fundamentals of iot cloud computing.pptx

Cloud Computing – Fundamentals
1
4/28/2024

Recent Trends in
Computing
• Shared pool of configurable
computing resources
• Ubiquitous, dynamic & on-demand access
2
Homogeneous computing nodes (connected loosely or tightly) working together
Heterogeneous computing nodes distributed over a wide area to perform very large
tasks
Packaged resources available for computing and storage
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Evolution of Cloud Computing
1950s
Time- shared
mainframe
computers
1970s
Virtual
Machines by
IBM
3
1996-97
‘Cloud
Computing'
2002
Amazo
n Web
Service
s
(AWS)
1969 1990s 1999 2006
ARPAN
ET
Expansion Salesforce. Amaz
on
of the com EC2
Internet.
Inception
of VPNs.
2008
Google
App
Engine
/ Micorsoft
Azure
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Cloud Computing
4
“Cloud computing is a model for
enabling convenient, on-demand
network access to a shared pool of
configurable computing resources (e.g.,
network infrastructures, servers,
storage, applications, etc.)” – NIST
Source: P Mell & T Grance, “A NIST Notional Definition of Cloud Computing”, version 15,2009.
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NISTVisual Model of Cloud
Computing
Software-as-a-
Service
(SaaS)
Platform-as-a-
Service
(PaaS)
Infrastructure
-as-a-
Service
(IaaS)
Essential
Characteristics Service Models
Deployment Models
Broad
Network
Access
Rapid Elasticity
Measured Services
On-demand
Self- services
Resource Pooling
Public
Private
Hybrid
Community
5
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BusinessAdvantages
6
 Nearly zero cost for upfront infrastructure investment
 Real-time Infrastructure availability
 More efficient resource utilization
 Usage-based costing
 Reduced time to market
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General Characteristics
• Improved agility in resource provisioning.
• Ubiquitous– independent of device or location
• Multitenancy– sharing of resources and costs across a large pool of
users
• Dynamic load balancing
• Highly reliable and scalable
• Low cost and low maintenance
• Improved security and access
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Essential Characteristics
8
 Broad network access
 Cloud resources should be available over the network
 Should support standard mechanisms for information
retrieval using traditional interfaces
 Supported clients: heterogeneous thin or thick client
platforms (e.g., mobile phones, laptops, and PDAs)
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9
Rapid elasticity
 Cloud resource allocation should be rapid, elastic and
automatic
 Dynamic allocation/release facility for scale-out and
scale-in
 Consumers should feel infinite resources
 Facility for add/remove of quantity should be there
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10
 Measured service
 Resource usage should be recorded and monitored
 Facility to dynamically control and optimize the resource
usage
 This facility should be transparent between the service
provider and consumer.
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 On-demand self-service
• Provide server time and network storage to users
automatically
• This facility should be available as a self-service
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12
 Resource pooling
• Automatically pool the whole available resources
• Serve multiple end-users using a multi-tenant model
• Resources should be allocated according to user’s
demand
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Components of Cloud Computing
13
Clients
Services
Applications
Platform
Storage
Infrastructure
Source:
Wikipedia
•Clients /end-users: Thick, Thin, Mobile
•Services: Products & solutions (Identity,
Mapping, Search, etc.)
•Applications: Web apps, SaaS, etc.
•Platform: Apps/Web hosting using PaaS
•Storage: Database, Data-Storage-as-a-Service
(DSaaS)
•Infrastructure: Virtualization, IaaS, EC2
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Service Models
 Software-as-a-Service (SaaS)
 Platform-as-a-Service (PaaS)
 Infrastructure-as-a-Service (IaaS)
14
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Software- as-a- Service(SaaS)
15
•Facility to execute service provider’s applications at user’s end
•Applications are available as ‘services’
•Services can be accessed via different types of client devices (e.g.
web browser, app)
•End-users do not posses the control of the cloud infrastructure
Examples: Google Apps, Salesforce, Learn.com.
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Platform-as-a-Service (PaaS)
16
 Facility for the consumer to execute consumer-created or acquired
applications onto cloud infrastructure
 Support for deployment of such applications
 The user does not control the cloud infrastructure
 User can control the deployed applications using given
configurations
Examples: Windows Azure, Google App Engine
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Infrastructure-as-a- Service (IaaS)
17
 Facility to access computing resources such as network, storage,
and operating system
 User can deploy, execute and control any software (Operating
systems and other applications)
 In some case, the user can control selected networking components
(e.g., host firewalls).
Examples: Amazon EC2, GoGrid, iland, Rackspace Cloud Servers.
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Deployment Models
 Public cloud
 Private cloud
 Hybrid cloud
 Others:
 Community cloud
 Distributed cloud
 Multi-cloud
 Inter-cloud
Private/
Internal
Off-
premise
cloud
service
Public/
Hosted
On-
premise
cloud
service
Hybrid
18
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Public Cloud
• Cloud set-up for the use of any person or industry
• Typically owned by an organization who offers the cloud service
• Examples: Amazon Web Service (AWS), Google Compute Engine,
Microsoft Azure
Advantages:
• Easy to set-up at low cost, as provider covers the hardware, application
and bandwidth costs
• Scalability to meet needs.
• Pay-per-use ensures that from user’s perspective no resources wasted.
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Private Cloud
• Cloud set-up functioned only for a single organization
• Typically managed by the organization itself (on-premises) or a third
party (off-premises)
Advantages:
• Total control over the system and data
• Minimum security concerns
Disadvantages:
Regular maintenance
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Public Cloud vs Private Cloud
Public Cloud Private Cloud
Virtualized
resources
Publicly shared Privately shared
Customer types Multiple Limited
Connectivity Over Internet Over Internet/private
network
Security Low High
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Hybrid Cloud
• Cloud set-up constructed by two or more unique
cloud set-up (private, community, or public).
• Pooled together by standardized tools.
• Supports data and application portability
(e.g., facility for load-balancing between clouds).
• Provides multiple deployment.
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Other Types of Cloud
Community cloud
• Shared set-up between several organizations having common
concerns (security, compliance, jurisdiction, etc.)
• Managed by internally or by third party
Distributed Cloud
• Collection of scattered set of computing devices in different
locations, however, connected to a single network
• Two types –Public-resource Computing and Volunteer Cloud
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Other Types of Cloud
Multi-cloud
• Multiple cloud computing services offered via single
heterogeneous architecture
• Increases fault-tolerance and flexibility
Inter-cloud
• Unified global ‘cloud of clouds 'based on the Internet
• Supports interoperability between cloud service providers
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Comparison of Different Deployment
Models
25
On-premise Off-premise
Dedicated
Access
Private cloud Hosted private cloud
Shared
Access
Community cloud Public cloud
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Cloud Computing – Service Models
1
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Comparison of Different Service
Models
Traditional
Application
s Data
Runtime
Middleware
OS
Virtualizatio
n Servers
Storage
Networking
By
Service
Provider
By
User
Application
s Data
Runtime
Middleware
OS
Virtualizatio
n Servers
Storage
Networking
By
Service
Provider
Application
s Data
Runtime
Middleware
OS
Virtualizatio
n Servers
Storage
Networking
By
User
By
Service
Provider
IaaS PaaS SaaS
Applications
Data
Runtime
Middleware
OS
Virtualization
Servers
Storage
Networking
By
User
3
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Infrastructure-as-a-Service (IaaS)
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Working Methodology
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Why IaaS?
6
 New businesses can operate without investing on computer
hardware.
 Scalable for rapidly growing businesses. (Organizations that
experience huge success immediately)
 Suitable for serving fluctuating computing demands. (Ex. Flipkart,
Amazon during festival seasons)
 Suitable for new business model trials.
 Helps in minimizing the capital expenses. (entrepreneurs
starting on a shoestring budget)
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7
 Scalability and elasticity:
 Dynamic scaling of required infrastructure resources
 Large amount of resource allocation/release in a short
span of time
 No variation in system performance while scale in or out
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8
 Manageability and interoperability
 Clients have control of the virtualized infrastructure resources
 Pre-configured facility for allocation of virtualized
resources
 The virtualized resources are to be monitored for their running
status
 The Usage and Billing system records the use of
infrastructure resources and accordingly calculate
payment
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9
 Availability and reliability
 Stored data can be retrieved at any time without failure
 The clients should be able to access the computational
resources without failure
 Uninterrupted facility for computation and
communication
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10
 Performance and optimization
 High utilization of physical resources among different
clients
 To enable high computing power with the large
pool of physical resources using parallel processing
 To optimize the deployment of physical resources
by dynamic configuration of virtual infrastructure
resources
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11
 Accessibility and portability
• Facility to client have various tasks – control, manage
and access infrastructure resources
• To facilitate easy reallocation and duplication of
allocated infrastructure resources
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IaaS Categories
12
resource
s
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IaaS – Challenges and Limitations
13
 Sometimes the regulatory approval does not allow outsourcing
the storage and processing of sensitive data.(Ex.: Medical
records)
 Network latency may degrade the level of expected
performance
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IaaS – Challenges and Limitations
(contd.)
14
 Users may require automated decision making of
job scheduling to available resources
 Seamless scaling of services independent of traffic variation
 Developers have to focus on low level system details
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PaaS (contd.)
16
 Facilitates development and managing applications
without the complexity of maintaining the underlying
infrastructure
 Allows customers to rent virtualized servers and
associated services
 Provide electronic scaling of the user’s deployed application
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Features of PaaS Offering
17
 Operating system
 Service side scripting environment
 Database management system
 Server Software
 Support
 Storage
 Network access
 Tools for design and development
 Hosting
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PaaS Working Model
18
•Allows users to create software applications using offered tools
•Provides preconfigured features that customers can subscribe
•Support available for managing the infrastructure and applications
for customers
•Services are regularly updated with new features
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Business Advantages
19
 Facility for accessing key middleware services without
worrying about the underlying complexities of managing
individual hardware and software elements.
 Ease of access for the development and deployment tools.
 Freedom from managing development and deployment tools
individually.
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Software- as-a- Service(SaaS)
20
“Software as a Service provides you with a completed product that is
run and managed by the service provider. In most cases, people
referring to Software as a Service are referring to end-user
applications.” – Amazon
Source: https://aws.amazon.com/types-of-cloud-computing/
 SaaS is a simplified model of software delivery over Internet.
 Operation, maintenance and technical support is provided by the
service provider.
 Typically offered via web browser working as a thin-client.
 Supports a fully pay-as-you-go model.
Source: Software Services for e-Business and e-Society: Proceedings of 9th IFIP WG 6.1 Conference on e-Business, e-Services and e-Society,
I3E 2009, Nancy, France, September 23-25, 2009.
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SaaS (contd.)
21
 Remote access of software via Internet where web-
browser acts as a thin-client
 Facility for access and control of commercial
software via Internet
 delivery in a one-to-many model
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Advantages
22
Traditional Software SaaS
Customers install, manage &
maintain
Customers uses over the Internet
Runs on individual
organization on dedicated
instantiation
Runs on multiple
customers
simultaneously
Cross platform support required No concerns for cross platform
support
Less frequent version
updates & purchased
separately
More frequent updates for
enhanced user satisfaction
Separate costs incurred for
upgrades
No separate cost
Vulnerable to software piracy Less vulnerable to software piracy
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SaaS Architecture
23
Scalability
To maximize application concurrency
•To optimize the shared pool of resources such as threads and network
connections
Multi-tenancy
•Important architectural shift from designing isolated, single-tenant
applications
•Ability to accommodate users from multiple companies at the same time
•Transparency to all the users
•Maximize the sharing of resources across tenants while distinguishing user’s
individual data
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SaaS Architecture (contd.)
24
 Configurability
 To facilitate parallel allocation of a single application on a
single server to several users.
 To customize the application for one customer will change
the application for other customers as well.
 Separate data space for different users
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Limitations of SaaS
25
 Centralized control
 Switching cost
 Limited flexibility
 Data security and privacy
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Cloud Computing – Case Studies
1
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Introduction
2
 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
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Introduction (contd.)
3
 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
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Cloud Simulators
4
 Different Cloud Simulators are:
 CloudSim
 CloudAnalyst
 GreenCloud
 iCanCloud
 GroudSim
 DCSim
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CloudSim
5
 A simulation framework
 Models cloud computing environments– Data Center,
VM, applications, users, network topology.
 Written on Java-based environment.
 Allows to examine the performance of application services.
 Dynamic addition/removal of resources during simulation.
 Developed at CLOUDS Lab. of University of Melbourne.
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Advantages of CloudSim
6
 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
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Features of CloudSim
7
 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
4/28/2024

