Unit-V

Cloud Computing
The term Cloud refers to a Network or Internet. In other words, we can say that
Cloud is something, which is present at remote location. Cloud can provide
services over public and private networks, i.e., WAN, LAN or VPN.
Applications such as e-mail, web conferencing, customer relationship
management (CRM) execute on cloud.
loud Computing refers to manipulating, configuring, and accessing the hardware
and software resources remotely. It offers online data storage, infrastructure, and
application.

Cloud computing offers platform independency, as the software is not required

to be installed locally on the PC. Hence, the Cloud Computing is making our
business applications mobile and collaborative.

Basic Concepts

There are certain services and models working behind the scene making the cloud
computing feasible and accessible to end users. Following are the working models
for cloud computing:

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 • Deployment Models
• Service Models

Deployment Models

Deployment models define the type of access to the cloud, i.e., how the cloud is
located? Cloud can have any of the four types of access: Public, Private, Hybrid,
and Community.

Public Cloud

The public cloud allows systems and services to be easily accessible to the
general public. Public cloud may be less secure because of its openness.

Private Cloud

The private cloud allows systems and services to be accessible within an

organization. It is more secured because of its private nature.

Community Cloud

The community cloud allows systems and services to be accessible by a group of

organizations.

Hybrid Cloud

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The hybrid cloud is a mixture of public and private cloud, in which the critical
activities are performed using private cloud while the non-critical activities are
performed using public cloud.

Service Models

Cloud computing is based on service models. These are categorized into three
basic service models which are -

• Infrastructure-as–a-Service (IaaS)
• Platform-as-a-Service (PaaS)
• Software-as-a-Service (SaaS)
Anything-as-a-Service (XaaS) is yet another service model, which includes
Network-as-a-Service, Business-as-a-Service, Identity-as-a-Service, Database-as-
a-Service or Strategy-as-a-Service.
The Infrastructure-as-a-Service (IaaS) is the most basic level of service. Each
of the service models inherit the security and management mechanism from the
underlying model, as shown in the following diagram:

Infrastructure-as-a-Service (IaaS)

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IaaS provides access to fundamental resources such as physical machines, virtual
machines, virtual storage, etc.

Platform-as-a-Service (PaaS)

PaaS provides the runtime environment for applications, development and

deployment tools, etc.

Software-as-a-Service (SaaS)

SaaS model allows to use software applications as a service to end-users.

History of Cloud Computing

The concept of Cloud Computing came into existence in the year 1950 with
implementation of mainframe computers, accessible via thin/static clients. Since
then, cloud computing has been evolved from static clients to dynamic ones and
from software to services. The following diagram explains the evolution of cloud
computing:

Benefits

Cloud Computing has numerous advantages. Some of them are listed below -
• One can access applications as utilities, over the Internet.
• One can manipulate and configure the applications online at any time.

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 • It does not require to install a software to access or manipulate cloud
application.
• Cloud Computing offers online development and deployment tools,
programming runtime environment through PaaS model.
• Cloud resources are available over the network in a manner that provide
platform independent access to any type of clients.
• Cloud Computing offers on-demand self-service. The resources can be
used without interaction with cloud service provider.
• Cloud Computing is highly cost effective because it operates at high
efficiency with optimum utilization. It just requires an Internet connection
• Cloud Computing offers load balancing that makes it more reliable.

Risks related to Cloud Computing

Although cloud Computing is a promising innovation with various benefits in the

world of computing, it comes with risks. Some of them are discussed below:

Security and Privacy

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It is the biggest concern about cloud computing. Since data management and
infrastructure management in cloud is provided by third-party, it is always a risk
to handover the sensitive information to cloud service providers.
Although the cloud computing vendors ensure highly secured password protected
accounts, any sign of security breach may result in loss of customers and
businesses.

Lock In

It is very difficult for the customers to switch from one Cloud Service Provider
(CSP) to another. It results in dependency on a particular CSP for service.

Isolation Failure

This risk involves the failure of isolation mechanism that separates storage,
memory, and routing between the different tenants.

Management Interface Compromise

In case of public cloud provider, the customer management interfaces are

accessible through the Internet.

Insecure or Incomplete Data Deletion

It is possible that the data requested for deletion may not get deleted. It happens
because either of the following reasons
• Extra copies of data are stored but are not available at the time of deletion
• Disk that stores data of multiple tenants is destroyed.

Characteristics of Cloud Computing

There are four key characteristics of cloud computing. They are shown in the
following diagram:

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On Demand Self Service

Cloud Computing allows the users to use web services and resources on demand.
One can logon to a website at any time and use them.

Broad Network Access

Since cloud computing is completely web based, it can be accessed from

anywhere and at any time.

Resource Pooling

Cloud computing allows multiple tenants to share a pool of resources. One can
share single physical instance of hardware, database and basic infrastructure.

Rapid Elasticity

It is very easy to scale the resources vertically or horizontally at any time. Scaling
of resources means the ability of resources to deal with increasing or decreasing
demand.

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The resources being used by customers at any given point of time are
automatically monitored.

Measured Service

In this service cloud provider controls and monitors all the aspects of cloud
service. Resource optimization, billing, and capacity planning etc. depend on it.
Deployment Models

Public Cloud allows systems and services to be easily accessible to general

public. The IT giants such as Google, Amazon and Microsoft offer cloud
services via Internet. The Public Cloud Model is shown in the diagram below.

Benefits

There are many benefits of deploying cloud as public cloud model. The following
diagram shows some of those benefits:

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Cost Effective

Since public cloud shares same resources with large number of customers it turns
out inexpensive.

Reliability

The public cloud employs large number of resources from different locations. If
any of the resources fails, public cloud can employ another one.

Flexibility

The public cloud can smoothly integrate with private cloud, which gives
customers a flexible approach.

Location Independence

Public cloud services are delivered through Internet, ensuring location

independence.

Utility Style Costing

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Public cloud is also based on pay-per-use model and resources are accessible
whenever customer needs them.

High Scalability

Cloud resources are made available on demand from a pool of resources, i.e., they
can be scaled up or down according the requirement.

Disadvantages

Here are some disadvantages of public cloud model:

Low Security

In public cloud model, data is hosted off-site and resources are shared publicly,
therefore does not ensure higher level of security.

Less Customizable

It is comparatively less customizable than private cloud.

Private Cloud allows systems and services to be accessible within an
organization. The Private Cloud is operated only within a single organization.
However, it may be managed internally by the organization itself or by third-
party. The private cloud model is shown in the diagram below.

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Benefits

There are many benefits of deploying cloud as private cloud model. The following
diagram shows some of those benefits:

High Security and Privacy

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Private cloud operations are not available to general public and resources are
shared from distinct pool of resources. Therefore, it ensures
high security and privacy.

More Control

The private cloud has more control on its resources and hardware than public
cloud because it is accessed only within an organization.

Cost and Energy Efficiency

The private cloud resources are not as cost effective as resources in public clouds
but they offer more efficiency than public cloud resources.

Disadvantages

Here are the disadvantages of using private cloud model:

Restricted Area of Operation

The private cloud is only accessible locally and is very difficult to deploy
globally.

High Priced

Purchasing new hardware in order to fulfill the demand is a costly transaction.

Limited Scalability

The private cloud can be scaled only within capacity of internal hosted resources.

Additional Skills

In order to maintain cloud deployment, organization requires skilled expertise.

Hybrid Cloud is a mixture of public and private cloud. Non-critical activities are
performed using public cloud while the critical activities are performed using
private cloud. The Hybrid Cloud Model is shown in the diagram below.

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Benefits

There are many benefits of deploying cloud as hybrid cloud model. The following
diagram shows some of those benefits:

Scalability

It offers features of both, the public cloud scalability and the private cloud
scalability.

Flexibility

It offers secure resources and scalable public resources.

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Cost Efficiency

Public clouds are more cost effective than private ones. Therefore, hybrid clouds
can be cost saving.

Security

The private cloud in hybrid cloud ensures higher degree of security.

Disadvantages

Networking Issues

Networking becomes complex due to presence of private and public cloud.

Security Compliance

It is necessary to ensure that cloud services are compliant with security policies of
the organization.

Infrastructure Dependency

The hybrid cloud model is dependent on internal IT infrastructure, therefore it is

necessary to ensure redundancy across data centers.
Community Cloud allows system and services to be accessible by group of
organizations. It shares the infrastructure between several organizations from a
specific community. It may be managed internally by organizations or by the
third-party. The Community Cloud Model is shown in the diagram below.

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Benefits

There are many benefits of deploying cloud as community cloud model.

Cost Effective

Community cloud offers same advantages as that of private cloud at low cost.

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Sharing Among Organizations

Community cloud provides an infrastructure to share cloud resources and

capabilities among several organizations.

Security

The community cloud is comparatively more secure than the public cloud but less
secured than the private cloud.

Issues

• Since all data is located at one place, one must be careful in storing data in
community cloud because it might be accessible to others.
• It is also challenging to allocate responsibilities of governance, security and
cost among organizations.
Cloud Service Models

Infrastructure-as-a-Service provides access to fundamental resources such as

physical machines, virtual machines, virtual storage, etc. Apart from these
resources, the IaaS also offers:

• Virtual machine disk storage

• Virtual local area network (VLANs)
• Load balancers
• IP addresses
• Software bundles
All of the above resources are made available to end user via server
virtualization. Moreover, these resources are accessed by the customers as if they
own them.

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Benefits

IaaS allows the cloud provider to freely locate the infrastructure over the Internet
in a cost-effective manner. Some of the key benefits of IaaS are listed below:
• Full control of the computing resources through administrative access to
VMs.
• Flexible and efficient renting of computer hardware.
• Portability, interoperability with legacy applications.

Full control over computing resources through administrative access to VMs

IaaS allows the customer to access computing resources through administrative

access to virtual machines in the following manner:
• Customer issues administrative command to cloud provider to run the
virtual machine or to save data on cloud server.
• Customer issues administrative command to virtual machines they owned to
start web server or to install new applications.

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Flexible and efficient renting of computer hardware

IaaS resources such as virtual machines, storage devices, bandwidth, IP addresses,

monitoring services, firewalls, etc. are made available to the customers on rent.
The payment is based upon the amount of time the customer retains a resource.
Also with administrative access to virtual machines, the customer can run any
software, even a custom operating system.

Portability, interoperability with legacy applications

It is possible to maintain legacy between applications and workloads between IaaS

clouds. For example, network applications such as web server or e-mail server
that normally runs on customer-owned server hardware can also run from VMs in
IaaS cloud.

