Traffic Rank Based QoS Routing in Wireless Mesh Network

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Traffic Rank Based QoS Routing in Wireless Mesh

Network

Deepa P Kamble#1, Sujatha P Terdal*2

#1 Department of Computer Science and Engineering, Poojya Doddapa Appa College of Engineering, Gulbarga, Karnataka,

India.

*2

Department of Computer Science and Engineering, Poojya Doddapa Appa College of Engineering, Gulbarga, Karnataka, India.

Abstract— Wireless Mesh Network (WMN) is a network to

provide Internet access to remote areas. As part of the Internet, WMN has to support multimedia applications to all its users. It is essential to provide efficient Quality-of-Service (QoS) support to the networks. Searching the better quality path with the maximum available utilized bandwidth is one of the fundamental issues for supporting QoS in the WMN. Real time applications such as type of media access like video, audio, mail services varies from one node internet access to another node internet access. A small variation of the efficient Quality-of-Service (QoS) metrics like jitter, delay, and throughput, has significant impact on the bandwidth and link capacity demands of the node. In this paper we present a computing of better rank based path, which captures the available path utilized bandwidth information is proposed. This paper also show, that the efficient routing protocol based on the new rank based path which provides the consistency. The consistency property guarantees that each node in the traffic makes an appropriate packet forwarding decision, based on the assigned ranks, so that a data packet does travels through the exact path. Once the current path fails the rerouting of the packet is supported. The OMNeT++ 4.2.2 simulation experiments also show that the proposed rank based path gives high-throughput paths.

Keywords— Wireless mesh networks, Quality-of-Service, Bandwidth, Rank of nodes (wireless hosts).

I. INTRODUCTION

A wireless mesh network is a communication network made up of radio nodes organized in a mesh topology. As part of the global Internet, Wireless Mesh Network has to support multimedia applications for all its users. It is essential to provide efficient Quality-of-Service support in this type of networks [1]. Searching the new link with the better rank is available to nodes in the network traffic and also searching the path with the maximum available utilized bandwidth is also one of the fundamental issues for supporting QoS in the WMN [2]. The available path bandwidth which is defined as the maximum additional rate a flow can push through saturating its path [3]. For that reason, if the traffic rate of a new flow on a path is no greater than the available bandwidth of the path, then accepting the new traffic will not violate the bandwidth which guarantees the existing flows. This paper focuses on the problem to identify the maximum utilized bandwidth path from the source to the destination, and also aims at developing a best transmission solution for QoS

routing in mesh network through vertical handover where nodes in mesh network can choose the best access point near them by measuring link quality through throughput of independent access category. The source nodes identify the best access point’s paths from the source to the destination, which is called as vertical handover technique.

Meeting uncertain demand and ensuring same throughput to all the nodes is one of the dominant and important issues in mesh network which affects the quality of services. Several protocols and updates in the protocols are proposed which guarantees more stable paths. But most of these protocols depends upon the instantaneous values of the stability factor and does not change the route if a better route is available than the selected route. Therefore in this work we propose a traffic rank base mesh routing protocol which estimates the link quality based on the bandwidth and link capacity and incorporate the same in routing table. There are several factors which affects the quality of transmission in mesh network. Congestion, Bandwidth, Jitter are the few of the parameters which affects the transmission quality. But all these parameters are result variation in traffic and effective throughput. Change in throughput results in change of rate of transmission, delay, packet collision. Bandwidth provides a good estimation of throughput. Therefore measuring the bandwidth is sufficient for measuring the link variability. The metric also changes due to factors like mobility and power losses. But accurately measuring power and used bandwidth gives a good estimation of this variability. Hence the objective is to estimate the bandwidth variability and power in the link as well as the variations in the rate and incorporate the stability value in the routing and existing route cache. The main work is to ensure QoS in the mesh network. The technique can be used to obtain better packet rate and throughput. The work can also be used to improve the variability in transmission or Jitter. It can be used to obtain the stable routes which can be used for Multimedia transmission. The technique is well suited for single radio mesh network where every link has fixed maximum bandwidth. In such single radio channel, Bandwidth and power losses does not vary based on radio interface. Thus is well suited for proposed system. In this work, we study how to perform better QoS routing in Wireless Mesh Networks and make the following contributions.

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1. Here we propose a new path rank that captures the idea of available utilized bandwidth. We give the system to access the media services; varies from one node internet access to another node internet access. Hence a bandwidth and link capacity demand of the nodes varies. The proposed mesh router and access points must also able to categories the link quality and demands, which is essentially important to offer best services. We officially prove that the proposed path rank is good for the traffic based QoS routing.