CloudSim Architecture
 User Code: Top most layer
 Presents different machine and application specifications
 CloudSim: Middle layer
 Provides cloud environment
 Enables modelling and simulation
 Core Simulation Engine: Bottom most layer
 Event scheduling
 Entity creation
 Interaction between components
 Clock management
4/28/2024 58

Top Layer: User Code
 Basic entities:
 Users
 Physical Machines
 Virtual Machines
 Applications &
services
 Scheduling policies
Fig: Functionalities at top
layer
Simulation
Specificatio
n
Schedulin
g
Policy
Use
r
Cod
e
Cloud
Scenari
o
User
Requireme
nt
Application
Configuratio
n
User Broker
Data
Center
Broker
9
4/28/2024

Middle Layer:CloudSim
10
 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
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User Interface
Structure
VM
Services
Cloud
Services
Cloud
Resources
Networ
k
CloudSim
Architecture
VM
Managemen
t
Cloudlet
Executio
n
Cloudle
t
Virtual
Machin
e
VM
Provisioning
Storage
Allocatio
n
CPU
Allocation
Memory
Allocatio
n
Bandwidt
h
Allocation
Networ
k
Topolog
y
Message
Delay
Calculation
Event
Handlin
g
Senso
r
Cloud
Coordinato
r
Data
Center
11
4/28/2024

CloudAnalyst
12
 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
4/28/2024

Features of CloudAnalyst
13
 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
4/28/2024

CloudAnalyst Design
Fig: CloudAnalyst
Architecture
CloudSim
Toolkit
CloudSi
m
Extension
s
GUI
CloudAnaly
st
14
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
• CloudAppServiceBroker: Entities for routing
between
UserBase & data center.
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GreenCloud
15
 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
4/28/2024

Features of GreenCloud
GU
I
 iUesnedrl-yfr
 Open source
 Facility for monitoring energy consumption of
network & devices
 Srtusppo simulation of cloud network components
 Supports monitoring of energy consumption of
individual components
 Enables improved power management schemes
 Dynamic management and configuration of devices
4/28/2024 66

Open Source and Commercial
Clouds
17
Open Source Clouds Commercial Clouds
Examples OpenStack,
CloudStack,
Eucalyptus
Amazon Web Services
(AWS), Microsoft Azure,
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
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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
VM
Container
Storage
Dashboard
GUI
Monitorin
g Tools
Apps Apps
Common Network
18
User’
s
Apps
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Microsoft Azure
20
 Previously Windows Azure
 Supports Iaas and PaaS
 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
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Azure Services
21
 Compute
 Mrv
iocbeislese
 Srvtoicreasgese
 Danaat
agemment
 Messaging
 r
M
v
i
e
c
d
e
i
s
ase
 Content Delivery Network
(CDN)
 Developer
 Management
 Machine Learning
4/28/2024

Azure as PaaS (Platform as a Service )
22
 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.
Source URL : https://azure.microsoft.com/en-in/overview/what-is-paas/
4/28/2024

Azure as IaaS (Infrastructure as a
Service )
23
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
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Amazon Elastic Compute Cloud (EC2)
24
 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
 Supports pay-per-use billing rather than making large and
expensive hardware purchases
Source: amazon web services Website https://aws.amazon.com/ec2/
4/28/2024

Amazon EC2 Instances
25
 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
4/28/2024

Features of Amazon EC2
26
 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
4/28/2024

Features of Amazon EC2
7
 Firewall Rules/Security Groups: Only predefined
protocols, ports, and source IP ranges reach the instances
 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
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Cloud Computing - Practical
77
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Contents
78
 Introduction toOpenstack
 Components
 Installation
 Creatingakey‐pairand manage security
group
 LaunceInstances
 Creating animage
 Accessing and Communicating withinstances
4/28/2024 78

Introduction to Openstack
79
 Asoftwaretocreateacloudinfrastructure
 Launched as ajointprojectofRackspaceHostingand NASAin 2010
 Opensource
 Presentlymany companies arecontributingtoopenstack
 Eg.IBM,CISCO, HP,Dell,Vmware, Redhat, suse, Rackspace hosting
 It has a very largecommunity
 Can be used todevelopprivatecloudor publiccloud
 Versions:
 Austin,Bexar,Cactus,Diablo,Essex, Folsom,Grizzly,Havana,Icehouse,
Juno, Kilo,Liberty,Mitaka, Newton, Ocata(Latest)
4/28/2024 79

Components
Horizon
Dashboard
Nova Swift
Glance Neutron Cinder Heat Ceilometer Keystone
80
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Components contd.
81
 Keystone
Identityservice
Provides authentication andauthorization
 Horizon
Dashboard
GUI of thesoftware
Provides overview of the othercomponents
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Components contd.
82
 Nova
 Compute service
 Where you launce yourinstances
 Glance
 Image service
 Discovering, registering, retrieving theVM
 Snapshots
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Components contd.
83
 Swift
Object storage
Helps in storing data safely,cheaply and efficiently
 Neutron
Provides networkingservice
Enables the other services to communicate with each other
Make your ownnetwork
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Components contd.
84
 Cinder
Block storage
Virtualizes the management of blockservice
 Heat
Orchestration
 Ceilometer
Billing
What service you areusing
How long are youusing
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Installation
85
 Canbe installedmanuallyor using scripts likeDevstack
 We will usedevstack
 Steps:
 Installgit(sudo apt‐getinstallgit)
 Clone devstack (git clone
https://git.openstack.org/openstack‐ dev/devstack)
 Go todevstackdirectory(cddevstack)
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Installation contd.
86
 Open local.conffileand pastethefollowingand savethefile
ADMIN_PASSWORD=<YOUR PASSWORD>
DATABASE_PASSWORD =<YOUR
PASSWORD> RABBIT_PASSWORD =<YOUR
PASSWORD> SERVICE_PASSWORD =<YOUR
PASSWORD>
HOST_IP=<the IP of yourPC>
 Run the stack.sh file(./stack.sh)
 Foruninstallation,go todevstackdirectoryand run unstack.shfile
4/28/2024 86

References
87
 1. https://www.openstack.org/
 https://docs.openstack.org/developer/devstack/
4/28/2024 87

Introduction
 It is not mere integration of sensors and cloud computing
 It is not only “dumping the sensor data into cloud”
Cloud
Cloud
2

Wireless Sensor Networks
(WSNs): Recap
3
 Contain sensor nodes which sense some physical phenomena
from the environment.
 Transmit the sensed data (through wireless communication) to a
centralized unit, commonly known as Sinknode.
 The communication between Sink node and other sensor
nodes in the network may besingle/multi‐hop.
 Sink node further processdata.