Issues

IaaS shares issues with PaaS and SaaS, such as Network dependence and browser
based risks. It also has some specific issues, which are mentioned in the following
diagram:

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Compatibility with legacy security vulnerabilities

Because IaaS offers the customer to run legacy software in provider's

infrastructure, it exposes customers to all of the security vulnerabilities of such
legacy software.

Virtual Machine sprawl

The VM can become out-of-date with respect to security updates because IaaS
allows the customer to operate the virtual machines in running, suspended and off
state. However, the provider can automatically update such VMs, but this
mechanism is hard and complex.

Robustness of VM-level isolation

IaaS offers an isolated environment to individual customers through hypervisor.

Hypervisor is a software layer that includes hardware support for virtualization to
split a physical computer into multiple virtual machines.

Data erase practices

The customer uses virtual machines that in turn use the common disk resources
provided by the cloud provider. When the customer releases the resource, the
cloud provider must ensure that next customer to rent the resource does not
observe data residue from previous customer.

Characteristics

Here are the characteristics of IaaS service model:

• Virtual machines with pre-installed software.
• Virtual machines with pre-installed operating systems such as Windows,
Linux, and Solaris.
• On-demand availability of resources.
• Allows to store copies of particular data at different locations.
• The computing resources can be easily scaled up and down.
Platform-as-a-Service offers the runtime environment for applications. It also
offers development and deployment tools required to develop applications. PaaS

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has a feature of point-and-click tools that enables non-developers to create web
applications.
App Engine of Google and Force.com are examples of PaaS offering vendors.
Developer may log on to these websites and use the built-in API to create web-
based applications.
But the disadvantage of using PaaS is that, the developer locks-in with a
particular vendor. For example, an application written in Python against API of
Google, and using App Engine of Google is likely to work only in that
environment.
The following diagram shows how PaaS offers an API and development tools to
the developers and how it helps the end user to access business applications.

Benefits

Following are the benefits of PaaS model:

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Lower administrative overhead

Customer need not bother about the administration because it is the responsibility
of cloud provider.

Lower total cost of ownership

Customer need not purchase expensive hardware, servers, power, and data
storage.

Scalable solutions

It is very easy to scale the resources up or down automatically, based on their

demand.

More current system software

It is the responsibility of the cloud provider to maintain software versions and

patch installations.

Issues

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Like SaaS, PaaS also places significant burdens on customer's browsers to
maintain reliable and secure connections to the provider’s systems. Therefore,
PaaS shares many of the issues of SaaS. However, there are some specific issues
associated with PaaS as shown in the following diagram:

Lack of portability between PaaS clouds

Although standard languages are used, yet the implementations of platform

services may vary. For example, file, queue, or hash table interfaces of one
platform may differ from another, making it difficult to transfer the workloads
from one platform to another.

Event based processor scheduling

The PaaS applications are event-oriented which poses resource constraints on

applications, i.e., they have to answer a request in a given interval of time.

Security engineering of PaaS applications

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Since PaaS applications are dependent on network, they must explicitly use
cryptography and manage security exposures.

Characteristics

Here are the characteristics of PaaS service model:

• PaaS offers browser based development environment. It allows the
developer to create database and edit the application code either via
Application Programming Interface or point-and-click tools.
• PaaS provides built-in security, scalability, and web service interfaces.
• PaaS provides built-in tools for defining workflow, approval
processes, and business rules.
• It is easy to integrate PaaS with other applications on the same platform.
• PaaS also provides web services interfaces that allow us to connect the
applications outside the platform.

PaaS Types

Based on the functions, PaaS can be classified into four types as shown in the
following diagram:

Stand-alone development environments

The stand-alone PaaS works as an independent entity for a specific function. It

does not include licensing or technical dependencies on specific SaaS
applications.

Application delivery-only environments

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The application delivery PaaS includes on-demand scaling and application
security.

Open platform as a service

Open PaaS offers an open source software that helps a PaaS provider to run
applications.

Add-on development facilities

The add-on PaaS allows to customize the existing SaaS platform.

Software-as–a-Service (SaaS) model allows to provide software application as a
service to the end users. It refers to a software that is deployed on a host service
and is accessible via Internet. There are several SaaS applications listed below:

• Billing and invoicing system

• Customer Relationship Management (CRM) applications
• Help desk applications
• Human Resource (HR) solutions
Some of the SaaS applications are not customizable such as Microsoft Office
Suite. But SaaS provides us Application Programming Interface (API), which
allows the developer to develop a customized application.

Characteristics

Here are the characteristics of SaaS service model:

• SaaS makes the software available over the Internet.
• The software applications are maintained by the vendor.
• The license to the software may be subscription based or usage based. And
it is billed on recurring basis.
• SaaS applications are cost-effective since they do not require any
maintenance at end user side.
• They are available on demand.
• They can be scaled up or down on demand.
• They are automatically upgraded and updated.

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 • SaaS offers shared data model. Therefore, multiple users can share single
instance of infrastructure. It is not required to hard code the functionality
for individual users.
• All users run the same version of the software.

Benefits

Using SaaS has proved to be beneficial in terms of scalability, efficiency and

performance. Some of the benefits are listed below:

• Modest software tools

• Efficient use of software licenses
• Centralized management and data
• Platform responsibilities managed by provider
• Multitenant solutions

Modest software tools

The SaaS application deployment requires a little or no client side software

installation, which results in the following benefits:

• No requirement for complex software packages at client side

• Little or no risk of configuration at client side
• Low distribution cost

Efficient use of software licenses

The customer can have single license for multiple computers running at different
locations which reduces the licensing cost. Also, there is no requirement for
license servers because the software runs in the provider's infrastructure.

Centralized management and data

The cloud provider stores data centrally. However, the cloud providers may store
data in a decentralized manner for the sake of redundancy and reliability.

Platform responsibilities managed by providers

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All platform responsibilities such as backups, system maintenance, security,
hardware refresh, power management, etc. are performed by the cloud provider.
The customer does not need to bother about them.

Multitenant solutions

Multitenant solutions allow multiple users to share single instance of different

resources in virtual isolation. Customers can customize their application without
affecting the core functionality.

Issues

There are several issues associated with SaaS, some of them are listed below:

• Browser based risks

• Network dependence
• Lack of portability between SaaS clouds

Browser based risks

If the customer visits malicious website and browser becomes infected, the
subsequent access to SaaS application might compromise the customer's data.
To avoid such risks, the customer can use multiple browsers and dedicate a
specific browser to access SaaS applications or can use virtual desktop while
accessing the SaaS applications.

Network dependence

The SaaS application can be delivered only when network is continuously

available. Also network should be reliable but the network reliability cannot be
guaranteed either by cloud provider or by the customer.

Lack of portability between SaaS clouds

Transferring workloads from one SaaS cloud to another is not so easy because
work flow, business logics, user interfaces, support scripts can be provider
specific.

Open SaaS and SOA

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Open SaaS uses those SaaS applications, which are developed using open source
programming language. These SaaS applications can run on any open source
operating system and database. Open SaaS has several benefits listed below:

• No License Required
• Low Deployment Cost
• Less Vendor Lock-in
• More portable applications
• More Robust Solution
The following diagram shows the SaaS implementation based on SOA:

Cloud Computing – Case Studies

Introduction:

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 ✓ Simulation tools provide reliable, scalable and repeatable
environment for performance evaluatio

✓ The simulator facilitate pre-deployment tests of services

✓ As the demand of cloud computing is growing everyday, the simulators and

technologies are needed to be studied

✓ Cloud simulators allow customers to

✓ Evaluate the services

✓ Testing at no cost

✓ Enable repeatable evaluation

✓ Control the environment

✓ Pre-detection of issues affecting performance

✓ Design of countermeasures

✓ Different Cloud Simulators are:

✓ CloudSim

✓ CloudAnalyst

✓ GreenCloud

✓ iCanCloud

✓ GroudSim

✓ DCSim

CloudSim:

✓ A simulation framework

▪ Models cloud computing environments – Data

Center, VM, applications, users, network topology.

▪ Written on Java-based environment.

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 ▪ Allows to examine the performance of application services.

▪ Dynamic addition/removal of resources during simulation.

▪ Developed at CLOUDS Lab. of University of Melbourne.

Advantages of CloudSim

✓ Time effectiveness: Cloud-based application implementation in

▪ Minimum time

▪ Minimum effort

✓ Flexibility and applicability:

▪ Support for diverse cloud environments

▪ Enables modelling of application services in any environment

Features of CloudSim

✓ Various cloud computing data centers

✓ Different data center network topologies

✓ Message-passing applications

✓ Virtualization of server hosts

✓ Allocation of virtual machins (VMs)

✓ User defined policies for allocation of host resources to VMs

✓ Energy-aware computational resources

✓ Dynamic addition/removal of simulation components

✓ Stop and resume of simulation

CloudSim Architecture

✓ User Code: Top most layer

▪ Presents different machine and application specifications

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 ✓ CloudSim: Middle layer

▪ Provides cloud environment

▪ Enables modeling and simulation

✓ Core Simulation Engine: Bottom most layer

▪ Event scheduling

▪ Entity creation

▪ Interaction between components

▪ Clock management

Top Layer: User Code

Basic entities:

▪ Users

▪ Physical Machines

▪ Virtual Machines

▪ Applications & services

▪ Scheduling policies

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Middle Layer:CloudSim:

✓ Creation and simulation of

▪ Dedicated management interfaces

▪ Memory, storage, bandwidth and VMs

✓ Helps in solving issues like

▪ Hosts provisioning to VMs

▪ Application execution management

▪ Dynamic system state monitoring

✓ Allows a cloud service provider to

▪ Implement customized strategies

▪ Evaluating the efficiency of different policies in VM provisioning

CloudAnalyst:

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 ✓ Simulation tool designed based on CloudSim

✓ Provides GUI

✓ Supports geographically distributed large-scale Cloud applications

✓ The purpose is to study the behavior of such applications under various

deployment configurations

Features of CloudAnalyst:

✓ Easy to use due to Graphical User Interface (GUI)

✓ High level of configurability

✓ Flexibility of adding components

✓ Repeatability of experiments

✓ Graphical output (e.g. charts, tables)

✓ Easy to extend (Java Swing) and uses blended technology

CloudAnalyst Design:

Main components

• GUI Package: Front end

• Simulation: Create, execute, hold

• UserBase: User traffic generation

• DataCenterController: Events of data center

• Internet: Internetworking & routing

• InternetCharacteristics: Properties of Internet (delay, Bandwidth,

throughput, etc.)

• VmLoadBalancer: Policies for load balancing

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• CloudAppServiceBroker: Entities for routing between

• UserBase & data center.