2. Then we illustrate how to construct the ranks for nodes in network and routing table, and develop a packet forwarding scheme. And we formally show that the routing protocol satisfies the consistency and optimality requirements.

3. Lastly, we implement the routing protocol based on the AODV (Ad hoc On-demand Distance Vector) protocol in the OMNeT++ 4.2.2 simulator. The general simulations experiments demonstrate that the routing protocol outperforms the present routing protocols for demands discover the maximum available bandwidth paths.

The rest of the paper is organized as follows: In Section II Related work is described; then in section III, we explain System Design. Section IV describes Simulation Description and in Section V the Simulation Results and Performance measurement are shown. Finally we conclude our paper in Section VI.

II. RELATED WORK

Wireless Mesh Networks is a “hot topic” in network research [4] in recent years. But very little research has been done in designing a traffic rank based QoS routing for WMNs. One of the more popular solutions is AODV, every node in the network traffic act as a mesh router and a relay. In this section we describe in brief how the reactive routing is well suited for this works. The WMR (Wireless mesh routing) protocol is based on AODV and includes the following aspects:

Topology Discovery: This is the procedure that maintains neighborhood information for each node by local information exchange. This is done by sending out periodic beacons (Airframe packets).

Route Discovery: This algorithm uses the topology information to obtain the route to destination. The routes are discovered on-demand. For internal traffic, the route is obtained from the source to the destination while for external traffic the route is obtained to the nearest node that provides external connectivity. Again, Route Discovery consists of two phases, Route Rank Exploration when the route is discovered, and Link capacity when with the receipt of the first data packet to all resources links for that flow are activated. Admission Control: This is the module that helps decide whether a flow with the given QoS constraints will be accepted or rejected. AODV uses the following methodology for calculating the Bandwidth consumed at each node: Bandwidth Measurement techniques;

For providing best QOS technique, accurate bandwidth estimation is very important. Generally bandwidth measurement technique concentrate towards estimating used bandwidth between all neighboring links of a node. But in

case of Burst traffic it is equally important that we take into consideration of self generated traffic by a node.

 

x

B

 

i

B

self N i cons X

….. (1) (1) Gives the bandwidth consumption at any node X where

 

i

B

self is the self traffic generated at Node X. We assume that the channel capacity in a network is C (channel capacity here is assumed as the Ideal available bandwidth in ideal time at any node). For simplicity we consider one way link here. We express bandwidth at any node i is

a

C

B

i N

….. (2) Where N is the number of nodes that shares the channel and C is termed as channel utilization rate. It is quite obvious that as number of nodes in an area increases( node density) channel will be shared by that much more number of nodes or in other terms utilization per node will decrease. Hence we can device a theory that a α Node_Density. We choose a=Node_Density by optimizing the proportionality constant to 1 as it will not have direct effect on the performance. Based on this we can say that bandwidth at any node i is

i i i

U

n

1

x

a

C

B

…. (3) Where ni is the total neighbours of i and Ui is the used

bandwidth by node i. This bandwidth measurement is very significant as a node only need to know about its neighbours and can still estimate the available bandwidth. From (1) and (2) we can device new bandwidth estimation (Bandwidth consumed at a node).

 

 

a

C

i

B

X

B

self N i cons X

... (4) From (4) and (3), Total available bandwidth can be calculated by.

 

B

 

i

U

 

x

n

1

x

a

C

x

B

self N i x a X

 ... (5) Where Ba(x) is the total available bandwidth at node x. from 5

it is very clear that if traffic generation at each node is controlled by the node, QOS can be provided to overall network.

Since AODV and QOS are very identical protocols, in the rest of this paper we use the term AODV for describing the semantics of AODV i.e., on demand route request and QOS to avoid confusion in providing better quality service. With all these mechanisms in place, AODV seems to be a good reactive routing protocol that guarantees efficient routing with QoS guarantees.Mesh routing protocols are used in environments where there is not necessarily a well-controlled infrastructure network but where there is a common routing policy. There are two main categories of routing: proactive and reactive.

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Proactive routing protocols are based on the ‘normal’ routing protocols used in wired networks, such as today’s internet. Algorithms based on distance vector and link state are common. Distance vector often uses the number of hops as a metric to minimise when selecting the best route, but can also go beyond this to consider more parameters, for example link bandwidth and delay. This is used to construct a route table, which is shared with other routers. Link state operation is more complex and requires each router to build its own map of the network. Thus, in proactive protocols, there is an attempt to build locally, at each node, a picture of roots within the network before they are required for use. The routing tables are usually built periodically through the normal operation of the protocol exchanging routing update packets. In normal operation, this has advantage that the routes are already pre-computed and so packet forwarding can take place as soon as a packet for a particular destination appears at a node. The drawback is that routes may be calculated and re-calculated (for e.g. due to node mobility) when they are actually not required for data. This wastes bandwidth and, for mobile nodes, also wastes battery power via the sending and receiving of unnecessary routing updates.

