Design and Implementation of Smart congestion control system

IDL -International Digital Library Of
Education & Research
Volume 1, Issue 6,June- 2017 Availableat: www.dbpublications.org
International e-Journal For Education And Research-2017
IDL -International Digital Library 1 | P a g e Copyright@IDL-2017
"Design and Implementation of Smart
congestion control system"
1.
Ms.Nandini M. 2
.Dr.Pushpamala Siddaraju
1
PG Scholar, 2
.Assistant professor.
Department of Electronics and Communication
Dayananda Sagar University, Bangalore, Karnataka, India
email :nandini1234m@gmail.com,email:pushpa.mala-ece@dsu.edu.in
ABSTRACT
The frequent traffic jams at major junctions
call for an efficient traffic management
system in place. The resulting wastage of
time and increase in pollution levels can be
eliminated on a city-wide scale by these
systems.
The project proposes to implement
an intelligent traffic controller using real
time image processing. The image
sequences from a camera are analyzed using
thresholding method to find the density.
Subsequently, the number of vehicles at
the intersection is evaluated and traffic is
efficiently managed. The project also
proposes to implement a real-time
emergency vehicle detection system. In case
an emergency vehicle is detected, the lane is
given priority over all the others. Hardware
control is done by microcontroller.
I. INTRODUCTION
Current traffic control techniques involving
magnetic loop detectors buried in the road,
infra-red and radar sensors on the side
provide limited traffic and require separate
systems for traffic counting and for traffic
surveillance.
Inductive loop detectors do provide a cost-
effective solution, however they are subject
to a high failure rate when installed in poor
road surfaces, decrease pavement life and
obstruct traffic during maintenance and

repair. Infrared sensors are affected to a
greater degree by fog than video cameras
and cannot be used for effective
surveillance.
In contrast, video-based systems offer many
advantages compared to traditional
techniques.
They provide more traffic information,
combine both surveillance and traffic
control technologies, are easily installed,
and are scalable with progress in image
processing techniques.
This project tries to evaluate the process and
advantages of the use of image processing
for traffic control.
Implementation of our project will eliminate
the need of traffic personnel at various
junctions for regulating traffic.
Thus the use of this technology is valuable
for the analysis and performance
improvement of road traffic.
II. BLOCK DIAGRAM
Figure: Embedded system is the
combination of both software and hardware
Software deals with the languages like ALP,
C, and VB etc., and Hardware deals with
Processors, Peripherals, and Memory.
Memory: It is used to store data or address.
Peripherals: These are the external devices
connected
Processor: It is an IC which is used to
perform some task

Figure: Block diagram of Embedded System
III. Applications of embedded
systems
 Manufacturing and process control
 Construction industry
 Transport
 Buildings and premises
 Domestic service
 Communications
 Office systems and mobile
equipment
 Banking, finance and commercial
 Medical diagnostics, monitoring
and life support
 Testing, monitoring and diagnostic
systems
IV. Basic Principle
A transformer makes use of Faraday's law
and the ferromagnetic properties of an iron
core to efficiently raise or lower AC
voltages. It of course cannot increase power
so that if the voltage is raised, the current is
proportionally lowered and vice versa.
Embedded
System
Software Hardware
o ALP
o C
o VB
Etc.,
o Processor
o Peripherals
o memory