GreenCloud:

✓ Why:

▪ The computing capacity has increased the cost and operational

expenses of data centers

▪ Energy consumption by data center is the major factor driving the

operational expense

✓ What:

▪ Operational cost is the energy utilized by computing and

communication units within a data center

✓ How:

▪ GreenCloud monitors the energy consumption of servers, switches,

etc.

▪ Developed as an extension of a packet-level network simulator NS2

Features of GreenCloud:

✓ Facility for monitoring energy consumption of network & devices

✓ Support simulation of cloud network components

✓ Supports monitoring of energy consumption of individual components

✓ Enables improved power management schemes

✓ Dynamic management and configuration of devices

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 Open Source and Commercial Clouds:

Open Source Clouds Commercial Clouds

Examples OpenStack, CloudStack, Amazon Web Services (AWS), Microsoft Azur

Eucalyptus Google App Engine

Facility Mostly offers IaaS IaaS, PaaS, SaaS Services on

subscription
Security Implemented by user Implemented by service provider

Type Private/On-premise Public/Off-premise/Hosted-private

OpenStack:

✓ Collection of open source technologies

✓ Managed by the OpenStack Foundation

✓ Supports vastly scalable cloud system

✓ Preconfigured software suit

✓ Different services available for users

✓ Considered Infrastructure as a Service (IaaS).

✓ Ease of use: add new instances quickly to run other cloud components

✓ Provides a platform to create software applications

✓ Developed software applications can be used by the end users

Microsoft Azure:

✓ Previously Windows Azure

✓ Supports Iaas and PaaS

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 ✓ Supports extensive set of services to quickly create, deploy and manage
applications

✓ Many programming languages and frameworks are supported

✓ Available across a worldwide Microsoft-managed datacenters

Azure as PaaS (Platform as a Service )

✓ Platform is provided to clients to develop and deploy software

✓ Clients focus on application development rather than worry about

hardware and infrastructure

✓ Low Cost

✓ less vulnerable to security attacks

✓ Ease to move on to new tools

✓ Solves the issues related to most of the operating systems, servers and
networking.

Azure as IaaS (Infrastructure as a Service )

✓ Offers total control of the OS and application stack

✓ Features to access, manage and monitor the data centers

✓ Ideal for the application where complete control is required

✓ Facility for loading of custom configurations

Amazon Elastic Compute Cloud(EC2):

✓ A web service for users to launch and manage

server instances in Amazon’s data centers

✓ Provides various APIs, tools and utilities

✓ Facilitate dynamic computation scaling in the Amazon Web Services

(AWS) cloud

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 ✓ Supports pay-per-use billing rather than making large and expensive
hardware purchases

Amazon EC2 Instances

✓ Virtual computing environments

✓ Instance templates of different configurations – CPU, memory, storage,

networking capacity

✓ Dynamic instance allocation by AWS according to user demand

✓ Instance types

▪ General purpose: T2, M4, M3

▪ Compute optimized: C4, C3

▪ Memory optimized: X1, R4, R3

▪ Accelerated computing instances: P2, G2, F1

Features of Amazon EC2

✓ Operating system:

▪ Supports all OS types

▪ Custom distribution: Amazon Linux AMI/Amazon Machine Images

✓ Persistent storage:

▪ Temporary: Local ‘Instance Store’

▪ Amazon Elastic Block Store (EBS)

▪ Simple Storage Service (S3)

✓ Automated scaling: Rule based / Schedule based

✓ Different “availability zones” in data centers increases fault-tolerance

✓ Firewall Rules/Security Groups: Only predefined protocols, ports, and

source IP ranges reach the instances

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 ✓ Elastic IP address: Mapping between IP and any VM of user

✓ Amazon CloudWatch: CPU, disk, network resource utilization monitoring

✓ Enhanced security for instances using public-private key pair

✓ Virtual private clouds (VPCs):

▪ Logically separate from the rest of the AWS cloud

▪ Optionally connected to user’s own network

Cloud Computing – Practical

Introduction to Openstack:

✓ A software to create a cloud infrastructure

✓ Launched as a joint project of Rackspace Hosting and NASA in 2010

✓ Opensource

✓ Presently many companies are contributing to openstack

✓ Eg. IBM, CISCO, HP, Dell, Vmware, Redhat, suse, Rackspace hosting

✓ It has a very large community

✓ Can be used to develop private cloud or public cloud

✓ Versions:

✓ Austin, Bexar, Cactus, Diablo, Essex, Folsom, Grizzly, Havana,

Icehouse, Juno, Kilo, Liberty, Mitaka, Newton, Ocata (Latest)

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Openstack Components:

❖
❖ Keystone

o Identity service

o Provides authentication and authorization

❖ Horizon

o Dashboard

o GUI of the software

o Provides overview of the other components

❖ Nova

o Compute service

o Where you launce your instances

❖ Glance

o Image service

o Discovering, registering, retrieving the VM

o Snapshots

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 ❖ Swift

o Object storage

o Helps in storing data safely, cheaply and efficiently

Neutron

o Provides networking service

o Enables the other services to communicate with each other

o Make your own network

❖ Cinder

o Block storage

o Virtualizes the management of block service

❖ Heat

o Orchestration

❖ Ceilometer

o Billing

o What service you are using

o How long are you using

✓ Installation:
Can be installed manually or using scripts like Devstack

✓ We will use devstack

✓ Steps:

▪ Install git ( sudo apt‐get install git )

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 ▪ Clone devstack ( git clone https://git.openstack.org/openstack‐
dev/devstack )

▪ Go to devstack directory ( cd devstack )

▪ Open local.conf file and paste the following and save the file

ADMIN_PASSWORD=<YOUR PASSWORD> DATABASE_PASSWORD

=<YOUR PASSWORD> RABBIT_PASSWORD =<YOUR PASSWORD>
SERVICE_PASSWORD =<YOUR PASSWORD>

HOST_IP=<the IP of your PC>

▪ Run the stack.sh file ( ./stack.sh)

▪ For un installation, go to devstack directory and run unstack.sh file.

Sensor-Cloud
Introduction
✓ It is not mere integration of sensors and cloud computing
✓ It is not only “dumping the sensor data into cloud”
✓ Not only the mere integration of cloud computing and sensor
networks, but sensor‐cloud is more than that.
✓ Concept of virtualization of sensor node.
✓ Pay‐per‐use.
✓ One sensor node/network appears as many.
✓ A stratum between sensor nodes and end‐users.
Virtualization Concept
✓ One computer host appears as many computers‐concept of Virtual
Machine (VM).

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✓ Improve IT throughput and costs by using physical resources as a
pool from which virtual resources can be allocated.
✓ Benefit
✓ Sharing of resources: Same resource can be shared, in turn
cost reduction.
✓ Encapsulation: A complete computing environment
✓ Independence: Runs independently of underlying hardware
✓ Portability: VM Migration
Difference with WSN

Actors and Roles

Attributes WSN Sensor Cloud

Ownership WSN‐user Sensor‐owner

Deployment WSN‐user Sensor‐owner

Redeployment WSN‐user SCSP

Maintenances WSN‐user SCSP

Overhead WSN‐user SCSP

Usage WSN‐user End‐user

Actors in Sensor-cloud
✓ End‐users

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 ✓ Enjoy SaaS through applications as per the requirements.
✓ Unknown about what and which physical sensor is/are
allocated to serve the application
✓ Sensor‐owner
✓ Plays a role from business perspective.
✓ They purchase physical sensor devices, deployed over
different geographical locations, and lend these devices to
the sensor‐cloud
✓ Sensor‐Cloud Service Provider (SCSP)
✓ A business actor.
✓ SCSP charges price from the end‐users as per their usage of
Se‐aaS.
Sensor-cloudArchitecture:

42
 ✓ End‐users: Registered themselves, selects templates, and request
for application(s)
✓ Sensor‐owner: Deploy heterogeneous/ homogeneous physical
sensor nodes over different geographical location
✓ SCSP: Plays managerial role
Management Issues in Sensor-Cloud:
✓ Optimal Composition of virtual sensor nodes
✓ Data Caching
✓ Optimal Pricing
Optimal Composition of Virtual Sensor:
✓ Efficient virtualization of the physical sensor nodes
✓ An optimal composition of VSs
✓ Consider same geographic region: CoV‐I
✓ Spanning across multiple regions: CoV‐II
Why Composition of Virtual Sensor?
✓ Resource‐constrained sensor nodes
✓ Dynamic change in sensor conditions
✓ The composition of virtual sensors are non‐traditional
CoV-I: Formation of Virtual Sensor
Optimal formation of sensor nodes are homogeneous sensor nodes with
in samw geographical boundaries.

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CoV-II: Formation of Virtual Sensor
Optimal formation of sensor nodes are heterogeneous sensor nodes with
in same geographical boundaries.

44
Dynamic and Adaptive Data Caching Mechanism:

✓ Introduces internal and external caching mechanisms

✓ Ensures efficiency in resource utilization

✓ Flexible with the varied rate of change of the physical environment

✓ End‐users request for the sensed information through a Web‐interface

✓ Allocation of physical sensor nodes and virtualization takes place

✓ Physical sensor nodes continuously sense and transmit data to sensor‐cloud

✓ Practically, in some cases, the change in environmental

condition are significantly slow

✓ Due to the slow change in environment, the sensed data of physical sensors
unaltered

✓ In such a situation, unnecessary sensing causes energy consumption

✓ Internal Cache (IC)

✓ Handles requests from end‐user

✓ Takes decision whether the data should be provided directly to the end
user or is it required to re‐cache the data from external cache

✓ External Cache (EC)

✓ After every certain interval data are required to re‐cache

✓ Initially, few data are used to be transmitted to IC

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Dynamic Optimal Pricing for Sensor-Cloud Infrastructure:

✓ Existing schemes consider homogeneity of service (e.g. for IaaS, SaaS)

✓ No scheme for SeaaS.

✓ The proposed pricing scheme comprises of two components:

✓ Pricing attributed to hardware (pH)

✓ Pricing attributed to Infrastructure (pI)

✓ Goal of the proposed pricing scheme:

✓ Maximizing profit of SCSP

✓ Maximizing profit of sensor owner

✓ End users’ satisfaction

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Fog Computing:

✓ Fog computing or fogging is a term coined by CISCO.

✓ The idea of fog computing is to extend the cloud nearer to the IoT devices.

✓ The primary aim: solve the problems faced by cloud computing during IoT
data processing.

✓ an intermediate layer between cloud and devices.

✓ 40% of the whole worlds data will come from sensors alone by 2020.

