C017641219

IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 6, Ver. IV (Nov – Dec. 2015), PP 12-19
www.iosrjournals.org
DOI: 10.9790/0661-17641219 www.iosrjournals.org 12 | Page
WMNs: The Design and Analysis of Fair Scheduling
M. Sudhakar1
, Vandana Khare2
Professor, Department of ECE, CMR College of Engineering & Technology, India1
Associate Professor, Department of ECE, CMR College of Engineering & Technology, India2
Abstract: In this paper an attempt has been made to address the matter of scheduling in wireless mesh
networks. First, we offer a comparison of existing scheduling algorithms and then classify them based on the
scheduling techniques in the degree of fairness and their implementation frameworks. Later we attempted good
scheduling approach victimization multiple gateways. The projected scheduling approach consists of 4
necessary steps namely, demand tables, demand propagation in group generation and schedule generation.
Simulation experiments area unit conducted to check the performance of fair scheduling with the strategy that
doesn't use fair scheduling. The simulation results assert that the projected scheduling has superior
performance with reference to the metrics used for performance analysis.
Keywords: WMN, QoS, Multiple Gateway, Mesh Router, Mesh Clients
I. Introduction
Wireless mesh networks (WMN) area unit is convenient and straightforward to setup and maintain.
They have quickly replaced ancient wired networks for several kinds of communication. For example, mobile
phone service is quickly turning into additional widespread than land based telephone services. This is often
very true in developing countries where infrastructure is non-existent or prohibitively expensive. To boot,
wireless native space networks (WLAN) area unit gaining quality compared with older technologies like local
area network for knowledge communications in each residential and businesses owing to the bated value and
simple setup compared with birth wires. These same engaging options are the explanation why wireless
technology is employed within the military and in disaster environment. Recently, wireless mesh networks
became the main target of analysis since they permit for exaggerated coverage vary whereas holding the
engaging options of low value and straightforward preparation [9]. However, there are still several challenges
left so as to attain all of the applications that the technology is capable of. Above all this paper focuses on
challenges of scheduling in wireless mesh networks.
Scheduling is a very important challenge to subsume, particularly in business wireless mesh network
applications. Several current deployments area unit optimized with reference to turnout, delay or another feature
that provides very little respect to fairness. The main target of this paper is on fair scheduling techniques that use
multiple gateways. The contributions of this paper area unit are two-fold. Firstly we have a tendency to provide
an in detail comparison and analysis of existing techniques within the space. Secondly we offer our own fair
scheduling algorithms in WMNs with multiple gateways. The performance of fair scheduling for WMNs with
multiple gateways is given and evaluated in conjunction with experimental results.
The remainder of this paper is organized as follows: Section 2 offers background, connected work and
motivation for finding out the matter. It conjointly provides analysis of assumptions in existing solutions.
Section 3 provides the careful description of the projected approach. Section 4 presents the performance analysis
of our projected approach. Finally in Section 5, we have a tendency to provide conclusions and discuss areas for
future analysis.
Fig. 1: Wireless mesh network (WMN).

