Salona Choudhury
(16100049)

 
Sanvida Tulam (16100050)

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Dept. of Computer Science & Engineering

Dept. of Computer Science & Engineering

IIIT Naya Raipur, Chattisgarh, India

IIIT Naya Raipur, Chattisgarh, India

                          
[email protected]

[email protected]

 

 

 

               Abstract – Internet of Everything (IoE)
is the fastest growing technological trend in the today’s world. Broader then
the trending concept of Internet of Things (IoT), IoE is not only limited to
connecting electronic devices and people to Internet, but also providing smart
& knowledge based solutions and services. The future technology will be
completely dominated on IoE, it will be all around us and will bring
revolutionary changes in our lives.

 

IoE
comprises of 3 basic components: millions of everyday objects which need to be
customised to connect to network, information-centred networks, and providing
smart and knowledge-based solution through real time analysis of collected
data. IoE is one of the most promising future technologies, for it integrates
the daily life with technology, making everything smart and artificially
intelligent.

 

 

               Index Terms – Internet of
Things, Artificially Intelligent, information-centric

 

 

I.  Introduction

               Internet
of Everything is the advancement and integration of technologies, like Cloud,
Internet of Things, Artificial Intelligence (AI), Deep Learning, Machine
Learning etc. The systems won’t only provide services, but will self-learn to
gain knowledge, will analyse the situation and provide solution based on that.
But each component to reach IoE has its own difficulties and limitations, which
have to be dealt with.

 

Fig.1 Internet of Everything (IoE)

 

Smart Objects

 

               The 1st
pillar of IoE consists of millions of everyday objects which have to be
connected with network. From industrial machines, electrical appliances, to
wearable items, packaging technologies, medical appliances etc. all are being
integrated to the net. At present, traditional silicon chips are the used to
connect devices, but to truly become ubiquitous, electronics has to be shaped
in such a way that it can be integrated with all type of objects – miniature
sized, soft, flexible and wearable.

 

      Recent developments in
Printed Electronics are paving the way towards low-cost and low-performance
electronics. It defines a technology for creating electronics on top of some
substrate using organic and inorganic inks. It gives the possibility of preparing stacks of micro-structured
layers and thus, helps in manufacturing thin-film devices.

 

      
Many Packaging companies are using Printed Electronics for developing
smart labels, for Anti-Counterfeiting
Packaging techniques. Especially, pharmaceuticals, healthcare, beauty products,
appliances, and food and beverage companies are increasingly using this
technology, which improves security as well as is disposable.

 

     
The concept of System in Packages (SiP) has led to miniaturized and more
powerful systems, where various components are stacked on top of another, to
create thin but highly efficient electronics.

 

 

Information-Centered Networks

 

      At
present, Internet is built on Internet Protocol (IP) based concept, where
point-to-point communication takes place on the basis of IP nomenclature of
source and destination. But with billions of smart objects connected to
Internet, there will be a burst of data flowing through net in the coming
years, which the present technology will not be able to handle. Also,
information and facts based search on a search engine is entirely different
thing from searching for sensor data from smart objects.

 

      For
this, Information-centric Network is being developed which makes data content
directly addressable and routable, also known as Content-centric network (CCN)
or Named-data networking. In the CCN security model, instead of using
additional layers for security features, individual units of data are made
secure using encryption. When user requests data by name, CCN transmits named
content to the user from the nearest cache, therefore less number of hops is
traversed, redundant requests gets eliminated, and ultimately less resources
are consumed.

 

 

Automated Real Time Insights

 

      After connecting different
devices to net and making the network suitable for IoE, the real use is when
these devices generate real time insights and take action accordingly.
Developments in the fields of Machine Learning, Deep Learning, Artificial
Intelligence, Big Data, Cloud etc. have made possible efficient and secure
processing of vast amounts of data to provide low-power and smart services. But
the objective is not only to make these devices smart, but also capable of
self-learning, so that they can use their previous experiences to generate more
accurate and better solutions and services. 

 

 

Fig.2 Self Learning Mechanism

 

 

      Fig.2 shows the proposed
architecture for self-learning and knowledge enhancement. It contains three
modules:

1) The data collection module collects real time data, and processes
it using the previous knowledge and training. Then the data is forwarded to
Machine Learning Module (MLM).

 2) Using machine learning
model from Knowledge Processing Module (KPM), learning process is initiated. If
it satisfies the standards set by the designer, the learning results and
parameters are transferred to KPM for generating knowledge. If the learning
results aren’t up to the standards, re-learning process is initiated under
different learning conditions.

 3) After updating knowledge,
this new knowledge is used to process the future inputs.

 

II.  CONCLUSION

       

      Thus, the IoE concept stands on three
pillars: Smart devices, Information based Networks, and self-learning devices
for smart and real time services.

 

      But to
implement it possesses a number of challenges, both in hardware and software
parts. For IoE applications, flexible, miniaturized, very low-power, low-cost
and extremely fast chips have to be designed. Many difficulties are arising:
like downscaling CMOS technology for miniaturization has led to compromised
amplifier performance. Designing techniques for chips with self-learning
algorithms, without compromising with its speed, size, and at low costs, have
to be explored.

 

 

Acknowledgment

        First of
all, we express our gratitude towards the Almighty, by whose grace we were able
to complete the task given on time. We are indebted to DR. RAMESH VADDI Sir,
for his guidance and supervision in completing this paper.

 

References

 

1     A Study on the Virtuous Circle Self-Learning Methods for Knowledge
Enhancement – Jaehak Yu, Young-Min Kim, SoonHyun Kwon, Kwihoon Kim, Nae-Soo
Kim, Sun-Jin Kim, * Cheol-Sig Pyo 

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Everything: Smart everyday objects, information-centric networks, and automated
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3     Named Data Networking Matthew N.O. Sadiku1 ,
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5     L, Atzori, A, Iera, and G, Morabito, “The Internet of Things: A survey,”
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6     Internet of Everything, Available online: http://ioeassessment.cisco.com/

7     M. Mosko, I. Solis, “CCNx Semantics”, IRTF
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8     L. Zhang et al, “Named Data Networking”, ACM SIGCOMM Computer
Communications Review, Volume 44, Number 3, July 2014, 66-73.

9     Mark Weiser, “The Computer for the 21st Century” – Scientific American,
September 1991, pages 94-104.