for a new data producer (or
for the same data producer in its new position). To this end,
flooding techniques could be used, but an intra-domain level only,
otherwise the network stability would be seriously impaired. For
handling mobility across different domains, more sophisticated
approaches could be used, including the adoption of a SDN-based
control plane.
5.5. Contextual Communication
Contextualization through metadata in ICN control or application
payload allows IoT applications to adapt to different environments.
This enables intelligent networks which are self-configurable and
enable intelligent networking among consumers and producers [36].
For example, let us look at the following smart transportation
scenario: "James walks on NYC streets and wants to find an empty cab
closest to his location." In this example, the context is the
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relative locations of James and taxi drivers. A context service, as
an IoT middleware, processes the contextual information and bridges
the gap between raw sensor information and application requirements.
Alternatively, naming conventions could be used to allow applications
to request content in namespaces related to their local context
without requiring a specific service, such as /local/geo/
mgrs/4QFJ/123/678 to retrieve objects published in the 100m grid area
4QFJ 123 678 of the military grid reference system (MGRS). In both
cases, trust providers may emerge that can vouch for an application's
local knowledge.
However, extracting contextual information on a real-time basis is
very challenging:
o We need to have a fast context resolution service through which
the involved IoT devices can continuously update its contextual
information to the application (e.g., each taxi's location and
Jame's information in the above example). Or, in the namespace
driven approach, mechanisms for continuous nearest neighbor
queries in the namespace need to be developed.
o The difficulty of this challenge grows rapidly when the number of
devices involved in a context as well as the number of contexts
increases.
5.6. In-network Computing
In-network computing enables ICN routers to host heterogeneous
services catering to various network functions and applications
needs. Contextual services for IoT networks require in-network
computing, in which each sensor node or ICN router implements context
reasoning [36]. Another major purpose of in-network computing is to
filter and cleanse sensed data in IoT applications, that is critical
as the data is noisy as is [44].
Named Function Networking [64] describes an extension of the ICN
concept to named functions processed in the network, which could be
used to generate data flow processing applications well-suited to,
for example, time series data processing in IoT sensing applications.
Related to this, is the need to support efficient function naming.
Functions, input parameters, and the output result could be
encapsulated in the packet header, the packet body, or mixture of the
two (e.g. [24]). If functions are encapsulated in packet headers,
the naming scheme affects how a computation task is routed in the
network, which IoT devices are involved in the computation task (e.g.
[35]), and how a name is decomposed into smaller computation tasks
and deployed in the network for a better performance.
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Another is challenge is related to support computing-aware routing.
Normal routing is for forwarding requests to the nearest source or
cache and return the data to the requester, whereas the routing for
in-network computation has a different purpose. If the computation
task is for aggregating sensed data, the routing strategy is to route
the data to achieve a better aggregation performance [32].
In-network computing also includes synchronization challenges. Some
computation tasks may need synchronizations between sub-tasks or IoT
devices, e.g. a device may not send data as soon as it is available
because waiting for data from the neighbours may lead to a better
aggregation result; some devices may choose to sleep to save energy
while waiting for the results from the neighbours; while aggregating
the computation results along the path, the intermediate IoT devices
may need to choose the results generated within a certain time
window.
5.7. Security and Privacy
Security and privacy is crucial to all the IoT applications
applications including the use cases discussed in Section 5. In one
recent demonstration,it was shown that passive tire pressure sensors
in cars could be hacked and used as a gateway into the automotive
system [38]. The ICN paradigm is information-centric as opposed to
state-of-the-art host-centric internet. Besides aspects like naming,
content retrieval and caching this also has security implications.
