School Papers

This important term to describe the extension of

This
paper provides how Big Data Analytics, Cloud and Fog Computing to support in
Internet of Things (IoT). The Big data analytics needed smart and efficient
storage. Fog computing extends the cloud computing paradigm to edge of the
network. If completely Big data analytics, Cloud and Fog computing  are 
implemented in IoT than every aspect of human life is continuously
changing. Fog and cloud computing provides everything as a services due to
social media and smartphones have dominated our daily lives. The IoT plays an important term to describe the
extension of connectivity and smart features in the year of 2020, to commonly
used appliances in our lives. Applications
and services of IOT to get smart cities, smart homes, smart buildings etc.. In
this paper we explore the relation between the IoT and emerging technologies like
Big Data Analytics, Cloud and Fog Computing. In addition to that we simulate
the protocol translator in application layer of IoT architecture.

Keywords—Cloud
computing; Big Data Analytics; Fog                          computing; Internet of Things.

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I.      Introduction

            The Internet of Things (IoT) provides latest development of Radio
Frequency Identification (RFID), internet protocols, smart sensors,
communication technologies and it improve quality of our lives1. The
applications of IoT are transportation, health care, industrial automation, the
present revolution of internet is mobile and machine-to-machine (M2M)
technologies 1. The IoT permits physical objects to see, hear, think and
perform jobs by having them talk together, the IoT transforms the objects to
underlying technologies are ubiquitous computing 1. The IoT provides
significant of human live and quality it deals with business applications and
it grow the world economy1,2.

                Suppose
for a example if smart city is made up of IoT then every home will  enable their residents to automatically open
the  door at the entrance when their
reaching home, prepare the coffee and breakfast automatically 1. To perform
the climate control system and pervasive and ubiquitous nature it required to
support emerging technologies 1. The emerging technologies which are
supporting to IoT are Big data Analytics, Cloud and Fog computing etc.

                 The Big data can store massive data in the
form of  data, emails, texts, consumer
transactions, social media interactions etc. which is connected by a large
number of devices and physical objects like humans, sensors, animals, plants,
smart phones, personal computers etc. equipped with sensors to generate 1. The support of Big data analytics in IoT is a
challenging task of organizations. In industries has required to  process amassed volumes of data, any industry
processed the amassed data with big data analytics three important factors i)
Save the budget  ii) Connect disparate
sources of data within the business iii) Improvement of the revenue 1. In IoT
the use of big data analytics will create more accurate market strategies and
enhanced Return on Investment (ROI) in the future sales2.

            The
cloud computing pay as you go model, it consist of millions of data centers and
it composed of trillions of virtual machines (VMs). The data centers are
performing higher utilization of VMs without degrading the performance. The
effective tasks allocation strategy performing on VMs. In internet deployments,
most notably the IoT, requires mobility support and geo-distribution and
location awareness in addition to that low latency of plat form is fog
computing 2. There are seven characteristics of Fog computing  there are 
i) Low latency and location awareness ii) Wide-spread geographical
distribution iii) Mobility iv) Very large number of nodes v) Predominant role
of wireless access vi) Strong presence of streaming and real time applications
vii) Heterogeneity 1. In Cloud and Fog computing provides developers and
providers to try everything together what customer wants. Cloud and Fog is
essential to success of IOT, The following difference have made to define the
role of IoT 1.

 

Table 1: Cloud computing
versus Fog computing in IoT

 

Cloud
Computing

Fog
Computing

There are huge Data and applications are processed in a cloud, which
is time consuming task for large volume of data in IoT 1.

Rather than presenting and working from centralized cloud, fog
operates on network edge. so it consumes less time in IoT 1

There is a Problem of bandwidth in IoT, as result of sending every bit
of data over cloud channels is a tedious task1.

Less demand for band width, as every bit of data’s where aggregated at
certain access points instead of sending over cloud channels1.

It was a slow response time and scalability problems as a result of
depending servers that are located at remote places2.

By setting small servers called edge servers in visibility of users,
it is possible for a fog computing platform to avoid response time and
scalability issues1.

