QOS Constrained Routing Algorithm UsingDirectional Antenna

Divya GuptaABV-IIITM

Gwalior, 474010, IndiaEmail: guptadivya.diva@gmail.com

Sangeeta SinghPDPM-IIITDM

Jabalpur, 482005, IndiaEmail: sangeetasingh.1409@gmail.com

Bharti ModiABV-IIITM

Gwalior, 474010, IndiaEmail: modi.bharti@gmail.com

Abstract—Directional antenna provides tremendous perfor-mance improvement in an ad hoc network. There are manyapplications such as multimedia and real time, which requiresguaranteed QoS (quality of service) in the network. In this paper,we present QoS routing algorithm which finds a optimal pathfrom source to destination as well as for other nodes in thenetwork. Route discovery is initiated by source node and routingpath is selected based on routing metric value which is havingdelay and bandwidth as constraints. The probability updationrule is used to increase the probability of beam in the directionof successful transmission and it reduces the routing overhead inretransmission. The simulation results demonstrate the routingoverhead which reduces as the number of iteration increases.It shows significant improvement in terms of average routingoverhead with directional transmission over omnidirectionaltransmission.

Index Terms—Ad hoc networks, QoS routing, Directionalantenna.

I. INTRODUCTION

The growth of high speed networks are expected to supportreal time applications which delivers large amount of datain the network. The basic Quality of service (QoS) routingproblem is to find optimal path for a given network, whichhas sufficient resources and satisfies the QoS constraints.

The routing protocols proposed in [1], [2] achieve betterperformance by using directional antenna. The maneuverabil-ity problem which occurs in case of directional transmissionand reception is not covered by these protocol. One of thekey issue is the QoS routing, which deals with the problemslike selection of routes with sufficient resources is also notaddressed by these protocols. In [3], the use of directional an-tenna systems for ad hoc networking is presented. It provides abroad understanding of directional antenna systems, associatedproblems and solution approaches for utilizing these antennasystems in ad hoc and sensor networks. In [4], benefits of usingdirectional antenna in a wireless network are presented. It alsodemonstrates the detailed study on advances and research chal-lenges in wireless network with directional antennas. In [5] and[6], multi-path unicast routing algorithms are proposed that arebased on some flooding like techniques, which considers thatrouting messages are flooded selectively through the networktowards the destination, and whenever destination receives arouting message a feasible path is found. In [7], the featuresof directional transmission and reception are considered and

it also deals with the maneuverability problem. In wirelesssystem, the use of directional antenna is increasing contin-uously due to its characteristics of providing power saving,high capacity, communication capabilities improvement andinterference reduction. But these benefits are attained at thecost of low maneuverability. Low maneuverability will causemany problems which includes low diffusion rate of the net-work information. For improving the transmission efficiency,it proposes a directional transmission mechanism based oncognitive approach. This approach reduces the convergencetime of routing algorithm.

In this paper, we have considered the directional communi-cation between nodes in ad hoc network. The proposed algo-rithm find the optimal path not only from source to destinationbut also for other nodes traversed in route discovery with QoSconstraints, i.e., bandwidth and delay constraint. On demandcommunication approach is used, i.e., route discovery is initi-ated by source node. To limit the flooding of real time routerequest (RREQ) packet which causes the routing overhead ina network, the probability updation rule given in [7] is used.Routing overhead is the average packet forwarded per nodeand is defined as the total number of packet forwarded amongall nodes to the total number of nodes in the network. Thisrule reduces the routing overhead by updating the probabilityof successfully communicated nodes.

The remaining of the paper is organized as follows. Insection II, the system model is discussed. In section III,proposed algorithm is given. In section IV, the simulationresults are presented and it is followed by conclusions insection V.

II. SYSTEM MODEL

In this section, all the assumptions and notations for the an-tenna model and network model are presented. The directionalcommunication model that is used in [7] is introduced.

A. Antenna Model

For improving the performance of ad hoc network direc-tional antenna offers enormous potential. Directional antennaprovides power saving, reduces interference, increase the net-work efficiency, and improve the communication capabilities.In this model, m nodes are assumed in the network and eachnode is associated with directional antenna. The beamwidth

978-1-4673-5999-3/13/$31.00 ©2013 IEEE

of antenna is denoted by an angle of θ. Hence the number ofbeams for an antenna can be calculated by:

N ,360

θ(1)

where N denotes the number of beams.An omnidirectional antenna transmits radio signals equally

in all the directions. On the other hand, in case of directionalantenna, there are certain desirable transmission and receptiondirections, i.e., it transmits or receives more energy in onedirection compared with the other. In case of omnidirectionalcommunication, two nodes are defined as the connected nodepairs, if they lies in each others mutual transmission rangesand communicate. In directional communication, two nodesare called connected node pairs, if one node is in transmittingmode and another is in receiving mode and both are pointing toeach other. Since nodes do not have any theoretical knowledgeabout their neighbors, so in ad hoc network neighbor discoveryis important for making connections between the nodes. Byusing neighbor discovery algorithm from [8], the profile ofneighbor nodes can be attained.

