a routing algorithm for supporting soft-qos in

8/3/2019 A Routing Algorithm for Supporting Soft-Qos In

1/4

A Routing Algorithm for Supporting Soft-Qos in

Mobile Ad hoc Networks

Xiaonan FengInstitute of Electrical and Electronic Engineering

North China Electric Power University

Beijing 102206, China

[email protected]

Abstract Recent work in mobile ad hoc network routing

protocol development has demonstrated how global route

discovery can be performed more efficiently by leveraging the

known topology of each nodes local surrounding area. In this

paper, we proposed a hybrid routing scheme based on a dynamic

clustering algorithm. The adaptive clustering algorithm deals

with mobility, transmit power conservation and improvingsystem throughput. Routing algorithm proposed in this paper is

decoupled from the clustering algorithm, it dynamically balance

the tradeoff between proactive and reactive routing, while also

dynamically tradeoff route optimality for routing overhead in a

novel way. Moreover, this paper describes how to gather three

metrics, which are used to support Qos aware route computation.

Keywords-routing algorithm; Soft-Qos;mobile ad hoc network

I. INTRODUCTIONThe routing problem in ad hoc networks is complicated

since any, or all, of the hosts involved may move at any time.Numerous challenges must be overcome to realize the practical benefits of ad hoc networking [1], for the network is highlydynamic and transmissions are susceptible to fading,interference, or collision from stations.

A central challenge in the design of ad hoc networks is thedevelopment of dynamic routing protocols that can efficientlyfind routes between two communicating nodes. The routing

protocol must be able to keep up with the high degree of nodemobility that often changes the network topology drasticallyand unpredictably, but routing overhead is the critical factorimpacting performance of routing protocol. In [2], a clusteringmanagement mechanism have developed to adjust the mobileterminals transmission power to smooth the drastic topologychange based on current mobility scenarios. Nasipuri et al in [3]use preemptive search by studying the statistical distribution ofthe link and route lifetimes to balance the optimality of theroutes as well as the overhead incurred from transmit routing

packet. In [4], a QOS-based robust multipath routing protocolis proposed to accomplishes increased packet delivery ratiowith reduced latency. In [5], Michail and Ephremides aimed tominimize energy expenditure in addition to minimizing theoverall blocking probability. Hybrid adaptive routing strategy

proposed in [6]is using a path stability to cluster network, the

clustering algorithm presents a logical topology to the routingalgorithm, and it accepts feedback from the routing algorithm

in order to adjust that logical topology and make clusteringdecisions. Routing protocol proposed in [7]

mainly can reduce

average end-to-end delay and loss rate, does not describe howto update the routing status.

In order to limit routing overhead, routing policy for ad hoc

wireless network has progressed from proactive, reactive tohybrid routing. Proactive routing protocols attempts tomaintain shortest-path routes by using periodic updates to trackchanges in the network topology. The main drawback of thistechnique is that there are routing updates even when there arenot needed, such as when nodes have not moved or are notinvolved in communication. Reactive routing protocols

provides the basic motivation for the on-demand routingprotocol, which create and maintain routes only when required,thus saving on redundant routing overhead from periodicupdates. Pure adopts proactive or reactive routing protocols cannot completely solve the problems. In this paper, we proposeda hybrid routing protocol utilizes a dynamic clusteringalgorithm that is adaptive with respect to node mobility. The

adaptive cluster organization supports a hybrid routingapproach dynamically balance the tradeoff between proactiveand reactive routing, while also dynamically tradeoff routeoptimality for routing overhead in a novel way.

II. NETWORKMODEL AND DEFINITION1) Network model

In a wireless network, each node has a transceiver forcommunication. The neighbors of a node, which a node candirectly communicate with, is not fixed but depends on the

power used by its radio transmitter. When the power of theradio transmitter is increased, a node can directly communicatewith a larger set of nodes.

The propagation function is represented as R: LLZ,where L is a set of location coordinates in the space. Function

),( ji ll gives the loss in dB due to propagation at location

Llj , when a packet is originated from location Lli .

