Unit
-2
Topics to be
covered
Machine-to-machine
(M2M), SDN (software defined networking) and NFV (network function
virtualization) for IOT, data storage in IOT, IOT Cloud Based Services.
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Machine-to-machine (M2M)
·
Machine to machine (M2M) is a broad label that can
be used to describe any technology that enables networked devices to exchange
information and perform actions without the manual assistance of humans. M2M
communication is often used for remote monitoring. In product restocking, for
example, a vending machine can message the distributor when a particular item
is running low. M2M communication is an important aspect of warehouse
management, remote control, robotics, traffic control, logistic services,
supply chain management, fleet management and telemedicine.
·
It forms the
basis for a concept known as the Internet of Things (IoT). Key components of an
M2M system include sensors, RFID, a
Wi-Fi or cellular communications link and autonomic computing software
programmed to help a networked device
interpret data and make decisions. The most well-known type of M2M
communication is telemetry, which has been used since the early part of the
last century to transmit operational data. Pioneers in telemetric first used
telephone lines and later, on radio waves -- to transmit performance
measurements gathered from monitoring instruments in remote locations. The
Internet and improved standards for wireless technology have expanded the role
of telemetry from pure science, engineering and manufacturing to everyday use
in products like home heating units, electric meters and Internet-connected
appliances. Products built with M2M communication capabilities are often
marketed to end users as being smart.
Fig. 2.1 M2M
Communication
SDN (software defined networking)-SDN deployment will enable Internet of Things devices to share
network resources efficiently and reliably, and further cut hardware
investment, but the possibilities are still emerging. Software-defined
networking will meet the Internet of Things (IoT) at the crossroads of VPN
exhaustion, uptime challenges and limited network resources. The expected
result is that SDN will help drive the expansion of IoT-enabled devices, enable
more efficient network resource sharing and improve IoT service-level
agreements (SLAs).
SDN Benefits-SDN brings three
important capabilities to IoT:
·
Centralization of control through
software that has complete knowledge of the network, enabling automated,
policy-based control of even massive, complex networks. Given the huge potential scale of IoT
environments, SDN is critical in making them simple to manage.
·
Abstraction of the details of the many devices and
protocols in the network, allowing IoT applications to access data, enable
analytics and control the devices, and add
new sensors and network control devices, without exposing the details of the
underlying infrastructure. SDN simplifies the creation, deployment and ongoing
management of the IoT devices and the applications that benefit from them.
·
The flexibility to tune the components within the
IoT (and manage where data is stored and analyzed) to continually maximize
performance and security as business needs and data flows change. IoT
environments are inherently dispersed with many end devices and edge computing.
As a result, the network is even more critical than in standard application
environments. “DN’s ability to dynamically change network behavior based on new
traffic patterns, security incidents and policy changes will enable IoT
environments to deliver on their promise.
Features of SDN-
·
SDN will make it easier to find and fight security
threats through the improved visibility they provide into network traffic right
to the edge of the network. They also make it easy to apply automated policies
to redirect suspicious traffic to, for example, a honey net where it can be
safely examined. By making networking management less complex, SDN allows IT to
set and enforce more segmented access controls.SDN can provide a dynamic,
intelligent self-learning layered model of security that provides walls within
walls and ensures people can only change the configuration of the devices
they’re authorized to “touch.” This is far more useful than the traditional
“wall” around the perimeter of the network, which won’t work with the IoT
because of its size and the fact the enemy is often inside the firewall, in the
form of unauthorized actors updating firmware on unprotected devices.
·
SDN will allow IT to effectively program the network
to make automatic, real-time decisions about traffic flow. They will allow the
analysis of not only sensor data, but data about the health of the network, to
be analyzed close to the network edge to give IT the information it needs to
prevent traffic jams and security risks. The centralized configuration and
management of the network, and the abstraction of network devices, also makes
it far easier to manage applications
that run on the edge of the IoT.
·
For example, SDN will allow IT to fine-tune data
aggregation, so data that is less critical is held at the edge and not
transmitted to core systems until it won’t slow critical application traffic.
This edge computing can also perform fast, local analysis and speed the results
to the network core if the analysis indicates an urgent situation, such as the
impending failure of a jet engine.
NFV (network function
virtualization) for IOT-
·
Utilizing NFV (network function virtualization)
capabilities is one way to address the IoT network challenges providing secure
network resources for IoT. Network functions virtualization (also Network function
virtualization or NFV) is a network architecture concept that uses the
technologies of IT virtualization to virtualize entire classes of network node
functions into building blocks that may connect, or chain together, to create communication services.