Wireless Sensor Networks (WSNs):
Recap
Sensing unit
Processing unit
Communication unit
Major Components ofa
Sensor Node
Sink
Wireless SensorNetworks
4
Applications
 T
argetTracking
 Wildlife Monitoring
 Healthcare
 Industrial Applications
 Smart Home
 Smart City
 Agriculture
 …

Cloud Computing: Recap
5
 An architecture which provides on‐demand
computingresources
 Advantages
 Elasticity:Scaling up/down
 Pay‐per‐use: Payment for the resource as per
requirement
 Self Service: Resource can be accessed byself

Cloud Computing: Services
App,
Web
browser
,
terminal
Cloud‐Clients
6
Software‐as‐a‐Service (SaaS)
Platform‐as‐a‐Service (PaaS)
Infrastructure‐as‐a‐Service (IaaS)

Cloud Computing: Services
7
 Software‐as‐a‐Service(SaaS)
 Athirdpartyprovidesahostapplication overinternet
 Example:Microsoft Office365
 Platform‐as‐a‐Service (PaaS)
 Provideaplatformtodevelopandrunapplications
 Example:WindowsAzure
 Infrastructure‐as‐a‐Service(IaaS)
 Provide computingresources
 Example:Storagespace

Virtualization Concept
8
 One computer host appears as many computers‐concept of Virtual
Machine (VM)
 Improve ITthroughput and costs by using physical resources as a pool
from which virtual resources can beallocated.
 Benefit
Sharing of resources: Same resource can be shared, in turn cost
reduction
Encapsulation:A complete computingenvironment
Independence: Runs independently of underlyinghardware
Portability: VM Migration

Limitations of WSNs
 Procurement
 Price
 Rightvendor
 Typesof sensor integratedwithit
 Deployment
 Right way ofdeployment
 Right placeofdeployment
 Maintenance
 Post deploymentmaintenance
 Batterylifetime
Change of Requirement
An example
Today Tomorrow
Agriculture Smart Home
Result: Change in Sensor type,deployment
area, topology design, and manymore….
9

Sensor-Cloud: Introduction
10
 Not only the mere integration of cloud computing and sensor
networks, but sensor‐cloud is more thanthat
 Concept of virtualization of sensornode
 Pay‐per‐use
 One sensor node/network appears asmany
 A stratum between sensor nodes and end‐users

Difference with WSN
Virtualization
WSN user
Aggregated
data
Dedicated to
a single user
Multiple
applications/ users
11
Serves
multiple
applications
Sensor‐cloud
infrastructur
e
WSN Sensor‐Cloud
Source: S. Misra; S. Chatterjee; M. S. Obaidat, "On Theoretical Modeling of Sensor Cloud: A Paradigm Shift From Wireless Sensor Network," in IEEE
Systems Journal , vol.PP, no.99, pp.1-10

Difference with WSN (Contd.)
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
12

Actors in Sensor-cloud
13
 End‐users
 EnjoySe‐aaS through applicationsas per therequirements.
 Unknown about what and which physical sensor is/areallocated to
serve the application
 Sensor‐owner
 Plays a role from businessperspective.
 They purchase physical sensor devices, deployed over different
geographical locations, and lend these devices to thesensor‐cloud
 Sensor‐Cloud ServiceProvider(SCSP)
 A businessactor.
 SCSPchargespricefromtheend‐usersas per theirusage of Se‐aaS.

Sensor-cloud: Architecture
 End‐users: Registeredthemselves,selects
templates, and request forapplication(s)
 Sensor‐owner: Deploy heterogeneous/
homogeneous physical sensor nodesover
differentgeographicallocation
 SCSP: Plays managerialrole
14

Sensor-cloud: View
Real
View
Application
1
Application
2
User
organizatio
n
Browser
Interfac
e
Data feed
Data feed Sensed
informatio
n
T
emplate
display
Templatespecification
User organization view
User Login Xml
interpretation
Dynamic Scaling
On‐demandphysical
sensor scheduling
Vast data storage and
specializedprocessing
Energy
management,QoS
Application
specific real‐time
data aggregation
Web
portal
Template
specificati
on
Sensed
data
Heterogeneous
pool ofphysical
sensors
Interaction
with physical
sensor
On‐deman
d sensor
data

Work Flow of Sensor-Clou d
Compatible
sensor
scheduling,
allocation,
deallocation
Operations
request Create virtual
sensor instance
User
organization
SensorML
interpretor
Virtual Sensor
Manager
Virtual Sensor
Controller
Resource
Manger
Manage
operations
Response
Response
Data request
XML template Decode
Physical sensordefinition,
Virtual sensor
Group definition
Client information
Metadata
Templates
Sensor
resourcepool
(WSN)
Data retrieval
Data
aggregation
Data
provisioning
Delete
virtual sensor
Release
instance
Release
resource
Source: S. Misra; S. Chatterjee; M. S.
Obaidat, "On Theoretical Modeling of
Sensor Cloud: A Paradigm Shift From
Wireless Sensor Network," in IEEE
Systems Journal , vol.PP, no.99,pp.1-10
16

Case Study: Target Tracking
17
“We consider a WSN‐based target tracking application, in which a WSN owner
refuses to share the sensed information with an external body, even in exchange
of money. Consequently, any organization that wishes to detect intrusion within
a particular zone has to deploy its own WSN. This leads to a long‐term
investment due to costly network setup and maintenance overheads. However,
in a sensor‐cloud environment, the same organization can use the same tracking
applicationand stillget the service without actually owning theWSN”

Management Issues in Sensor-Cloud
2
 Optimal Composition of virtual sensornodes
 Data Caching
 Optimal Pricing

Optimal Composition of Virtual Sensor
3
Source: S. Chatterjee and S. Misra, “Dynamic Optimal Composition of a Virtual Sensor for Efficient Virtualization Within
Sensor‐cloud”, IEEE ICC2015.

Introduction
4
 Efficientvirtualization of the physical sensornodes
 An optimal composition ofVSs
 Consider same geographic region:CoV‐I
 Spanning across multiple regions:CoV‐II

Why Composition of Virtual
Sensor?
Introduction to Internet of
Things
5
 Resource‐constrained sensor nodes
 Dynamic change in sensorconditions
 The composition of virtual sensors arenon‐traditional

CoV-I: Formation of Virtual Sensor
 Optimal formation of Virtual
sensor nodes
geographical
Sensor (VS)
 Homogeneous
within same
boundary
V
S
6
Source: S. Chatterjee and S. Misra, "Optimal composition of a virtual sensor for
efficient virtualization within sensor‐cloud," 2015 IEEE International Conference
onCommunications (ICC), London, 2015, pp.448‐453

CoV-II: Formation of Virtual Sensor
Group
of Virtual Sensor
 Formation
Group(VSG)
 Heterogeneous physical sensor
nodes across different
geographical locations
VS
1
7
VS
2
VS
3
VS
G
Source: S. Chatterjee and S. Misra, "Optimal composition of a virtual sensor for
efficient virtualization within sensor‐cloud," 2015 IEEE International Conference
onCommunications (ICC), London, 2015, pp.448‐453

Performance
Source: S. Chatterjee and S. Misra, "Optimal composition of a virtual sensor for efficient virtualization within sensor‐cloud," 2015 IEEE InternationalConference on
Communications (ICC), London, 2015, pp.448‐453
Things
8

Dynamic and Adaptive Data
Caching Mechanism
9
Source: S. Chatterjee, S. Misra, “Dynamic and Adaptive Data Caching Mechanism for Virtualization within Sensor‐Cloud”,IEEE ANTS
2014.

Introduction
10
 Introduces internal and external cachingmechanisms
 Ensures efficiency in resourceutilization
 Flexiblewith the variedrateof change of the physical environment
2014.

Why Caching in Sensor-Cloud?
11
 End‐users requestforthesensed informationthroughaWeb‐interface
 Allocationofphysical sensor nodesandvirtualization takesplace
 Physicalsensor nodescontinuously sense andtransmit datatosensor‐cloud
2014.

Why Caching in Sensor-Cloud?
(Contd.)
12
 Practically, in some cases, the change in environmental
conditionare 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
2014.

External and Internal Caching
Mechanism
13
 Internal Cache(IC)
 Handles requests fromend‐user
 Takesdecision whether the datashould be provided directlyto the end
useroris itrequiredtore‐cachethedatafromexternalcache
 ExternalCache(EC)
 After every certain intervaldataare required tore‐cache
 Initially,fewdataareused tobetransmitted toIC
2014.

Architecture of Caching
ExistingArchitecture Cache‐enabledArchitecture
App1 Appn
Sensor‐Cloud
Resource
pooling
App2 . . .
App1 App2 Appn
. .
.
14
EC
Sensor‐Cloud
IC
2014.

Performance
2014.
15

Dynamic Optimal Pricing for Sensor-Cloud Infrastructure
Things
16
Source: S. Chatterjee, R. Ladia, and S. Misra, “Dynamic Optimal Pricing for Heterogeneous Service‐Oriented Architecture of
Sensor‐Cloud Infrastructure”, IEEE TSC2017.

