Internati onal Journal of Computer & Mathematical Sciences
IJCMS
ISSN 2347 – 8527
Volume 4, Issue 6
June 2015
Improving Performance Metrics in Distributed Multihopping
CSMA Network
M.Bhuvaneswari1 , C.Kalaiselvi2 , M.Dhivya3
Assistant professor, Dept. of ECE, Ratnavel Subramaniam College of Engineering and Technology, Dindigul, Tamilnadu, India 1
Assistant professor, Dept. of ICE, Ratnavel Subramaniam College of Engineering and Technology, Dindigul, Tamilnadu, India 2
Assistant professor, Dept. of ECE, Ratnavel Subramaniam College of Engineering and Technology, Dindigul, Tamilnadu, India 3
Abstract- One of the challenges in multihop wireless networks is to maximize throughput over a communication
channel. The utmost confront is to attain optimal throughput in a distributed manner. Recently, it has been shown that
queue length based carrier sense multiple access (Q-CSMA) type random access algorithms can achieve the maximum
possible throughput in adhoc wireless networks. A lot of work has been completed in this area of studies since the late
70s.In general, single channel to communication network is considered. In this kind of network, if more than one links
are active in a certain neighborhood or more than one entity try to access a certain link, then collision will occur. When
collision occurs, packets are not successfully transmits over the medium. So, a hybrid algorithm is defined, to improve
the efficiency of collision avoided communication and to improve throughput and reduce latency. This algorithm based
on hybrid Q-CSMA and DSR .In finally combining CSMA with DSR leads to very good d elay performance. It is proved
that it should be straightforward to extend all algorithms to be applicable to networks with multihop traffic and
congestion controlled sources.
Index Terms: Greedy maximal scheduling (GM S), Maximal weighted scheduling (MWS), Longest queue
first(LQF ),Que ue carrier sense multiple access(Q-CSMA)
I. INTRODUCTION
For wireless networks with limited sources, efficient resource allocation and optimization play an important
role in achieving high network performance & reducing energy consumption. The performance metrics of
interest in this paper are throughput and delay. The throughput performance of a scheduling algorithm is often
characterized by the largest set of arrival rates under which the algorithm can keep the queues in the network
stable. The delay performance of a scheduling algorithm can be characterized by the average delay
experienced by the packets transmitted in the network. Although the recent results on CSMA-type random
access algorithms show throughput-optimality, simulation results indicate that the delay performance of these
algorithms can be quite bad and much worse than MWS and GMS. Thus, one of our goals in this paper is to
design distributed scheduling algorithms that have low complexity, are provably throughput optimal and have
good delay performance. Towards this end, we design a discrete-time version of the CSMA random access
algorithm. The algorithm generates collision-free data transmission schedules while allowing for collisions
during the control phase of the protocol. The performance metrics of interest are throughput and delay. The
throughput performance of a scheduling algorithm is characterized by the largest set of arrival rates under
which the algorithm can keep the queues in the network stable. The delay performance of a scheduling
algorithm can be characterized by the average delay experienced by the packets transmitted in the network.
Since many wireless applications nowadays have stringent bandwidth and delay requirements.
Designing high-performance scheduling algorithms to achieve maximum throughput and low delay is of great
importance, which is the focus of this paper. We also want the scheduling algorithms to be distributed and
have low complexity/overhead since, in wireless scenarios. Normally there is no central ent ity and the
resources at the nodes are very limited. It has been shown that carrier sense multiple access (CSMA) type
random access algorithms can achieve the maximum possible throughput in adhoc wireless networks.
However these algorithms assume an idealized continuous-time CSMA protocol where collisions can never
occur In addition simulation results indicate that the delay performance of these algorithms can be quite bad.
Although some simple heuristics can yield much better delay performance for a large set of arrival rates. In
general they may only achieve a fraction of the capacity region. In this we propose a discrete time version of
the CSMA algorithm. Central to our results is a discrete time distributed randomized algorithm that is based
on the generalization of the Glauber dynamic physics, where multiple links are allowed to update their states
in a single time slot.
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M.Bhuvaneswari, C.Kalaiselvi , M.Dhivya
Internati onal Journal of Computer & Mathematical Sciences
IJCMS
ISSN 2347 – 8527
Volume 4, Issue 6
June 2015
II. SYSTEM ARCHITECTURE
Fig.2. System Architecture
III. BASIC SCHEDULING ALGORITHM
A scheduling algorithm is a procedure to decide which schedule to be used (which set of links to be activated)
