“Design of Efficient Mobile Femtocell by Compression and Aggregation Technology in Cellular Network ”

Seminar
on
“Design of Efficient Mobile Femtocell by Compression
and Aggregation Technology in Cellular Network ”
Presented By
Mr. Virendra A. Uppalwar
IV Semester M. Tech. (Communication Engineering)
Mr. Akshay P. Nanote
Assistant Manager
Gupta Energy Pvt.Ltd.
Deoli, Wardha
Prof. S.M. Sakhare
Ass. Professor
P.G. Department of Ele. & Comm.
Suresh Deshmukh College of Engineering,
Selukate, Wardha.
P. G. Department of Electronics (Communication Engineering)
Suresh Deshmukh College of Engineering, Selukate, Wardha.
Under the Guidance of
Guide
Co-Guide

Contents
• Introduction
• Literature Survey
• Problem Definition
• Objectives
• Network Formation
• Project Simulation
Simulation Parameters
Simulation Process
• Results And Analysis
• Conclusion
• Future Scope
• References

Introduction
• Femtocell is a small size base station, low power domestic access point, cellular base station.
• Femtocell allows service providers to extend service coverage in indoors at the edge and Improve indoor coverage
and capacity.
• It connects to the service providers network via broadband , such as DSL or Cable, Support 2 to 6 active User’s.
• When users walk outside or out of range, calls are automatically handed over to the external mobile network.
• Unlike Wi-Fi access points, Femtocells operate using licensed spectrum and thus must be supplied and operated in
conjunction with the mobile operator.
• The concept is applicable to all wireless standards, including UMTS, GSM, CDMA and Wi-MAX solutions also
for LTE.
• In 3GPP Terminology
A Home Node B (HNB) is a 3G Femtocell.
A Home eNode B (HeNB) is a 4G Femtocell.

Small Cell ?
• In homes and buildings where coverage decreases
considerably as soon as you go indoors.
• It has become necessary to work on new technology that will
facilitate calling coverage both indoor and outdoor.
• Improvement Small Cell technology could be the answer.

Evolution of Cellular System : Cell Size

• Title of Paper 1 :
“A Survey on Power Control Technique in Femtocell N/W”
• Name of Author :
Mohamod Ismail , Rosdiadee Nordin
• Publication :
IEEE, Journal of Communications Vol. 8, No. 12, December
2013.
• The Objective of paper :
Focus on power control technique in Femtocell.

 Idea Represented
In this paper , Femtocell base stations (FBS’s) perform Self-Optimization function , that
continually adjust the transmit power. So, the femtocell coverage does not leak into an
outdoor area while sufficiently covering the indoor femtocell area.
 Methodology
Paper explains the different power control technique.
a) Fixed HeNB power setting.
b) Location based power control scheme.
c) Power control based interference avoidance schemes.
 Conclusion
Femtocell Base Stations (FBS’s) is a small low power device, but it should be able
to handle the complexity of different power control techniques as highliated in
literature.

“Energy Efficient Power Management for 4G Heterogeneous
Cellular Networks”
Xiang Xu, Gledi Kutrolli, Rudolf Mathar.
• Year of Publication :
1st International Workshop on Green Optimized Wireless
Network , GROWN’2013.
Explain energy efficient power management technique in Femtocell.

Authors explains how Heterogeneous n/w is promising solution to improve the energy
efficiency of cellular system.
 Methodology
Using user & service classification , the proposed algorithm balances the N/W
coverage , average data rate & energy consumption. Algorithm Used :
a) Resource Allocation Algorithm
b) Power control algorithm with adaptive data rate offset.
 Conclusion
The energy efficiency of the proposed scheme is much higher than the
conventional scheme with less HUE’s per femtocell. The simulation result confirms
the superiority of proposed algorithm in energy efficiency & coverage.

“On the Potential of Handover Parameter Optimization for Self-
Organizing Networks”
Pablo Munoz, Raquel Barco, Isabel de la Bandera.
IEEE, Transactions on Vehicular Technology, Vol.62, No.5 June
2013.
Explain HO parameter optimization for Self – Organizing network.