·
NFV relies upon, but differs from, traditional
server-virtualization techniques, such as those used in enterprise IT. A
virtualized network function, or VNF, may consist of one or more virtual
machines running different software and processes, on top of standard
high-volume servers, switches and storage devices, or even cloud computing
infrastructure, instead of having custom hardware appliances for each network
function.
·
For example, a virtual session border controller
could be deployed to protect a network without the typical cost and complexity
of obtaining and installing physical network protection units. Other examples
of NFV include virtualized load balancers, firewalls, intrusion detection
devices and WAN accelerators.
The NFV framework consists of three main components-
·
Virtualized network functions
(VNFs) are software implementations of network functions that can be deployed
on a network functions virtualization infrastructure (NFVI).
·
Network functions virtualization infrastructure
(NFVI) is the totality of all hardware and software components that build the
environment where VNFs are deployed. The NFV infrastructure can span several
locations. The network providing connectivity between these locations is
considered as part of the NFV infrastructure.
·
Network functions virtualization
management architectural framework (NFV-MANO Architectural Framework) is the
collection of all functional blocks, data repositories used by these blocks,
and reference points and interfaces through which these functional blocks
exchange information for the purpose of managing and orchestrating NFVI and VNFs.
Software defined networking (SDN) and Network
Virtualization (NV):
· It is two of the
most prominent technologies to serve as key enablers for the IoT networks of
the near future. The main idea behind SDN is to separate the control plane
(where the logical procedures supporting the networking protocols are executed
and all the relevant decisions are taken) from the data plane (where the
forwarding of packets on the most suitable interface towards the intended
destination is executed).
· The main entity
behind this separation is the controller, which communicates with the network
applications through the so-called northbound interface and translates their
requirements into appropriate network decisions. The controller also
communicates with the network switches that forward packets according to the
controller-installed rules. This way, SDN provides increased possibilities to
smartly route traffic, for example to balance
the load over the network or to exploit underutilized network resources in an
optimal way, thereby alleviating the burden on the network by the data
onslaught of IoT.
·
The term network virtualization concerns a network
that allows multiple service providers to form multiple separate and isolated
virtual networks by sharing physical resources provided by one or more
different physical network infrastructure providers.SDN
(software defined networking) is another element that works in combination with
NFV while creating IoT network infrastructure. NFV in combination with SDN
enable network to utilize distributed intelligence capacity to analyze and
manage traffic flows across the network. NFV enables SPs to assemble and
provide cost effective and secure IoT networks along with ISV partners. The
complexity part can be handled with effective implementation of NFV
capabilities with IoT network.
Relevance of NFV in IoT
System-NFV can play a crucial role in achieving the
goal with IoT network combining both hardware and software network features in
a single virtual network. NFV helps accelerate the deployment of new services,
operations, and maintenance of a network allowing high level of network
optimization. It brings multiples benefits to service operators and service
providers including ROI. The relevance of NFV lies with the promise of benefits
across network architecture.
NFV to Enhance IoT
Networking Capacity-NFV leverages couple of IT
technologies to build flexible and agile IoT network such as virtualization,
standard servers, and open software. It distributes intelligence throughout the
IoT network enabling real time analytics and business intelligence. NFV creates
menu for virtual network functions (VNFs) that includes gateways, mobile core,
deep packet inspection (DPI), security, routing, and traffic management that
helps delivering customized network services for IoT. Conversely IoT drives NFV
opportunity for service providers too financially and technologically.
Data storage in IOT-The Internet of Things is creating an enormous amount of data. To
manage, access, and make use of this data, digital storage becomes a critical
factor. Data management is a broad concept referring to the architectures,
practices, and procedures for proper management of the data lifecycle needs of
a certain system. In the context of IoT, data management should act as a layer
between the objects and devices generating the data and the applications
accessing the data for analysis purposes and services. The devices themselves
can be arranged into subsystems or subspaces with autonomous governance and
internal hierarchical management. The functionality and data provided by these
subsystems is to be made available to the
IoT network, depending on the level of privacy desired by the subsystem owners.
·
IoT data has distinctive characteristics that make
traditional relational-based database management an obsolete solution. A
massive volume of heterogeneous, streaming and geographically-dispersed
real-time data will be created by millions of diverse devices periodically
sending observations about certain monitored phenomena or reporting the
occurrence of certain or abnormal events of interest .