ICN advocates the model of trust in content rather than trust in
network hosts. This brings in the concept of Object Security which
is contrary to session-based security mechanisms such as TLS/DTLS
prevalent in the current host-centric internet. Object Security is
based on the idea of securing information objects unlike session-
based security mechanisms which secure the communication channel
between a pair of nodes. This reinforces an inherent characteristic
of ICN networks i.e. to decouple senders and receivers. In the
context of IoT, the Object Security model has several concrete
advantages. Many IoT applications have data and services are the
main goal and specific communication between two devices is
secondary. Therefore, it makes more sense to secure IoT objects
instead of securing the session between communicating endpoints.
Though ICN includes data-centric security features the mechanisms
have to be generic enough to satisfy multiplicity of policy
requirements for different applications. Furthermore security and
privacy concerns have to be dealt in a scenario-specific manner with
respect to network function perspective spanning naming, name-
resolution, routing, caching, and ICN-APIs. In general, we feel that
security and privacy protection in IoT systems should mainly focus on
the following aspects: confidentiality, integrity, authentication and
non-repudiation, and availability.
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Implementing security and privacy methods faces different challenges
in the constrained and infrastructure part of the network.
o In the resource-constrained nodes, energy limitation is the
biggest challenge. As an example, let us look at a typical sensor
tag. Suppose the tag has a single 16-bit processor, often running
at 6 MHz to save energy, with 512Bytes of RAM and 16KB of flash
for program storage. Moreover, it has to deliver its data over a
wireless link for at least 10,000 hours on a coin cell battery.
As a result, traditional security/privacy measures are impossible
to be implemented in the constrained part. In this case, one
possible solution might be utilizing the physical wireless signals
as security measures [46] [36].
o In the infrastructure part, we have several new threats introduced
by ICN-IoT [52]:
1. We need to ensure the name of a network element is issued by a
trustworthy organization entity such as in [48], or by its
trusted delegate. As name securely binds to data in ICN,
security constraints of content that has not yet been
published yet should also be taken into consideration.
2. An intruder may gain access or gather information from a
resource it is not entitled to. As a consequence, an
adversary may examine, remove or even modify confidential
information.
3. An intruder may mimic an authorized user or network process.
As a result, the intruder may forge signatures, or impersonate
a source address.
4. An adversary may manipulate the message exchange process
between network entities. Such manipulation may involve
replay, rerouting, mis-routing and deletion of messages.
5. An intruder may insert fake/false sensor data into the
network. The consequence might be an increase in delay and
performance degradation for network services and applications.
5.8. Self-Orgnization
General IoT deployments involves heterogeneous IoT systems or
subsystems within a particular scenario. Here scope-based self-
organization is required to ensure logical isolation between the IoT
subsystems, which should be enabled at different levels -- device/
service discovery, naming, topology construction, routing over
logical ICN topologies, and caching [69]. These challenges are
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extended to constrained devices as well and they should be energy and
device capability aware. In the infrastructure part, intelligent
name-based routing, caching, in-network computing techniques should
be studied to meet the scope-based self-configuration needs of ICN-
IoT.
5.9. Communications Reliability
ICN offers many ingredients for reliable communication such as multi-
home interest anycast over heterogeneous interfaces, caching, and
forwarding intelligence for multi-path routing leveraging state-
based forwarding in protocols like CCN/NDN. However these features
have not been analyzed from the QoS perspective when heterogeneous
traffic patterns are mixed in a router, in general QoS for ICN is an
open area of research [71]. In-network reliability comes at the cost
of a complex network layer; hence the research challenges here is to
build redundancy and reliability in the network layer to handle a
wide range of disruption scenarios such as congestion, short or long
term disconnection, or last mile wireless impairments. Also an ICN
network should allow features such as opportunistic store and forward
mechanism to be enabled only at certain points in the network, as
these mechanisms also entail overheads in the control and forwarding
plane overhead which will adversely affect application throughput.
5.10. Energy Efficiency
All the optimizations for other components of the ICN-IoT system
(described in earlier subsections) can lead to optimized energy
efficiency. As a result, we refer the readers to read sections
5.1-5.9 for challenges associated with energy efficiency for ICN-IoT.