Authors explain the SON , with one of the important field of SON i.e. Handover
process in mobile N/W’s. Self Organizing N/W (SON’s) aim to raise the level of
automated operation in next-generation N/W’s.
 Methodology
In this paper , a sensitivity analysis of the two main HO parameters i.e. the HO
margin (HOM) and the time–to-trigger (TTT) , is carried out for different system load
levels and user speed in LTE. In this case, different parameter optimization levels like
N/W-wide, call wide & call pair wide and the impact of measurement errors have been
considered.
 Conclusion
Result of this sensitivity analysis show that tuning HOM is an effective
solution for HO optimization in LTE N/W’s . In addition , the adjustment of TTT
does not provide greater benefits that the obtained by adjusting HOM.

“Efficient SON Handover Scheme for Enterprise Femtocell
Networks”
Chaganti Ramarjuna, Shaikh Asif Ahammed, Riddhi Rex.
IEEE International Conference on Advanced Networks and
Telecommunications Systems (ANTS) 2014
Provide efficient SON handover scheme for Femtocell network.

In this paper authors propose an efficient SON handover scheme to mitigate (minimize)
unnecessary handovers. The proposed approach uses building information and estimated
UE (user equipment) position for making handover decision.
 Methodology
In this paper authors explains , the SON (Self-Organizing-Network) is an automation
technology designed to make the planning, configuration & management of mobile N/W.
SON has a Plug-n-Play feature i.e.(connect and disconnect automatically) which is very
important in next generation communication industry.
 Conclusion
The simulation results show that SON HO scheme achieves 31.5% improvement
in reducing HO delay compared to traditional HO scheme.

Overall Conclusion
• As surveying all this four papers , we see that , the SON triggers
the HO whenever necessary, because of that increment in
femtocell power efficiency.
• Ultimately improvement in power efficiency reduces the
radiation power.
• As the SON follows user it provide a better network coverage
and improve the spectral efficiency.

• As the cellular world expand people potentially requesting diverse data services such as
web browsing, video streaming, and gaming.
• Users inside a building faces network problem, as the reports says 26% calls place at
home, 57% mobile usage at indoor, 75% of 3networkG traffic to originate in- building as
2013.
• In urban area customers faced network problems because of poor coverage.
• The small cell enhancement is the only option for better services in future to provide a
better quality of services to the Users.
Problem Definition

Objectives
• To set up a platform for performing the simulations. The platform could be chosen from
Windows and various Linux flavors such as Fedora.
• To install and set up appropriate software such as NS2, Trace graph, Xgraph, NAM
(Network Animator) on the selected platform.
• Develop Compression and Aggregation Algorithm.
• To evaluate the performance of chosen protocol based on their performance metrics such
as average delay, Energy, Throughput, and Spectral Efficiency in the simulated
environment of AODV(Ad hoc on Demand Distance Vector).
• To conclude the results and suggest the future work related to the performance of the
protocols.

Lampel Ziv Markov Chain Algorithm

Data Compression and Aggregation
 Data is a combination of alphanumeric characters.
 Data compression is nothing but the encoding of data.
 Data Compression algorithm compresses data file so that it takes less storage space.
 In order to store and transmit such a data as it is, requires larger memory and increase
bandwidth utilization.
 Hence, before storage or transmission the size of data has to be reduced without affecting
the information content of the data.

 Among the various encoding algorithms, the Lempel Ziv Markov Chain Algorithm (LZMA) to be
effective in unknown byte stream compression for reliable lossless data compression.
 Lossless compression technique is free from loss of data. Using this technique original message can
be exactly decoded.
 Lossless data compression works by finding repeated patterns in a message and encoding those
patterns in an efficient manner.
 Jacob Ziv and Abraham Lempel (LZMA) drew attention towards dictionary-based methods to
achieve better compression ratios.
 The first simple compression algorithm described by Ziv and Lempel is commonly referred to as
LZ77.

LZMA Data Compression Algorithm
 In LZMA all data follow path :
• Address to already coded contents
• Sequence length
• First deviating symbol
 If no identical byte sequence is available from former contents, the address is 0, the
sequence length is 0 and the new symbol will be coded.

LZMA Coding Scheme
• LZMA uses a dynamic dictionary to compress unknown data with the use of sliding
window algorithm
• Delta Filter and Range Encoder in addition to improve the compression technique.

Delta Encoding and Decoding / Delta Filter
 The Delta Filter shapes the input data stream for effective compression by the sliding window.
 It stores or transmits data in the form of differences between sequential data rather than
complete files.
 The output of the first byte delta encoding is the data stream itself.
 The subsequent bytes are stored as the difference between the current and its previous byte.
 For a continuously varying real time data, delta encoding makes the sliding dictionary more
efficient.