Introduction
17
 Existing schemes consider homogeneity of service(e.g.forIaaS, SaaS)
 No scheme forSeaaS.
 Theproposed pricingscheme comprises oftwocomponents:
 Pricingattributed to hardware(pH)
 Pricingattributed to Infrastructure(pI)
 Goal of the proposed pricingscheme:
 Maximizing profit ofSCSP
 Maximizing profit of sensorowner
 End users’satisfaction

Base
Station
Web Portal
Sensor‐Cloud
18
Pricing and
negotiation
Pricing in Sensor-Cloud
Set of endusers
Set of sensor owner

Focus on
19
 Maximizing the profit made bySCSP
 Optimal pricingto theend‐users
 End userssatisfaction
 Pricingattributed to hardware(pH)
 Dealswithusageofphysicalsensor nodes
 Pricingattributeto infrastructure(pI)
 Dealswiththepriceassociatedwithinfrastructure ofsensor‐cloud

References
20
 Madoka Yuriyama and Takayuki Kushida ,“Sensor‐Cloud Infrastructure ‐ Physical Sensor Management with
Virtualized Sensors on Cloud Computing”, Research Report ,IBM Research ‐ Tokyo IBM Japan, Ltd.,2010
(http://domino.research.ibm.com/library/cyberdig.nsf/papers/70E4CC6AD71F2418852577670016F2DE/$F
ile
/RT0897.pdf)
 S. Chatterjee, R.Ladia and S. Misra, "Dynamic Optimal Pricing for HeterogeneousService‐Oriented
Architecture of Sensor‐Cloud Infrastructure," in IEEE Transactions on Services Computing, vol. 10, no. 2,
pp. 203‐216, 2017
 S. Chatterjee and S. Misra, "Optimal composition of a virtual sensor for efficient virtualization within
sensor‐ cloud," 2015 IEEE International Conference on Communications (ICC),London, 2015, pp. 448‐453
 S. Misra; S. Chatterjee; M. S. Obaidat, "On Theoretical Modeling of Sensor Cloud: A Paradigm Shift
From Wireless Sensor Network," in IEEE Systems Journal ,vol.PP
,no.99,pp.1‐10

 Fogcomputingor foggingisatermcoinedbyCISCO.
 Theideaoffogcomputingis toextendthecloudnearertotheIoTdevices.
 Theprimaryaim:solvetheproblemsfacedbycloudcomputingduringIoT
dataprocessing.
 an intermediate layerbetween cloud anddevices.
Introduction

Cloud
Fog
Device
Fig. Fog as intermediate layer between cloud and
device

 40% ofthewholeworldsdatawillcomefromsensorsaloneby2020.
 90% oftheworld’sdataweregenerated onlyduringtheperiodoflasttwo
years.
 2.5quintillionbytesofdatais generatedperday.
 totalexpenditure on IoTdeviceswillbe$1.7Trillionby2020

 the totalnumber of connected vehicles worldwide will be 250
millionsby 2020.
 therewill be more than 30 billion IoTdevices
 The amount of datagenerated by IoTdevices is simply huge.

Why Fog Computing
 Theabilityofthecurrentcloudmodelis insufficient tohandlethe
requirements ofIoT.
 Issues are:
 Volume
 Latency
 Bandwidth

Why Fog Computing (contd.)
Cloud
Sends data
for analysis
andstorage
Sends back
commandor
action
required
Devices
Fig.1: Present day cloud model

Why Fog Computing
(contd.)
 DataVolume:
 By2020,about50 billiondeviceswillbeonline.
 Presentlybillionsofdevicesproduceexabytesofdataeveryday.
 Device density is stillincreasingeveryday.
 Currentcloudmodelis unabletoprocess thisamountofdata.

Cloud
Privatefirms Factories Airplane firms
 Privatefirms, Factories,
airplane companiesproduces
colossus amount of data
everyday
 Currentcloud modelcannot
store allthesedata
 Data need to befiltered
Storingdata

 Latency
 Timetakenbyadatapacketforaroundtrip
 Animportant aspectfor handing a time sensitive data.
 Ifedgedevicessend timesensitive datatocloudforanalysisandwait for the
cloud to givea proper action,then itcanlead to many unwantedresults.
 Whilehandlingtimesensitivedata,amillisecond canmakeahuge
differences.

 Sending time‐sensitivedatato cloudfor
analysis
 Latency= Tfron device to cSoud +Tdata
anaSycic
+ Tfron cSoud to device
where T = Time
 Latencywill beincreased
 When the action reaches thedevice,
accidentmay have already occured
Cloud
Analysisof
data
Appropriate
action
Sending time
sensitive datafor
analysis

 Bandwidth:
 Bit‐rate of dataduringtransmission
 Ifallthedatagenerated byIoTdevicesaresenttocloudforstorage
and analysis, then,the trafficgenerated by these devices will be
simplygigantic.
 consumes almost allthebandwidths.
 Handlingthiskindoftrafficwillbesimplyaveryhardtask.

Cloud
 Billionsof devicesconsumingbandwidth
 If allthe devicesbecome online evenIPv6 will
not be ableto provide facilityto all thedevices
 Data may be confidential which thefirms do
not want to share online
Sending datafor
analysis and
storage
Appropriate
action

Requirements of IoT
Reduce latency ofdata:
Appropriate actions at the right time prevents major accidents
machine failure etc.
Aminute delay while taking a decision makes a huge difference
Latency can be reduced by analyzingthe data close to the data
source

Requirements of IoT (contd.)
 Datasecurity:
 IoTdatamust besecuredandprotectedfromtheintruders.
 Data are required to be monitored24x7
 An appropriateactionshould betakenbeforetheattackcausesmajor
harm to thenetwork

 Operation reliability:
Thedatageneratedfrom IoTdevices areused to solve real
time problem
Integrity and availabilityof the datamust be guaranteed
Unavailability and tampering of data can behazardous

 Processing of data at respective suitable place:
 Data can be divided into three types based on sensitivity
time sensitive data
less time sensitive data
data which are not timesensitive
 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 dataacross largegeographicalarea:
 ThelocationofconnectedIoTdevicescanbespreadacross alarge
geographicalregion
 E.g.monitoring therailwaytrackofacountryorastate
 thedevicesareexposedtotheharshenvironmentscondition

When should we use fog
 If the datashould ne analyzewithfractionof second
 If there are hugenumber of devices
 If the devices are separated by alargegeographical distance
 If the devices are needed to be subjected toextreme conditions

Architecture of Fog
 CloudservicesareextendedtoIoTdevicesthroughfog
 Fogisalayerbetween cloudandIoTdevices
 many fog nodes can bepresent
 Sensordataareprocessedinthefogbeforeitissenttothecloud
 Reduceslatency,savebandwidth andsave thestorageofthecloud

Fog nodes
 Characteristicsfor a fognode:
 Storage ‐Togive transientstorage
 Computing facility
‐ Toprocess the databefore itis sent tocloud
‐ Totakequick decisions
 Network connectivity ‐Toconnectwith IoTdevices, other fognodes and
cloud

Fog nodes (contd.)
 E.g.‐ routers, embedded servers, switches, video surveillancecameras, etc.
 deployable anywhere inside thenetwork.
 Each fog nodes havetheir aggregatefog node.

Working of Fog
 Threetypes ofdata
 Very time‐sensitivedata
 Less time‐sensitivedata
 Data which are nottime‐sensitive
 Fognodes worksaccordingtothetypeofdatatheyreceive.
 An IoTapplication should beinstalled toeachfognodes

Working of Fog (contd.)
Devices
Cloud
Nearest
FogNode
Aggregate
fog
node
Sends the summaryfor
historical analysis and
storage
Sends the summaryfor
historical analysis and
storage
Sends the summary for historical analysis and storage
Non‐time‐sensitiv
e data
Lesstime‐sensitive
data
Ingest data
If time‐sensitive
data then take
immediate action
Action
Fig :Working offog

 Thenearestfognode ingestthedatafromthedevices.
 Most time‐sensitivedata
 Datawhichshould beanalyzedwithinfractionofasecond
 Analyze atthe nearest nodeitself
 Sends thedecisionor actiontothedevices
 Sends andstoresthesummary tocloudforfutureanalysis

 Less time‐sensitivedata
 Datawhichcanbeanalyzedaftersecondsorminutes
 Aresenttotheaggregatenode foranalysis
 Afteranalysis,theaggregatenode send thedecisionoractiontothe device
through the nearestnode
 Theaggregate node sends thesummary tocloudforstorageand future
analysis.

 Non‐time‐sensitive data
Data whichcan be wait for hours, days,weeks
Sent to cloudfor storage and future analysis.
Those summaries fromfog nodes can be considered as
less time sensitive data.