Reactive routing takes an alternative approach by building routes only upon demand. It may also cache route information according to some short time-out or stale-ness policy. Cached routes can be used as required, but if a route is not known then it has to be ‘discovered’. This has advantage that routes are only evaluated when needed, although this approach adds latency to packet forwarding when routes are not already known. Generally, the reactive routing approach is the one that has received most attention in the ad hoc networking community.All these optimizations applied to AODV results in WMESH, our routing protocol for Wireless Mesh Net-works.

III. SYSTEM DESIGN

Systems design is the process of defining the architecture, components, modules, interfaces, and data flow for a system to satisfy specified requirements. System design is classified into two designs they are logical design and physical design. Logical Design: The logical design of a system pertains to an abstract representation of the data flows, inputs and outputs of the system. Physical Design: The physical design relates to the actual input and output processes of the system.

A Data Flow Diagram (DFD) is a graphical representation of the “flow” of data throughput the system. Data Flow models are used to show how data flows through a sequence of processing steps. The data is transformed at each step before moving on to the next stage. These processing steps or transformations are program functions where as data flow diagrams are used to document a software design. The Data Flow Diagram (DFD) for assigning rank of node and rank updating is shown in fig.1.

Link (RANK) Rank (node)

Rank Update

Fig. 1 Data Flow Diagram of assigning rank of node.

The rank of a node, Rank (node), determines its priority in assigning channels to the links emanating from it [5]. The rank encompasses the dynamics of channel assignment and is computed on the basis of three factors:

• The aggregate traffic at a node based on the offered load of the mesh network as computed in [7]

• The distance of the node, measured as the minimum number of hops from the gateway node

• The number of radio interfaces available on a node

The gateway node is assigned the highest rank as it is expected to carry the most traffic. The rank for the remaining nodes is given by:

Rank (node) = ) node ( gateway the from hops Min ) node ( Node the at Traffic Aggregate

Clearly, the aggregate traffic flowing through a mesh node has an impact on the channel assignment strategy. The rationale behind this observation stems from the fact that if a node relays more traffic, assigning it a channel of least interference will increase the network throughput. Thus, aggregate traffic increases the rank of a node with its traffic. The aggregate traffic (total traffic traversing a node) is a key factor in computing the rank of the node.

Once the rank of each node has been computed, the algorithm traverses the mesh network in decreasing order of Rank (node). ALGORITHM:

Let traffic rate be P packets/second for fixed traffic and ex where x is random Gaussian distribution with probability density function 0.5. (We have selected Gaussian distribution for our traffic generation, because of its burst nature).

Let L=

2

P

is the queue length at any node. Total Available bandwidth C= 11.2 MBPS Total area of simulation be A m2

Let total Number of Nodes be N a=

A

N

Source Route Cache Transmission , Access Percentage Updating

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Let P be total Number of sessions selected and S= {s1, s2...sp} be the set of source node and D= {d1, d2…dp} be the set of destination node

For each source in S Generate RREQ do

At every node I that has received RREQ Calculate available bandwidth at i. If (Bi< L)

Drop RREQ End

End Till i=destination

//Now we have QOS path, formed based on CBR (Constant Bit Rate) traffic pattern.

For every path p in P Select traffic pattern For every node I in p do

If (Bi<L)

// Bi measured by MAC layer and information is passed to

application layer. Store L traffic in queue

Restrict self traffic so that

L

B

self

 

i

N i X

is minimum // by application layer End Till i=destination of p End End

Throughput, delay and control overhead are estimated at every transmission. Here we define delay as summation of latency and waiting time of the traffic in a node.

IV. SIMULATION DESCRIPTION

The OMNeT++ 4.2 released on Thursday, 17 November 2011 14:12. The OMNeT++ 4.2.2 Integrated Development Environment is based on the Eclipse platform, and extends it with new editors, views, wizards, and additional functionality. OMNeT++ adds functionality for creating and configuring models (NED and ini files), performing batch executions, and analyzing simulation results, while Eclipse provides C++ editing, SVN/GIT integration, and other optional features (UML modeling, bug tracker integration, database access, etc.) via various open-source and commercial plug-ins.