Sliding Dictionary
 There are two types of dictionaries namely static dictionary and adaptive dictionary.
 In static dictionaries the entries are predefined and constant according to the application of the text.
 In adaptive dictionaries, the entries are taken from the text itself and created on-the-fly.
 A search buffer is used as dictionary , Patterns in text are assumed to occur within range of the search buffer.
 Use of suitable data structure for the buffers will reduce the search time for longest matches.
 Sliding Dictionary encoding is more difficult than decoding as it needs to find the longest
match.

Range Encoder
 Range encoder encodes all the symbols of the message into a single number to achieve greater
compression ratios.
 The range encoder uses the following steps.
1) Provide a large-enough range of integers, and probability estimation for the
symbols.
2) Divide the initial range into sub-ranges whose sizes are proportional to the
probability of the symbol they represent.
3) Encode each symbol of the message by reducing the current range down to just that
sub-range which corresponds to the next symbol to be encoded.
 The decoder must have the same probability estimation the encoder used, which can either be sent
in advance, derived from already transferred data.

Delay
• The average time taken by a data packet to arrive in the
destination
∑ ( arrive time – send time ) / ∑ Number of connections.
• The average time from the beginning of a packet transmission at a
source node until packet delivery to a destination

Energy
• The average energy consumed by the nodes in receiving & sending
the packets.
• It includes energy spent in channel listening & packet transmission
forwarding in the whole network.
• It is measured in joule.
• Energy permit the network to work.

Throughput
• The system throughput is the sum of data rates that are delivered to all terminals
in the network.
• It is the ratio of the number of packet received successfully & the total number
of packets transmitted.
• Measured in bits/sec or some time data packets/sec i.e.(p/s or pps)

Project Simulation
• The simulation experiment is carried out in LINUX (FEDORA 7).
• The detailed simulation model is based on Network Simulator-2 (ver-2.34) ,
is used in the evaluation.
• The NS instructions can be used to define the topology structure of the
network and the motion mode of the nodes, to configure the service source and
the receiver, to create the statistical data track file and so on.

Development of Data Aggregation and Compression Technique
for Communication in Femtocell Network.

Simulator NS-2.34
Protocol AODV
Simulation duration 300 Seconds
Simulation area 300 m x 300 m
Number of nodes 30
Transmission range 1.5 Km
Movement model Radio propagation Model
MAC Layer Protocol IEEE 802.11
Pause Time 0.0001sec
Maximum speed 1000m/s
Packet rate 100 psec
Traffic type CBR
Datapay load 100 bits
Parameter values for Simulation

Screenshot showing use of AWK Command

Step 1 : Command for Data Compression and Aggregation
• Here we provide commands
to the system for operation
• “0” represents we don’t select
Compression & Aggregation
Operation

Step 2 : Comparing Every Two Access Point Nodes to Obtain Maximum Energy at Nodes
• We compare every two
nodes for detecting
maximum energy
• Each node have it’s own
energy
• Node which have
maximum energy always
initiating for data
transmission or receiving.

Step 3 : Enter the Source and Destination
• Source node 3
• Destination node 18
• Both Source and
Destination Nodes have
minimum energy
compared to Node 2 and
Node 19.

Step 4 : Data Transfer From Source to Destination
• Source Node -3
• But the Data Transfer
from Node 3 to Node 2
• Because we know that,
Node 2 has maximum
energy than Node 3

Step 5 : Data loss between Source and Destination
• At the time of Data
transmission from
Node 3 to Node 2 the
Black Square Box
represents the Data
loss.
• Data loss is more at
the time of
Uncompressed Data
transmission.

Step 6 : Data transferring Source Node 2 to Femto Node 30
Data
Loss
is on
large
scale

Step 7: Data Transfer Femto Node 30 to Node 18 via Node 19
• Data Transfer from
Femto Node 30 to
Destination Node 18
• But as we know that
Node 19 has the
maximum energy
compared to Node 18 so
the Data transfer via
Node 19

Step 8: Applying Data Compression and Aggregation Technique
• As select “1” for compression we
send command to the system that
Data Compression start

Step 9: Data Loss is Minimum in Compressed Data
• Compressed Data
Transfer from Node
3 to Node 2
• Data loss is
minimum in
compressed data
compared to the
uncompressed data.