✓ 90% of the world’s data were generated only during the period of last two
years.

✓ 2.5 quintillion bytes of data is generated per day.

✓ total expenditure on IoT devices will be $1.7 Trillion by 2020

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 ✓
✓ the total number of connected vehicles worldwide will be 250 millions by
2020.

✓ there will be more than 30 billion IoT devices

✓ The amount of data generated by IoT devices is simply huge.

Why Fog Computing:

✓ The ability of the current cloud model is insufficient to handle the

requirements of IoT.

✓ Issues are:

✓ Volume

✓ Latency

✓ Bandwidth

Data Volume:

✓ By 2020, about 50 billion devices will be online.

✓ Presently billions of devices produce exabytes of data everyday.

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 ✓ Device density is still increasing everyday.

✓ Current cloud model is unable to process this amount of data.

✓ Private firms, Factories, airplane companies produces colossus amount of

data everyday

✓ Current cloud model cannot store all these data

✓ Data need to be filtered

Latency

✓ Time taken by a data packet for a round trip

✓ An important aspect for handing a time sensitive data.

✓ If edge devices send time sensitive data to cloud for analysis and wait for
the cloud to give a proper action, then it can lead to many unwanted results.

✓ While handling time sensitive data, a millisecond can make a huge

differences.

✓ Sending time‐sensitive data to cloud for analysis

✓ Latency = T
fron device to cSoud + Tdata anaSycic
+T
fron cSoud to device

where T = Time

✓ Latency will be increased

✓ When the action reaches the device, accident may have already occured

Bandwidth:

✓ Bit‐rate of data during transmission

✓ If all the data generated by IoT devices are sent to cloud for storage
and analysis, then, the traffic generated by these devices will be
simply gigantic.

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 ✓ consumes almost all the bandwidths.

✓ Handling this kind of traffic will be simply a very hard task.

✓ Billions of devices consuming bandwidth

✓ If all the devices become online even IPv6 will not be able to provide
facility to all the devices

✓ Data may be confidential which the firms do not want to share online

Requirements of IoT:

✓ Reduce latency of data:

✓ Appropriate actions at the right time prevents major accidents

machine failure etc.

✓ A minute delay while taking a decision makes a huge difference

✓ Latency can be reduced by analyzing the data close to the data source

✓ Data security:

✓ IoT data must be secured and protected from the intruders.

✓ Data are required to be monitored 24x7

✓ An appropriate action should be taken before the attack causes major

harm to the network

✓ Operation reliability:

✓ The data generated from IoT devices are used to solve real time
problem

✓ Integrity and availability of the data must be guaranteed

✓ Unavailability and tampering of data can be hazardous

✓ Processing of data at respective suitable place:

✓ Data can be divided into three types based on sensitivity

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 ✓ time sensitive data

✓ less time sensitive data

✓ data which are not time sensitive

✓ Extremely time sensitive data should be analyzed very near to the data
source

✓ Data which are not time sensitive will be analyzed in the cloud.

✓ Monitor data across large geographical area:

✓ The location of connected IoT devices can be spread across a large

geographical region

✓ E.g. monitoring the railway track of a country or a state

✓ the devices are exposed to the harsh environments condition

When should we use fog:

✓ If the data should ne analyze with fraction of second

✓ If there are huge number of devices

✓ If the devices are separated by a large geographical distance

✓ If the devices are needed to be subjected to extreme conditions

Architecture of Fog:

✓ Cloud services are extended to IoT devices through fog

✓ Fog is a layer between cloud and IoT devices

✓ many fog nodes can be present

✓ Sensor data are processed in the fog before it is sent to the cloud

✓ Reduces latency, save bandwidth and save the storage of the cloud

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Fog nodes:

✓ Characteristics for a fog node:

✓ Storage ‐ To give transient storage

✓ Computing facility

‐ To process the data before it is sent to cloud

‐ To take quick decisions

✓ Network connectivity ‐ To connect with IoT devices, other fog nodes

and cloud

✓ E.g. ‐ routers, embedded servers, switches, video surveillance cameras, etc.

✓ deployable anywhere inside the network.

✓ Each fog nodes have their aggregate fog node.

Working of Fog:

✓ Three types of data

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 ✓ Very time‐sensitive data

✓ Less time‐sensitive data

✓ Data which are not time‐sensitive

✓ Fog nodes works according to the type of data they receive.

✓ An IoT application should be installed to each fog nodes

✓ The nearest fog node ingest the data from the devices.

✓ Most time‐sensitive data

✓ Data which should be analyzed within fraction of a second

✓ Analyze at the nearest node itself

✓ Sends the decision or action to the devices

✓ Sends and stores the summary to cloud for future analysis

✓ Less time‐sensitive data

✓ Data which can be analyzed after seconds or minutes

✓ Are sent to the aggregate node for analysis

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 ✓ After analysis, the aggregate node send the decision or action to the
device through the nearest node

✓ The aggregate node sends the summary to cloud for storage and
future analysis.

✓ Non‐time‐sensitive data

✓ Data which can be wait for hours, days, weeks

✓ Sent to cloud for storage and future analysis.

✓ Those summaries from fog nodes can be considered as less time

sensitive data.

Advantages of Fog:

✓ Security

✓ Provides better security

✓ Fog nodes can use the same security policy

✓ Low operation cost

✓ Data are processed in the fog nodes before sending to cloud

✓ Reduces the bandwidth consumption

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✓ Reduces unwanted accidents

✓ Latency will be reduce during decision making

✓ Quick decision making

✓ Better privacy

✓ Every industry can analyze their data locally

✓ Store confidential data in their local servers

✓ Send only those data which can be shared to the cloud

✓ Business agility

✓ Fog application can be easily developed according to tools available

✓ Can be deployed anywhere we need

✓ Can be programed according to the customer’s need

✓ Support mobility

✓ Nodes can be mobile

✓ Nodes can join and leave the network anytime

✓ Deployable in remote places

✓ Can be deployed in remote places

✓ Can be subjected to harsh environmental conditions

✓ Under sea, railway tracks, vehicles, factory floor etc

✓ Better data handling

✓ Can operate with less bandwidth

✓ Data can be analyzed locally

✓ Reduce the risk of latency

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Applications of Fog:

✓ Real time health analysis

✓ Patients with chronic illness can be monitored in real time

✓ Stroke patients

✓ Analyze the data real time

✓ During emergency, alerts the respective doctors immediately

✓ Historical data analysis can predict future dangers of the patient

✓ Intelligence power efficient system

✓ Power efficient

✓ Reports detail power consumption report everyday

✓ Suggest economical power usage plan

✓ Real time rail monitoring

✓ Fog nodes can be deployed to railway tracks

✓ Real time monitoring of the track conditions

✓ For high speed train, sending the data in cloud for analysis is
inefficient

✓ Fog nodes provide fast data analysis

✓ Improve safety and reliability

✓ Pipeline optimization

✓ Gas and oils are transported through pipelines

✓ Real time monitoring of pressure, flow, compressor is necessary

✓ Terabytes of data are created

✓ Sending all this data to cloud for analysis and storage is not efficient

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 ✓ Network latency is not acceptable

✓ Fog is a solution

✓ Real time wind mill and turbine analysis

✓ Wind direction and speed analysis can increase output

✓ Data can be monitored real time

Challenges of FOG:

✓ Fault tolerance

✓ Failure of a node should be immediately fixed

✓ Individual failure should not affect the whole scenario

✓ Real time analysis

✓ Real time analysis is a primary requirement for minimizing latency

✓ Dynamic analysis and decision making reduces danger and increase

output

✓ Monitor huge number of nodes is not easy

✓ Programming architecture

✓ Fog nodes may be mobile

✓ Nodes can connect and leave the network when necessary

✓ Many data processing frameworks are statically configured

✓ These frameworks cannot provide proper scalability and flexibility

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Smart Cities and Smart Homes:

Introduction:

✓ A Smart City is-

▪ An urban system

▪ Uses Information & Communication Technology (ICT)

▪ Makes infrastructure more interactive, accessible and efficient.

✓ Need for Smart Cities arose due to-

▪ Rapidly growing urban population

▪ Fast depleting natural resources

▪ Changes in environment and climate

Analogy:

Humans Smart Cities

Skeleton Buildings, Industries, People

Skin Transportation, Logistics

Organs Hospital, Police, Banks, Schools

Brain Ubiquitously embedded intelligence

Nerves Digital telecommunication networks

Sensory Organs Sensors, Tags

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Cognition Software

Current Focus Areas:

✓ Smart Homes

▪ Health monitoring.

▪ Conservation of resources (e.g. electricity, water, fuel).

▪ Security and safety.

✓ Smart Parking Lots

▪ Auto routing of vehicles to empty slots.

▪ Auto charging for services provided.

▪ Detection of vacant slots in the parking lot.

✓ Smart Vehicles

▪ Assistance to drivers during bad weather or low-visibility.

▪ Detection of bad driving patterns or driving under the influence of substances.

▪ Auto alert generation during crashes.

▪ Self diagnostics.

✓ Smart Health

▪ Low cost, portable, at-home medical diagnosis kits.

▪ Remote check-ups and diagnosis.

▪ On-body sensors for effortless and accurate health monitoring.

▪ Auto alert generation in case of emergency medical episodes (e.g. Heart attacks,
seizures).

✓ Pollution and Calamity Monitoring

▪ Monitoring for weather or man-made based calamities.

▪ Alert generation in case of above-threshold pollutants in the air or water.

▪ Resource reallocation and rerouting of services in the event of calamities.

✓ Smart Energy

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 ▪ Smart metering systems.

▪ Smart energy allocation and distribution system.

▪ Incorporation of traditional and renewable sources of energy in the same grid.

✓ Smart Agriculture

▪ Automatic detection of plant water stress.

▪ Monitoring of crop health status.

▪ Auto detection of crop infection.

▪ Auto application of fertilizers and pesticides.

▪ Scheduling harvesting and arranging proper transfer of harvests to warehouses or

markets.

IoT Challenges in Smart Cities:

✓ Security and Privacy
▪ Exposure to attacks (e.g. cross-site scripting, side channel,
etc.).
▪ Exposure to vulnerabilities.
▪ Multi-tenancy induces the risk of data leakage.
✓ Heterogeneity
▪ Integration of varying hardware platforms and specifications.
▪ Integration of different radio specifications.
▪ Integration of various software platforms.
Accommodating varying user requirements.
✓ Reliability
▪ Unreliable communication due to vehicle mobility.