Fog node
closest
to
devices
Fog aggregate nodes Cloud
Analysis duration Fraction ofsecond Seconds tominutes Hours toweeks
IoT data storage
duration
Transient Hour
,days Months toyears
Geographical
coverage
Very local Wider Global

Advantages of Fog
 Security
 Provides bettersecurity
 Fognodescanuse thesame securitypolicy
 Low operationcost
 Dataareprocessedinthefognodesbeforesendingtocloud
 Reduces the bandwidthconsumption

Advantages of Fog (contd.)
 Reduces unwantedaccidents
 Latencywill be reduceduring decisionmaking
 Quick decisionmaking
 Betterprivacy
 Every industry can analyze their datalocally
 Store confidential datain their localservers
 Sendonlythosedatawhichcanbeshared tothecloud

 Businessagility
 Fogapplication canbeeasilydevelopedaccordingtotoolsavailable
 Can be deployed anywhere weneed
 Canbeprogramedaccordingtothecustomer’sneed
 Supportmobility
 Nodes can bemobile
 Nodescanjoinandleavethenetworkanytime

 Deployable in remoteplaces
 Can be deployed in remoteplaces
 Can be subjectedto harsh environmentalconditions
 Under sea, railway tracks,vehicles, factoryflooretc
 Betterdatahandling
 Can operatewith lessbandwidth
 Data can be analyzedlocally
 Reduce the risk oflatency

Applications ofFog
 Realtime healthanalysis
 Patients withchronicillness canbemonitored inrealtime
 Strokepatients
 Analyze the datarealtime
 During emergency,alerts the respective doctorsimmediately
 Historical dataanalysis can predictfuturedangers ofthe patient

Applications of Fog (contd.)
 Intelligencepower efficientsystem
 Powerefficient
 Reports detail power consumption reporteveryday
 Suggest economical power usageplan

 Realtime railmonitoring
 Fognodescanbedeployedtorailwaytracks
 Realtime monitoring of the trackconditions
 Forhighspeed train,sendingthedataincloudforanalysisisinefficient
 Fog nodes provide fastdataanalysis
 Improve safety andreliability

 Pipeline optimization
Gas and oils are transported throughpipelines
Real time monitoring of pressure, flow,compressor is
necessary
Terabytesof data arecreated
Sending allthis datato cloud for analysisand storage is not
efficient
Network latencyis notacceptable
Fog is asolution

 Realtime wind mill and turbineanalysis
 Wind directionand speedanalysis can increaseoutput
 Data can be monitored realtime

Challenges
 Powerconsumption
 Fog use additionnodes
 Power consumption is higher than centralizedcloud
 DataSecurity
 Data generatingnodes aredistributed
 Providingauthenticationandauthorizationsystem forthewholenodesis
not an easytask
 Reliability
 Maintaining dataintegrity andavailabilityformillionsofnodes is difficult
 failureof a node c
e
a
c
n
t
notaff thenetwork

Challenges (contd.)
 Faulttolerance
 Failure of a node shouldbe immediately fixed
 Individual failureshould not affectthe wholescenario
 Realtimeanalysis
 Realtime analysisisaprimaryrequirementforminimizinglatency
 Dynamicanalysisanddecisionmakingreducesdangerandincreaseoutput
 Monitor hugenumberofnodesisnoteasy

Challenges (contd.)
 Programmingarchitecture
 Fog nodes may bemobile
 Nodes canconnectandleave thenetworkwhennecessary
 Many dataprocessing frameworks are staticallyconfigured
 These frameworks cannotprovide proper scalability andflexibility

Conclusion
 FogisaperfectpartnerforcloudandIoT
 Solves theprimaryproblemsfacedbycloudwhilehandlingIoTdata
 Benefitsextendsfromanindividualpersontohugefirms
 Providesrealtime analysisandmonitoring

SmartCitiesand SmartHomes
16
8

Introduction
 ASmartCity is-
 Anurban system
 UsesInformation & Communication Technology(ICT)
 Makesinfrastructure more interactive, accessibleand efficient.
 Needfor SmartCitiesarosedueto-
 Rapidly growing urban population
 Fastdepleting natural resources
 Changesin environment and climate
16
9

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
Cognition Software
17
0

Application FocusAreas
SmartEconomy
• SmartEconomy
•Smart Economy----------------Competitiveness
Smart Governance
•SmartGovernance
•Smart Governess----------------Citizen participation
Smart People
•Smart People----------------------Socialand HumanCapital
Smart Mobility
•Smart Mobility-----------------------Transportand ICT
Smart Environment
•Smart Environment--------------Natural resources
Smart Living
•SMart Living ----------------------------Quality of life
17
1

CurrentFocusAreas
 SmartHomes
 Health monitoring.
 Conservation of resources(e.g. electricity, water,
fuel).
 Security and safety.
 SmartParkingLots
 Auto routing of vehicles to emptyslots.
 Auto charging for servicesprovided.
 Detection of vacant slots in the parkinglot.

CurrentFocusAreas (contd.)
 Smart Vehicles
 Assistance to drivers during bad weather orlow-visibility.
 Detection of bad driving patterns or driving under the influence
of substances.
 Auto alert generation duringcrashes.
 Self diagnostics.

 Smart Health
 Low cost, portable, at-home medical diagnosis kits.
 Remote check-ups and diagnosis.
 On-body sensorsfor effortless and accurate healthmonitoring.
 Auto alert generation in caseof emergency medical episodes
(e.g. Heart attacks, seizures).

 Pollution and CalamityMonitoring
 Monitoring for weather or man-made basedcalamities.
 Alert generation in case of above-threshold pollutants in the air
or water.
 Resource reallocation and rerouting of services in the event of
calamities.
18
1

 Smart Energy
 Smart metering systems.
 Smart energy allocation and distribution system.
 Incorporation of traditional and renewable sources of
energy in the same grid.

 SmartAgriculture
 Automatic detection of plant waterstress.
 Monitoring of crop healthstatus.
 Auto detection of cropinfection.
 Auto application of fertilizers andpesticides.
 Schedulingharvesting and arranging proper transfer of
harvests to warehousesor markets.
18
3

IoTChallenges inSmartCities
 Security andPrivacy
 Exposureto attacks (e.g. cross-sitescripting, side channel,etc.).
 Exposureto vulnerabilities.
 Multi-tenancy induces the risk of dataleakage.
 Heterogeneity
 Integration of varying hardware platforms andspecifications.
 Integration of different radiospecifications.
 Integration of various softwareplatforms.
 Accommodating varying user requirements.

IoTChallenges inSmartCities (contd.)
 Reliability
 Unreliable communication due to vehicle mobility.
 Devicefailures still significant
 Largescale
 Delaydue to large scaledeployments.
 Delaydue to mobility of deployednodes.
 Distribution of devicescanaffect monitoring tasks.
18
5

 Legaland Socialaspects
 Servicesbasedon user provided information may be subject to
localor
international laws.
 Individual and informed consent required for using humansasdata
sources.
18
6

 Bigdata
 Transfer,storageand maintenance of huge volumes of data is
expensive.
 Datacleaning and purification is time consuming.
 Analytics on gigantic data volumes is processingintensive.

 SensorNetworks
 Choiceof appropriate sensorsfor individual sensingtasksis crucial.
 Energyplanning is crucial.
 Deviceplacement and network architecture is important for
reliable
end-to-end IoTimplementation.
 Communication medium and means play an important role in
seamlessfunction of IoTin smart cities.
18
8

SmartCities-DataFusion
 Enormousvolume of datais produced periodicallyin asmart city
environment.
 Challengesinclude makingthe available/incominglargedata
volume precise andaccurate.
 Quality of data precisionand accuracyaffects the quality of decision
making in IoT-enabled smartcities.
 Datafusion enablesoptimum utilization of massivedata gathered
from multiple sources,and acrossmultiple platforms.
Source: Alam, Furqan, et al. "Data Fusion and IoT for Smart Ubiquitous
2

Multi-sensorData Fusion
 Combinesinformation from multiplesensorsources.
 Enhancesthe ability of decisionmakingsystemsto includea
multitudeof variablesprior to arrivingat adecision.
 Inferencesdrawn from multiple sensortype datais
qualitatively superior tosinglesensortype data.
 Information fusion generatedfrom multiple heterogeneous sensors
providesfor betterunderstandingof the operational surroundings.
Environments: A Survey." IEEE Access (2017). 3

Challenges
Imperfection Inaccurate or uncertain WSNsensordata
Ambiguity Outliers, missingdata
Conflicts Samesensortype reports different data for the samelocation.
Alignment Arises when sensor data framesare converted to asingular frame prior to
transmission
Trivialfeatures Processingof trivial data features may bring down the accuracyof the whole
system
Source: Alam, Furqan, et al. "Data Fusion and IoT for Smart Ubiquitous Environments: A Survey." IEEE Access(2017).
4

DataFusionOpportunitiesin IoT
 Collectivedataisrich in information andgeneratesbetter
intelligencecomparedto datafrom singlesources.
 Optimal amalgamation ofdata.
 Enhancingthe collective information contentobtainedfrom multiple
low-power, low-precisionsensors.
 Enableshidingof critical datasourcesandsemantics(useful in military
applications, medical cases,etc.).
Environments: A Survey." IEEE Access (2017).
5

StagesofData Fusion
Decision level
• Ensemble of
decisions
Feature level
• Fusionof
information
prior to
decision
making
Pixel level
• Fusionof
information
at the
imaging
device level
itself
Signallevel
• Fusionof
information
at thesensor
node/ within
the local
network
itself.
6

MathematicalMethodsofData
Fusion
Probability based
• Bayesiananalysis, Statistics, Recursive methods
AI based
• ANN,Machine Learning, CNN
Theory of Evidencebased
• Belief functions, Transferable belief models
7

AIinIoTDecisionMaking
8

DataFusionforAutonomous Vehicles
9

SmartParking
 Shortensparking searchtime ofdrivers.
 Reducestraffic congestion.
 Reducespollution by keepingunnecessarilylingeringvehicles off the
roads.
 Reducesfuel consumption andcosts.
 Increasesurbanmobility.
 Shorterparkingsearchtime resultsin more parked time, and hence,
more revenue.
Source: Lin, Trista, Hervé Rivano, and Frédéric LeMouël. "A Survey of Smart
Parking Solutions." IEEETransactionson Intelligent Transportation
10

FunctionalLayersinSmart parking
Information Collection
SystemDeployment
ServiceDissemination
Source:Lin,Trista, Hervé Rivano, and Frédéric LeMouël. "A Surveyof SmartParking Solutions." IEEETransactionson Intelligent Transportation
Systems(2017).
11
Introduction to Internet of Things