Step 10 : Data transmission from Source Node to Femto Node with data minimum loss
• Compressed Data
transfer from Source
Node 2 to Femto
Node 30
• Data loss is also
reduce.
Data Loss
is minimum

Step 11 : Data transfer from Femto Node 30 to Destination Node 18
Data Loss
Reduces

Step 12 : Final Cycle of Compressed Data Transmission from Source Node to
Destination Node
• Data transfer from Femto
Node 30 to Destination
Node 18 via Node 19
• Because the Node19 has
maximum energy compared
with Node 18
• Data loss is reduces with
Compressed Data

Delay Graph
X axis – time – 0.2 sec
Y axis – delay – 0.2 msec
Delay
Time
Uncompressed Data
Compressed Data

1
Analysis of Delay Values
Simulation Time Compressed Data Uncompressed Data
0.1 sec 0.9 msec 1.0 msec
Sr. No.
1
2
3
4
5
6
7
8
9
10
Delay for
Compressed
Data
Delay for
Uncompressed
Data
<
• According to parameter
Delay , Compressed data
transmission rate is fluent
than Uncompressed Data

Energy Graph
Y axis – energy – 0.05 mJ
X axis – time – 0.2 Sec
Energy
Time
Uncompressed Data
Compressed Data

Analysis of Energy Values
Sr. no. Simulation Time Compressed Data Uncompressed
Data
1 0.1sec 1.98 mJ 2 mJ
2 0.2 sec 1.93 mJ 1.97 mJ
3 0.3 sec 1.87 mJ 1.9 mJ
4 0.4 sec 1.84 mJ 1.87 mJ
5 0.5 sec 1.80 mJ 1.83 mJ
6 0.6 sec 1.78 mJ 1.79 mJ
7 0.7 sec 1.73 mJ 1.75 mJ
8 0.8 sec 1.68 mJ 1.78 mJ
9 0.9 sec 1.65 mJ 1.68 mJ
10 1.0 sec 1.7 mJ 1.68 mJ
Energy
required for
Compressed
Data
Energy
required for
Uncompressed
Data
<
• According to the parameter
Energy Consumption,
Compressed Data consume
less energy compared to the
Uncompressed Data

Throughput Graph
Bits
Time
Uncompressed Data
Compressed Data
X axis - Time – 0.2 sec
Y axis – Bits – 20 bits

Throughput Values
Sr. No. Simulation Time Compressed
Data
Uncompressed
Data
1 0.2 sec 210 bits/sec 210 bits/sec
3 0.6 sec 230 bits/sec 230bits/sec
• The values of Throughput is
nearly same for Compressed &
Uncompressed Data.

Spectral Efficiency
For Uncompressed Data
For Compressed Data
• Compressed Data achieve
improved Spectrum Efficiency

Analysis
• Compressed Data perform much better than Uncompressed Data on performance
metrics likes Delay, required Energy, and Spectral Efficiency.
• The Throughput of Compressed and Uncompressed Data is nearly same.

Conclusion
• The Compression and Aggregation technique tone down data loss very
accurately.
• Improvement in Femtocell Based Handoff, Operational Time decreases,
and also improvement in Energy Efficiency.
• We obtain improved Spectral Efficiency in Femtocell Network.

Future Scope
• In the future, simulations could be carried out using project codes, in
order to gain a more in-depth performance analysis of the Data
Compression Technique in Femtocell Network.
• Our work can be extended by evaluating & comparing to various other
technique in Femtocell like Self-Organizing Network.
• Also, we may proceed hardware implementation for efficient Femtocell
devices in a real world.