6. Appendix
Several types of IoT applications exists, where the goal is efficient
and secure management and communication among objects in the system
and with the physical world through sensors, RFIDs and other devices.
Below we list a few popular IoT applications. We omit the often used
term "smart", though it applies to each IoT scenario below, and posit
that IoT-style interconnection of devices to make these environments
"smart" in today's terms will simply be the future norm.
6.1. Homes
The home [10] is a complex ecosystem of IoT devices and applications
including climate control, home security monitoring, smoke detection,
electrical metering, health/wellness, and entertainment systems. In
a unified IoT platform, we would inter-connect these systems through
the Internet, such that they can interact with each other and make
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decisions at an aggregated level. Also, the systems can be accessed
and manipulated remotely. Challenges in the home include topology
independent service discovery, common protocol for heterogeneous
device/application/service interaction, policy based routing/
forwarding, service mobility as well as privacy protection. Notably,
the ease-of-use expectations and training of both users and
installers also presents challenges in user interface and user
experience design that are impacted by the complexity of network
configuration, brittleness to change, configuration of trust
management, etc. Finally, it is unlikely that there will be a single
"home system", but rather a collection of moderately inter-operable
collaborating devices. In addition, several IoT-enabled homes could
form a smart district where it becomes possible to bargain resources
and trade with utility suppliers.
Homes [12][13] faces the following challenges that are hard to
address with IP-based overlay solutions: (1) context-aware control:
home systems must make decisions (e.g., on how to control, when to
collect data, where to carry out computation, when to interact with
end-users, etc.) based upon the contextual information [14]; (2)
inter-operability: home systems must operate with devices that adopt
heterogeneous naming, trust, communication, and control systems; (3)
mobility: home systems must deal with mobility caused by the movement
of sensors or data receivers; (4) security: a home systems must be
able to deal with foreign devices, handle a variety of user
permissions (occupants of various types, guests, device
manufacturers, installers and integrators, utility and infrastructure
providers) and involve users in important security decisions without
overwhelming them; (5) user interface / user experience: homes need
to provide reasonable interfaces to their highly heterogeneous IoT
networks for users with a variety of skill levels, backgrounds,
cultures, interests, etc.
Smart homes have the following specific requirement for the
underlying architecture:
o Smart homes require names that can enable local and wide area
interactions; Also, security, privacy, and access control is
particularly important for smart homes.
o Smart homes may use in-network caching at gateway to enable
efficient content access.
o In smart homes, we need local, intra-domain and inter-domain
routing protocols.
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o In smart homes many control loops and actions depend heavily on
the context, and the contexts evolve with time, e.g., temperature,
weather, number of occupants, etc.
o In smart homes, local services can provide value-added
contributions to a standardized home gateway network, through
features such as reporting, context-based control, coordination
with mobile devices, etc.
o In smart homes, the access to networked information should be
shielded to protect the privacy of people, for example, cross-
correlation of device activity patterns to infer higher-level
activity information.
6.2. Enterprise
Enterprise building deployments, from university campuses [15] [65]
[66] [67] to industrial facilities and retail complexes, drive an
additional set of scalability, security, and integration requirements
beyond the home, while requiring much of its ease of use and
flexibility. Additionally, they bring requirements for integration
with business IT systems, though often with the additional support of
in-house engineering support.
Increasing number of enterprises are equipped with sensing and
communication devices inside buildings, laboratories, and plants, at
stadiums, in parking lots, on school buses, etc. A unified IoT
platform must integrate many aspects of human interaction, H2M and
M2M communication, within the enterprise, and thus enable many IoT
applications that can benefit a large body of enterprise affiliates.
The challenges in smart enterprise include efficient and secure
device/data/resource discovery, inter-operability between different
control systems, throughput scaling with number of devices, and
unreliable communication due to mobility and telepresence.