SmartParking:Information Collection
Sensors
Parking Meters
SensorNetworks
Crowd sensing
Systems(2017).
12

SmartParking:SystemDeployment
Software System
Information Management
E-parking
Guidance
DataAnalytics
Systems(2017).
13

SmartParking:Service Dissemination
Dynamic Pricing
Strategies
Infrastructure-based information
Infrastructure-free information
Parking Choice
VehicularActivities
Systems(2017).
14

InformationSensinginSmart Parking
Largenumber of
Sensors
Stationary
Detects
presence/absence
in-place
Sensing
Fewer sensors
Mobile
Collects
information along
the route
Source:Lin, Trista, Hervé Rivano, and Frédéric LeMouël. "A Survey of SmartParking Solutions." IEEETransactions on Intelligent TransportationSystems(2017).
15

EnergyManagementinSmartCities
 Energyefficient solutions
 Lightweight protocols
 Scheduling optimization
 Predictive models for energy
consumption
 Cloud-based approach
 Low-power transceivers
 Cognitive management framework
16

 Energyharvestingsolutions
 Ambient energyharvesting
 RFsources
 Wind
 Sun
 Heat
 Vibration
17

 Energyharvestingsolutions
 Dedicatedenergy harvesting
 Energysourcesintentionally deployed near
IoT sources.
 Amount of energy harvested depends upon:
1. Sensitivity of the harvestingcircuit
2. Distance between the device and source
3. Environment
18

SmartHome-Introduction
 Smart home infrastructure consistsof:
 Intelligent networking deviceinfrastructure
 Seamlessintegration of various devicesusingwired/wireless
technologies
 Allows easeof usefor householdsystems.
 Createsahighly personalizedandsafehome space
 Corporations seriously indulging in smart homesystems include
GE,Cisco,Google,Microsoft, andothers.
2

SmartHome
 Providesproductive and cost-efficientenvironment.
 Maximizesthe effectiveness of the occupants.
 Providesefficient management with minimum life-time costs
of hardware andfacilities.
 Optimizes-
 Structures
 Systems
 Servicesand management
 Interrelationships between the abovethree
3
Introduction to Internet ofThings

SmartHome Aspects
Source:Toschi,Guilherme Mussi, LeonardoBarreto Campos,and Carlos EduardoCugnasca."Home automation networks:Asurvey." Computer
Standards& Interfaces 50 (2017):42-54.
4

HomeArea Networks(HANs)
Elements
Standards
Architectures
Initiatives
 Network contained withina
home.
 Enablesremote accessand
control of devicesandsystems.
 Providesamalgamation of various
systemswithin ahome, suchas–
security systems,home automation
systems,personal media,
communication, etc.
5

HANElements
 Internet Protocol(IP)
 Multi-protocol gateway bridges
non-IPnetwork toIP network.
 Bridging between new
technologies islimited.
 For new technologies or
networks, anew mappingis
required for bridging to
perform satisfactorily.
Internet Protocol

HANElements
 Wired HAN
 Easyintegration withpre-
existing houseinfrastructure.
 Lowcost.
 Canusepower lines, coaxial
cables,telephone lines,optical
fibers, and other such
technologies for
communication.
Wired HAN
7

HANElements
 Wireless HAN
 Canusepopular home Wi-
Fi, ZigBee,and evennew
standards, suchas
6LoWPAN.
 Wireless makes
implementation
easy.
WirelessHAN
8

HANStandards
 Universal Plugand Play(UPnP).
 Application layer technology,mainly
web-based.
 TCP/IPprotocol stackprovides support
for the lower layers,and enables seamless
integration of various technologies.
 Providestransparent networking with
support for zero-configuration
networking and automatic discoveryof
devices.
UPnP
DLNA
Konnex
LonWorks
Zigbee
X-10
10

HANStandards
 Digital LivingNetworkAlliance
(DLNA)
 Tradeorganization created bySony,
Intel, andMicrosoft.
 Connectscable-basednetworks with
wireless networks for increased
sharing of media,control and access.
 Domestically sharesnetwork media
resources.
UPnP
DLNA
Konnex
LonWorks
Zigbee
X-10
11

HANStandards
 Konnex(KNX):anopen
important standard for home
and building networks.
 Utilizes the full rangeof home
communication infrastructure –
Power lines, coaxialcables,twisted
pair, RF
,etc.
 Must be setup and configured viaa
software before its properusage.
UPnP
DLNA
Konnex
LonWorks
Zigbee
X-10

HANStandards
 LocalOperation Networks(LonWorks).
 Everydevice includes aNeuron Chip,a
transceiver and the application electronics.
 Neuron chip is aSOCwith multiple
microprocessors,RAM,ROMand IO
interface ports.
 Splits device groups into intelligent
elements, which can communicate through
aphysicalcommunication medium.
UPnP
DLNA
Konnex
LonWorks
Zigbee
X-10
13

HANStandards
 Zigbeeconsistsof four layers–Physical,
MediumAccessControl, Network, and
Application.
 Physicaland MAClayers are defined by
IEEE802.15.4,whereas Network and
Application are defined byZigbee.
 Aims at low-cost, low-energydevices.
 ZigBeeAlliance is composed of
Mitsubishi, Honeywell,Invensys,
Motorola andPhilips
UPnP
DLNA
Konnex
LonWorks
Zigbee
X-10
14

HANStandards
 X-10enables remote control of compliant
transmitters and receivers over power lines and
electrical wirings present in the house.
 Adopted by GEandPhilips.
 Standarddefines procedures for
transmission of bits overACcarrier
signals.
 Low-speed and low data rate.
 Mainly usedfor control of lighting, appliance
networks and security sensors.
UPnP
DLNA
Konnex
LonWorks
Zigbee
X-10
15

HANArchitectures
 UsesXMLfor description and web- services
for control.
 Follows aServiceorientedArchitecture
(SOA).
 Not tied toanysoftware, languageor
architecture.
 Acentral gateway connects different
technologies.
 Atech Manager for eachtechnology provides
web servicesfor controland access.
DomoNet
Jini
16

HANArchitectures
 Connects various devices sharing their resources
with auto-configuration and auto-installation.
 Basedon JA
V
Aenvironment andpursued
by SunMicrosystems (Now, Oracle).
 Constructs an organized distribution system
without acentral node (federation).
 Jini appsusebytecode to run JVM,and are
portable.
 Follows Object Oriented Paradigm.
DomoNet
Jini
17

HANInitiatives
 Middleware for embedded
intelligent systems.
 ConnectsaService Oriented
Architecture Network.
 Connecteddevicesmay havelimited
resources,low processing power,
memory or energyconsumption.
 Eachdevice hasan embedded HYDRA
client which acts asaproxy between
the device and themiddleware.
Project
HYDRA
Amigo
18

HANInitiatives
 Aimed at:-
 Ambient intelligent systems
 For networked homesystems
 Features user-friendly interfaces,
interoperability, and automatic
discovery of devices and
services.
Project
HYDRA
Amigo
20

Introduction
 Vehiclesequipped with
 Sensors
 Networking and communicatingdevices
 Capableof :
 Communicating with other deviceswithin the vehicle
 Communicating with other similarvehicles
 Communicating with fixedinfrastructure
Source:Kim,Younsun,HyunggoyOh,and SunghoKang."Proof of Conceptof HomeIoTConnectedVehicles." Sensors17.6 (2017): 1289.
2

Challenges
 Security
 Privacy
 Scalability
 Reliability
 Quality of service
 Lackof globalstandards
3

ConnectedVehicles
4

Vehicle-to-Everything(V2X)Paradigm
 Main component of futureIntelligent
Transportation System(ITS).
 Enablesvehiclesto wirelessly sharea
diverserangeof information.
 Information sharing maybe with other
vehicles, pedestrians, or fixed
infrastructures (mobile towers,parking
meters, etc.)
 Allows for traffic management,
ensuringon-road andoff-roadsafety,
mobility fortraveling.
5

V2X
 Follows adistributed architecture, where contents are widely
distributed over the network.
 Not restricted to single source informationprovider.
 Designedmainly for highly mobileenvironments.
 Canshareinformation to nodes in vicinity, aswell asremotelylocated.
 Hasgreatly enhanced travel efficiency, aswell assafety.
 Thenetwork is mainly usedasatool for sharing and
disseminating information.
Source:Zhu, Z.,et al. "Recent advancesin connected vehicles via information-centric networking." Intelligent and ConnectedVehicles (ICV2016),
IETInternational Conferenceon. IET
,2016.
6

FailuresofTCP/IPinV2X
 Designedmainly for handling information exchangebetween a
singlepair of entities.
 Information exchangedependent on the location ofdata.
 Canonly identify the addressesof endpoints, which aloneis not
useful for contentdistribution.
 Increasein number of wirelessdevices,restricts themobility of
thenodes.
,2016.
7

ContentCentricNetworking(CCN)
 CCNis derived from Information Centric Networking (ICN)
architecture.
 Focusesmore on the data than its actuallocation.
 Hierarchically named data.
 Hierarchical data is transmitted directly instead of being
part ofa conversation.
 Enablesscalable and efficient datadissemination.
 In-network caching allows for lowdata traffic.
 Works well in highly mobileenvironments.
8

VehicularAd-hoc Networks(VANETs)
 Basedon:
 Dedicated Short-Range
Communication (DSRC)
 WirelessAccessin
Vehicular Environment
(WAVE)
 Routing protocols derivedfrom
MANETs.
 High throughput achievable inmobile
environments.
 Guaranteed low-latency in mobile
environments. 9