References
[1] Sawsan A. Sadd, Mohamod Ismail , Rosdiadee Nordin. National University of Malaysia. “A Survey on Power Control
Techniques in Femtocell Networks”Journal of Communications Vol. 8, No. 12, December 2013.
[2] Lan Wang, Yongsheng Zhang, Zhenrong Wei. DOCOMO Beijing Communications Laboratories Co. Ltd., Beijing, China.
“Mobility Management Schemes at Radio Network Layer for LTE Femtocells”, IEEE 69TH , Vehicular Technology Conference,
Barcelona, 2009.
[3] Haijun Zhang, XiangmingWen, Bo Wang, Wei Zheng and Yong Sun. School of Information and Communication
Engineering Beijing University of Posts and Telecommunications Beijing, China. “A Novel Handover Mechanism between
Femtocell and Macrocell for
LTE based Networks”, , IEEE- ICCSN’10, Second International Conference on Communication Software and Networks,
Singapore.
[4] Dionysis Xenakis, Nikos Passas, Christos Verikoukis, “A Novel Handover Decision Policy for Reducing Power
Transmissions in the two-tier LTE network”, IEEE ICC 2012 - Communication QoS, Reliability and Modeling Symposium,
2012.
[5] Liu Xiang, Student Member, IEEE, Jun Luo, Member, IEEE, and Catherine Rosenberg, Fellow, IEEE, “Compressed Data
Aggregation: Energy Efficient and High Fidelity Data Collection”, IEEE/ACM Transactions on Networking
(Volume:21,Issue:6), 2012.
[6] Tian Hey, Lin Gu_, Liqian Luoz, Ting Yan_, John A. Stankovic_, Sang H. Son,
“An Overview of Data Aggregation Architecture for1 Real-Time Tracking with Sensor Networks”. 20th International Parallel
and Distributed Processing Symposium , Greece , April 2006.

[7] N. Sinclair , D. Harle , I. glover ,J. Irvine, R. Atkinson ,”An advanced SOM algorithm applied to handover
management within LTE” , 2013.
[8] J. A. del Peral – Rosado , J. A. Lopez Salcedo , “ Joint channel and time delay estimation for LTE positioning
reference signals “ , 2012.
[9] S. Nielsen , “ LTE evolving towards local area in release 12 and beyond “, 2012.
[10] Views on rel-12 and onwards for LTE and units ,” Future Radio in 3GPP , Huawei Technologies, 2012.
[11] “Enterprise multi-femtocell deployment guidelines”, Qualcomm Inc. 2011.
[12] Next-Generation Mobile Networks (NGMN) Alliances, Recommendation on SON & O&M , Tech Rep. 2014
[13] M. Toril , V. Wille ,”Optimization of handover parameters for traffic sharing in GERAN “, 2008
[14] Ardian Ulvan, Robert Bestak, Deprtment of Telecommunication Engineering, Czech Technical University in Prague,
Prague Czech Republic. Melvi Ulvan Department of Electrical Engineering, The University of Lampung, Indonesia.
“Handover Scenario and Procedure in LTE-Based Femtocell Networks”, UBICOMM2010 : The Fourth International
Conference on Mobile Ubiquitous Computing, System, Services, Technologies.
[15] Mansou Zuair, Department of Computer Engineering, Kings Saud University, Riyadh, Saudi Arabia. “Development
of an Access Mechanism for Femtocell Networks”, Journal of Theoretical and Applied Information Technology,
vol.51no.3, May 2013.
[16]Yoram Haddad, Jerusalem Collge of Technology, Computer Science and Networks Department, Jerusalem, Israel,
Yisroel Mirsky, Be Gurion University of the Negev, Dept. of Communication Syst. Eng., Beer-Sheva, Israel.
IEEE2013

[17] Fourat Haider, Cheng-Xiang Wang, “Spectral-Energy Efficiency Trade-off of CellularSystems with Mobile Femtocell
Deployment”, IEEE Transactions on Vehicular Technology 2015
[18] Rand Raheem, Aboubaker Lasebae, Mahdi Aiash, Jonathan Loo, “From Fixed to Mobile Femtocells in LTE system: Issues
and Challenges”, FGCT 2nd International conference, IEEE, Nov 2013
[19] Azwan Mahmud and Khairi Ashour Hamdi, “Hybrid Femtocell Resource Allocation Strategy in Fractional Frequency Reuse”,
Wireless Communications and Networking Conference (WCNC): NETWORKS, IEEE, 2013
[20]Maryam Nasr Esfahani, Behrouz Shahgholi Ghahfarokhi, “Improving Spectral Efficiency in Fractional Allocation of Radio
Services to Self Organizing Femtocells using Learning Automata”,7th International Symposium on Telecommunication on 2014
[21] You-Chiun Wang, “Data Compression Techniques in Wireless Sensor Networks”, IEEE Pervasive Computing 2012
[22] Rony Kumer Saha, Poompat Saengudomlert, “Novel Resource Scheduling for Spectral Efficiency in L TE-Advanced Systems
with Macrocells and Femtocells”,The 8th Electrical Engineering/ Electronics, Computer, Telecommunications and Information
Technology (ECTI) Association of Thailand - Conference 2011
[23] Antonio De Domenico and Emilio Calvanese Strinati, “A Radio Resource Management Scheduling Algorithm for Self-
Organizing Femtocells”, 21st International Symposium on Personal, Indoor and Mobile Radio Communications Workshops, IEEE
2010
[24] Dr. Jay Weitzen, VP of Technology, Airvana. “Advantage of Femtocell for Mobile Broadband Data Services”, Airvana Report
2009.
[25] Dionysis Xenakis, Nikos Passas, Ayman Radwan, Jonathan Rodriguez, Christos Verikoukis. “Energy Efficeent Mobility
Management for the Macrocell – Femtocell LTE Network”, Chapter 3 INTECH Open Source , 2012.
[26] L. Richards, M. Sharrock, J. Sekine, A. Uchida, “Indoor Wireless Innovation : Preparing and Deploying Advanced Indoor
Coverage End-to-End in Japan”, Alactel-Lucent 2007.