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 ▪ Device failures still significant
✓ Large scale
▪ Delay due to large scale deployments.
▪ Delay due to mobility of deployed nodes.
▪ Distribution of devices can affect monitoring tasks.
✓ Legal and Social aspects
▪ Services based on user provided information may be subject
to local or international laws.
▪ Individual and informed consent required for using humans
as data sources.
✓ Big data
▪ Transfer, storage and maintenance of huge volumes of data is
expensive.
▪ Data cleaning and purification is time consuming.
▪ Analytics on gigantic data volumes is processing intensive.
✓ Sensor Networks
▪ Choice of appropriate sensors for individual sensing tasks is
crucial.
▪ Energy planning is crucial.
▪ Device placement and network architecture is important for
reliable end-to-end IoT implementation.
▪ Communication medium and means play an important role in

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seamless function of IoT in smart cities.
Smart Cities-Data Fusion:
✓ Enormous volume of data is produced periodically in a smart city
environment.
✓ Challenges include making the available/ incoming large data
volume precise and accurate.
✓ Quality of data precision and accuracy affects the quality of
decision making in IoT-enabled smart cities.
✓ Data fusion enables optimum utilization of massive data gathered
from multiple sources, and across multiple platforms.
✓ Combines information from multiple sensor sources.
✓ Enhances the ability of decision making systems to include a
multitude of variables prior to arriving at a decision.
✓ Inferences drawn from multiple sensor type data is qualitatively
superior to single sensor type data.
✓ Information fusion generated from multiple heterogeneous sensors
provides for better understanding of the operational surroundings.
✓ Combines information from multiple sensor sources.
✓ Enhances the ability of decision making systems to include a
multitude of variables prior to arriving at a decision.
✓ Inferences drawn from multiple sensor type data is qualitatively
superior to single sensor type data.

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 ✓ Information fusion generated from multiple heterogeneous sensors
provides for better understanding of the operational surroundings.
Challenges:

Data Fusion Opportunities in IoT:

✓ Collective data is rich in information and generates better
intelligence compared to data from single sources.
✓ Optimal amalgamation of data.
✓ Enhancing the collective information content obtained from
multiple low-power, low-precision sensors.
✓ Enables hiding of critical data sources and semantics (useful in
military applications, medical cases, etc.).

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 Smart Home-Introduction:
✓ Smart home infrastructure consists of:
▪ Intelligent networking device infrastructure
▪ Seamless integration of various devices using wired/wireless
technologies
✓ Allows ease of use for household systems.
✓ Creates a highly personalized and safe home space
✓ Corporations seriously indulging in smart home systems include
GE, Cisco, Google, Microsoft, and others.
✓ Provides productive and cost-efficient environment.
✓ Maximizes the effectiveness of the occupants.
✓ Provides efficient management with minimum life-time costs of
hardware and facilities.
✓ Optimizes-
✓ Structures
✓ Systems
✓ Services and management
✓ Interrelationships between the above three.
Smart Home Aspects:

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Home Area Networks (HANs)
✓ Network contained within a home.
✓ Enables remote access and control of devices and systems.
✓ Provides amalgamation of various systems within a home, such as
– security systems, home automation systems, personal media,
communication, etc.
HAN Elements:
Internet Protocol (IP)
✓ Multi-protocol gateway bridges non-IP network to IP network.
✓ Bridging between new technologies is limited.
✓ For new technologies or networks, a new mapping is required for
bridging to perform satisfactorily.
Wired HAN
▪ Easy integration with pre- existing house infrastructure.

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 ▪ Low cost.
▪ Can use power lines, coaxial cables, telephone lines, optical fibers,
and other such technologies for communication.
Wireless HAN
▪ Can use popular home Wi-Fi, ZigBee, and even new
standards, such as 6LoWPAN.
▪ Wireless makes implementation easy.
HAN Medium Classification:

HAN Standards:
UPnP:
✓ Universal Plug and Play (UPnP).
✓ Application layer technology, mainly web-based.

66
 ✓ TCP/IP protocol stack provides support for the lower layers, and
enables seamless integration of various technologies.
✓ Provides transparent networking with support for zero-
configuration networking and automatic discovery of devices.
DLNA:
✓ Digital Living Network Alliance (DLNA)
✓ Trade organization created by Sony,
Intel, and Microsoft.
✓ Connects cable-based networks with wireless networks for
increased sharing of media, control and access.
✓ Domestically shares network media resources.
KNX:
✓ Konnex (KNX): an open important standard for home and
building networks.
✓ Utilizes the full range of home communication infrastructure –
Power lines, coaxial cables, twisted pair, RF, etc.
✓ Must be setup and configured via a software before its proper
usage.
LonWorks:
✓ Local Operation Networks (LonWorks).
✓ Every device includes a Neuron Chip, a transceiver and the
application electronics.

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✓ Neuron chip is a SOC with multiple microprocessors, RAM, ROM
and IO interface ports.
✓ Splits device groups into intelligent elements, which can
communicate through a physical communication medium.
Zigbee:
✓ Zigbee consists of four layers – Physical, Medium Access Control,
Network, and Application.
✓ Physical and MAC layers are defined by IEEE802.15.4, whereas
Network and Application are defined by Zigbee.
✓ Aims at low-cost, low-energy devices.
✓ ZigBee Alliance is composed of Mitsubishi, Honeywell, Invensys,
Motorola and Philips.
X-10.
✓ X-10 enables remote control of compliant transmitters and
receivers over power lines and electrical wirings present in the
house.
✓ Adopted by GE and Philips.
✓ Standard defines procedures for transmission of bits over AC
carrier signals.
✓ Low-speed and low data rate.
✓ Mainly used for control of lighting, appliance networks and
security sensors.

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HAN Architectures:
✓ Uses XML for description and web- services for control.
✓ Follows a Service oriented Architecture
(SOA).
✓ Not tied to any software, language or architecture.
✓ A central gateway connects different technologies.
✓ A tech Manager for each technology provides web services for
control and access.
✓ Connects various devices sharing their resources with auto-
configuration and auto-installation.
✓ Based on JAVA environment and pursued
by Sun Microsystems (Now, Oracle).
✓ Constructs an organized distribution system without a central node
(federation).
✓ Jini apps use bytecode to run JVM, and are portable.
✓ Follows Object Oriented Paradigm.
✓ Middleware for embedded intelligent systems.
✓ Connects a Service Oriented
Architecture Network.
✓ Connected devices may have limited resources, low processing
power, memory or energy consumption.

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✓ Each device has an embedded HYDRA client which acts as a
proxy between the device and the middleware.
HYDRA Protocol:

HAN Initiatives:
▪ Ambient intelligent systems
▪ For networked home systems
▪ Features user-friendly interfaces, interoperability, and automatic
discovery of devices and services.

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 Smart Grid
Introduction:
✓ Advancement of traditional electrical grid
✓ Traditional electrical grid
✓ Energy generation is done in centralized power plants
✓ Energy distribution is one directional – from the power plant
to the homes or industries.
✓ Monitoring and restoration of grid is done manually
✓ Uni‐directional communication
✓ Smart Grid –
✓ Achieve high reliability in power systems
✓A cyber‐physical system equipped with sustainable
models of energy production, distribution,
and usage
✓ Smart grid is conceptualized as a planned nation wide network that
uses information technology to deliver electricity efficiently,
reliably, and securely.
✓ Smart grid is also named as –
✓ Electricity with a brain
✓ The energy internet
✓ The electronet
✓ According to the definition given by NIST, smart grid is – “a
modernized grid that enables bidirectional flows of energy and

71
 uses two‐way communication and control capabilities that will
lead to an array of new functionalities and applications.”
Benefits of Smart Grid:
✓ Benefits associated with the Smart Grid include:
✓ More efficient transmission of electricity
✓ Quicker restoration of electricity after power disturbances
✓ Reduced operations and management costs for utilities, and
ultimately lower power costs for consumers
✓ Reduced peak demand, which will also help lower electricity
rates
✓ Increased integration of large‐scale renewable energy
systems
✓ Better integration of customer‐owner power generation
systems, including renewable energy systems
✓ Improved security
✓ Using smart grid, both the consumers and the energy service
providers or stakeholders get benefited.
Benefits of Customers:
✓ For consumers, the benefit of using smart grid are as follows:
✓ Updated information on their energy usage in real‐time
✓ Enabling electric cars, smart appliances, and other smart
devices to be charged

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 ✓ Program the smart devices to run during off‐peak hours to
lower energy bills
✓ Different pricing options
Benefits to Stakeholders:
✓ For stakeholders, the benefit of using smart grid are as follows:
✓ Increase grid reliability
✓ Reduce the frequency of power blackouts and brownouts
✓ Provide infrastructure for monitoring, analysis, and
decision‐making
✓ Increase grid resiliency by providing detailed information
✓ Reduce inefficiencies in energy delivery
✓ Integrate the sustainable resources of wind and solar
alongside the main grid
✓ Improve management of distributed energy resources,
including micro‐grid operations and storage management.
Properties of Smart Grid:
✓ Power System Efficiency
✓ Power Monitoring
✓ Asset Management and optimal utilizations
✓ Distribution Automation and Protection
✓ Power Quality
✓ Self‐Healing

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 ✓ Frequency Monitoring and Control
✓ Load Forecasting
✓ Anticipation of Disturbances
✓ Consumer Participation
✓ Real‐time monitoring of consumption
✓ Control of smart appliances
✓ Building Automation
✓ Real‐time Pricing
✓ Distributed Generation
✓ Integration of renewable energy resources
✓ Integration of micro‐grid
Components of Smart Grid:

74
Smart Home:
✓ Smart home uses emerging smart grid technologies to save energy,
seek out the lowest rates, and contribute to the smooth and
efficient functioning of our electric grid
✓ The interactive relationship between the grid operators, utilities,
and consumers helps in proper functioning of smart grid
technologies
✓ Computerized controls in smart homes helpseto minimiz
energy use at times when the power grid is under stress from
high demand, or even to shift some of their power use to times
when power is available at a lower cost, i.e., from on‐ peak hours
to off‐peak hours
✓ Smart home depends on –
✓ Smart meters and home energy management systems
✓ Smart appliances
✓ Home power generation
✓ Smart Meters
✓ Provide the Smart Grid interface between consumer and the
energy service provider
✓ Operate digitally
✓ Allow for automated and complex transfers of information
between consumer‐end and the energy service provider
✓ Help to reduce the energy costs of the consumers

