476870776-Internet-of-Things-IoT-RGPV-syllabus-Ec-703-b-Unit-2.ppt

ecognition-TCs

1. Internet of Things (IoT)
EC- 703 (B)
Prof. Ashish Verma
Asst. Professor
Electronics & Communication
Department
Mahakal Institute of Technology

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Unit 2
•Machine-to-machine (M2M)
•SDN (software defined networking) for IOT
•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 an important aspect of
warehouse management, remote control,
robotics, traffic control, logistic services,
supply chain management, fleet management
and telemedicine.
Prof. Ashish Verma Asst Prof ECE MIT

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•Machine-to-machine communication is often used for
remote monitoring. In product restocking, for
example, a vending machine can message the
distributor's network, or
 machine, when a particular
item is running low to send a refill. An enabler of
asset tracking and monitoring, M2M is vital in
warehouse management systems (WMS) and supply
chain management (SCM).
•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.

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Key features of M2M
•Low power consumption, in an effort to improve the system's
ability to effectively service M2M applications.
•A Network operator that provides
 packet-switched service.
•Monitoring abilities that provide
 functionality to detect events.
•Time tolerance, meaning data transfers can be delayed.
•Time control, meaning data can only be sent or received at
specific predetermined periods.
•Location specific triggers that alert or wake up devices when
they enter particular areas.
•The ability to continually send and receive small amounts of
data.

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M2M vs. IoT
•While many use the terms interchangeably, M2M and IoT are
not the same. IoT needs M2M, but M2M does not need IoT.
•Both terms relate to the communication of connected devices,
but M2M systems are often isolated, stand-alone networked
equipment. IoT systems take M2M to the next level, bringing
together disparate systems into one large, connected
ecosystem.
•M2M systems use
 point-to-point communications between
machines, sensors and hardware over cellular or wired
networks, while IoT systems rely on IP-based networks to
send data collected from IoT-connected devices to gateways,
the
 cloud or middleware platforms.

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M2M security
•Machine-to-machine systems face a number of
security issues, from unauthorized access to
wireless intrusion to device hacking. Physical
security, privacy, fraud and the exposure of
mission-critical applications must also be
considered.
•Typical M2M security measures include
making devices and machines tamper-
resistant, embedding security into the
machines, ensuring communication security
through
 encryption and securing back-
end
 servers, among others. Segmenting M2M
devices onto their own network and managing
device identity, data confidentiality and device
availability can also help combat M2M
security risks.

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M2M standards
•Machine-to-machine technology
does not have a standardized
device platform, and many M2M
systems are built to be task- or
device-specific. Several key
M2M standards, many of which
are also used in IoT settings,
have emerged over the years,
including:
–OMA DM (Open Mobile
Alliance Device Management), a
device management protocol
–OMA LightweightM2M, a device
management protocol
–MQTT, a messaging protocol
–TR-069 (Technical Report 069),
an application layer protocol
–HyperCat, a data discovery
protocol
–OneM2M, a communications
protocol
–Google Thread, a wireless mesh
protocol
–AllJoyn, an open source software
framework

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SDN (software defined
networking) for IOT
•Internet of things (IoT) 
poses challenges that
are different from traditional Internet in
different aspects heterogeneous
communication technologies, application
specific QoS requirements, massive influx of
data, and unpredictable network conditions.
•On the other hand, software-defined
networking (SDN) is a promising approach to
control the network in a unified manner using
rule-based management. The abstractions
provided by SDN enable holistic control of the
network using high-level policies, without
being concerned about low-level configuration
issues.
•Hence, it is advantageous to address the
heterogeneity and application-specific
requirements of IoT.

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What is SDN?
•SDN is a framework to allow network
administrators to automatically and
dynamically manage and control a large
number of network devices, services,
topology, traffic paths, and packet handling
(quality of service) policies using high-level
languages and APIs.
•Management includes provisioning, operating,
monitoring, optimizing, and managing FCAPS
(fault, configuration, accounting, performance,
and security) in a multi-tenant environment.