[27] Ian Sharp, Kegen Yu. Senior Member, IEEE, “Enhanced Least- Squares Positioning Algorithm for Indoor Positioning”, IEEE
Transactions on Mobile Computing, Vol. 12 No.8, August 2013.
[28] Christos Bouras, Georgios Kavourgias, Vasileios Kokkinos, Andres Ppazois, Computre Technology Institute & Press
“Diophantus” and Computer Engineering and Informatics Department, University of Patras, Greece. “Interference Management in
LTE Femtocell System Using an Adaptive Frequency Reuse Scheme” IEEE 2012.
[29] M.Jada, J. Hamalaimen, R. Jantti. Department of Communications and Networking Aalto University, School of Science and
Technology Espoo, Finland. “Energy Savings in Mobile Networks : Case Study on Femtocells”. IEEE2014.
[30] Atta ul Quddus, Tao Guo, Mehrdad Shariat, Bernard Hunt, Ali Imran, Youngwook Ko., Rahim Tafazoli. Centre for
Commmunnication System Research, University of Survey Guildford, .UK. “Next Generation Femtocells : An Enabler for High
Efficiency Multimedia Transmission”, IEEE COSMOS MMTC E- Letter, Vol. 5, N0. 5, September 2010.
[31] Hakyung Jung, Ji Hoon Lee, Chulhyum Park, Youngbin Im, Taekyong Kwon, Yanghee, Choi, Schoole of Computer Science
and Engineering, Seoul National University, Seoul, Korea. “A Femtoell – Based Testbed for Evaluating Futur Cellular Networks”,
IEEE 2013.
[32] Prof. R.K. Jain, Sumit Katiyar, Research Scholar, Singhania University, Jhunjhunu, India. Prof. N. K. Agrawal Sr. M IEEE
Indraprastha Engineering College, Ghaziabad, India. “Hierarchical Cellular Structures in High – Capacity Cellular
Communications Systems”, International Journal of Advanced Computer Science and Applications”, Vol.2, N0.9, 2011.
[33] Ayaskant Rath, Sha Hua, Shivenda S. Panwar , Dept. of Electrical and Computer Engineering, Polytechnic Institute of NYU,
Brooklyn, NY,11201. “FemtoHaul : Using Femtocells with Relays to Increase Macrocell Backhaul Bandwidth” IEEE INFOCOM
2010.

Sr.
No. Paper Title Conference Name Journal Publication
Impact
Factor
1 Review On : Design of
Efficient Femtocell by
Lampel Ziv Markov Chain
Algorithm.
4th International
Conference on Emerging
Trends & Research in
Engineering, Technology
and Science, IBSS College
of Engineering, Amravati.
International Journal of
Pure and Applied
Research in Engineering
and Technology,
IJPRET, Vol.4 (9) : 579-
583, 2016
4.226
2 Design of Efficient
Femtocell using LZMA Data
Compression Technique.
International Conference
on Science & Technology
for Sustainable
Development , ICS&TSD
2016, Jhulelal Institute of
Technology, Nagpur.
International Journal on
Recent and Innovation
Trends in
Communication ,
IJRITCC, Vol.4, Issue 5,
May 2016
5.0998

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