II. Background And Related Work
In this section we have a tendency to outline fairness with reference to wireless mesh networks. We
have then provided a transient introduction to fair scheduling techniques. This is often followed by a
classification, comparison, and analysis of current scheduling solutions. The literature analysis establishes
wherever our proposals interchange comparison to the prevailing work. We have also attempted to establish
areas wherever additional analysis may well be accomplished within the future. Lastly, we have described how a
cross-layer style will improve scheduling in wireless mesh networks and why a mixed-biased cross-layer
approach may be a promising technique for cross-layer scheduling.
2.1. Fairness in wireless networks
The resource allocation and a number of scheduling and techniques are projected for WMN in literature
[8], [15], [16], [18], [22–24]. The trend may be an exchange between the turnout and fairness employing a
constant coefficient system or a dynamic coefficient system that changes the weights over time to attain a long
fairness. It‘s necessary to notice that fairness might occur at completely different points in a wireless mesh
network. Some researchers have projected per-mesh-router fairness or per-link fairness [26]. There‘s conjointly
a notion of ―uplink–downlink fairness‖ [19], [20], [[28] as a result of the mechanisms in some current solutions,
like IEEE Distributed Coordination Operate (DCO) [28] leave difference between the directions of flow in
WMNs. In alternative words, AN improvement in downlink turnout could severely have an effect on
performance of the transmission or vice-versa. However, additional recently [26], [31] have targeted on per-
client fairness. The motivation behind this is often that in business applications every user is paying AN equal
quantity of cash for services from the network therefore every user ought to get equal Quality of Service (QoS).
It‘s conjointly necessary to contemplate that metrics fairness is outlined. As an example, a scheduling
algorithmic program might give fairness in terms of the attainable turnout obtainable but the delay might not be
equal. Bound nodes within the network could stay starved for traffic whereas alternative nodes area unit
liberated to communicate for numerous reasons. It's conjointly necessary to contemplate that fairness and
scheduling is littered with intruders within the system. Several of the prevailing solutions for scheduling in
WMNs admit the idea of co-operation between nodes, and this is often not invariably the case in universe
networks. Scheduling algorithms typically provide preference to flows that area unit least dearly-won by some
criteria. These criteria could also be distance from the entranceway, delay, tiny flows and alternative similar
metrics. However, this approach could leave starvation or reduced QoS for flows that don't meet the standards.
Preference could also be given to greedy flows. This aspect offers very little priority to turnout and ensures that
every shopper gets a good share of the network resources. This might be achieved by employing a time division
mechanism or alternative similar approaches.
The problem with this approach is that not all flows need identical quantity of resources in the slightest
degree times that the resources could stay unused occasionally leading to poor turnout. One approach that aims
for a balance between the competitive goals of fairness and turnout, denoted as maxmin fairness [35] works by
increasing the minimum knowledge rates for every flow. It leads to higher turnout than hard-fairness; however,
the general turnout remains a lot of but maxi-mum turnout and leaves a lot of to be desired. The foremost
attention-grabbing definition of fairness then may be a compromise between hard-fairness and most turnouts. In
[4, 19, 22, 29, 31, and 33] this approach has been denoted as proportional fairness. Proportional fairness assigns
priority to bound flows supported criteria like the number of hops or amount of resources requested. Similarly,
the maxmin approach has conjointly been changed with a proportional issue yet yielding improved results. A
replacement approach known as mixed-bias may be a hybrid approach that emphasizes turnout whereas still
providing a basic level of fairness. Within the theme projected in [31], a little of the resources area unit is
appointed to a powerful biasing against nodes that are far from one another. Another portion is appointed to a
proportional or maxmin theme yet, so as to stop starvation. It is usually one amongst the primary approaches
that may provide a minimum level of fairness whereas holding turnout that is even bigger than of proportional
fairness or maxmin.
III. The planned approach
This section provides the planned approach for our truthful programming algorithmic and program
simulation. The performance analysis has been conducted through simulation exploitation. The truthful
programming algorithmic program utilized in the simulation is predicated based on the algorithmic program
provided in [26], extended to support multiple gateways.
3.1. Assumptions and summary of the planned approach
Like most existing analysis within the space, we've made certain assumptions. We have assumed that
MRs and GWs don't seem to be mobile. Their positions are fastened throughout the simulation. This assumption
is very common in several of the present solutions. There are several advantages and applications of this kind of

network. It may well be utilized in transit systems, military applications or disaster relief. Instead of having to
upset multiple handoffs of many moving purchasers, the moving purchasers might keep company with a moving
adult male. This enables the network to target addressing just one football play whereas all of the MCs related to
the adult male retain their attachment to the network. The topology of the network for this simulation remains
fastened throughout the whole simulation. In distinction to existing solutions but, we tend to assume that the
network could contain multiple gateways. This can be as important assumption as a result of limiting the
network to 1 entranceway causes associate degree extreme bottleneck at this entranceway. Notwithstanding the
traffic at intervals the network is balanced and truthful, having just one entranceway will decrease the
performance of the network. There are 2 solutions to the present drawback. One is to assume that the
entranceway invariably has enough capability to serve the wants of the network, despite its size. The opposite
choice that we‘ve chosen during this experiment is to permit multiple gateways in order that the load of the
traffic is unfolded around a lot equally. For the initial results given during this paper, we tend to assume there's
no load equalization mechanism at intervals the gateways. Lastly, the idea of downlink and transmission
equivalence is another common assumption with existing solutions. Several existing proposals solely simulate
one style of traffic and assume that identical approach may well be in love the opposite vogue. For instance a
proposal could simulate transmission programming and assume downlink can work equally. In our approach,
each transmission and downlink traffic are simulated.
3.2. Elaborate description of the planned approach
This section can give a discussion of the truthful programming approach with multiple gateways, and
highlight the most contributions we created to the present approach.
We have planned associate degree sweetening of the initial truthful programming approach planned by
[26] that we tend to devise the distributed demand table. The initial work planned is solely programming,
however it doesn't give a mechanism for maintaining and aggregation necessities. The necessities are needed for
generating the programming since this information tells however busy every link is. Therefore we tend to
propose a distributed manner of its accomplishment. Every mesh router keeps track of an area demand table.
During this demand table, the demand on every link between the router and a neighbour is unbroken. Once a
brand new schedule is requested, every entranceway asks for the partial demand tables from every mesh router
related to it.
Fig. 2: Distributed requirement tables combined at GW.
3.2.1. Demand propagation
The requirement propagation algorithmic program given in algorithmic program one, permits the
entranceway to stay track of the necessities across all of the links. At the MR, a table containing a partial
illustration of the network is unbroken for all of the MRs on the thanks to the entranceway. Once associate
degree megacycle associates with a given adult male, the necessity is incremented for all the MRs on the thanks
to the entranceway within the native table. once a brand new schedule generation is to be completed, the GW
requests for the necessities from all of the MRs and combines the results from the partial tables to work out that
links should be activated and for a way long.
1: Associate megacycle with MR //issued once the megacycle connects to the network
2: Generate a shopper demand at adult male for the megacycle //notify MC that adult male requests resources
3: For every link between adult male and GW
4: - Requirement(current-link) ++//increment the necessity within the native table
5: End For
6: For every Hop
7: - Requirement(current-link) −−//decrement the necessity within the native table