The successful reception of a transmitted signal depends,

SlltP ji ),( (1)

978-1-4244-6252-0/11/$26.00 2011 IEEE


2/4

Along with the propagation function R, on the transmit

powertPof sender and the receiving sensitivity Sof receiver.The receiving sensitivity is the threshold signal strength needfor reception and is assumed to be a previously knownconstant, same for all nodes. In particular, for successful

reception, we assume that R, is a monotonically increasing

function of the geographical distance ),( ji lld between iland

jl . This is generally true for free space propagation. We can

then combine S and R, into one function as fellows,

Slldd ji += )),(()( (2)

Clearly, tPmust be at least )(d for successful reception.

So given an Ad hoc network M=(N, L)(N is the nodes ofnetwork and L is the link between the nodes), a transmit power

functionp, and a least-power function , we can represent theinduced graph as G=(V, E), where V is a set of verticescorresponding to nodes in N, and E is a set of undirected edges

so that (u, v)E if and only ifp(u) (d(u,v)), and p(v)

(d(u,v)). Then, we can think of the links as beingsymmetric and the resulting graph as undirected. Furthermore,we assume that there exists a medium access scheduler so thateach node can transmit at a certain bit rate without interference.

2) Routing parametersBefore given routing metrics, we give some abbreviations

in table I for simplifying next descriptions

TABLE I. ABBREVIATIONS

tP(w) Transmit Power

rP(w) Receive Power sensitivity

AP(w) Residual Available Power

Tw(w) Estimation of Total power consumption based on the last periods

For improving Qos routing guarantee intensity, we will takethe following three parameters as main reference.

Definition 1 . Aggregate mobility metrics

Presented in [8], the aggregate mobility metriccs is used tomeet the biggest requirements of QoS stricter application.

The relative mobility metric is defined as:

)(log10 10

old

ij

old

ji

new

ij

new

ji

j

i

tP

rP

tP

rP

mR

=

(3)

Then calculate the aggregate local mobility valued:

[ ]iNj

jii mRmR = var (4)

A node that has the lowest M among its neighbors denotesit has more stable connection to its neighbor, also means it iswith easier to find alternate link for abrupt disconnection thanother nodes.

Definition 2 Residual power

This metric is important in wireless network because mostportables are powered by batteries with limited weight and life.Furthermore, the remaining power is essential to maintain astable network and achieve good communication quality. Theremain power will be get by the formula:

)(twaverageaP

resP=

(5)

When some nodes leave far from each other, then the nodeswill increase their power of communicating. On the other hand,if the nodes get closer, then the power of communicating willdecrease and thus save the valuable power resource.

Definition 3. Available Bandwidth

In adaptive clustering for mobile wireless networks, theavailable bandwidth is limited. The first data packet of the flowmakes reservations along the path. Once the packet is received,a transmission window is reserved for the subsequence packetsin the connection. The window is released until several idlecycles occur. If the link isnt broken due to mobility, thesubsequence packets will be received successfully.

The bandwidth is given by:

B=[cycle time/ frame time]

And the available Bandwidth (aB) can be as:

= iNj jii uBmBaB (6)

Where mB denotes Maximum Bandwidth of nodecapability and uB denotes Used Bandwidth (uB) for activeconnection.

III. HYBRID QOS ROUTING ALGORITHM(HQRA)Here we propose a hybrid routing algorithm. It is the

combination of proactive policy and reactive policy. It dividesthe nodes into several clusters through suitable size clusteringalgorithm(SSCA) in [2]. Based on shis algorithm intra-clusterrouting use a proactive policy, whereas the inter-cluster routingis reactive. In networks with low rates of mobility, clustering

provides an infrastructure, which is more proactive. Thisenables more optimal routing by increasing the distribution oftopology information when the rate of change low. Whenmobility rates become very high. Cluster size will diminish andreactive routing will dominate. Decoupling the routingalgorithm specification from the clustering algorithm, thus, it isflexible enough to support evolving ad hoc network routingstrategies in both intra and inter-cluster domains. The hybrid

policy accompanies a better balance between power saving andquick routing.

When a node needs to discover a route to the destination,except considering the least time-consuming route, stabilityalso should be take into consideration. Thus those nodes whichhave enough power and bandwidth remain should be give some

priority.