·
Periodic observations are most demanding in terms of
communication overhead and storage due to their streaming and continuous
nature, while events present time-strain with end-to-end response times
depending on the urgency of the response required for the event. Furthermore,
there is metadata that describes “Things” in addition to the data that is
generated by “Things”; object identification, location, processes and services
provided are an example of such data. IoT data will statically reside in fixed-
or flexible-schema databases and roam the network from dynamic and mobile
objects to concentration storage points. This will continue until it reaches
centralized data stores. Communication, storage and process will thus be
defining factors in the design of data management solutions for IoT.
·
A data management framework for IoT is presented
that incorporates a layered, data-centric, and federated paradigm to join the
independent IoT subsystems in an adaptable, flexible, and seamless data
network. In this framework, the “Things” layer is composed of all entities and
subsystems that can generate data. Raw data, or simple aggregates, are then
transported via a communications layer to data repositories. These data
repositories are either owned by organizations or public, and they can be
located at specialized servers or on the cloud.
Organizations or individual
users have access to these repositories via query and federation layers that
process queries and analysis tasks, decide which repositories hold the needed
data, and negotiate participation to acquire the data. In addition, real-time
or context-aware queries are handled through the federation layer via
·
·
a sources
layer that seamlessly handles the discovery and engagement of data sources. The
whole framework therefore allows a two-way publishing and querying of data.
This allows the system to respond to the immediate data and processing requests
of the end users and provides archival capabilities for later long-term
analysis and exploration of value-added trends.
IOT Data Management-Traditional data management systems handle the storage, retrieval,
and update of elementary data items, records and files. In the context of IoT,
data management systems must summarize data online while providing storage,
logging, and auditing facilities for offline analysis. This expands the concept
of data management from offline storage, query processing, and transaction
management operations into online-offline communication/storage dual
operations. We first define the data lifecycle within the context of IoT and
then outline the energy consumption profile for each of the phases in order to
have a better understanding of IoT data management.
IOT Data Lifecycle-The lifecycle of data within an IoT system proceeds from data
production to aggregation, transfer,
optional filtering and preprocessing,
and finally to storage and archiving. Querying and analysis are the end points
that initiate (request) and consume data production, but data production can be
set to be pushed to the IoT consuming services. Production, collection,
aggregation, filtering, and some basic querying and preliminary processing
functionalities are considered online, communication- intensive operations.
Intensive preprocessing, long-term storage and archival and in-depth
processing/analysis are considered offline storage-intensive operations.
Fig 2.2 data
Production
Storage operations aim at making data available on the long term for
constant access/updates, while archival is concerned with read-only data. Since
some IoT systems may generate, process, and store data in-network for real-time
and localized services, with no need to propagate this data further up to
concentration points in the system, edges that combine both processing and
storage elements may exist as autonomous units in the cycle. In the following
paragraphs, each of the elements in the IoT data lifecycle is explained.
·
Querying: Data-intensive
systems rely on querying as the core process to access and retrieve data. In
the context of IoT, a query can be issued either to request real-time data to
be collected for temporal monitoring purposes or to retrieve a certain view of
the data stored within the system. The first case is typical when a (mostly
localized) real-time request for data is needed. The second case represents
more globalized views of data and in-depth analysis of trends and patterns.
·
Production: Data production
involves sensing and transfer of data by the “Things” within the IoT framework
and reporting this data to interested parties periodically (as in a
subscribe/notify model), pushing it up the network to aggregation points and
subsequently to database servers, or sending it as a response triggered by
queries that request the data from sensors and smart objects. Data is usually
time-stamped and possibly geo-stamped, and can be in the form of simple
key-value pairs, or it may contain rich audio/image/video content, with varying
degrees of complexity in- between.
·
Collection: The sensors and
smart objects within the IoT may store the data for a certain time interval or
report it to governing components. Data may be collected at concentration
points or gateways within the network where it is further filtered and
processed, and possibly fused into compact forms for efficient transmission.
Wireless communication technologies such as Zigbee, Wi- Fi and cellular are used by objects to send data to collection points.
·
Aggregation/Fusion:
Transmitting
all the raw data out of the network in real-time is often prohibitively
expensive given the increasing data streaming rates and the limited bandwidth.
Aggregation and fusion techniques deploy summarization and merging operations
in real-time to compress the volume of data to be stored and transmitted.