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 ✓ Provides information about usage of electricity in different
service areas to the energy service providers
✓ Home energy management systems
✓ Allows consumers to track energy usage in detail to better
save energy
✓ Allows consumers to monitor real‐time information and price
signals from the energy service provider
✓ Allows to create settings to automatically use power when
prices are lowest
✓ Avoids peak demand rates
✓ Helps to balance the energy load in edaifferent ar
✓ Prevents blackouts
✓ In return, the service provider also may choose to provide financial
incentives
✓ Smart Appliances
✓ Automated and robust in nature
✓ Response to signals from the energy service provider to avoid
using energy during times of peak demand
✓ Include consumer controls to override the automated controls
✓ By overriding, the consumer can consume energy as per their
requirement, while paying minimum is not ensured
✓ Home Power Generation
✓ Power generation system at consumers‐end

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 ✓ Rooftop solar electric systems
✓ Small wind turbines
✓ Small hydropower System
✓ Home fuel cell systems – produce heat and power from
natural gas
✓ Surplus energy generated by the home power generation systems
can be fed back into the grid
✓ In case of “Islanding”, a home can have power from distributed
resources, i.e., home power generation systems
Renewable Energy:
✓ According to the International Energy Agency –
✓ “Renewable energy is derived from natural processes that
are replenished constantly. In its various forms, it derives
directly from the sun, or from heat generated deep within the
earth. Included in the definition is electricity and heat
generated from solar, wind, ocean, hydropower, biomass,
geothermal resources, and biofuels and hydrogen derived
from renewable resources.”
✓ Reduced environmental pollution
✓ Consumers capable of generating energy from renewable energy
resources are less dependent on the micro‐grid or main grid
✓ In addition to that, they can supply surplus amount of energy from
the renewable resources and can make profit out of it
Consumer Engagement:

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✓ Consumers can –
✓ Save energy with proper scheduling of smart home
appliances
✓ Pay less for consuming energy in off‐peak hours
✓ Energy service provider gives incentives based on the energy
consumption of the consumer and they can save money
✓ Consumers’ involvement in following ways:
✓ Time‐of‐Use pricing
✓ Net metering
✓ Financial incentives
✓ In Time‐of‐Use pricing
✓ The consumers are encouraged to consume energy in
off‐peak hours when the energy load is less
✓ Throughout the day, the energy load on the grids are dynamic
✓ In on‐peak hours, if the requested amount of energy is higher, it
leads to –
✓ Less‐efficient energy distribution
✓ More pollution – it depends on the non‐renewable energy
resource to meet the peak requirement
✓ Home energy management system tries to schedule the smart
appliances in off‐ peak hours
✓ To ensure efficient service
✓ To pay less

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✓ Net metering
✓ It is feasible with the installation of smart meters
✓ Consumers are paid high, if they are supplying excess
amount of generated energy to the grid in on‐peak hours
✓ The price is less in case of off‐peak hours
✓ Final bills to be paid by the consumers depends on
✓ The in‐flow of energy (from the grid to the consumers‐end)
✓ The out‐flow of energy (from the consumers‐end to the grid)
✓ The consumer may get incentives from the energy service
provider at the end of the year based on the net metering
value
✓ Financial Incentives
✓ Energy service provider offers some financial incentives for
the consumers’ participation
✓ Incentives for shifting operation of appliances to the off‐peak
hours
✓ Incentives for using stored energy at the battery installed at
the consumers‐end or at the plug‐in hybrid electric vehicles
(PHEVs)
✓ Smart grid enables consumers engagement to a large extend
✓ Consumers get financial incentives by different means from the
energy service providers
✓ Energy service providers maintain efficient and load balancing
energy distribution

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Operation Centers:
✓ Drawbacks of traditional operation centers
✓ Tries to make sure the amount of generated energy is getting
used
✓ The grid is unstable, if the grid voltage drops due to excess
energy generation
✓ Limited control capabilities
✓ No means to detect oscillation which leads to blackout
✓ Limited information about the energy flow through the grid
✓ Smart grid
✓ Provides information and control on the transmission system
✓ Makes the energy grid more reliable
Minimize the possibility of widespread blackouts
✓ For monitoring and controlling the transmission System in smart
grid, phasor measurement unit (PMU) is used
✓ PMU samples voltage and current with a fixed sample rate at the
installed location
✓ It provides a snapshot of the active power system at that location
✓ By increasing the sampling rate, PMU provides the dynamic
scenario of the energy distribution system
✓ PMU helps to identify the possibility of blackout in advance
✓ Multiple PMUs form a phasor network

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✓ Collected information by the phasor network is analyzed at
centralized system, i.e., Supervisory Control And Data
Acquisition (SCADA) system
✓ Self‐healing of grid
✓ Dampen unwanted power oscillations
✓ Avoid unwanted flows of current through the grid
✓ Reroute power flows in order to avoid overloading in a
transmission line
✓ This is part of distribution intelligence
✓ Demand side energy distribution
✓ Energy supply is done based on the requirement of the
consumers
✓ The consumers pay according the consumed energy and price
decide by the energy service provider at that time
✓ In smart grid, the energy distributors can form coalition and serve
the energy requirement in a specific geographic location
Distribution Intelligence:
✓ Distribution intelligence means the energy distribution systems
equipped with smart IoT devices
✓ Along with smart meters, distribution intelligence can –
✓ Identify the source of a power outage
✓ Ensure power flow automatically by combining automated
switching

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 ✓ Optimize the balance between real and reactive power
✓ Reactive power:
✓ Devices that store and release energy
✓ Cause increased electrical currents without consuming real
power
✓ Intelligent distribution System
✓ Maintains the proper level of reactive power in the System
✓ Protect and control the feeder lines
Plug-In Electric Vehicles:
✓ Smart Grids have the infrastructure needed to enable the efficient
use of plug‐in electric vehicle (PEVs)
✓ Using PEVs –
✓ Reduce dependency on oil
✓ No pollution when running on electricity
✓ PEVs rely on power plants to charge their batteries .
✓ Energy service provider encourages the consumers to charge
batteries of PEVs in off‐peak hours.
✓ PEVs also can be used as an energy source in on‐peak hours.
✓ PEVs get incentives from energy service provider for providing
energy to the grid through discharging .

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Plug-In Electric Vehicles:
✓ Smart Grids have the infrastructure needed to enable the efficient
use of plug‐in electric vehicle (PEVs)
✓ Using PEVs –
✓ Reduce dependency on oil
✓ No pollution when running on electricity
✓ PEVs rely on power plants to charge their batteries
✓ Energy service provider encourages the consumers to charge
batteries of PEVs in off‐peak hours
✓ PEVs also can be used as an energy source in on‐peak hours
✓ PEVs get incentives from energy service provider for providing
energy to the grid through discharging
Smart Grid Communic ation:

✓ Components for smart grid communication are as follows:

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 ✓ Smart Home Appliances
✓ Smart Meters
✓ Gateways
✓ Data Aggregator Units (DAUs)
✓ Meter Data Management Systems (MDMSs)
✓ Different networks associated with smart grid communication
✓ Home Area Networks (HANs)
✓ Neighborhood Area Networks (NANs)
✓ Wide Area Networks (WANs)
✓ IP Networks
✓ Sensors and Actuators Networks (SANETs)
✓ For Smart Home Appliances, the available protocol are as follows:
✓ C‐Bus:
✓ Data Rate: 3500 bits/sec
✓ Able to handle cable lengths upto 1000 m
✓ DECT
✓ Data rate: 64000 bits/sec
✓ Operates in 1880 – 1930 MHz
✓ EnOcean
✓ Data rate: 9600 bits/sec
✓ Operates in 902 MHz in North America

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✓ Universal Power line Bus
✓ Data rate: 480 bits/sec
✓ Enable two‐way communication protocol
✓ Thread
✓ Data Rate: 20‐250 Kbits/sec
✓ IPv6 addressing based 6LowPAN networking protocol
✓ Zigbee
✓ Data Rate: 20‐250 Kbits/sec
✓ Operates in 2.4 GHz band
✓ IEEE 802.15.4 protocol
✓ Communication range ~100 m
✓ Simplified Cable Solution (SCS)
✓ Data rate: 9.6 Kbits/sec
✓ Works on twisted pair
✓ Developed based on OpenWebNet
✓ Smart Meters and Gateways
✓ Each gateway connects few closely located smart meters
✓ Gateways communicate mostly based on WiFi, i.e., IEEE
802.11
✓ Gateways helps in two‐way communication
✓ Smart meters

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 ✓ Forward the energy consumption information fro the home
appliances to the gateways
✓ Forward the billing amount and the control information from
the gateways to the home appliances
✓ Gateway acts as link between the smart meters and the data
aggregator units (DAUs)
✓ Data Aggregator Units (DAUs)
✓ Aggregate the energy consumption or energy request of
certain geographical area
✓ Forward the energy consumption information to the
centralized coordinator – meter data management system
(MDMS)
✓ Maintains a buffer to queue the energy consumption
information of the consumers
✓ Meter Data Management Systems (MDMSs)
✓ Act as the centralized coordinator for smart grid
communication
✓ Handled by the energy service providers
✓ Part of operation center
✓ Decide the price per unit energy to be paid by the consumers
Smart Grid Security:
✓ Smart grid is a cyber physical system
✓ Following vulnerabilities are there in smart grid

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 ✓ Integrity – credibility of the data collected and transferred
over the grid
✓ Availability – accessibility to every grid component as well
as to the information transmitted and collected
✓ Dynamic system attacks – based on the previous information
same type of request can be replicated by the attacker
✓ Physical threats – physical attack to the smart
mgrpidocnoents
✓ Coordinated attacks – cascading failure of systems in smart
grid
✓ Integrity
✓ Data injection attacks (DIAs)
✓ Manipulation of exchanged data such as sensor
readings, feedback control signals, and electricity price
signals
✓ Performed by compromising the hardware components
(as in the case of Stuxnet), or intercepting the
communication links
✓ System Damage
✓ An attacker can manipulate system measurements so
that a congested transmission line falsely seems to not
have reached its thermal transmission limit
✓ Induce large fluctuations in system dynamics that can
lead to tripping additional lines, disconnecting
generators, load shedding, or even a system blackout

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✓ Integrity
✓ Financial benefit
✓ Manipulating the electricity prices
✓ Doing this one can buy energy with lesser price from a
service provider and make high profit
✓ Time synchronization attacks
✓ An adversary can manipulate the time reference of the
time stamped measured phasors to create a false
visualization of the actual system conditions thus
yielding inaccurate control and protection actions
✓ Attacks that target PMU time synchronization are
known as time synchronization attacks (TSAs)
✓ Availability
✓ Accessibility unavailable to every grid component as well as
to the information transmitted and collected, whenever
needed
✓ Attacks compromising this availability are known as denial
of service (DoS) attacks
✓ Block key signals to compromise the stability of the grid and
observability of its states
✓ Manipulating generation‐load balance
✓ Dynamic System Attacks
✓ Replay attacks (RAs)