VANETFeatures
HighDynamic Topology
• Vehiclesin highly mobile environments causesconstant changesin networkpartitioning
and topology.
Hightransmissionandcomputation capability
• Vehicle-stored energy sourcesand computational power do notrestrict capabilities.
Unstableconnectivity
• Link durations are short due to highly dynamic nature of VANET
s.
LargescaleCanbe easily scaledup to include all vehicles onroads.
Predictablemobility pattern
• Vehicular restriction within roads, makesmobility patternpredictable.
Source:Zhu, Z.,et al. "Recent advancesin connected vehicles via information-centric networking." Intelligent and ConnectedVehicles(ICV2016),
,2016.
10

ApplicationsofVANETs
Safety
• Emergencybraking, lane change warning, collision avoidance,hazard
notification
Efficiency
• Congestion management, electronic toll collection, parkingavailability
Commercial
• Internet access,multimedia stream
Comfort
• Weather information, autonomous driving, journey timeestimation
,2016.
11

CCN forVANETs
 Routing
 Forwardingandrouting basedon nameof content (not location).
 Individual content’s nameprefixesare advertised by routers acrossthe
network.
 Thishelpsto build aForwardingInformation Base(FIB)for eachrouter.
 Thenameof content remains sameand uniqueglobally.
 Noissuesof IPaddressmanagement or addressexhaustion.
 Communication doesnot dependon speedor direction of nodes.
12

CCN forVANETs
 Scalability
 Anin-network caching mechanism at eachrouter.
 Uniquely identifiable (named) data chunksare stored in ContentStore
(CS),which acts asa cache.
 Subsequentrequestsfor astored data chunk canbe made to aCS.
 Thenaming systemin the CSenables adata to be usedmultipletimes,
unlike normal IP-basedrouters.
 Reducednetwork load during increasednetwork size,asaresult of the
caching mechanism.

Bodyand BrainArchitecture
• Brain
Decision
Networkand
Transmission
• Nervoussystem
• Body
Senseand Execution
14

Bodyand BrainArchitecture
An in-vehicle networking architecture.
Three layered architecture.
Thebody consists of intelligent networking nodes
(INN) which constantly collect information from
the vehicle.
Thebrain manages central
• coordination.

ClassificationofINN
Resistor
Type
Open
C
lose
Motor
Type
Clockwise
Counter
clockwise
S
top
S
witch
Type
Knob
Pneumatic
Electromagnetic
Sensor
Type
Rheostatic
Transformer
Transducer
Assembled
Separate
electronic
control
Source:J.Wang,D.Yangand X.Lian, "Researchon electrical/electronic architecture for connected vehicles," IETInternational Conferenceon
Intelligent and ConnectedVehicles (ICV2016), Chongqing, 2016, pp.1-6
16

Networkand Transmission Layer
For
communication
17

DecisionLayer
For
monitoring
For
control
of
sensors
18

IntelligentConnected Vehicles(ICVs)
Intelligent
T
ransportation
Pedestrian
ICV
Communication
Channel
Transport
Infrastructure
2

TechnologicalBackground
 TheUSDepartment of Transportand FederalCommunications
Commission allocated 75MHz(5850-5925MHz) asthededicated
spectrum for ICVs.
 It isbasedon DedicatedShort RangeCommunication(DSRC)
technology.
 IEEEdeveloped IEEE802.11p and IEEE1609 as DSRCstandards.
 Societyof Automotive Engineers(SAE)cameup with SAEJ2735and
J2945asDSRCstandards.
Source:Li,Yan,et al. "Big wave of the intelligent connected vehicles." ChinaCommunications 13.Supplement2 (2016): 27-41.
3

IEEE1609Family
 IEEEP1609.0Draft Standardfor WirelessAccessin Vehicular Environments(W
A
VE)-
Architecture
 IEEE1609.1-2006- TrialUseStandard for WirelessAccessin Vehicular Environments
(W
A
VE)- ResourceManager
 IEEE1609.2 -2006- Trial UseStandard for WirelessAccessin VehicularEnvironments
(W
A
VE)- Security Servicesfor Applications and ManagementMessages
 IEEE1609.3 -2007 - Trial UseStandard for WirelessAccessin VehicularEnvironments
(W
A
VE)- Networking Services
 IEEE1609.4 -2006- Trial UseStandard for WirelessAccessin VehicularEnvironments
(W
A
VE)- Multi-Channel Operations
 IEEEP1609.11Over-the-Air DataExchangeProtocol for Intelligent Transportation
Systems(ITS).
4

DSRCOutline
5

PhasesofICV Development
Phase-1
• Infotainment service with remote informationprocessing
• Basedon 2G/3G
Phase-2
• Intelligent transportation service
• Basedon 4GL
TEor DSRC
Phase-3
• Vehiclesconnected to the cloud
6

ForwardCollisionWarning (V2V)
7

VulnerableRoadUserSafety (V2P)
8

VANET
s
Domains
In-vehicle
Ad-hoc
Infrastructure
Source:Pressas,Andreas,et al. "Connected vehicles in smart cities: interworking from inside vehicles to outside." Sensing,Communication, and
Networking (SECON),201613th Annual IEEEInternational Conferenceon. IEEE,2016.
9

In-VehicleDomain
 Composedof oneor moreon-boardunits(OBUs).
 Additional presenceof AdvancedDriverAssistanceSystems(ADAS)
sensorssuchas-
 cameras
 proximity sensors
 Enginesensors
 Radars
 Actuators
 CommunicationismainlythroughControllerAreaNetwork(CAN),
VehicularPowerline Networks(VPLN),andEthernet.
10

Ad-hoc Domain
 Composedof vehiclesand road-sideunits.
 Thevehicles(OBUs)are mobile.
 Theroad-side units (RSUs)arestatic.
 Communication mode maybe either V2VorV2I.
 Communication through DSRCstack(IEEE802.11p)
12

InfrastructureDomain
 RSUsconnected to Internet by meansofGateways.
 In the presence of RSUs, the vehicles may communicate to the
Internet via V2Iinterfaces.
 In the absence of RSUs,the vehicles may communicate with each
other or the Internet through cellular networks such as 3G/4G,
L
TE,etc.
14

V2XCommunication:Advantages
 Increasedtraffic safety.
 Increaseddriversafety.
 Optimized time oftravel.
 Efficiency of fuelconsumption.
 Securetravel.
 Easierdrive in low-visibility orunfavorable weather
conditions.
Source:Schmidt,Teresa,et al. "Public perception of V2X-technology-evaluation of general advantages,disadvantagesand reasonsfor data
sharing with connected vehicles." Intelligent VehiclesSymposium(IV), 2016IEEE.IEEE,2016.
17

V2XCommunication: Disadvantages
 Violation ofprivacy.
 Lossof datacontrol.
 Collection of personaldata.
 Seconduseof data.
 Datauseby unauthorizedentities.
 Trackingof movements.
 Localization of position.
Source:Schmidt,Teresa,et al. "Public perception of V2X-technology-evaluation of general advantages,disadvantagesand reasonsfor data
sharing with connected vehicles." Intelligent VehiclesSymposium(IV), 2016IEEE.IEEE,2016.
18

IIoT: Industrial Internet of Things
1

“IoTas a concept has crossed the chasm from slideware
to reality with many industries implementing IoT
solutions.”
‐ Paul Howarth, Senior Manager, Corporate Development,CISCO
2

Introduction
3
 The main aim of Internet ofThings (IoT) is
to globally connect smart ‘things’or‘objects’.
objects are uniquelyidentified.
interoperability among theobjects.
 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 productlifecycle.

Internetof
Things
Industry4.0
IIoT
Fig 1(a) :IIoT as an intersection of industries and
IoT
‐ Automation and data exchange in
manufacturing technologies
4
‐Cyber‐physical systems, the
Internet of things and cloud
computing
‐Smart factory

Internetof
Things
Industries
4.0
IIoT
Fig 1(a) :IIoT as an intersection of industries and
IoT
Fig 1(b) :IIoT ≠IoT
Fig 1 :IIoTPlatform
Industrial
Internetof
Things Internet
ofThings
5
Enterprise IoT
Consumer IoT

 IIoT includes–
machine learning
big datatechnology
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.
(Source : http://www.mhi.org)

Industrialization
(1870)
Power Generation
& Mechanical
Automation (1782)
Electronic
Automation
(1969)
Smart
Automation
(today)
 1st Industrial Revolution :Mechanized
production
 2nd Industrial Revolution :Mass
production
 3rd Industrial Revolution :Internet
evolution and automation
 4th Industrial Revolution :IIoT
Fig 2: Industry 4.0
Source:http://www.industry40wood.com

Source:https://www.artika.info Source:http://www.rehm‐group.com
Cloud
computing
IIoT :2nd generation of Internet evolution and 4th IndustrialAutomation

 IIoT is a networkof
physicalobjects
systems
platforms
applications
 These networks can communicate with each other,externalenvironment
and otherpeople.
 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
connectivit
y
Cloud
platfor
m
Application
Development
Big Data
analytic
s

IIoT Requirements (contd.)
Physical Plant
VirtualPlant
Machine
instructions
Sensor
readings

Design Considerations
 Touse an IoTdevicefor industrial applications,the followingdesign
objectivesare to be considered–
 Energy:TimeforwhichtheIoTdevicecanoperatewithlimitedpower
supply.
 Latency:Timerequired to transmit thedata.
 Throughput :Maximum datatransmitted across thenetwork.
 Scalability :Number o
fd
e
e
v
i
c
e
ssupported.
 Topology:Communication among the devices,i.e.interoperability.
 SafetyandSecurity:Degreeofsafetyandsecurityoftheapplication.