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Open flow protocol : Common platform for different
routers service providers like juniper, Cisco

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South bound
Interface
North
bound
Interfac
e

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Summary
•SDN is the framework to automatically
manage and control a large number of
network devices and services in a multi-tenant
environment
•OpenFlow originated SDN but now many
different southbound and northbound APIs,
intermediate services and tools are being
discussed and implemented by the industry,
e.g., XMPP, ForCES, PCE, ALTO

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SDN Vs NVF
•Network function virtualization (NFV) and
software-defined networks (SDN) are two
closely related technologies that often exist
together, but not always.
•An SDN can be considered a series of network
objects (such as switches, routers, firewalls)
that deploy in a highly automated manner.
The automation may be achieved by using
commercial or open source tools customized
according to the administrator's
requirements.
•A full SDN may only cover relatively
straightforward networking requirements,
such as VLAN and interface provisioning.
•In many cases, SDN will also be linked to
server virtualization, providing the glue that
sticks virtual networks together. This may
involve NFV, but not necessarily.
•NFV is the process of moving services, such as
load balancing, firewalls and IPS, away from
dedicated hardware into a virtualized
environment. This is, of course, part of a wider
movement toward the virtualization of
applications and services.
•Functions such as caching and content control
can easily be migrated to a virtualized
environment but won't necessarily provide
any significant reduction in operating costs
until some intelligence is introduced.

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NFV(Network function
virtualization) for IOT
•Network functions virtualization (NFV) is the
process of decoupling network functions from
hardware and running them on a software
platform.
•It is a complementary approach to software-
defined networking (SDN) for network
management. While both manage networks,
they rely on different methods.
•While
 SDN separates the control and
forwarding planes to offer a centralized view
of the network NFV
 primarily focuses on
optimizing the network services themselves.
•With NFV, you don’t need to have dedicated
hardware for each network function. NFV
improves scalability and agility by allowing
service providers to deliver new network
services and applications on demand, without
requiring additional hardware resources

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•Network functions virtualization (NFV) is a
way to
 virtualizes network services, such as
routers, firewalls, and load balancers, that
have traditionally been run on proprietary
hardware. These services are packaged
as
 virtual machines (VMs) on commodity
hardware, which allows service providers to
run their network on standard servers
instead of proprietary ones.
 
•The European Telecommunications Standards
Institute ETSI Industry Specification Group for
Network Functions Virtualization (ETSI ISG
NFV), a group charged with developing
requirements and architecture for
virtualization for various functions within
telecoms networks, such as standards like NFV
MANO. ETSI is also instrumental in
collaborative projects like the newly
announced OPNFV.

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OPEX & CAPEX
•Capital expenditures (CAPEX) are a company's
major, long-term expenses, while operating
expenses (OPEX) are a company's day-to-day
expenses.
•Examples of CAPEX include physical assets
such as buildings, equipment, machinery, and
vehicles.
 
•Examples of OPEX include employee salaries,
rent, utilities, property taxes, and cost of
goods sold (COGS).

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The Benefits of Network Functions
Virtualization
•NFV virtualizes network services via software to
enable operators to:
–Reduce CapEx: reducing the need to purchase
purpose-built hardware and supporting pay-as-you-
grow models to eliminate wasteful over-
provisioning.
–Reduce OpEX: reducing space, power and cooling
requirements of equipment and simplifying the roll
out and management of network services.