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 ✓ Injects input data in the system without causing changes
to the measurable outputs
✓ In RAs –
✓ Compromises sensors, monitors their outputs
✓ Learns the outputs and repeats them while injecting its
attack signal
✓ Dynamic data injection attacks (D‐DIA)
✓ Uses knowledge of the grid’s dynamic model to inject
data that causes unobservability of unstable poles
✓ Can lead to a system collapse
✓ Covert attack
✓ Closed loop version of replay attacks
✓ Physical Threats
✓ Attacks a physical component such as a generator, substation,
or transmission line is prominent
✓ Physical manipulation of smart meters for energy theft
purposes
✓ Coordinated Attacks
✓ Power system typically incorporates robustness measures
✓ An attack leading to the failure of one or few components
✓ Exploit the dense interconnections between grid components
to launch simultaneous attacks of different types targeting
various components

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 ✓ In smart grid, cloud applications take a lead in several aspects
✓ Energy management
✓ Information management
✓ Security
Energy Management and Cloud Application
✓ The energy management in smart grid can be more efficient by
using cloud applications
✓ Cloud‐Based Demand Response for fast response times in
large scale deployment
✓ Two cloud‐based demand response models are proposed as
follows:
✓ Data‐centric communication and
✓ Topic‐based group communication
✓ With the integration of cloud, requests from customers are
scheduled which are to be executed depending on the available
resources, priority, and other applicable constraints
✓ Incoming jobs from users are scheduled according to their priority,
available resources, and applicable constraints
✓ Integrating cloud computing applications for micro‐grid
management in the form of different modules such as
infrastructure, power management, and service
✓ The number of supported customers increases

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 ✓ With cloud application, integrate and analyze information
streaming from multiple smart meters simultaneously can be done,
in order to balance the real‐ time demand and supply curves
✓ Real‐time energy usage and pricing information can be shared
✓ Mobile agent can be used to monitor power system using cloud
computing platform due to the smart grid’s heterogeneous
architecture
Information Management and Cloud Application:
✓ Information processing in smart grid fit well with the computing
and storage mechanisms available for cloud applications
✓ Information from different components, and the supply and
demand state conditions can be shared with the help of cloud
computing
✓ Real‐time distributed data management and parallel processing of
information can be utilized using smart grid data cloud
application
✓ With the flexibility of cloud computing, information is retrieved
from the data cloud more conveniently in smart grid
✓ Dynamic pricing mechanism in smart grid is feasible with the use
of cloud application
✓ Cloud computing services are used as a dynamic data centers to
store the real‐ time information from the smart meter.
✓ Use of multi‐mobile agent combined with cloud computing for
profitable smart grid operation

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 ✓ Interactive cooperation using cloud services to support multiple
customers and multiple energy sources for large‐scale
development of smart grid for energy management
Security in Smart Grid and Cloud Application:
✓ An electric power information security and protection system can
be developed using based on cloud security
✓ Private cloud platforms are suitable for scaling out and processing
millions of data from users
✓ Using the cloud computing platform, the electrical utilities can
quickly and effectively deal with malicious software
✓ Security and protection system for electrical power
✓ Servers act as cloud and take decision according to the
clients’ data
✓ Privacy issue in smart grid
✓ Quickly and effectively deal with malicious software with the
implementation of cloud computing applications
✓ Data storage security for distributed verification in smart grid
using cloud application
✓ Real‐time data can be analyzed and estimated using cloud in smart
grid.
✓ Cloud‐based information privacy scheme can be used for smart
grid data privacy.

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Connected Vehicles:
Introduction:
✓ Vehicles equipped with
▪ Sensors
▪ Networking and communicating devices
✓ Capable of :
▪ Communicating with other devices within the vehicle
▪ Communicating with other similar vehicles
▪ Communicating with fixed infrastructure
Challenges:
✓ Security
✓ Privacy
✓ Scalability
✓ Reliability
✓ Quality of service
✓ Lack of global standards
Vehicle-to-Everything (V2X) Paradigm:
✓ Main component of future Intelligent Transportation System
(ITS).
✓ Enables vehicles to wirelessly share a
diverse range of information.

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✓ Information sharing may be with other vehicles, pedestrians, or
fixed infrastructures (mobile towers, parking meters, etc.)
✓ Allows for traffic management, ensuring on-road and off-road
safety, mobility for traveling.

✓ Follows a distributed architecture, where contents are widely

distributed over the network.
✓ Not restricted to single source information provider.
✓ Designed mainly for highly mobile environments.
✓ Can share information to nodes in vicinity, as well as remotely
located.
✓ Has greatly enhanced travel efficiency, as well as safety.
✓ The network is mainly used as a tool for sharing and disseminating
information.
Failures of TCP/IP in V2X:

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 ✓ Designed mainly for handling information exchange between a
single pair of entities.
✓ Information exchange dependent on the location of data.
✓ Can only identify the addresses of endpoints, which alone is not
useful for content distribution.
✓ Increase in number of wireless devices, restricts the mobility of
the nodes.
Content Centric Networking (CCN):
✓ CCN is derived from Information Centric Networking (ICN)
architecture.
✓ Focuses more on the data than its actual location.
✓ Hierarchically named data.
✓ Hierarchical data is transmitted directly instead of being part of a
conversation.
✓ Enables scalable and efficient data dissemination.
✓ In-network caching allows for low data traffic.
✓ Works well in highly mobile environments.
Vehicular Ad-hoc Networks (VANETs)
✓ Based on:
▪ Dedicated Short-Range Communication (DSRC)
▪ Wireless Access in Vehicular Environment (WAVE)
✓ Routing protocols derived from MANETs.

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 ✓ High throughput achievable in mobile environments.
✓ Guaranteed low-latency in mobile
environments.

CCN for VANETs:

✓ Routing
▪ Forwarding and routing based on name of content (not
location).
▪ Individual content’s name prefixes are advertised by routers
across the network.
▪ This helps to build a Forwarding Information Base (FIB) for
each router.
▪ The name of content remains same and unique globally.
▪ No issues of IP address management or address exhaustion.

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 ▪ Communication does not depend on speed or direction of
nodes.
✓ Scalability
▪ An in-network caching mechanism at each router.
▪ Uniquely identifiable (named) data chunks are stored in
Content Store (CS), which acts as a cache.
▪ Subsequent requests for a stored data chunk can be made to a
CS.
▪ The naming system in the CS enables a data to be used
multiple times, unlike normal IP-based routers.
▪ Reduced network load during increased network size, as a
result of the caching mechanism.
Body and Brain Architecture:
✓ An in-vehicle networking architecture.
✓ Three layered architecture.
✓ The body consists of intelligent networking nodes (INN) which
constantly collect information from the vehicle.
✓ The brain manages central coordination.
Sense and Execution Layer:

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Classification of INN:

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Network and Transmission Layer
For communication

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Intelligent Connected Vehicles (ICVs):

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✓ The US Department of Transport and Federal Communications
Commission allocated 75MHz (5850-5925MHz) as the dedicated
spectrum for ICVs.
✓ It is based on Dedicated Short Range Communication (DSRC)
technology.
✓ IEEE developed IEEE 802.11p and IEEE 1609 as DSRC
standards.
✓ Society of Automotive Engineers (SAE) came up with SAE J2735
and J2945 as DSRC standards.
IEEE 1609 Family
✓ IEEE P1609.0 Draft Standard for Wireless Access in Vehicular
Environments (WAVE) -
Architecture
✓ IEEE 1609.1-2006 - Trial Use Standard for Wireless Access in
Vehicular Environments (WAVE) - Resource Manager
✓ IEEE 1609.2 -2006- Trial Use Standard for Wireless Access in
Vehicular Environments
(WAVE) - Security Services for Applications and Management
Messages
✓ IEEE 1609.3 -2007 - Trial Use Standard for Wireless Access in
Vehicular Environments
(WAVE) - Networking Services
✓ IEEE 1609.4 -2006- Trial Use Standard for Wireless Access in
Vehicular Environments

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 (WAVE) - Multi-Channel Operations
✓ IEEE P1609.11 Over-the-Air Data Exchange Protocol for
Intelligent Transportation Systems (ITS).
Ad-hoc Domain:
✓ Composed of vehicles and road-side units.
✓ The vehicles (OBUs) are mobile.
✓ The road-side units (RSUs) are static.
✓ Communication mode may be either V2V or V2I.
✓ Communication through DSRC stack (IEEE 802.11p)

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✓ RSUs connected to Internet by means of Gateways.
✓ In the presence of RSUs, the vehicles may communicate to the
Internet via V2I interfaces.
✓ In the absence of RSUs, the vehicles may communicate with each
other or the Internet through cellular networks such as 3G/4G,
LTE, etc.

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104
V2X Communication Advantages:
✓ Increased traffic safety.
✓ Increased driver safety.
✓ Optimized time of travel.
✓ Efficiency of fuel consumption.
✓ Secure travel.

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 ✓ Easier drive in low-visibility or unfavorable weather conditions.
V2X Communication Disadvantages:
✓ Violation of privacy.
✓ Loss of data control.
✓ Collection of personal data.
✓ Second use of data.
✓ Data use by unauthorized entities.
✓ Tracking of movements.
✓ Localization of position.
IIoT: Industrial Internet of Things
Introduction:
✓ The main aim of Internet of Things (IoT) is
✓ to globally connect smart ‘things’ or ‘objects’ .
✓ objects are uniquely identified.
✓ interoperability among the objects.
✓ The Industrial Internet of Things (IIoT) is an application of IoT in
industries to modify the various existing industrial systems. IIoT
links the automation ,,planning and product lifecycle.