The routing update includes two procedures, intra-clusterrouting update and inter-cluster routing update. Intra-cluster


3/4

routing update cycle is shorter than that of inter-cluster routingcycle. Different from [9] adapting to the mobility by adjustingcluster size, our scheme decouples clustering and routing,routing algorithm adjusts the inter-cluster update scope to adaptthe mobility. Fig. 1 briefly describes the relationship betweenclustering algorithm SSCA and routing algorithms HQRA.

Figure 1. Relationship between Clustering and Routing

The intra-cluster routing update is implemented byclusterhead. The clusterhead sends intra-cluster route status

packet to its cluster members periodically, and the members

will update their routing table after they receive the packet.The inter-cluster routing update can employ any proposed

proactive routing schemes. Any clusterheads designate aninternal node as inter-cluster routing updater by some criteria,such as maximal residual power. These updaters execute inter-cluster routing update procedure as follow:

1) Inter-cluster Updater initiates intra-cluster route statuspackage and send it to its direct neighbors cluster periodically.

2) Any inter-cluster Updater receiving a inter-clusterupdate package performs below actions:

a) Integrates the routing status information of thispackage to its local routing table and records the updating path

of the source cluster.

b) Refreshes timers of routing table items according tothe new arrival inter-cluster update package.

c) Checks the travel path of this package with that oflast update. If successive update packages initiating from the

same cluster have traveled on the same cluster path, the

updater forwards new update package to its direct neighbors

except of the coming cluster, Or else no forwarding is

performed.

3) The timeout route items are removed from local routingtable when its timer event arrives.

AB

E

D

F

C

H

G

43

21

65

7

AB

E

D

F

C

H

G

43

21

65

7

AB

E

D

F

C

H

G

43

21

65

7

Figure 2. Example of inter-cluster routing update

Fig. 2 illustrates the process of above procedure. Node Ainitiates an inter-cluster route update package and sends tonode B transferred by node 1 (similar to node C and D), if nodeB has received inter-cluster update package of the clusterdelegated by node A in the successive inter-cluster update

periods, node B forward this update package to node E and F,so node E and other nodes which is in the same cluster with Eknow the topology and link status of the cluster delegated by

A. On this time, local routing table of node E at least includesthe status of three clusters, which delegated by E, B, Frespectively.

The inter-cluster routing information delivers to innernodes in the intra-cluster update procedure. But only intra-cluster route status is packed into the inter-cluster update

package. For example, in figure 2, when node A initiates inter-cluster update package it does not include the route status ofnode B though its local route table containing available route toB.

IV. PERFORMANCE EVALUATIONA. Simulation Environmentt

We implemented our routing scheme within the Glomosimlibrary [10]. The Glomosim library is a scalable simulationenvironment for wireless network system using the paralleldiscrete-event simulation language called PARSEC [11]. Oursimulation models a network within 2000*2000 meter squareand the nodes in the network is placed uniformly. Radio

propagation range for each node is 150 meters and channelcapacity is 2 Mbits/sec. In most of experiments unlessspecified, the network consists of 100 nodes and the averagemovement speed varies from 5m/s to 45m/s. Each simulationexecuted for 600 seconds of simulation time. We run eachscenario three times and the data collected are averaged over

those runs.

B. Simulation ResultsWe compare the routing overhead between Fisheye and

HQRA from two aspects, number of nodes and node mobilityspeed. The following figures are the simulation results showingthe impact of routing overhead from number of nodes and thenode mobility speed.

Figure 3. Overhead comparison by number

SSCA

Topology

HQRAProactiveRouting ReactiveRouting

Topology

0

100

200

300

400

500

600

700

800

900

1000

50 60 70 80 90 100110 120 130 140 150Number of Nodes

Average

Contro

l

Overhead

HQRA Fisheye


4/4

Figure 4. Overhead comparison by speed

Fig.3 reports the comparison of routing overhead betweenFisheye and HQRA by the number of nodes. When the numberof nodes gains, the routing overhead of the two algorithms bothincreased. But the routing overhead of HQRA is always lowerthan Fisheye. Because in Fisheye, every node makes itself thecentre of a circle and sends routing update message to othernodes with the frequency corresponding to the scope radius,while in HQRA, the routing update process is based on cluster.So when the number of nodes increased only a part of newnodes participate in the routing update process. Therefore therouting overhead of Fisheye is increasing faster than that ofHQRA. Especially when the number of node is more than 100,HQRA reduces more than 40% of routing overhead comparedwith Fisheye.