Figure: Basic Principle
Advantages:
 Reliability, Ease of Operation
 As we can improve transportation
security by implementing it.
 No need of human supervision
 People abiding rules can be caught.
Spontaneous output.
V. RELATED WORK
There has been a good deal of work on
building multipath transport protocols [13,
27, 18, 12, 14, 6, 23, 7]. Most of this work
focuses on the protocol mechanisms needed
to implement multipath transmission, with
key goals being robustness to long term path
failures and to short term variations in
conditions on the paths. The main issues are
what we discussed in §6: how to split
sequence numbers across paths (i.e. whether
to use one sequence space for all subflows
or one per subflow with an extra connection-
level sequence number), how to do flow
control (subflow, connection level or both),
how to ack, and so forth. Our protocol
design in §6 has drawn on this literature.
However, the main focus of this paper is
congestion control not protocol design. In
most existing proposals, the problem of
shared bottlenecks (§2.1) is considered but
the other issues in §2 are not. Let us
highlight the congestion control
characteristics of these proposals. pTCP
[12], CMT over SCTP[14] and M/TCP [23]
use uncoupled congestion control on each
path, and are not fair to competing single-
path traffic in the general case. mTCP [27]
also performs uncoupled congestion control
on each path. In an attempt to detect shared
congestion at bottlenecks it computes the
correlation between fast retransmit intervals
on different subflows. It is not clear how
robust this detector is. R-MTP [18] targets

wireless links: it probes the bandwidth
available periodically for each subflow and
adjusts the rates accordingly. To detect
congestion it uses packet interarrival times
and jitter, and infers mounting congestion
when it observes increased jitter. This only
works when wireless links are the
bottleneck.
VI. SCOPE OF THE PROJECT
The study showed that image processing is a
better technique to control the state change
of the traffic light. It shows that it can
reduce the traffic congestion and avoids the
time being wasted by a green light on an
empty road. It is also more consistent in
detecting vehicle presence because it uses
actual traffic images. It visualizes the reality
so it functions much better than those
systems that rely on the detection of the
vehicles’ metal content. Overall, the system
is good but it still needs improvement to
achieve a hundred percent accuracy
APPLICATIONS:
Main roads, Highways,Signal crossings.,
Tollgates.
RESULTS

CONCLUSION
We have demonstrated a working multipath
congestion control algorithm. It brings
immediate practical benefits.
Wireless personal area network and wireless
sensor networks are rapidly gaining
popularity, and the IEEE 802.15 Wireless
Personal Area Working Group has defined
no less than different standards so as to cater
to the requirements of different applications.
The ubiquitous home network has gained
widespread attentions due to its seamless
integration into everyday life. This
innovative system transparently unifies
various home appliances, smart sensors and
energy technologies. The smart energy
market requires two types of ZigBee
networks for device control and energy
management.
ensures that it does not harm other traffic,
and that there is always an incentive to turn
it on because its aggregate throughput is at
least as good as would be achieved on the
best of its available paths. It should be
beneficial to the operation of the Internet,
since it selects efficient paths and balances
congestion
REFERENCES:
[1]Vivek, Tyagi, Senior Member IEEE,
Shivakumar Kalyanaraman, Fellow, IEEE,
and Raghuram Krishnapuram,Fellow, IEEE
“Vehicular Traffic Density State Estimation
Based On Cumulative Road Acoustics” in
IEEE Transaction on Intelligent
Transportation System. Vol.23. No.3
September 2012.
[2]MD.Hazrat ALI, Syuhei KUROKAWA,
et al,“Autonomous Road Surveillance
System proposed Model For Vehicle
Detection and Traffic Signal Control “in
Procedia Computer Science
19(2013)Density Based Traffic Signal
System M.R. Thansekhar and N. Balaji
(Eds.): ICIET’14 2222.
[3] R. WEIL, J. WOOTTON AND A.
GARCIA-ORTIZ” Traffic Incident
Detection Sensor and Algorithms “Mathl.
Comput.Modeling Vol.27.