Enterprises face the following challenges that are hard to address
with IP-based overlay solutions: (1) efficient device/data/ resource
discovery: enterprise devices must be able to quickly and securely
discover requested device, data, or resources; (2) scalability: a
enterprise system must be able to scale efficiently with the number
and type of sensors and devices across not only a single building but
multi-national corporations (for example); (3) mobility: a enterprise
system must be able to deal with mobility caused by movement of
devices; (4) security: security for IoT applications in the
enterprise should integrate with other enterprise-wide security
components.
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6.3. Smart Grid
Central to the so-called "smart grid"[16] is data flow and
information management, achieved by using sensors and actuators,
which enables important capabilities such as substation and
distribution automation. In a unified IoT platform, data collected
from different smart grids can be integrated to achieve more
optimizations that include reliability, real-time control, secure
communications, and data privacy.
Deployment of the smart grid [17] [21] faces the following issues
that are hard to address with IP-based overlay solutions: (1)
scalability: future electrical grids must be able to scale gracefully
to manage a large number of heterogeneous devices; (2) real time:
grids must be able to perform real-time data collection, data
processing and control; (3) reliability: grids must be resilient to
hardware/software/networking failures; (4) security: grids and
associated systems are often considered critical infrastructure --
they must be able to defend against malicious attacks, detect
intrusion, and route around disruption.
Smart grids have the following specific requirements for the
underlying IoT architecture:
o Smart grids require names and name resolution system that can
enable networked control loops, real-time control, and security.
o Smart grids may use in-network caching to back up valuable data
improving reliability.
o In smart grids, we often require very timely data delivery.
Therefore, it is important to be able to locate the closest
information. In addition, routing/forwarding robustness and
resilience is also critical.
o In smart grids, contextual information such as location, time,
voltage fluctuations, depending on the specific segment of the
grid, can be used to optimize several power distribution
objectives.
o In smart grids, we often rely on in-network computing to increase
the scalability and efficiency of the system, putting computation
closer to the data sources.
o In smart grids, energy consumptions profiles should never be
disclosed at a fine granularity as it can be used to violate user
privacy.
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6.4. Transportation
We are currently witnessing the increasing integration of sensors
into cars, other vehicles transportation systems [22]. Current
production cars already carry many sensors ranging from rain gauges
and accelerometers over wheel rotation/traction sensors, to cameras.
These sensors can not only be used for internal vehicle functions,
but they could also be networked and leveraged for applications such
as monitoring external traffic/road conditions. Further, we can
build vehicle-to- infrastructure (V2I),Vehicle-to-Roadside (V2R), and
vehicle-to- vehicle (V2V) communications that enable many more
applications for safety, convenience, entertainment, etc. The
challenges for transportation include fast data/device/service
discovery and association, efficient communications with mobility,
trustworthy data collection and exchange.
Transportation [22][25] faces the following challenges that are hard
to address with IP-based overlay solutions: (1) mobility: a
transportation system must deal with a large number of mobile nodes
interacting through a combination of infrastructure and ad hoc
communication methods; ; also, during the journey the user might
cross several realms, each one implementing different stacks (whether
ICN or IP); (2) real-time and reliability: transportation systems
must be able to operate in real-time and remain resilient in the
presence of failures; (3) in-network computing/filtering:
transportation systems will benefit from in-network computing/
filtering as such operations can reduce the end-to-end latency; (4)
inter-operatibility: transportation systems must operate with
heterogeneous device and protocols; (5) security: transportation
systems must be resilient to malicious physical and cyber attacks.
Smart transportation applications have the following specific
requirements for the underlying IoT architecture:
o Smart transportation systems require names and name resolution
system to be able to handle extreme mobility, short latency and
security. In addition, the mobility patterns of transportation
systems increase the likelyhood that a user migrates from one
network realm to another one during the journey. In this case,
names and NRS should be designed in such a way to enable
interoperability between different heterogeneous ICN realms and/or
ICN and IP realms [68].
o Smart transportation may implement in-network caching on vehicles
for efficient information dissemination
o In smart transportation, vehicle-to-vehicle ad-hoc communication
is required for efficient information dissemination.