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Automation and data exchange in manufacturing technologies
‐ Cyber‐physical systems, the Internet of things and cloud
computing Smart factory.
‐ IIoT includes –
‐ machine learning
‐ big data technology
‐ machine ‐ to ‐ machine interaction (M‐2‐M)
‐ automation.
‐ IIoT is supported by huge amount of data collected from sensors. It
is based on “wrap & re‐use” approach, rather than “rip & replace”
approach.
‐ 1st Industrial Revolution : Mechanized production
‐ 2nd Industrial Revolution : Mass production
✓ 3rd Industrial Revolution : Internet evolution and automation
✓ 4th Industrial Revolution : IIoT

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✓ IIoT is a network of
✓ physical objects
✓ systems
✓ platforms
✓ applications
✓ These networks can communicate with each other, external
environment and other people.
✓ The acquisition of IIoT has led to availability and affordability of
sensors, processors, and other technologies which facilitates
capture and access to real‐time information
✓ IIoT is a network of
✓ physical objects
✓ systems
✓ platforms
✓ applications

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 ✓ These networks can communicate with each other, external
environment and other people.
✓ The acquisition of IIoT has led to availability and affordability of
sensors, processors, and other technologies which facilitates
capture and access to real‐time information
IIoT Requirements:

• Hardware and software connectivity

• Application Development
• Big data Analytics
• Cloud Platform
Design Considerations:
✓ To use an IoT device for industrial applications, the following
design objectives are to be considered –
✓ Energy : Time for which the IoT device can operate with
limited power supply.
✓ Latency : Time required to transmit the data.
✓ Throughput : Maximum data transmitted across the network.
✓ Scalability : Number of deevices supported.
✓ Topology: Communication among the devices, i.e.
interoperability.
✓ Safety and Security: Degree of safety and security of the
application.
Difference between IoT and IIoT :

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Service Management in IIoT:
✓ Service management refers to the implementation and management
of the quality of services which meets the end‐users demand”
✓ “Service is a collection of data and associated behaviors to
accomplish a particular function or feature of a device or portions
of a device”.
✓ Service can be of two types, which are ‐

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 ✓ Primary service ‐ The basic services which are responsible
for the primary node functions are termed as primary
service.
✓ Secondary service ‐ The auxiliary functions which provide
services to the primary service or secondary services are
termed as secondary service.
Applications of IIoT:
Manufacturing Industry:
✓ The devices, equipment, workforce, supply chain, work platform
are integrated and connected to achieve smart production. This
will led to –
✓ reduction in operational costs
✓ improvement in the productivity of the worker
✓ reduction in the injuries at the workplace
✓ resource optimization and waste reduction
✓ end‐to‐end automation.
Health care Service Industry:
✓ Patients can be continuously monitored due to the implanted
on‐body sensors. This has led to –
✓ improved treatment outcome
✓ costs has reduced
✓ improved disease detection
✓ improved accuracy in the collection of data

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 ✓ improved drugs management.
Transportation & logistics:
✓ To improve safety, efficiency of transportation, Intelligent
Transportation system (ITS) is developed which consists of
connected vehicles. ITS provides –
✓ Vehicle – to – sensor connectivity
✓ Vehicle – to – vehicle connectivity
✓ Vehicle – to – internet connectivity
✓ Vehicle – to – road infrastructure
✓ Dedicated short‐range communications (DSRC) is the key
enabling technology for V2V and V2R communications.
✓ In IIoT scenario the physical objects are provided with
✓ bar codes
✓ RFID tags
hence, real‐time monitoring of the status and location of the physical
objects from destination to the origin, across the supply chain is
possible.
✓ Security and privacy of the data should be maintained.
Mining:
✓ To prevent accidents inside the mines ‐ RFID, Wi‐Fi and other
wireless technologies are used, which
✓ provides early warning of any disaster
✓ monitors air‐quality

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 ✓ detects the presence of poisonous gases inside the mines
✓ oxygen level inside the mines.
Firefighting:
✓ Sensor networks, RFID tags are used to perform
✓ automatic diagnosis
✓ early warning of disaster
✓ emergency rescue
✓ provides real‐time monitoring Hence, improves public
security.
Examples of IIoT:
✓ Examples of IIoT are ‐
✓ unmanned aerial vehicles (UAVs) to inspect oil pipelines.
✓ monitoring food safety using sensors.
✓ minimizing workers’ exposure to noise, chemicals and other
hazardous gases.
✓ unmanned marine vehicle which can collect data up to a year
without fuel or crew.
Examples of IIoT:
✓ Examples of IIoT are ‐
✓ unmanned aerial vehicles (UAVs) to inspect oil pipelines.
✓ monitoring food safety using sensors.

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 ✓ minimizing workers’ exposure to noise, chemicals and other
hazardous gases.
✓ unmanned marine vehicle which can collect data up to a year
without fuel or crew.
Challenges in IIoT:
Standardization
✓ Standardization plays an important role in the development of the
system.
✓ Goal: To improve the interoperability of the different systems/
applications and allow the products/services to perform better.
✓ The problems related to standardization are:
✓ Interoperability
✓ Semantic interoperability (data sematics)
✓ Security and privacy
✓ Radio access level issues.
Privacy and security issues
✓ The two most important concerns related with IIoT are ‐
✓ information security
✓ data privacy protection
✓ The devices/things can be tracked, monitored and connected. So
there are chances of attack on the personal and private data.
Risks associated with IIoT in Manufacturing:

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✓ Though IIoT provides new opportunities, but few factors may
cause hindrance in the path to success, which are :
✓ lack of vision and leadership
✓ lack of understanding of values among management
employees
✓ costly sensors
✓ inadequate infrastructure.
Meet the challenges : Manufacturing
✓ Manufacturers use software capabilities to improve operational
efficiency through –
✓ predictive maintenance
✓ savings on scheduled repairs
✓ reduced maintenance costs
✓ reduced number of breakdowns.

Recent Research trends in IIoT:

✓ Recent research challenges in IIoT are ‐
✓ To improve the communications among the different things
or objects.
✓ To develop energy‐efficient techniques so as to reduce power
consumption by sensors.
✓ To develop context‐aware IoT middleware for better
understanding of the sensor data.

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 ✓ To create smart objects with larger memory, processing
capabilities.
Case Study: Agriculture
Future of IoT application in agriculture:
✓ Soil moisture and water level monitoring
✓ Automated irrigation system
✓ Automation in Recycling of Organic Waste and
Vermicomposting
✓ Automated sowing and weeding system .
Smart Water Management Using IoT:
✓ Objectives
▪ More yields with less water
▪ Save limited water resource in a country
▪ Automatic irrigation
▪ Dynamic irrigation treatments in the different phases of a
crop’s life cycle
Remote monitoring and controlling.
✓ Proposed architecture
▪ Sensing and actuating layer.
▪ Processing, storage, and service layer.
▪ Application layer.
✓ Design

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 ▪ Integrated design for sensors
▪ Integrated design for sensor node
▪ Integrated design for remote server
✓ Integrated design for sensors

Fig 4: Designed water‐level sensor

✓ Integrated design for sensor node

Fig 2: The block diagram of a sensor node

✓ Integrated design for sensor node

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✓ Integrated design for remote server
▪ Repository data server: Communicates with the deployed IoT
gateway in the field by using GPRS technology
▪ Web server: To access field data remotely
▪ Multi users server: Sends field information to farmer’s cell
using SMS technology and also executes farmer’s query and
controlling messages
✓ Implementation
▪ Field demo
▪ Website demo
▪ Project details from website

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Case study: Healthcare
Emergence of IoT Healthcare:
✓ Advances in sensor and connectivity
▪ Collect patient data over time
▪ Enable preventive care
▪ Understanding of effects of therapy on a patient
✓ Ability of devices to collect data on their own
▪ Automatically obtain data when and where needed by
doctors
▪ Automation reduces risk of error
▪ Lower error implies increased efficiency and reduced cost
Components of IoT Healthc are
✓ Components of IoT is organized in 4 layers
▪ Sensing layer: Consists of all sensor, RFIDs and wireless
sensor networks (WSN). E.g: Google glass, Fitbit tracker
▪ Aggregated layer: Consists of different types of aggregators
based on the sensors of sensing layer. E.g: Smartphones,
Tablets
▪ Processing layer: It consists of servers for processing
information coming from aggregated layer.
▪ Cloud platform: All processed data are uploaded in cloud
platform, which can be accessed by large no. of users
IoT Healthc are : Remote Healthc are

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 ▪ Many people without ready access to effective healthcare
▪ Wireless IoT driven solutions bring healthcare to patients rather
than bring patients to healthcare
▪ Securely capture a variety of medical data through IoT based
sensors, analyze data with smart algorithms
▪ Wirelessly share data with health professionals for appropriate
health recommendations
▪ IoT‐driven non‐invasive monitoring
▪ Sensors to collect comprehensive physiological information
▪ Gateways and cloud‐based analytics and storage of data
▪ Wirelessly send data to caregivers
▪ Lowers cost of healthcare
▪ Fall detection for seniors
▪ Emergency situation detection and alert to family members
▪ Machine learning for health trend tracking and early anomaly
detection
AmbuSens: Use-c ase of Healthc are system using IoT:
✓ Telemedicine and Remote Healthcare:
▪ Problem ‐ Physical presence necessary
▪ Solution ‐ Wireless sensors
✓ Emergency Response Time:
▪ Problem – Not equipped to deal with complications.

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 ▪ Solution
▪ Instant remote monitoring
▪ Feedback by the skilled medical professionals
✓ Real Time Patient Status Monitoring:
▪ Problem – Lack of collaboration.
▪ Solution ‐ Real‐time monitoring.
✓ Digitized Medical History:
▪ Problem
▪ Inconsistent
▪ Physical records vulnerable to wear and tear and loss.
▪ Solution ‐ Consistent cloud‐based digital record‐keeping
system
✓ Single hop wireless body area network (WBAN)
✓ Communication protocol used is Bluetooth i.e. IEEE 802.15.1
✓ Power management and data‐rate tuning
✓ Calibration of data
✓ Filtering and noise removal
✓ Health‐cloud framework
✓ The developed system is strictly privacy‐aware
✓ Patient‐identity masking involves hashing and reverse hashing of
patient ID

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 ✓ Scalable architecture
✓ URL: ambusens.iitkgp.ac.in
✓ Paramedic and Doctor portals for ease of use.
✓ Provision for recording medical history and sending feedback.
✓ Allows sensor initialization and
data streaming.
✓ Includes data visualization
tools for better understanding.
Activity Monitoring
Introduction:
✓ Wearable sensors have become very popular for different purposes
such as:
▪ Medical
▪ Child‐care
▪ Elderly‐care
▪ Entertainment
▪ Security
✓ These sensors help in monitoring the physical activities of humans
Advantages:
✓ Continuous monitoring of activity results in daily observation of
human behavior and repetitive patterns in their activities.

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 ✓ Easy integration and fast equipping
✓ Long term monitoring
✓ Utilization of sensors of handheld devices
▪ Accelerometer
▪ Gyroscope
▪ GPS
▪ Others
Data Analysis Tools:
✓ Statistical
▪ Sensor data
✓ Machine Learning Based
▪ Sensor data
✓ Deep Learning Based
▪ Sensor data
▪ Images
▪ Videos

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