in every timeslot for data transmission. The capacity region of the network is the set of all arrival rates for
which there exists a scheduling algorithm that can stabilize the queues, i.e., the queues are bounded in some
appropriate sense depending on the arrival model used. I say that a scheduling algorithm is throughputoptimal, or achieves the maximum throughput, if it can keep the network stable for all arrival rates in .
Divide each timeslot into a control slot and a data slot. (Later, we will further divide the control slot into
control mini slots.)The purpose of the control slot is to generate a collision-free transmission schedule used for
data transmission in the data slot. To achieve this, the network first selects a set of links that do not conflict
with each other, denoted by. Note that these links also form a feasible schedule, but it is not the schedule used
for data transmission. We call the decision schedule in time slot.
A. Packet Generation
A packet generation model is a traffic generation model of the packet flows or data sources in a packetswitched network. These models are useful in the development of telecommunication technologies, in view to
analysis the performance and capacity of various protocols. The network performance can be analyzed
by network traffic measurement in a test Bed network, using a network generator such
as iperf, bwping and Mausezahn. The traffic generator sends dummy packets, often with a unique packet
identifier, making it possible to keep track of the packet delivery in the network.
First generates the certain no of packets and transmit the packet in the network traffic. There the packet must
be identified and there will be two conditions. If the packet matches then the process will be stopped. else also
the packet generation will be stopped.
Packet filters act by inspecting the "packets" which transfer between computers on the Internet. If a packet
matches the packet filter's set of rules, the packet filter will drop the packet, or reject.
B. User Polling
User polling is a variation of the traditional polling technique and allows emulation of an information push
from a server to a client. With user polling, the client requests information from the server in a similar way to
a normal poll. The server does not have any information available for the client, instead of sending an empty
response; the server holds the request and waits for some information to be available. The performance of a
simple distributed scheduling policy, maximal scheduling, which had earlier been investigated in context of
node-exclusive spectrum sharing model and input-queued switches. The characterizations demonstrate that the
performance bounds depend heavily on the nature of communication and interference models.
Traditional maximal weight scheduling (MWS), although through put optimal, is difficult to implement in
distributed networks. CSMA scheduling algorithm that can achieve them axial through put distributive. The
client will normally then immediately re-request information from the server. so that the server will almost
always have an available waiting request that it can use to deliver data in response to an event. User polling is
itself not a true push, but can be used under circumstances where a real push is not possible, and offers many
of the same benefits in terms of rapid delivery.
C. Network Monitoring
The term network monitoring describes the use of a system that constantly monitors a computer network for
slow or failing components and that notifies the network administrator. A network monitoring system
monitors an internal network for problems.
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M.Bhuvaneswari, C.Kalaiselvi , M.Dhivya
Internati onal Journal of Computer & Mathematical Sciences
IJCMS
ISSN 2347 – 8527
Volume 4, Issue 6
June 2015
It can find and help resolve snail-paced webpage downloads, lost-in-space e-mail, questionable user activity
and file delivery caused by overloaded, crashed servers, dicey network connections or other devices.
Monitoring an active communications network in order to diagnose problems and gather statistics for
administration and fine tuning. Network monitoring can be achieved using various software or a combination
of plug-and-play hardware and software appliance solutions. Virtually any kind of network can be monitored.
It doesn't matter whether it's wireless or wired, a corporate LAN, VPN or service provider WAN.
D. Packet Scheduling
IP-packets sent from a station or AP is first converted from bit streams to radio signal, which is modulated to
a frequency for a channel and after that transmitted into the air medium.
All network nodes within the BSS network are using the same channel to communicate and all stations within
the LAN have equal opportunity to access to the medium using DCF protocol, including AP. AP, which is the
bridging node, has no privileges to transmit more on its downlink when sharing resources .But all flows from
AP are treated equally and there is no contention among these. First in First out (FIFO), where aggregate
flows are queued in an arrival manner. With N flows on the downlink, each flow receives 1/N of the availab le
bandwidth capacity fairly.
IV. ALGORITHM
A. Q-CSMA Algorithm