B. Network Model

Consider wireless ad hoc network scenario in which nodesare uniformly distributed. Whenever, it is desired to communi-cate data between nodes, source node initiates route discoveryby transmitting RREQ packet to its neighbor nodes and theintermediate node which receives the RREQ packet forward itto other neighbor nodes in the directional transmission mode.

Fig. 1. Node communication using directional antenna [9]

1) QoS Constraints: For QoS routing, the primary concernis to calculate the QoS metric. The QoS metric is composed oftwo QoS variables, i.e., minimum Bandwidth and cumulativedelay and the constraints on these variables are Bc and Dc

respectively as follows:

• Delay for each link in a path should be less than equalto Dc.

• Bandwidth for each link in a path should be greater thanequal to Bc.

2) Probability Updation Rule: In [7], lemmas have beenproved which shows the comparative study of omnidirectionaland directional antennas probabilities. In case of omnidi-rectional antenna, node which receives this RREQ packetbroadcasts it to its neighbor nodes with equal probability.So, probability for a pair of neighbor nodes will alwaysbe the same. Every time, node will retransmit packet withequal probability, i.e., RREQ packet is forwarded in all thedirections despite of the fact that the node has communicatedsuccessfully with its neighbor nodes in a particular direction.This will cause same routing overhead in every route discoverytime in the network and makes the retransmission ineffective.

In case of directional antenna model, probability is updatedin the direction of successful transmission to its neighbornodes. Number of beams (N) that are possible for a directionalantenna can be calculated by using ( 1).

Initially, node will scan all the beams with equal probabilityfor transmitting route request to its neighbor nodes. After suc-cessful transmission to its neighbor node, it will increase theprobability of that beam which has communicated successfullyand reduces the probability of other directions beams by factorλ, where λ denotes the probability updation factor. This canbe illustrated by equations [7]:

Prob(noden,j) = λ ∗ Prob(noden,j) (2)

Prob(noden,i) = 1−N∑

j=1,j 6=i

Prob(noden,j) (3)

where noden,i denote the ith direction of transmission of noden. ith and jth denote the beam direction in which route isfound and not found respectively.

This probability updation rule limits the flooding of RREQpacket by updating the probability, i.e., increasing the prob-ability of successfully communicated direction and hence re-ducing the routing overhead for the same route in the network.

III. ALGORITHM DESIGN

In this section, a routing algorithm is proposed with QoSconstraints. Frame format of RREQ packet of QoS routingalgorithm is shown below:

Fig. 2. Frame format of RREQ packet [7]

where Sid, Did, RREQID, Bc, Dc are obtained bysource node while Dcum, Bmin, Nr are updated with thepropagation of RREQ packet. Here Sid is a identity of sourcenode, Did is a identity of destination node, RREQID is a

identity of RREQ packet, Bc is a minimum bandwidth limitfor the route, Dc is maximum delay limit for the route, Dcum

is accumulative delay for the link up to this hop, Bmin isminimum bandwidth of the link up to this hop, Nr is a list ofnodes that have forwarded RREQ packet, η and 1− η denotethe weight associated with delay and bandwidth respectively.

The route discovery is performed by source node by trans-mitting the RREQ packet to all of its neighbor nodes withdelay and bandwidth constraints. The intermediate node whichreceives this route request calculate Dcum and Bmin for thislink, then update the value of Dcum and Bmin and calculatethe route metric by using the equation [7]:

Rmetric = η ∗ Dc

Dcum+ (1− η) ∗ Bmin

Bc(4)

If the intermediate node is having route metric from anotheralternate paths, then compare these route metrics and select themetric which is having less delay and minimum bandwidth.Now, this intermediate node will forward this RREQ packetto its neighbors. Finally we will get the optimal routing pathfrom source to destination. The list of nodes forwarding thisRREQ is added in Nr. This can be easily demonstrated by thefollowing steps:

1) Initialize source node S2) Set Blink, Dc, Bc, maxhop, η3) Set RREQID, Did, Sid, Dcum, Bmin, Nr

4) Initially hop = 0• Find neighbors of S• Encapsulate RREQ packet with S

5) While hop is less than equal to maxhop and S is havingneighbor nodes. If not,exit the loop, else go to next step• Calculate Dcum and Bmin for the node which re-

ceives the RREQ from S• Update route bandwidth• Update route delay• Calculate routing metric by using ( 4)• Check neighbor node is having RREQ from alternate

paths or not, if yes then compare calculated routingmetric with other routing metrics.

• Finally put the routing metric which is having lessdelay and minimum bandwidth

• Add the neighbor node in a listing of Nr

6) if(next hop == destination node)then check if it has already received RREQ with sameRREQID, if yes, then compare the metric and select thebest metric RREQ.