[4] K.Thatsanavipas,N.Ponganunchoke ,et
al., “Wireless Traffic Light Controller”2nd
International Scien
ce,SocialScience,Engineering and Energy
Conference 2010:Engineering Science and
Management.
[5] Wanjing MA and Xiaoguang YANG
“Design and Estimation of an Adaptive Bus
Signal Priority System Base on Wireless
Sensor Network “Proceeding of the 11th
International IEEE Conference on Intelligent
Transportation Systems.
[6]Hikaru Shimizu,Masa-aki
Kobayashi,Haruko,et al.,”A Development of
Deterministic Signal Control System in
Urban Road Networks” in SICE Annual
Conference 2008 The University of Electro-
Communications Japan.
[7] Muhammad Hassam Malhi, Muhammad
Hassan Aslam].et al.,”Vision Based
Intelligent Traffic Management System”
IEEE Computer Society 2011 Frontiers of
Information Technology.
[8] H.R.Kashani and G.N.Saridis
“Intelligent Control for Urban Traffic
Systems” International Federation of
Automatic Control Automatica, vol.19, No.
[9] Umar Farooq, Hafiz Muhammad Atiqs,
Muhammad Us man Assad et al.,”Design
and Development of an Image Processing
Based Adaptive Traffic control
System”IEEE Computer Society
2011Second International Conference on
Machine Vision.
[10] H. Han, S. Shakkottai, C. V. Hollot, R.
Srikant, and D. Towsley. Multi-path TCP: a
joint congestion control and routing scheme
to exploit path diversity in the Internet.
IEEE/ACM Trans. Networking, 14(6), 2006.
[11] M. Honda, Y. Nishida, L. Eggert, P.
Sarolahti, and H. Tokuda. Multipath
Congestion Control for Shared Bottleneck.
In Proc. PFLDNeT workshop, May 2009.
[12] H.-Y. Hsieh and R. Sivakumar. A
transport layer approach for achieving
aggregate bandwidths on multi-homed
mobile hosts. In Proc. MobiCom ’02, pages
83–94, New York, NY, USA, 2002. ACM.

[13] C. Huitema. Multi-homed TCP. Internet
draft, IETF, 1995.
[14] J. R. Iyengar, P. D. Amer, and R.
Stewart. Concurrent multipath transfer using
SCTP multihoming over independent end-
to-end paths. IEEE/ACM Trans. Netw.,
14(5):951–964, 2006.
[15] F. Kelly and T. Voice. Stability of end-
to-end algorithms for joint routing and rate
control. CCR, 35(2), Apr. 2005.
[16] T. Kelly. Scalable TCP: improving
performance in highspeed wide area
networks. SIGCOMM Comput. Commun.
Rev., 33(2):83–91, 2003.
[17] P. Key, L. Massouli´e, and D.
Towsley. Path selection and multipath
congestion control. In Proc. IEEE Infocom,
May 2007. Also appeared in proceedings of
IEEE ICASSP 2007.
[18] L. Magalhaes and R. Kravets.
Transport level mechanisms for bandwidth
aggregation on mobile hosts. ICNP, page
0165, 2001. [19] PF: the OpenBSD Packet
Filter. OpenBSD 4.7,
www.openbsd.org/faq/pf, retrieved Sep
2010.
[20] B. Radunovi´c, C. Gkantsidis, D.
Gunawardena, and P. Key. Horizon:
balancing TCP over multiple paths in
wireless mesh network. In Proc. MobiCom
’08, 2008.
[21] C. Raiciu, M. Handley, and A. Ford.
Multipath TCP design decisions. Work in
progress, www.cs.ucl.ac.uk/staff/C.Raiciu/
files/mtcp-design.pdf, 2009.
[22] C. Raiciu, C. Pluntke, S. Barre, A.
Greenhalgh, D. Wischik, and M. Handley.
Data center networking with multipath TCP.
In Hotnets, 2010.
[23] K. Rojviboonchai and H. Aida. An
evaluation of multi-path transmission
control protocol (M/TCP) with robust
acknowledgement schemes. IEICE Trans.
Communications, 2004.
[24] K. Tan, J. Song, Q. Zhang, and M.
Sridharan. A Compound TCP approach for
high-speed and long distance networks. In
Proc. IEEE INFOCOM 2006, pages 1–12,
April 2006.
[25] D. Thaler and C. Hopps. Multipath
Issues in Unicast and Multicast Next-Hop
Selection. RFC 2991
control is done by microcontroller.

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