 

The IoT interconnecting millions and billions or trillions of
connections with sensors or large volume of data through the internet. In this
scenario the IoT layer architecture is drawn in the Figure 1, The IoT layered
architecture is a flexible and There are four IoT architectures had drawn
Figure (a)  baisc architecture model
consisting of Application layer, Network layer and Perception layers. Figure
(b), (c),(d)  are illustrated the common
architecture of the five layer TCP/IP model, then it follow the brief
discussion of some layers

Figure 1: The
architecture of IoT 1  (a) Three
layered (b) Middle ware based layered (c) SOA based layered (d) Five layered
(TCP/IP)

 Perception Layer: This is first layer of the three layered
architecture, the big data created by the IoT and initiated at this layer. In
this layer digitizes the data and transfers to the Network layer through the
secure channels1.  Object Layer: The objects layer is first layer of the SOA and Five
layered which is also called perception layer or devices layer. The physical
sensor devices are connected to the objects layer. The object layer contains
sensors and actuators, it performing the various  functionalities such as temperature humidity,
weight, motions, vibration1.Object Abstraction
Layer: This is second layer of the SOA and Five layered, it transfers the
data to the objects layer and service management layer through the secure
channels1.Service Management Layer:
Service Management Layer is called as Middleware or pairing layer, this layer
receives the data and make the decisions and delivers the required services
over the network 1.Application Layer:
The application layer provides the environment of the services requested by the
customer. The customer needs temperature, humidity and high quality of smart
device1. Business Layer : The
business layer is also called as management layer it manage the overall system
activities and services, this layer builds the responsibility of the business
model such as graphs, statistical analysis etc. it receives the data and
process the data based on the big data analytics and making decisions1. Network Layer: In this layer transfer
the data to IoT platform and performing the important role in IoT and wireless
sensor Networks (WSNs)1.

The five layer model is most applicable model in IoT applications 1.
There are  six elements of combinations
are needed to build the functionalities of IoT 
identification, sensing, communication, computation, services and
semantics as shown in F igure 2.

Figure 2: The
elements of IoT 1

Identification is the first element it allocate the unique
address of the objects for identifying unique objects. The addressing modes
which are using  identification is
IPv4/IPv61. Sensing is the second
element combination of IoT, it gathering the data from related objects within
the network and sending it back to a data base or data ware house or cloud 1.
Communication element provides
communication protocols used for IoT are WiFi, Bluetooth, IEE 802.15.4, Z-wave 1.
Computation element processing the
microcontrollers, microprocessors, SOCs, FPGPAs and utilizing to provide the
IoT hardware or software functionalities 1. Services Under the services of IoT can be  categorized as four classes Identity-related
Services, Information Aggregation Services, Collaborative- Aware Services and
Ubiquitous services are the main service 1. These services are useful to
build smarthome, smart buiding, Intelligent Transportation System (ITS),
Industrial automation, Smart health care, SmartGrid and Smart city 1.        

                                             
II.    The Support Of Big Data Analytics, Fog And
Cloud                 Computing Platform
in IoT

                The
IoT is basically a complex network that seamlessly connects people and things
together through the Internet which is incorporated with Bigdata Analytics, Fog
and Cloud computing. Theoretically, anything that can be connected through the
M2M technologies (smart watches, cars, homes, thermostats, vending machines,
servers) will be connected in the near future using sensors and RFID
tags 2. IoT will have the advantage of bringing us smart cities with smart
cars, secure and efficient buildings, and smart traffic management systems 3.
It will achieve major efficiency in industry, healthcare and retail and will
save millions of dollars 3. IoT allows connected objects to continuously send
data over the Web and from anywhere 3.

 

 

 

A. Bigdata Analytics platform

                The companies need to work with big data  innovate their business, processing data, run
smarter applications and to deliver new values to their customers3. The data
collected through IoT becomes more useful if collected from different types of
devices and then combined in a creative way3. This should be followed by
building connections and correlations between data units that lead to
intelligent decision making processes3.
There are various software development tools are used for Big data. Hadoop and its incorporated technologies such as HDFS,
MapReduce, Hive, Pig and others that support Big data paradigms that will
embrace a large part of the IoT functionality1.