Fig.4 reports the comparison of routing overhead betweenFisheye and HQRA by the node mobility speed. When thenode mobility speed is slow the routing overhead of HQRA ismuch lower than Fisheye. With the speed increasing, therouting overheads of HQRA and Fisheye are both increasing,

but even in the worst case that the node mobility speed is veryfast and the cluster can not be maintained, because the HQRAcan dynamically reduce the scope of routing update, so therouting overhead of HQRA is still lower than that of Fisheye.

V. CONCLUSIONIn this paper, a hybrid QoS aware routing update algorithm

has proposed to minimize the total routing overhead. It is basedon an adaptive clustering algorithm but decouples from

clustering schemes. By adjusting the inter-cluster update scopeaccording to network dynamic degree to get the goal ofminimizing routing overhead. In addition, it calculates Qosaware status parameters and updates to foreign clusters forsupport local Qos route computation. The future work isneeded to assess the performance of this algorithm combinedwith routing selection scheme.

REFERENCES

[1] Chakrabarti, S., Mishra, A., Qos Issues in Ad hoc Wireless Network,IEEE Communications Magazine, Feb. 2001, pp. 142148.

[2] Xin Jin, Hongbo Wang, Yaoxue Zhang, A cooperative two-tierframework for efficient routing in MANET, IEEE Proceedings ofInternational Conference on Computer Networks and MobileComputing(ICCNM), Oct. 2003, pp. 465469.

[3] Nasipuri, A., Burleson, R., Hughes, B., Roberts, J., Performance of aHybrid Routing Protocol for Mobile Ad hoc Networks, IEEEProceedings of Tenth International Conference on ComputerCommunications and Networks, Oct. 2001, pp. 296302.

[4] Venkatasubramanian, S.,Gopalan, N.P., A QoS-based robust multipathrouting protocol for mobile ad hoc networks, IEEE First AsianHimalayas International Conference on Internet(AHICI), Nov. 2009, pp.17.

[5] Michail, A., Ephremides, A., Energy Efficient Routing for Connection-Oriented Traffic in Ad hoc Wireless Networks, The 11th IEEEInternational Symposium on Personal Indoor and Mobile RadioCommunications (PIMRC), 2000, Vol. 2, pp. 762766.

[6] McDonald, A.B., Znati, T.,A dual-hybrid adaptive routing strategy forwireless ad-hoc networks, IEEE Wireless Communications andNetworking Conference (WCNC), 2000. Vol. 3, pp. 11251130.

[7] Yanmei Yang, Wendong Han, A QoS Routing Protocol for Mobile AdHoc Networked Control Systems. IEEE Second InternationalConference on Networks Security Wireless Communications andTrusted Computing (NSWCTC), Jun. 2010. pp. 8992.

[8] Basu, P., Khan, N., Little, T.D.C., A Mobility Based Metric forClustering in Mobile Ad Hoc Networks, IEEE International Conferenceon Distributed Computing Systems Workshop (ICDCS), Apr. 2001, pp.413418.

[9] Lin, C.R., Chung-Ching Liu, An on-demand QoS routing protocol formobile ad hoc networks, IEEE International Conference on Networks(ICON), Sep. 2000, pp. 160164.

[10] Gerla, M., Tsai, J.T.C., Multicluster, mobile, multimedia radio network,Wireless Networks, vol. 1, 1995, pp. 255265.

[11] Malnar, M.Z., Neskovic, N.J., Comparison of ETX and HOP countmetrics using Glomosim simulator, IEEE 9th International Conferenceon Telecommunication in Modern Satellite, Cable and BroadcastingServices(TELSIKS), Oct. 2009, pp. 8588.

0

100

200

300

400

500

600

700

5 10 15 20 25 30 35 40 45Number of Speed

Average

Control

Overhead

HQRA Fisheye

a routing algorithm for supporting soft-qos in

Documents