Link i selects a random (integer) back off time Ti uniformly in[0 ,W − 1] and waits for Ti control
mini-slots.

IF link i hears an INTENT message from a link in C (i) before the (Ti +1)-th control mini-slot, i will
not be included in m (t)and will not transmit an INTENT message anymore.
Link i will set xi (t)= xi ( t − 1).

IF link i does not listen to an INTENT message from any link in C

before the ( Ti +1) -th control mini-slot, it will send(broadcast) an INTENT message to all
links in C

at the beginning of the (Ti +1)-th control mini-slot.

If there is a collision (i.e) if there is another link in C
Transmitting an INTENT message in the same mini-slot), link i will not be included in m (t) and will
set xi (t)= xi ( t − 1).

If there is no collision, link i will be included in m (t) and decide its state as follows:

If no links in C (i) were active in the previous data slot
xi (t)=1 with probability pi , 0 <pi < 1 ;
xi (t)=0 with probability ¯pi =1 − pi .
else
xi (t)=0 .

IF xi (t)=1 , link i will transmit a packet in the data slot.
B. D-GMS Algorithm

Link i selects a random backoff time

Ti = W ×└B − log b(qi (t)+1)┘++ Uniform[0 ,W − 1]
and waits for Ti control mini-slots.

IF link i hears an RESV message (e.g., an RTS/CTS pair) from a link in C (i) before the (Ti +1)-th
control mini-slot, i will not be included in x(t) and will not transmit an RESV message. Link i will set xi (t)=0
.

If there is a collision, link i will set
xi (t)=0 .

If there is no collision, link i will set
xi (t)=1

IF xi (t)=1 , link i will transmit a packet in the data s lot.
C. Hybrid Q-CSMA Algorithm

IF wi (t) >w0 ( Q-CSMA Procedure)
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M.Bhuvaneswari, C.Kalaiselvi , M.Dhivya
Internati onal Journal of Computer & Mathematical Sciences
IJCMS
ISSN 2347 – 8527
Volume 4, Issue 6
June 2015

Link i selects a random backoff time Ti = Uniform [0 ,W0 − 1].

If link i hears an INTENT message from a link in C (i) before the (Ti+1)-th control mini-slot, then it
will set xi (t)= xi ( t −1) and go to Step 1.4

If link i does not hear an INTENT message from any link in C (i) before the (Ti +1) -th control minislot, it will send an INTENT message to all links in C (i) at the beginning of the (Ti +1)-th control mini-slot.

If there is a collision If there is a collision, link i will set xi (t)= xi (t − 1).

If there is no collision, link i will decide its state as follows:

If no links in C were active due to the Q-CSMA procedure in the previous data slot, i.e., NAi =0,
xi (t)=1 with probability pi , 0 <pi < 1 ;
xi (t)=0 with probability ¯pi =1 − pi .
else
xi ( t)=0 .

If xi (t)=1 , link i will send an RESV message to all links in C (i) at the beginning of the (W0 +1)-th
control mini-slot. It will set NAi =0 and transmit a packet in the data s lot

If xi ( t)=0 and link i hears an RESV message from any link in C ( i) in the ( W0 +1)-th control minislot, it will set NAi =1 ;otherwise, it will set NAi =0 .
If wi ( t) ≤ w0 ( D-GMS Procedure)
If link i hears an RESV message from any link in C
in the(W0 +1) -th control mini-slot, it will set NAi =1 and xi (t)=0and
keep silent in this time slot
else
link i will set NAi =0 and s elect a random backoff time

Ti =( W0 +1)+ W1 ×└B − log b(qi ( t)+1)┘++Uniform [0 ,W1 − 1] and wait for Ti control mini-slots.

f link i hears an RESV message from a link in C (i) before the (Ti +1)-th control mini-slot, it will set
xi ( t)=0 and beep silent in this time slot.

If link i does not hear an RESV message from any link in C

before the (Ti +1)-th control mini-slot, it will send an RESV message to all

at the beginning of the (Ti +1)-th control
mini-slot. If there is a collision,
link i will set xi ( t)=0 .

If there is no collision, link i will set xi ( t)=1 .