·
Delivery:
As data is filtered, aggregated, and possibly processed
either at the concentration points or at the autonomous virtual units within
the IoT, the results of these processes may need to be sent further up the
system, either as final responses, or for storage and in-depth analysis. Wired
or wireless broadband communications may be used there to transfer data to
permanent data stores.
·
Preprocessing:
IoT data will come from different sources with varying
formats and structures. Data may need to be preprocessed to handle missing
data, remove redundancies and integrate data from different sources into a
unified schema before being committed to storage. This preprocessing
is a known procedure in data mining called data cleaning.
Schema integration does not imply brute- force fitting of all the data into a
fixed relational (tables) schema, but rather a more abstract definition of a
consistent way to access the data without having to customize access for each
source's data format(s). Probabilities at different levels in the schema may be
added at this phase to IoT data items in order to handle uncertainty that may
be present in data or to deal with the lack of trust that may exist in data sources.
·
Storage/Update—Archiving:
This
phase handles the efficient storage and organization of data as well as the
continuous update of data with new information as it becomes available.
Archiving refers to the offline long-term storage of data that is not
immediately needed for the system's ongoing operations. The core of centralized
storage is the deployment of storage structures that adapt to the various data
types and the frequency of data capture. Relational database management systems
are a popular choice that involves the organization of data into a table schema
with predefined interrelationships and metadata for efficient retrieval at
later stages. NoSQL key-value stores are gaining popularity as storage
technologies for their support of big data storage with no reliance on
relational schema or strong consistency requirements typical of relational
database systems. Storage can also be decentralized for autonomous IoT systems,
where data is kept at the objects that generate it and is not sent up the
system. However, due to the limited capabilities of such objects, storage
capacity remains limited in comparison to the centralized storage model.
·
Processing/Analysis:
This
phase involves the ongoing retrieval and analysis operations performed and
stored and archived data in order to gain insights into historical data and
predict future trends, or to detect abnormalities in the data that may trigger
further investigation or action. Task-specific preprocessing may be needed to
filter and clean data before meaningful operations take place. When an IoT
subsystem is autonomous and does not require permanent storage of its data, but
rather keeps the processing and storage in the network, then in-network
processing may be performed in response to real-time or localized queries.
Data Management Framework
for IOT-Most of the current data management
proposals are targeted to WSNs, which are only a subset of the global IoT
space, and therefore do not explicitly address the more sophisticated
architectural characteristics of IoT.
·
WSNs are a mature networking paradigm
whose data management solutions revolve mainly around in-network data
processing and optimization. Sensors are mostly of stationary, resource-
constrained nature, which does not facilitate sophisticated analysis and services.
·
The main focus in WSN-based data management
solutions is to harvest real-time data promptly for quick decision making, with
limited permanent storage capacities for long-term usage. This represents only
a subset of the more versatile IoT system, which aims at harnessing the data
available from a variety of sources; stationary and mobile, smart and embedded,
resource- constrained and resource-rich, real-time and archival.
·
The main focus of IoT-based data
management therefore extends the provisions made for WSNs to add provisions of
a seamless way to tap into the volumes of heterogeneous data in order to find
interesting global patterns and strategic opportunities.
IOT Cloud Based
Services-As these devices start to become
connected, we need a place to send, store, and process all of the information.
Setting up your own in-house system isn’t practical anymore. The cost of
maintaining, upgrading and securing a system is just too high, and there are
some great services available.
·
Amazon Web
Services IOT Platform-Amazon dominates the consumer cloud market. They
were the first to really turn cloud
computing into a commodity way back in 2004. “ince
then they’ve put a lot effort into innovation and building features, and
probably have the most comprehensive set of tools available.
·
Microsoft Azure
IoT Hub-Microsoft is taking their Internet of Things cloud services very
seriously. They have cloud storage, machine learning, and IoT services, and
have even developed their own operating system for IoT devices. This means they
intend to provide a complete IoT solution provider.
·
IBM Watson IoT
Platform-IBM is another IT giant trying to set itself up as an Internet of
Things platform authority. They try to make their cloud services as accessible
as possible to beginners with easy apps and interfaces. You can try out their
sample apps to get a feel for how it all works. You can also store your data
for a specified period, to get historical information from your connected
devices.
·
Google Cloud
Platform-Search giant Google is also taking the Internet of Things very
seriously. They claim that Cloud Platform is the best place to build IoT
initiatives, taking advantage of
Google’s heritage of web-scale processing, analytics, and machine intelligence.
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