8: End For
9: On Drop: for every link between adult male and GW
10: - Requirement(current-link) −−//decrement the necessity within the native table
Algorithm: demand propagation [10].
In this theme, every entranceway is answerable for generating the centralized programming for all of
the links routing thereto. The distribution and coordination of the programming is completed through the
utilization of begin and finish packets. The entranceway sends a begin packet to the adult male once it's regular
time to send associate degreed, and finish packet once it now not has permission. It's assumed that these
management packets are sent on a special channel from the information and therefore don't interfere with data
traffic. At the top of 1 cycle of programming, the method is recurrent with a brand new programming set up,
being computed and distributed throughout the network. In order to work out that teams of links ought to be
regular along, a thought of gain that was introduced in [26] is employed to pick teams of links that have the best
load. Gain is outlined because the sum of the demands of all the links minus the best requirement. The
programming algorithmic program uses the trail and necessities info to provide permission to sure MRs to
transfer at the specified timeslots. Once the truthful programming algorithmic program is enabled, associate
degree adult male could solely send packets once it has permission. If it doesn't have permission, it retries till a
waiting threshold has been crossed at that purpose the packet is born. Once collision happens as a result of a
buffer is full the packet is born. The performance of the network may well be improved more if a rehear or
backup mechanism was enforced or if load equalization was applied at the GWs.
3.2.2. Schedule generation
Scheduling is generated for the all of the mesh routers within the network exploitation the thought of a
compatibility matrix like that utilized in [26], [30]. The compatibility matrix is then wants to verify that links are
enabled at identical time while not inflicting interference. In our network model, this suggests that the 2 MRs
don't have a typical neighbour and don't seem to be neighbours with one another. Attributable to the positioning
of the MRs and also the communication ranges, if 2 MRs don't seem to be neighbours and don't share a typical
neighbour, they're not shut enough to cause interference with one another and that they don't vie for the
resources of a typical neighbour. In this fashion each could communicate at identical time. The spatial TDMA
programming permits multiple links to be activated at identical time once they don't interfere. Therefore the
network is used more efficiently than it might if just one link within the entire network were active [7].
Moreover, as the algorithmic program uses the thought of compatibility, no 2 links are active that vie for
resources thus collisions are avoided. the answer given here is totally different from several alternative TDMA
solutions as a result of it solely allocates time for links that even have necessities related to them.
IV. Performance Analysis Of The Projected Approach
In this section we have dealt intimately the simulation atmosphere, performance metrics and simulation
parameters. This is followed by a discussion of the results of the experiments.
4.1. Simulation atmosphere
The performance analysis was carried exploitation simulation experiments. The simulation focuses on
packet transmission from MRs to GWs. MCs area unit generated (using a regular random distribution) at the
beginning of the simulation and area unit at random distributed at intervals the simulation atmosphere. Every
megahertz is related to the nearest man and every man routes its packets to the nearest GW. This implies that
any packets that have a collision at the association stage don't seem to be counted within the rumoured results.
We have contemplated this downside cut loose and attempted to deal with during this paper. The
management packets for distributing the planning area unit assumed to be sent on another channel and so don't
impact the performance of the network. To boot, the simulation atmosphere acts as AN all-knowing observer
therein it performs the planning and distributes in to the gateways. In an exceedingly real-world implementation
this ought to either be performed through a centralized GW or via some quite distributed GW resolution. The
interference model assumes that 2 nodes interfere if they're at intervals vary and sending at constant time or if
there's a buffer collision. Once interference happens, retransmission is allowed till a threshold timeout is
reached.
4.2. Performance metrics and simulation parameters
This simulation study uses 2 performance metrics. The primary metric is average packet delivery
quantitative relation. It‘s computed because the quantitative relation of the full variety of packets delivered to
the full variety of packets sent. The second metric employed in the simulation is the average delay. It measures
the time taken by a packet to achieve its destination. These metrics will facilitate measurement of the