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–Accelerate Time-to-Market: reducing the time to
deploy new networking services to support
changing business requirements, seize new market
opportunities and improve return on investment of
new services.
– Deliver Agility and Flexibility: quickly scale up
or down services to address changing demands;
support innovation by enabling services to be
delivered via software on any industry-standard
server hardware.
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NFV framework consists of three main components:
•Virtualized network functions 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.
•Network functions virtualization management
and orchestration architectural framework is
the collection of all functional blocks and
interfaces. In its NFV-MANO role it consists of
VNF and NFVI managers and virtualization
software operating on a hardware controller. It
consists of both virtual and physical processing
and storage resources, and virtualization
software.
•carrier-grade features used to manage and
monitor the platform components, recover from
failures and provide effective security - all
required for the public carrier network.

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Then find below a
summary of SEVEN
key
  blocks in  NFV
architecture,
 
which is all you
need to know to
get started with
the NFV
architecture.
Follow the block
numbers and
definitions below.
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“NFV Architecture”
•1. VNF (Virtual Network Function):
–A VNF is the basic block in NFV Architecture. It is the virtualized
network element. For example when a router is virtualized, we call it
Router VNF; another example is base station VNF.
•2. EM (Element Management ):
–This is the element management system for VNF. This is responsible
for the functional management of VNF i.e. FCAPS ( Fault,
Configuration, Accounting, Performance and Security Management).
•3. VNF Manager:
–A VNF Manager manages a VNF or multiple VNFs i.e. it does the life
cycle management of
  VNF instances. Life cycle management means
setting up/ maintaining and tearing down VNFs.
–Additionally VNFM ( VNF Manager) does the FCAPS for the virtual part
of the VNF.

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•4. NFVI (Network Function
Virtualization Infrastructure): NFVI is
the environment in which VNFs run. This
includes Physical resources, virtual
resources and virtualization layer, described
below.
–4.1 Compute, Memory and Networking
Resources:
•This is the physical part in NFVI. Virtual
resources are instantiated on these physical
resources. Any commodity switch or
physical server/storage server is part of this
category.
–4.2 Virtual Compute, Virtual Memory and
Virtual Networking Resources:
•This is the virtual part in NFVI. The
physical resources are abstracted into
virtual resources that are ultimately utilized
by VNFs.
–4.3 Virtualization Layer:
•This layer is responsible for abstracting
physical resources into virtual resources.
The common industry term for this layer is
“Hypervisor”. This layer decouples
software from hardware which enables the
software to progress independently from
hardware.
•Suppose, there is no virtualization layer, one
may think that VNFs can run on physical
resources directly; However, as such by
definition we CANNOT call them VNF nor it
would be
  NFV architecture. They may
appropriately be called PNFs ( Physical Network
Functions).

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•5. VIM (Virtualized Infrastructure Manager):
–This is the management system for NFVI.
  It is
responsible for controlling and managing the
NFVI compute, network and storage resources
within one operator’s infrastructure domain. It is
also responsible for collection of performance
measurements and events.
•6. NFV Orchestrator:
–Generates, maintains and tears down network
services of VNF themselves. If there are
multiple VNFs, orchestrator will enable creation
of end to end service over multiple VNFs. NFV
Orchestrator is also responsible for global
resource management of NFVI resources
•7. OSS/BSS(Operation Support
System/Business Support System)
–OSS deals with network management, fault
management, configuration management and
service management. BSS deals with customer
management, product management and order
management etc.
Prof. Ashish Verma Asst Prof ECE MIT

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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.
•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.

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Functionality Provided by
subsystem
•IoT data has distinctive characteristics that make
traditional relational-based database management
an obsolete solution through periodically
sending observations.
• 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.
•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.
•Organizations or individual users have access to
these repositories via query.

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IOT Data Management
•Traditional data management systems handle
the storage, retrieval, and update of
elementary data items, records and files.
•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.

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Hadoop 
is an open-source software framework for storing 
data 
and running
applications on clusters of commodity hardware.
Commodity hardware, sometimes known as off-the-shelf
 
hardware, is a computer
device or IT component that is relatively inexpensive, widely available and basically
interchangeable with other
 
hardware 
of its type.

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IOT Data Lifecycle
•lifecycle of data within an IoT system
proceeds from data production to
aggregation(summarized format), 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.