                There are three
challenges are involved in processing the data, first one is data collection it
gathering data from different sources IOT connects all manner of end points,
second one is data storage which is several possible directions in this regard
such as storing the data from analytics 
in a relational database, in the cloud or in a NoSQL database MongoDB
and CouchDB, third one is data analytics and business intelligence tools
empower decision markers as never before by extracting and presenting meaning
full information in real time 1. The relation between IoT, Big data and Big data
analytics as shown Figure 3. The IoT connects end points to separation of data
and it extract value from the huge amount of collected big data and analytics. This
requires the development of applications that can analyze the data 4.

Figure 3: The role of Big data Analytics in
IoT4

The main advantage of Big Data to IoT is that predictive analytics is
provided over all the data, not only a small part of it. This allows to dig up
for patterns, correlations, and build insights from data stored in Big Data
databases in a way never expected before. For IoT, the use of Big Data will
create more accurate market strategies and enhanced Return on Investment (ROI)
in the future sales. This is because data analysis will be implemented over the
complete product lifecycle, and we will get feedback from devices as well as
from customers.

 

 

B.  Fog and Cloud computing
platform

                In Fog Computing
cludlets are edge computing, fog  is highly
virtualized by supplementing
the cloud and providing intermediate layers of computation, networking, and
storage, fog nodes can optimize Internet of Everything (IoE) deployments
greatly enhancing latency, bandwidth, reliability, security, and overall IoE
network performance. Fog can be act as bridge between the smart devices. Fog
and cloud perform the massive computational, storage capabilities. Fog and
cloud perform the IoT Services to the end users. There are fog computing having
the various features Location, Distribution, scalability, density of devices,
Mobility support, Real-time, Standardization, On the fly analysis. Fog and
cloud computing perform the big data analytics through IoT edge devices as
shown in figure 4.

 Figure
4: The role of Big Data Analytics, Fog and Cloud               in IoT services 1

 

                In Figure 4 ,
hundreds of cloud resources are data centers, datacenters interacted with
thousands of fog gateways interacted with IoT services. IoT services delivers
the millions of services to end users. Big data analytics, Fog and Cloud
computing services are provide everything as service through mobile network
operators are at the cell towers. The role of fog and cloud-based
architecture of IoT where application intelligence and storage are centralized
in server side data centers, satisfies the need of most of the IoT
applications.

                                       
III.   Analytics Of IoT And Interplay Between Big Data
Analytics, Fog And Cloud Computing

 

                The IoE billions
and  trillion opportunity over past 10
years. Eight hundred million devices will be connected to various networks in
2020 as per the intelligence estimate statistical reports as shown in Figure 5
5. To bringing new technical
challenges in big data analytics,  Fog
and cloud domains and specifically the rate may increase in the data processing
the number of devices which are connected .

                Big data contains huge
amount of data, The IoT is allowing us to generate more data with big data
analytics, and it will generate the eye- popping numbers. The IoT  which consists of all people and things
connected to the Internet, will generate 507.5 zettabytes of data by 2019, according
to Cisco one zettabyte  is equal to one
trillion gigabyte 7,8.

 

           

Figure 5: The devices
which are connected to IoT 5, 6

                BI Intelligence
report believes that fog computing will be instrumental in analyzing all data,
as it offers several advantages that a cloud computing model, it
include quicker data analysis, reduced costs tied to data transmission,
storage, and management, as well as enhanced network and application
reliability 7.

            Fog computing is extending of cloud
computing, it also involves delivering data, applications, photos, videos, and
more over the Internet to data centers 3. IoT is a connection of devices
to the Internet, automobiles, kitchen appliances, and even heart monitors
could all be connected through the IoT 6. IoT in the future large number
of devices will join that list. The IoT generates massive amounts of data, and
fog and cloud computing provides a pathway for that data to travel to its
destination 7. Some of the most  popular
IoT cloud platforms on the market include Amazon Web Services, GE Predix,
Google Cloud IoT, Microsoft Azure IoT Suite, IBM Watson, and Salesforce IoT
Cloud 7,8.