OMNeT++ is an object-oriented modular discrete event network simulation framework. It has a generic architecture, so it can be (and has been) used in various problem domains:

 modeling of wired and wireless communication networks

 protocol modeling  modeling of queuing networks

 modeling of multiprocessors and other distributed hardware systems

 validating of hardware architectures

 evaluating performance aspects of complex software systems

 In general, modeling and simulation of any system where the discrete event approach is suitable, and can be conveniently mapped into entities communicating by exchanging messages.

OMNeT++ simulations can be run under various user interfaces. Graphical, animating user interfaces are highly useful for demonstration and debugging purposes, and command-line user interfaces are best for batch execution. The simulator as well as user interfaces and tools are highly portable. They are tested on the most common operating systems (Linux, Mac OS/X, Windows), and they can be compiled out of the box or after trivial modifications on most Unix-like operating systems. OMNeT++ is free only for academic and non-profit use; for commercial purposes, one needs to obtain OMNEST licenses from Simulcraft Inc. A. Simulation Parameters

The simulation focuses on some of the network properties such as:

i. Jitter ii. Delay iii. Throughput iv. SNIR

The throughput is analyzed with time. The other parameters are analyzed with various numbers of nodes.

B. Simulation Experimental Setup

Channel Type Wireless Channel

Hosts Wireless Hosts, Adhoc Hosts No. of nodes N no. of nodes

Routing protocol AODV Time of simulation start 0.035 Time of simulation end 1000s

Simulation Type OMNeT++ 4.2.2

Table. 1 Simulation Settings

C. Network Simulation Scenarios

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Fig. 3Inter-Access point1 communicates with Access point2 by sending Airframe packets.

Fig. 4 Node Probe packets to all Neighbours

Fig. 5 Performance measurement at each node upon receiving Packets.

The simulation experiment settings which are listed in Table1 are implemented in OMNeT++ 4.2.2 simulation. The simulation network consists of 8 wireless hosts and 3 mesh access points one mesh router and one mesh gateway shown in fig. 2. In fig.3 inter access communication is shown. The fig. 4 above gives node probe packets to all neighbours. In this setup five nodes are chosen as source and one node is chosen as destination. First, the distance is calculated based on utilized bandwidth from all the nodes to the destination. Based on this, path rank selection is done from source to destination; there may be one or more paths. Available Bandwidth is set as 11.2Mbps. Then each links bandwidth is estimated which is the path rank, also the maximum available utilized bandwidth path is found and hop by hop routing is done. Packet forwarding is performed successfully to provide the qos consistency. The routing table is constructed and also the routes are updated. Once the route fails then rerouting is also supported. Fig. 5 shows performance measurement.

V. RESULTS & PERFORMANCE EVALUATION

SIMULATION RESULTS

i. Jitter

In voice over IP (VoIP), jitter is the variation in the time between packets arriving, caused by network congestion, timing drift, or route changes. A jitter buffer can be used to

handle jitter. The types of jitter are random jitter and deterministic jitter. Jitter is defined as a variation in the delay of received packets. The jitter occurs because of traffic, interference etc. Also while sending more data in the routing path the data packet overhead is occurred. As shown in Fig. 6, the Mac jitter is compared with existing speed, whenever speed increases the proposed jitter is decreased hence from 4mps to 6mps period of time is the optimization time, which shows that the jitter is less for the proposed system.

Fig. 6 Speed versus Jitter

ii. Delay

End-to-end delay refers to the time taken for a packet to be transmitted across a network from source to destination. End to End delay versus speed is plotted in Fig. 7. The different delays are queuing delay, propagation delay, processing delay, processing time. The delay is calculated for different number of nodes. As shown in Fig. 7, the end to end delay is compared with existing speed, which shows that the delay is less for the proposed system. The optimized result is got at the 4mps.

Fig. 7 Speed versus Delay

iii. Throughput

Throughput is the average rate of successful message delivery over a communication channel. This data may be delivered over a link, or pass through a certain network node. In Fig. 8, the speed versus throughput comparison is plotted. It is carried out with different evaluation time. As observed in the graph throughput of the network increases in both existing speed and proposed Bandwidth method. Due to the maximum available bandwidth path routing, throughput is significantly increased in proposed method. This is because the delivery ratio is extremely high and packet drop is comparatively less. Optimizing result got at period 6mps.

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Fig. 8 Speed versus Throughput

iv. SNIR

In telecommunications, the ratio of signal to noise plus interference, or signal-to-noise-plus-interference ratio (SNIR), is defined as the ratio of signal power to the combined noise and interference power:

erference int noise signal P P P SNIR  

Where P is the averaged power. Values are commonly quoted in decibels. As shown in Fig. 9, the optimized result got at 4mps, & the snir is compared with existing speed, which shows that the snir is less for the proposed system.