IoT
• Focused on convenience
of individuals
• M‐2‐M communication:
Limited
• Applications areasare
at consumer‐level
IIoT
• Focused on efficiency,
safety and security of
the operation.
• M‐2‐M communication:
Extensively.
• Application areas are at
industries.
Difference between IoT and IIoT
The main differences between IoT and IIoT are :

Difference between IoT and IIoT
(contd.)
Devices
Network
(connectivity)
Service
enablement
Application and
data
System
integration
M‐2‐M focus
IoTfocus

Service Management in IIoT
 “Service management refers to the implementation and
managementof the quality of services which meets the
end‐users demand”
 “Serviceis a collection of data and associated behaviors to
accomplish a particular functionor featureof a device or
portions of a device”.

Service Management in IIoT
 Service canbe of two types,which are ‐
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 servicesto the primary service or secondary
services are termed as secondary service.

Applications ofIIoT
 The keyapplication areas of
IIoTare ‐
Manufacturing industry
Healthcare Serviceindustry
Transportation & logistics
Mining
Firefighting

Manufacturing Industry
3
 The devices, equipment, workforce, supply chain, work
platform are integrated and connected to achieve smart
production. This will led to –
reduction in operationalcosts
improvement in the productivity of theworker
reduction in the injuries at theworkplace
resource optimization and wastereduction
end‐to‐end automation.

Healthc are ServiceIndustry
 Patients can be continuously monitored due to the
implanted on‐body sensors. This has led to–
improved treatmentoutcome
costs hasreduced
improved diseasedetection
improved accuracy in the collection ofdata
improved drugs management.

Transportation & logistics
 Toimprove 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 technologyforV2V andV2R communications.

Transportation & logistics
 In IIoTscenario thephysical objectsare providedwith
bar codes
RFID tags
hence, real‐time monitoring of the status and location of the
physical objects fromdestination to the origin, across the
supply chain is possible.
 Security and privacy of the datashould be maintained.

Mining
 Toprevent accidents inside the mines ‐RFID,Wi‐Fi and
other wireless technologies are used,which
provides early warning of anydisaster
monitors air‐quality
detects the presence of poisonous gases inside the
mines
oxygen level inside themines.

Firefighting
 Sensor networks, RFID tags are used to perform
automaticdiagnosis
 early warning ofdisaster
emergencyrescue
provides real‐timemonitoring Hence, improves public
security.

Examples of IIoT
9
 Examples of IIoT are‐
unmanned aerial vehicles (UAVs) to inspect oilpipelines.
monitoring food safety usingsensors.
minimizing workers’exposure to noise, chemicals and
otherhazardous gases.
unmanned marine vehiclewhich can collectdataup to a
year without fuel or crew.

Connected Ecosystems in IIoT
scenario
 Traditional supplychains in industries are linear in nature.
 Toshift the business focus from products to outcomes, new
ecosystem should befollowed.
 Digital ecosystems progress ata much faster ratethan
physical industries. Hence, itcan quickly adapt to the changes
in the external environments.

Integration of Digital and
Human Workforce
 In IIoT,machines become more intelligent.Hence, the
automatedtasks canbe done in the industries atlower costs
and higher quality level.
 Humans will work with machines, the outcome will be
higher overall productivity.
 IIoTwill reformand redefine the skills of the workers.

Creation of New Jobs
 The creation of new composite industries, such as precision
agriculture, digital healthcare system, digital mines etc., will
lead to developmentof new jobopportunities.
 Highly automated machines will require lesser number of
unskilled workers, but will require skilled experts with
digitaland analyticalskills.

Reformation of Robots
 In IIoTenvironment, robots are featured with three capabilities :sensing,
thinking and acting.They will be reformed with the ability to carry out
repetitivetasks.
 Robots will be more intelligent butwill work under the supervision of
human beings. Their availability willincrease.
 Robots will be reprogrammable toperform nasekws.t They havethe
capability to ‘learn’faster.

Challenges in IIoT
 Primary challenges
Identification of objectsor
things
Managehuge amount of data
Integrate existing
infrastructures into newIIoT
infrastructure
Enabling datastorage

Challenges in IIoT(contd.)
 SafetyChallenges
Worker health andsafety
Regulatory compliance
Environmental protection
Optimized operations

 Hazards (related)
Handling, storing or using hazardoussubstances
Oxygendeficiency
Particulates
Radiation
Physiological stress

Standardization
 Standardization plays an important role in the development of
the system.
 Goal:Toimprove the interoperability of the different systems/
applications and allow the products/services to performbetter.

Standardization
 The problems related to
standardizationare:
Interoperability
Semantic interoperability (data
sematics)
Security andprivacy
Radio access levelissues.

Privacy and securityissues
 The two most important concerns relatedwithIIoTare ‐
information security
data privacyprotection
 The devices/thingscan be tracked,monitored and connected.
So there are chances of attackon the personal and privatedata.

Privacy and securityissues
 Examples –
Healthcare industry – the medical data of a patient
must not be tampered, or altered by any person in the
middle.
Food industry – the deterioration of any food item being
sent to the company must be kept confidential as it will
affectthe reputation of the company.

Risksassociated with IIoT in
Manufacturing
 Though IIoTprovides new opportunities, but few
factors may cause hindrance in the path tosuccess,
which are :
lackof vision andleadership
lackof understanding of values among management
employees
costly sensors
inadequate infrastructure.

Meet the challenges: Sensor
improvement
 Improvement in sensor technologies–
miniaturization
performance
cost and energyconsumption.

Meet the challenges : Manufacturing
 Manufacturers use software capabilities to improve
operationalefficiency through –
predictivemaintenance
savings on scheduledrepairs
reduced maintenancecosts
reduced number ofbreakdowns.

Case study : RtTech Software
24
 Rt Techparticularizes in software which–
improves industrial facilities’efficiency
improves productivity.
 Energymanagement solution,which leads to reductionin
the plant’s highest variablecost.
 RtTechautomates the process of mapping and
managing energy consumption.

PRODUCTS DEVELOPED
 M‐2‐M communication :Intelligent Radio Modem(IRM)
IRM 1500 &ACE 1000 ‐IRM
simple
M‐2‐M connectivity
datatransmission
These devices provide easy maintenance and installation.They
canbe connected to IP and non‐IP serial devices to extend the
capability to monitor and communicate with othertechnologies.

PRODUCTSDEVELOPED (contd.)
 Comtrol – IO Link Master Gateway
It can be easily integrated into the
industrial network with existing and
newinstallations.
 It supports Ethernet/IP,
PROFINET(PNIO) and
ModbusTCP
.

Benefits of IIoT
 The benefitsof IIoTare
Improved
connectivity
among devices
Improved
efficiency
Upgraded
scalability
Reduces operation
time
Remote diagnosis
Costeffective
27

Recent Research trends in IIoT
 Recent research challenges in IIoT are‐
Toimprove the communications among the different things or
objects.
Todevelop energy‐efficient techniques so as toreduce power
consumption bysensors.
Todevelop context‐aware IoT middleware for better understanding
of the sensor data.
Tocreatesmart objects with larger memory,
processingaasnodnirneg capabilities.

Conclusion
 IIoT system requires the following:
Smaller,less expensive sensors which makes them
easilyaccessible.
Distributed control of assembly line,automated
monitoring,control and maintenance.

Future of IoTapplication inagriculture
Image template source: https://pixabay.com/p‐747175/?no_redirect
 Soil moisture and water level monitoring
 Automated irrigation system
 Automation in Recycling of Organic Waste and
Vermicomposting
 Automated sowing and weeding system
2

Future of IoT application inagriculture
Soil moisture and water level monitoring.
Automated irrigation system.
Automation in Recycling of Organic Waste and
Vermicomposting.
Automated sowing and weeding system.

Case study on
Smart Water Management Using IoT
3

AgriSens: Smart Water Management using IoT
4
 Objectives
 More yields with lesswater
 Save limited water resource in acountry
 Automatic irrigation
 Dynamic irrigation treatments in the different phases of a
crop’slife cycle
 Remote monitoring andcontrolling
Source: Project name: Development of a Sensor based Networking System for Improved Water Management for Irrigated Crops, funded by MHRD, Govt. of India

AgriSens: Smart Water
Management using IoT (Contd.)
 Proposedarchitecture
 Sensing and actuatinglayer.
 Processing, storage, andservice layer.
 Application layer.
5

6
Design
 Integrated design forsensors
 Integrated design for sensornode
 Integrated design for remoteserver

 Integrated design forsensors
Fig 4: Designed water‐levelsensor
Fig 5: EC‐05 soil moisturesensor
7

 Integrated design for sensornode
Fig 2:The blockdiagram of a sensor node
8

 Integrated design for sensornode
Fig 3: Designed sensornode
9

10
 Integrateddesignforremoteserver
 Repositorydataserver:Communicates with the deployed IoTgateway
in the fieldby using GPRStechnology
 Web server:Toaccess field dataremotely
 Multi users server:Sends field information to farmer’s cellusing SMS
technology and also executes farmer’s query andcontrollingmessages

11
 Implementation
 Field demo
 Website demo
 Project details fromwebsite

 Results
Fig. 6:Average soilmoisture
12
Vegetativephase Reproductive phase Maturityphase

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