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IoT data lifecycle and data management system

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•Querying: 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.
•Production: Data production involves sensing and
transfer of data by the "Things" within the loT
framework and reporting this data to interested
parties periodically (as in a subscribe/notify model).
•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.

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•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(integration) techniques deploy summarization
and merging operations in real-time to compress the
volume of data to be stored and transmitted.
•Delivery: 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.

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•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.
•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.
•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.

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IOT Cloud Based Services
•A simple definition of cloud computing involves
delivering different types of services over the Internet.
From software and analytics to
 secure and safe data
storage
 and networking resources, everything can be
delivered via the cloud.
•You probably use different cloud-based applications
every day. You are benefiting from cloud solutions
every time you send a web, use a mobile app,
download an image, Netflix show, or play an online
video game. All these services are stored in the cloud
and exist in some digital space.

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•Normal servers, in fact, refers to the regular physical
technology you’re installing somewhere in the room,
while cloud server
 is perceived as an online system
able to store a large amount of data, deliver software
services, automate business process and operations,
and allow enough customization for the user to make
the desired changes.
•For businesses, cloud computing means improved
collaboration and productivity, as well as significant
cost reductions. It means better data protection,
improved availability, and expanded access to
cutting-edge technologies.

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 Most-recommended cloud
platforms used for IoT
development•Microsoft Azure IoT Suite
–It provides multiple services to create IoT
 solutions. It enhances your
profitability and productivity with pre-built connected solutions. It
analyzes untapped data to transform business. Azure Suite can easily
analyze and act on new data.
• Google Cloud’s IoT Platform
–Google's platform is among the best platforms we currently have.
Google has an end-to-end platform for Internet-of-Things solutions. It
allows you to easily connect, store, and manage IoT data. This
platform helps you to scale your business.
•Thingworx 8 IoT Platform
–Thingworx 8 is a better, faster, easier platform, providing the
functionality to build, deploy, and extend industrial projects and apps.

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•AWS IoT Platform
–Amazon made it much easier for developers to collect data from
sensors and Internet-connected devices. They help you collect and
send data to the cloud and analyze that information to provide
the ability to manage devices.
•Cisco IoT Cloud Connect
–Cisco Internet of Things accelerates digital transformation and
actions from your data.
 Cisco IoT Cloud Connect is a mobile,
cloud-based suite. It offers solutions for mobile operators to
provide phenomenal IoT experience. It provides flexible
deployment options for your device.
•Salesforce IoT Cloud
–Salesforce IoT Cloud
 is powered by Salesforce Thunder. It gathers
data from devices, websites, applications, and partners to trigger
actions for real-time responses. Salesforce combined with IoT
delivers improved customer service.
•Kaa IoT Platform
–It
 is an open-source, multipurpose, middleware platform for complete end-to-end IoT development and smart devices. It
reduces cost, risk, and market time. Also, Kaa offers a range of IoT
tools that can be easily plugged in and implemented in IoT use
cases.

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•Oracle IoT Platform
–Oracle offers real-time Internet of Things data analysis, endpoint
management, and high speed messaging where the user can get
real-time notification directly on their devices. Oracle IoT cloud
service is a Platform as a Service (PaaS), cloud-based offering that
helps you to make critical business decisions.
•Thingspeak IoT Platform
–Thingspeak
 is an open-source platform that allows you to collect
and store sensor data to the cloud. It provides you the app to
analyze and visualize your data in Matlab. You can use Arduino,
Raspberry Pi, and Beaglebone to send sensor data. You can create
a separate channel to store data.
• GE Predix IoT Platform
–Predix
 is the world’s first industrial platform. Predix was designed
to target factories and provides simple ecosystem. It can directly
analyze data from the machine and store. GE wants to provide the
growing industrial Internet of Things for its cloud platform. This
platform is secure and scalable.

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