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o In smart transportation, many different contexts exist,
intertwined to each other and highly changing, which include
location - both geographical and jurisdictional, time - absolute
and relative to a schedule, traffic, speed, etc.
o In smart transportation, in-network computing is very useful to
make vehicle become an active element of the system and to improve
response time and scalability.
o In smart transportation, the habits of users can be inferred by
looking at their movement patterns -- privacy protection is
essential.
6.5. Healthcare
As more embedded medical devices, or devices that can monitor human
health become increasingly deployed, healthcare is becoming a viable
alternative to traditional healthcare solutions [26]. Further,
consumer applications for managing and interacting with health data
are a burgeoning area of research and commercial applications. For
future health applications, a unified IoT platform is critical for
improved patient care and consumer health support by sharing data
across systems, enabling timely actuations, and lowering the time to
innovation by simplifying interaction across devices from many
manufacturers. Challenges in healthcare include real-time
interactions, high reliability, short communication latencies,
trustworthy, security and privacy, and well as defining and meeting
the regulatory requirements that should impact new devices and their
interconnection. In addition to this dimension, assistive robotics
applications are gaining momentum to provide 24/24 7/7 assistance to
patients [59].
Healthcare [26][27] faces the following challenges that are hard to
address with IP-based overlay solutions: (1) real-time and
reliability: healthcare systems must be able to operate on real-time
and remain resilient in the presence of failures; (2) inter-
operability: healthcare systems must operate with heterogeneous
devices and protocols; (3) security: healthcare systems must be
resilient to malicious physical and cyber attacks and meet the
regulatory requirement for data security and interoperability; (4)
privacy: user trust in healthcare systems is critical, and privacy
considerations paramount to garner adoption and continued user; (5)
user interface / user experience: the highly heterogeneous nature of
real-world healthcare systems, which will continue to increase
through the introduction of IoT devices, presents significant
challenges in interface design that may have architectural
implications.
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Smart healthcare applications have the following specific
requirements for the underlying IoT architecture:
o Smart healthcare system requires names and name resolution system
to enable real- time interactions, dependability, and security.
o Smart healthcare may use in-network caching for rapid information
dissemination.
o In smart healthcare, timely and dependable routing and information
forwarding is the key.
o In smart healthcare several contexts can be used to delineate
between levels of care and urgency, for example delineating
between chronic, everyday, urgent, and emergency situations. Such
contexts can evolve rapidly with significant impact to individuals
health. Hence timely and accurate detection of contexts is
critical.
o In smart healthcare, in-network computing can help resolve
contexts and ensure security and dependability, as well as provide
low-latency responses to urgent situations.
o In smart healthcare, personal medical data about patients should
remain shielded to protect their privacy, implementing both
regulatory requirements and current industry best practices.
6.6. Education
IoT technologies enable the instrumentation of a variety of
environments (from greenhouses to industrial plants, homes and
vehicles) to support not only their everyday operation but an
understanding of how they operate -- a fundamental contribution to
education. The diverse uses of hobbyist-oriented micro-controller
platforms (e.g., the Arduino) and embedded systems (e.g., the
Raspberry PI) point to a burgeoning community that should be
supported by the next generation IoT platform because of its
fundamental importance to formal and informal education.
Educational uses of IoT deployments include both learning about the
operation of the system itself as well as the systems being observed
and controlled. Such deployments face the following challenges that
are hard to address with IP-based overlay solutions: (1) relatively
simple communications patterns are obscured by many layers of
translation from the host-based addressing of IP (and layer 2
configuration below) to the name-oriented interfaces provided by
developers; (2) security considerations with overlay deployments and
channel-based limit access to systems where read-only use of data is
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not a security risk; (3) real-time communication helps make the
relationship between physical phenomena and network messages easier
to understand in many simple cases; (4) integration of devices from a
variety of sources and manufacturers is currently quite difficult
because of varying standards for basic communication, and limits
experimentation; (5) programming interfaces must be carefully
developed to expose important concepts clearly and in light of
current best practices in education.