IV. SIMULATION RESULTS

In this section, the performance of our proposed QoSconstrained routing algorithm is performed via simulationusing Matlab 7.8.0 (R2009b). Consider a wireless networkhaving 100 nodes that are randomly distributed in an area of30X30m2. Directional antennas are being shown by equiva-lent nodes and the range of each node is set to 10

√2m. The

bandwidth of each link between nodes is uniformly distributedbetween 1 Mbps to 10 Mbps. The antenna beamwidth istaken as 36o, Dc is uniformly distributed between 0 and 1,Bc is uniformly distributed between 1MHz to 10MHz, η isuniformly distributed between 0 and 1. These parameters aretaken from [7]. Maximum hop is calculated by taking squareroot of number of nodes. The value of probability updationfactor λ is taken to be 0.75.

In Fig. 3, the path from source to destination is shown,which fulfill the QoS requirements.

Fig. 3. Network scenario for route discovery

The routing overhead for different values of η is shown inFig. 4. It shows that in case of omnidirectional communication,the routing overhead is same for all values of η as it istransmitting RREQ packet in all the directions with equalprobability. In Fig. 4, the effect of probability updation in caseof directional antenna is also shown, as it is transmitting RREQpacket in a successfully communicated direction so the routingoverhead decreases as the probability of different directionsare updated at next retransmission. The routing overhead iscalculated for different value of weight factor η from 0 to1. This figure shows the average routing overhead for 100iterations in case of omnidirectional antenna and directionalantenna with different values of η.

Now, in Fig. 5 and 6, comparison of the routing overheadfor 100 transmissions without and with probability updationrule is plotted for different value of the metric weight factorη. Fig. 5 shows routing overhead of route discovery withomnidirectional antenna i.e., with no probability updation rule,we can see that as RREQ is transmitted with equal probabilityevery time so the routing overhead is similar as the numberof retransmission increases for different values of η.

In this Fig. 6, we analyze that as RREQ is transmitted ini-tially with equal probability but when next time the probabilityis updated according to probability updation rule the routingoverhead decreases as the number of iterations increases for

Fig. 4. Routing overhead using omnidirectional antenna(OD) and directionalantenna(D)

Fig. 5. Routing overhead using omnidirectional antenna

different values of η. It limits the unlimited flooding of RREQpacket in a network.

V. CONCLUSION

In this paper, we have considered QoS constrained routingin directional antenna based ad hoc network. The proposedrouting algorithm found optimal path not only from source todestination, but also for other nodes traversed in route discov-ery. The probability updation rule increases the probability ofa successfully communicated beam direction and reduces theother directions probability. RREQ packet is retransmitted inthat direction which is having high probability thus reduces therouting overhead in a network. The simulation results clearlyshows that compared with omnidirectional communication, thedirectional communication reduces the routing overhead in anetwork.

Fig. 6. Routing overhead using directional antenna

REFERENCES

[1] R. Roy Choudhury and N. Vaidya, “Performance of ad hoc routing usingdirectional antennas,” Ad Hoc Networks, vol. 3, no. 2, pp. 157–173, 2005.

[2] H. Gossain, T. Joshi, C. De Morais Cordeiro, and D. Agrawal, “Drp: Anefficient directional routing protocol for mobile ad hoc networks,” IEEETransactions on Parallel and Distributed Systems, vol. 17, no. 12, pp.1438–1451, 2006.

[3] C. Cordeiro and D. Agrawal, Ad hoc & sensor networks: theory andapplications. World Scientific Pub Co Inc, 2006.

[4] H. Dai, K. Ng, M. Li, and M. Wu, “An overview of using directionalantennas in wireless networks,” International Journal of CommunicationSystems, 2011.

[5] K. Shin and C. Chou, “A distributed route-selection scheme for establish-ing real-time channels,” in Proceedings of the IFIP Sixth InternationalConference on High Performance Networking VI. Chapman & Hall,Ltd., pp. 319–330, 1995.

[6] S. Chen and K. Nahrstedt, “Distributed quality-of-service routing in high-speed networks based on selective probing,” in IEEE Proceedings., 23rdAnnual Conference on Local Computer Networks LCN’98., pp. 80–89,1998.

[7] P. Feng, Y. Ding, B. Liu, J. Wu, L. Gui, and H. Zhou, “A qos constrainedcognitive routing algorithm for ad hoc networks based on directionalantenna,” in IEEE International Conference on wireless Communicationsand Signal Processing (WCSP), pp. 1–5, 2011.

[8] Z. Zhang and B. Li, “Neighbor discovery in mobile ad hoc self-configuring networks with directional antennas: algorithms and compar-isons,” IEEE Transactions on Wireless Communications, vol. 7, no. 5, pp.1540–1549, 2008.

[9] I. Jawhar, J. Wu, and D. Agrawal, “Resource scheduling in wirelessnetworks using directional antennas,” IEEE Transactions on Parallel andDistributed Systems, vol. 21, no. 9, pp. 1240–1253, 2010.