                Fog computing is a
clever name it also known as edge computing, it provides a way to gather and
process data at local computing devices instead of in the cloud or at a remote
data center 7. Under this model, sensors and other connected devices send
data to a nearby edge computing device 7. 
This could be a gateway device, such as a switch or router, that
processors and analyzes this data 8. This is not just about
aggregation or concatenation of sensed physical data like a typical gateway but
really about distributed intelligence, where effective real time and
deterministic processing is needed to implement a functionality 8. To move
from cloud computing, or centralized computing, to edge computing according to
a 2014 Cisco survey and 2017, the  37
percent of IoT computing will be located at the edge of the network as shown in
Figure 5 & 6 7,8.

 

Figure 6: The
government and enterprises IOT devices                      connected to an edge solution 7, 8

                                                                     
IV.   The Simulation Of Protocol Translator Provides   Application Layer

               In gateway entity within the context of IoT
nodes can be deployed by the thousands or even millions in support of a single
application. Thus, having self-management Fault, Configuration, Accounting,
Performance and Security (FCAPS) capabilities is a must1,2. In IoT process the
communication over long distances between different systems, a range of
communication protocols are involved in such as Wi-Fi, Bluetooth, GPRS, 3G,
LTE, ZigBee, Z-Wave, home automation communication protocol, Near Field
Communication (NFC) which is an ensemble of protocols that allow electronic
devices to establish radio communication either by touching them together or by
bringing them into proximity and many other forms of data connectivity 3.

                The
IoT devices can be classified as two major categories first one is
resource-constrained and resource-rich devices. Most of IoT applications are
low-rate but the large number of IoT devices participating on a single
application needed gate way protocols. We believe that there is a re-programmability
of the IoT gateway through a rule-based language can put the gateway in a
unique position to offer smart autonomic management, data aggregation or flow
aggregation, and protocol adaptation services. 
There is a huge IoT load among the gateways it become multiple gateways
it required unique solution.

                It
needs an efficient solution for protocol conversion, it required a
protocol-friendly mechanism inside the Protocol Translator that can increase
the conversion speed. The key point of this mechanism is a protocol Name-Value
index table of data which is carried in the optional headers of the different
application protocols. In TCP/IP protocol suite contains at different levels
different application protocols, some of the important protocols are  CoAP, REST, MQTTM, MQTT-SN, AMQP. When a
packet reach at the gateway, the Protocol Translator examines the optional
header. If it determines the index table there, then it grabs the data
immediately from the payload it place the packets in destination protocol. In
index table is stored as on optional header, 
application protocols may not use the index tables. In such cases, the
conversion is done in the conventional form and consequently it takes span of
time.

Figure 5: (a) Optional header of the application protocol and Index table (b) The conversion
mechanism inside the gateway.

In Figure 5, In
optional header there  are various
application protocol formats that assigned index number in index table.  In index table where
a packet consisting of  Name-Value pairs suppose for example x-97, y-99 etc. they  needed to be converted in gate way from a
source protocol to a desired protocol in the protocol translator format. The data is stored
in a linear structure inside the payload of each packet.

The analysis of
protocol translator has been implemented XMPP, in which data are stored in XML
tags. In application layer format, it need O(n/2)
operations are required to find a data item in the payload before inserting it into the
desired protocol.  There are O(n2) operations are
required in name-value pairs data inside packets. The conversion of XMPP takes
the if the position of each Name-value 
item is available then the conversion time will be reduced to O(n). 

                                                                                                                                                                 
V.    Conclusion

                In
this paper outlined the vision of Big Data Analytics, Fog and Cloud computing
their role in IoT and future of IoT. The layered architecture of IoT system
performed by the IoT framework and IoT elements. The IoT architecture of this
massive infrastructure has been defined, store and network devices. the
simulation of protocol translator provided by application layer in IoT is
defined. In future there is scope to investigate new Big data analytics
platform, Fog and Cloud computing platform of IoT, deliver a portfolio of new
services in IoT and develop better and 
services of IoT.      

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