If xi ( t)=1 , link i will transmit a packet in the data s lot.
V. SIMULATION RESULTS
In this section, we evaluate the performance of different scheduling algorithms via simulations, which include
MWS (only for small networks), GMS (centralized), D-GMS,Q-CSMA, and the hybrid Q-CSMA algorithm.
In addition,we have implemented a distributed algorithm to approximate maximal scheduling (called D-MS),
Fig.3. Comparison Graph
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M.Bhuvaneswari, C.Kalaiselvi , M.Dhivya
Fig.4. Transmission Graph
Internati onal Journal of Computer & Mathematical Sciences
IJCMS
ISSN 2347 – 8527
Volume 4, Issue 6
June 2015
Fig.4. Time Line Chart for Packet Transfer Energy Calculation:
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M.Bhuvaneswari, C.Kalaiselvi , M.Dhivya
Internati onal Journal of Computer & Mathematical Sciences
IJCMS
ISSN 2347 – 8527
Volume 4, Issue 6
June 2015
VI. CONCLUSION
A slotted distributed queue-length based CSMA/CA protocol that leads to collision-free data transmission
schedules. The protocol is provably throughput-optimal. The discrete-time formulation allows us to
incorporate mechanisms to dramatically reduce the delay without affecting the theoretical throughputoptimality property. In particular, combining CSMA with distributed GMS leads to very good delay
performance. Our design focuses on routing that also balances the energy consumption, and forward the
packet toward the sink through dense energy areas so as to protect the nodes with relatively low residual
energy.
A. FUTURE ENHANCEMENT
In future, this project is discrete time version is going to be used to reduce the delay in order to get better
performance than the previous performances .simulation results will give good delay performance. Designing
of distributed scheduling algorithm is an important design aspect in this project in future .This algorithm
generates collision free data transmission schedules while allowing for collisions during the control phase of
the protocol.
More concentration will be given to an energy balanced method, it takes a consideration of
location and energy information. Simulation results show that the network lifetime will be increased.
VII.REFERNCES
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[2] A.Dimaki and J.Warland,”Sufficient conditions For stability of longest queue First scheduling:second order
properties using fluid limits,”Adv.APPL.Probab vol 38,no 2,PP 505 -525,2006
[3] J.Ghaduri and R.Srikant,”On design of efficient CSMA algorith ms for wireless networks,”mar2010online]
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[4] L.Jiang and J.Walrand,”A distributed CSMA Algorith m fo r throughput and utility
maximizat ion in wireless
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[5] C.Joo,X.Lin,and .B.Shroff,”Understanding The capacity region of the greedy maximal scheduling algorithm in
mu ltihop wireless,”IEEE/A CM rans.Networks”.,Vo l 17,no 4.pp.1132-1145 Aug 2009
[6] M.Leconte,J.Ni,andR.Srikant,’Improved bounds On the throughput and efficiency of greedyMaximal scheduling in
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[7] J.Ni,B.R.Tan and R.Srikant, ” Q-CSMA :Queue Length based CSMA/CA algorith ms for achieving maximu m
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[8] Q-CSMA :Queue- length- based CSMA/CA Algorith ms achieving maximu m throughput And low delay in wireless
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M.BHUVANESWARI
The author is working as an Assistant Professor in department of Electronics and Co mmunication
Engineering at RatnaVel Subraman iam Co llege of Eng ineering and Technology, Dindigul. She
received her BE(ECE) & M.E(Co mputer Science Engineering) degree fro m RatnaVel Subramaniam
College of Engineering and Technology, Dindigul, affiliated to Anna University, Chennai. Her
research interest is Networking.
C.KALAISELVI
The author is working as an Assistant Professor in department of Instrumentation and Control
Engineering at RatnaVel Subramaniam College of Engineering and Technology, Dindigul. She
received her BE(EEE) degree fro m PSNA College of Engineering and Technology, Dindigul,
affiliated to Anna University, Chennai and M.E(Control & Instrumentation) fro m College of
Engineering Guindy, Anna University, Chennai. Her research interests are Power Electronics, Digital
Electronics & Electrical Mach ines.
M.DHIVYA
The author is working as an Assistant Professor in department of Electronics and Commun ication
Engineering at RatnaVel Subramanian Co llege of Engineering and Technology, Dindigul. She received
her BE (ECE) degree fro m the Lord Venkateshwara Eng ineering College, Kanchipuram, TN, India, in
2011 and the Master of Engineering (M.E.) degree fro m the PSNA CET, Dindigu l, TN, India. Her
research interests include Networking and Embedded.
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M.Bhuvaneswari, C.Kalaiselvi , M.Dhivya