Smart campus systems have the following specific requirements for the
underlying IoT architecture:
o Smart campus systems usually consist of heterogeneous IoT
services, thus requiring names and name resolution system to
enable resource/ service ownership, and be application-centric.
o Smart campus systems may use in-network caching to enable social
interactions and efficient content access.
o In smart campus, inter-domain routing protocols are required which
often need short latency.
o In smart campus, due to the existence of many services, relevant
contextual inputs can be used to improve the quality and
efficiency of different services.
o In smart campus, in-network computing services can be used to
provide context for different applications.
o In smart campus, it is required to differentiate among different
profiles and to allocate different rights and protection levels to
them.
6.7. Entertainment, arts, and culture
IoT technologies can contribute uniquely to both the worldwide
entertainment market and the fundamental human activity of creating
and sharing art and culture. By supporting new types of human-
computer interaction, IoT can enable new gaming, film/video, and
other "content" experiences, integrating them with, for example, the
lighting control of the smart home, presentation systems of the smart
enterprise, or even the incentive mechanisms of smart healthcare
systems (to, say, encourage and measure physical activity).
Entertainment, arts, and culture applications generate a variety of
challenges for IoT: (1) notably, the ability to securely "repurpose"
deployed smart systems (e.g., lighting) to create experiences; (2)
low-latency communication to enable end-user responsiveness; (3)
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integration with infrastructure-based sensing (e.g., computer vision)
to create comprehensive interactive environments or to provide user
identity information; (4) time synchronization with audio/video
playback and rendering in 3D systems (5) simplicity of development
and experimentation, to enable the cost- and time-efficient
integration of IoT into experiences being designed without expert
engineers of IoT systems; (6) security, because of integration with
personal devices and smart environments, as well as billing systems.
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Authors' Addresses
Prof.Yanyong Zhang
WINLAB, Rutgers University
671, U.S 1
North Brunswick, NJ 08902
USA
Email: yyzhang@winlab.rutgers.edu
Prof. Dipankar Raychadhuri
WINLAB, Rutgers University
671, U.S 1
North Brunswick, NJ 08902
USA
Email: ray@winlab.rutgers.edu
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Prof. Luigi Alfredo Grieco
Politecnico di Bari (DEI)
Via Orabona 4
Bari 70125
Italy
Email: alfredo.grieco@poliba.it
Prof. Emmanuel Baccelli
INRIA
Room 148, Takustrasse 9
Berlin 14195
France
Email: Emmanuel.Baccelli@inria.fr
Jeff Burke
UCLA REMAP
102 East Melnitz Hall
Los Angeles, CA 90095
USA
Email: jburke@ucla.edu
Ravishankar Ravindran
Huawei Technologies
2330 Central Expressway
Santa Clara, CA 95050
USA
Email: ravi.ravindran@huawei.com
Guoqiang Wang
Huawei Technologies
2330 Central Expressway
Santa Clara, CA 95050
USA
Email: gq.wang@huawei.com
Zhang, et al. Expires October 24, 2016 [Page 38]
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Andres Lindgren
SICS Swedish ICT
Box 1263
Kista SE-164 29
SE
Email: andersl@sics.se
Bengt Ahlgren
SICS Swedish ICT
Box 1263
Kista, CA SE-164 29
SE
Email: bengta@sics.se
Olov Schelen
Lulea University of Technology
Lulea SE-971 87
SE
Email: lov.schelen@ltu.se
Zhang, et al. Expires October 24, 2016 [Page 39]
