As Internet of things and allied applications are
spreading their facets at faster rate, use of sensors is
increasing rapidly. Every sensor produce big amount of data
which needs processing, reliable delivery. There are many
use cases where information is to be sensed from multiple
sensory resources and fused together. An objective of
information fusion from multiple sources is to interpret
single parameter for effective decision making. In the sequel,
this paper presents an overview of information fusion and its
mathematical model. Next part of the paper presents
motivation and challenges in information fusion for different
uses of Internet of Things. Comprehensive literature survey
and evaluation of the literature survey is also presented in
order to compare existing potential works with respect to
performance parameters like reliability, scalability,
computational time etc. The proposed data science approach
using cognitive computing is also presented and discussed in
detail. Experimental results of information fusion for one
sensor and three sensors are also presented which produces
significant contribution towards information fusion. Results
show that there is marginal difference in the access time to
sense information from one sensor and multiple sensors.
Finally, paper also presents challenges and future outlook.
Published In:IJCSN Journal Volume 5, Issue 4
Date of Publication : August 2016
Pages : 628-637
Figures :06
Tables : 02
Parikshit N Mahalle : has obtained his B.E degree in Computer
Science and Engineering from SantGadge Baba Amravati
University, Amravati, India and M.E. degree in Computer
Engineering from SavitribaiPhule Pune University, Pune, India. He
completed his Ph. D in Computer Science and Engineering
specialization in Wireless Communication from Aalborg University,
Aalborg, Denmark. He has more than 16 years of teaching and
research experience. He has been a member board of studies in
computer engineering, SavitribaiPhule Pune University (SPPU),
Pune, India. He has been a member – Board of studies in
computer engineering, SPPU. He is member – BoS coordination
committee in computer engineering, SPPU. He is also serving as
member- Technical committee, SPPU. He is IEEE member, ACM
member, Life member CSI and Life member ISTE.
Saudagar S. Barde : received the Master in Computer
Engineering (Software System) from BITS Pilani Maharashtra,
India in the year 2011. He is currently working as an Assistant
Professor in Department of Computer Engineering, STES’s Smt.
KashibaiNavale College of Engineering, Pune, India. He has
published 12 plus papers at international journals and
conferences. He has guided more than 30 plus under-graduate
students and 6 plus postgraduate students for projects. His
research interests are Discrete Mathematics, Artificial
Intelligence& Data Mining.
Poonam N. Railkar : received the Master in Computer
Engineering (Computer Networks) from Pune University
Maharashtra, India in the year 2013. From September 2012, she is
currently working as an Assistant Professor in Department of
Computer Engineering, STES’s Smt. Kashibai Navale College of
Engineering, Pune, India. She has published 15 plus papers at
national and international journals and conferences. She has
guided more than 10 plus under-graduate students and 3 plus
postgraduate students for projects. Her research interests are
Mobile Computing,Identity Management, Security and Database
Management System Applications.
Pankaj R. Chandre : received the Master in Computer
Engineering (Computer Engineering) from Mumba1 University
Maharashtra, India in the year 2011. He is currently working as an
Assistant Professor in Department of Computer Engineering,
STES’s Smt. Kashibai Navale College of Engineering, Pune, India.
He has published 45 plus papers at international journals and
conferences. He has guided more than 30 plus under-graduate
students and 20 plus postgraduate students for projects. His
research interests are Network Security, Operating System Design
& Information Secirity.
Data fusion, Information fusion, Multi-sensor
This paper presented an overview of information fusion and
its mathematical model. This information fusionhas great
potential to provide smart decision making in all Internet of
Things use cases. Information fusion from multi-sensory
data is challenging inapplications like space and wireless
domain. The proposed data science approach using
cognitive computing surely helps to design lightweight and
scalable algorithm forinformation fusion. Experimental
results also show that there is marginal difference in the
access time with the increasing number of sensors.This is
indeed a significant contribution in the field and there is a
need of smart information fusion algorithm.
Future outlook is to investigate and demonstrate the
usefulness of information fusion in remote sensing and
GIS applications equipped with visual and non-visual
sensors. Next step is to model an intelligent remote
sensing environment as a knowledge ecosystem to perform
multi-sensor data fusion. Another interesting future work
will be to propose lightweight and reliable algorithms for
information fusion and to evaluate the reliability of the
proposed algorithms based on the decision making ability
in comparison with the alternative approaches.
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