Fig. 9 Speed versus SNIR

VI. CONCLUSION &FUTURE WORK

Link quality in WMN varies due to factors like mobility, energy consumption, and power losses. Variation in link quality results in fluctuations in packet delivery ratio, latency and other performances. So under link variations, routes cannot be considered as stable. Therefore incorporating link quality and deriving a suitable technique to include the same as cost metric in routing is essential. There are several techniques which estimates the link quality based on either movement or power loss. As SINR and signal power directly or indirectly affects all the other parameters, we have considered SINR based link quality metric for Link State routing and vertical handoff in WMN. Therefore in this work we measure link stability as consistency of data and control packet rate in the links. Links with consistent rates irrespective of high or low are considered as more stable Links. Through OLSR we find the routes that incorporate the most stable links. Further if the current router's link fails or degrades, the next best router is selected. Result shows that the technique results in better QoS in terms of packet delivery ratio, control overhead under different link variability constraints like high mobility. The system can be further

improved by incorporating other factors that marks link variability like channel capacity, bandwidth, throughput or jitter. By also incorporating variation in received signal we can improve the system performance.

REFERENCES

[1] T.S. Starlin, D. Jasmiine David, “Routing Analysis in Wireless Mesh Network with Bandwidth Allocation,” proc. IJEAT, ISSN: 2249, vol. 2, issue 3, 2013.

[2] Q. Zhang and Y.-Q. Zhang, “Cross-Layer Design for QoS Support in Multihop Wireless Networks,” Proc. IEEE, vol. 96, no. 1, pp. 234-244, Jan. 2008.

[3] T. Salonidis, M. Garetto, A. Saha, and E. Knightly, “Identifying High Throughput Paths in 802.11 Mesh Networks: A Model-Based Approach,” Proc. IEEE Int’l Conf. Network Protocols (ICNP ’07), pp. 21-30, Oct. 2007.

[4] Vinod Kone, Sudipto Das University of California, Santa Ba7rbara, “QoS Routing in Wireless Mesh Networks”.

[5] Habiba Skalli, IMT Lucca Institute for Advanced Studies, Samik Ghosh and Sajal K. Das, The University of Texas at Arlington, Luciano Lenzini, University of Pisa, Marco Conti, Italian National Research Council (CNR), “Channel Assignment Strategies for Multiradio Wireless Mesh Networks: Issues and Solutions,” proc. IEEE, Nov 2007. [6] L. Chen and W.B. Heinzelman, “QoS-Aware Routing Based on

Bandwidth Estimation for Mobile Ad Hoc Networks,” IEEE J. Selected Areas in Comm., vol. 23, no. 3, pp. 561-572, Mar. 2005.

[7] A. Raniwala, K. Gopalan, and T. Chiueh, “Centralized Channel Assignment and Routing Algorithms for Multichannel Wireless Mesh Networks,” ACM Mobile Comp. and Commun. Rev., Apr. 2004, pp. 50–65.

[8] Q. Xue and A. Ganz, “Ad Hoc QoS On-Demand Routing (AQOR) in Mobile Ad Hoc Networks,” J. Parallel and Distributed Computing, vol. 63, pp. 154-165, 2003.

[9] W. Liao, Y. Tseng, and K. Shih, “A TDMA-Based Bandwidth Reservation Protocol for QoS Routing in a Wireless Mobile Ad Hoc Networks,” Proc. IEEE Int’l Conf. Comm. (ICC ’02), pp. 3186-3190, Apr. 2002. .

[10] J.L. Sobrinho, “Algebra and Algorithms for QoS Path Computation and Hop-by-Hop Routing in the Internet,” Proc. IEEE INFOCOM, pp. 727-735, Apr. 2001.

BIOGRAPHY

Deepa P Kamble received the B.E degree in Computer Science and Engineering (CSE) from PDA Engineering College, Gulbarga (Karnataka), India in 2011. Currently pursuing her master’s degree in Computer Science and Engineering (CSE). Her area of interest includes Wireless Mesh Networks, QoS routing in Wireless Mesh Networks. Currently working on the Traffic Rank Based QoS routing, towards the progress of increasing the network throughput of wireless mesh networks.

Sujatha.P.Terdal is working as Associate Professor in the Computer Science and Engineering Department, P.D.A College of Engg, Gulbarga, and Karnataka. She received her M.Tech.degree from Visveshwariah Technological University, Belgaum, India, in 2002. Currently, she is pursuing her research at Jawaharlal Nehru Technical University, Hyderabad, India. Her fields of interest are Mobile Ad Hoc Networks and wireless network.

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