performance of the protocol effectively. In order to stay the planning rule straightforward, several authors
assume one or no gateways [26–28], [30], [31] within the WMN. However, one among the most uses for WMN
is to supply web access with dilated service areas from ancient WLANs and thence the bulk of the traffic flow is
between the gateways and also the MCs [35] via MRs. Having only one entree during this situation could be a
major bottleneck that the existing solutions ought to be extended to be able to support any variety of gateways to
form a very climbable WMN.
4.3. Analysis of the experimental results
The performance of the honest planning was studied exploitation the 2 simulation parameters
delineated within the preceding section. The result conferred compare each honest planning against no planning
and honest planning with multiple entrees against honest planning with one gateway. Transmission traffic solely
is contemplated for these results since we have a tendency to consider downlink planning a separate downside
which may make the most of caching and multicast to yield additional enhancements.
Fig. 3: Average packet delivery ratio with varying mesh routers.
Fig. 4. Average delay with varying mesh routers.
Fig. 5: Average packet delivery ratio with varying gateways.

Fig. 6: Average delay with varying gateways.
Fig. 6 shows the common delay as an operate of the quantity of gateways with a network size of fifty
five. During this case, the results of honest planning will best be seen with one entree. This is often seemingly as
a result of every extra gateway is other, each the honest planning and also the no planning cases profit
considerably by easing congestion on the one bottleneck entree.
Fig. 7 shows a comparison of packet delivery quantitative relation as an operate of the quantity of mesh
routers for one entree and multiple gateways. The aim of this result is to highlight the importance of multiple
gateways and to check our approach to it[26]. Evidently, multiple gateways yield higher delivery ratios for all
network sizes from ten to fifty five. This is often seemingly as a result of on the average there area unit fewer
hops between any given man and its GW. This is often vital as a result of every hop will increase the chance of
encountering that's busy that might lead to packet loss at the worst or delay at the best.
Fig. 8 shows the common delay as operate of the quantity of mesh routers and another time compares
the case with one entree to it with multiple gateways. Once more the case with multiple gateways enhanced
performance in terms of delay. As within the previous figure, this is often seemingly as a result of the lower
average hops any given node should go for get an entree. The delay isn't solely accumulated thanks to a larger
quantity of hops within the single GW case however conjointly thanks to the time spent awaiting a free buffer.
V. Conclusions And Future Work
We have conferred the state of the art in honest planning techniques in WMNs. It had been noted that
it's vital to realize per-client which fairness ought to be a balanced between hard-fairness and most outturn so
finish users understand fair service whereas the network resources area unit used efficiently. The techniques
investigated during this study were classified in line with the kind of planning and cargo equalization, metrics or
mechanism used, and also the management approaches (centralized or distributed). We‘ve projected and
evaluated good planning technique exploitation multiple GWs. The experimental results have shown that the
performance of the network is way higher with the honest planning enabled than while not in terms of packet
delivery quantitative relation.
In the future, we have a tendency to conceive to experiment with the projected approach in an
exceedingly test-bed atmosphere. It's typically difficult to predict however a protocol or rule performs with real
hardware. Another goal is that eventually the belief of static nodes may be relaxed leading to a mobile mesh
network wherever the mesh purchasers and mesh routers don't seem to be mounted and also the topology of the
network is very dynamic. To further enhance the projected approaches, load equalization and cross-layer style
approaches are accustomed cut back the entree load and conjointly modify intelligent planning by exchanging
network and link layer standing info.
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Author Biography
Dr. M. Sudhakar1
: Graduated from JNTU College of Engineering, Hyderabad in 1979, with
specialization in ECE. He completed his M.Tech from Indian Institute of Technology Madras in 1986 with the
specialization in Instrumentation, Control & Guidance. Obtained doctoral degree from Annamalai University.
Successfully headed R&D Project assigned by IAF on ―Mathematical Modelling & Simulation of Aero Engine
Control System‟ at Aeronautical Development Establishment, Bangalore and Gas Turbine Research
Establishment, Bangalore. He is presently working as a Professor in the department of ECE and Vice Principal
at CMR College of Engineering & Technology, Hyderabad.

Mrs. Vandana Khare2
is pursuing PhD in Communication Engineering JNTU Hyderabad (A.P).
She completed M.E (Digital techniques) in 1999 from SGSITS, INDORE (M.P) India and B.E in ECE in the
year 1994 from GEC Rewa (M.P). She is Associate Professor in ECE at CMR College of Engineering &
Technology. Secunderabad. She has 18 years of teaching experience and has published 13 research papers in
International journals &presented 5 papers in National & International conferences. She is life member of ISTE,
IETE & IEEE Technical societies. Her research Interest includes Wireless Communication, Computer
Networks, Mobile Computing and Bio-Medical Imaging.

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