research article a clustering routing protocol for mobile...

Research ArticleA Clustering Routing Protocol for Mobile Ad Hoc Networks

Jinke Huang Xiaoguang Fan Xin Xiang Min Wan Zhenfu Zhuo and Yongjian Yang

Aeronautics and Astronautics Engineering College Air Force Engineering University Xirsquoan 710038 China

Correspondence should be addressed to Jinke Huang 86297609qqcom

Received 13 January 2016 Revised 9 April 2016 Accepted 19 April 2016

Academic Editor Luis J Yebra

Copyright copy 2016 Jinke Huang et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

The dynamic topology of a mobile ad hoc network poses a real challenge in the design of hierarchical routing protocol whichcombines proactive with reactive routing protocols and takes advantages of both And as an essential technique of hierarchicalrouting protocol clustering of nodes provides an efficientmethod of establishing a hierarchical structure inmobile ad hoc networksIn this paper we designed a novel clustering algorithm and a corresponding hierarchical routing protocol for large-scale mobile adhoc networks Each cluster is composed of a cluster head several cluster gateway nodes several cluster guest nodes and other clustermembers The proposed routing protocol uses proactive protocol between nodes within individual clusters and reactive protocolbetween clusters Simulation results show that the proposed clustering algorithm and hierarchical routing protocol provide superiorperformance with several advantages over existing clustering algorithm and routing protocol respectively

1 Introduction

A mobile ad hoc network (MANET) [1 2] is a wireless com-munication network atwhich nodes use peer-to-peer packetstransmission and multihop routes to communication It canoperate without fixed based station or any wireless backboneinfrastructureHenceMANETs bear great application poten-tial in these scenarios including battlefield communicationsemergency operation disaster relief survival search andsensor dust But due to the mobility and constant topologychanges of MANETs designing routing protocols for thosenetworks is a challenging process especially in the large-scalenetwork

Routing protocols of MANETs presented currently canbe classified into four categories according to the mechanismof updating [3 4] (1) Proactive (table-driven) routingthis method requires nodes broadcast routing informationperiodically tomaintain valid routing table at all times whichwill costmuch bandwidth Hence thismechanism is not suit-able for large dynamic networks (2) Reactive (on-demand)routing in this case nodes in the network do not regularlymaintain routing table for all destinations just only when anode has data packets to send to some given destination itchecks its routing table to determine whether it has an activeroute to the destination If not the nodemust perform a route

discovery procedure to acquire a route to the destinationThus this method does well to large population and highmobility networks (3) Hybrid routing it is a better com-promise of the first two approaches namely it uses reactive(proactive) routing between nodes within individual clustersand proactive (reactive) between clusters (4) Geographicrouting thismethod uses nodesrsquo location information as theiraddresses and forwards data packets towards the destinationrsquodirection It is efficient and scalable as nodes only keep statefor their neighbors and support a fully general any-to-anycommunication pattern without explicit route establishment

According to the topology of networks routing protocolscan be classified into flat-based routing and hierarchical-based routing [5 6] In flat-based routing all nodes playan equal role and can establish a route by local operationand information feedback among themselves easily But tolarge scale networks the frequent topology detection mayinvalidate the discovered routes which would lead to highdelay and network spending This phenomenon can besolved efficiently in hierarchical-based routing network Inhierarchical-based routing [7ndash10] all nodes are divided intodifferent clusters (zones) Each cluster elects a cluster headaccording to specific rules Data exchanging between clusterswas relayed by gateway node disregarding the details of howthe relayed data would be transmitted to the destination

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2016 Article ID 5395894 10 pageshttpdxdoiorg10115520165395894

2 Mathematical Problems in Engineering

In a word hierarchical-based routing not only plays downthe network spending by decreasing the number of nodeswhich participate in routing maintenance but also increasesthe network stability by dividing the network into easilycontrolled subnets

ZRP (Zone Routing Protocol) [11] is typical hybridrouting which groups nodes into geographic zones It usesDVA (Distance Vector Algorithm) proactive routing betweennodes within individual zones and DSR (Dynamic SourceRouting) reactive routing between zones ZHLS (Zone-BasedHierarchical Link-State Routing) [1 12] protocol incorporateslocation information into a novel peer-to-peer hierarchicalrouting approach The ZHLS network is divided into non-overlapping zones and aggregating nodes into zones concealsthe detail of the network topology CBRP (Cluster BasedRouting Protocol) [13] uses an on-demand protocol betweennodes both intrazone and interzone communication As aresult it will cost much time in routing maintenance Theabove three routings have a similar shortcoming namelyrouting maintenance will consume a large portion of band-width and even may paralyze the network when the trafficis too large HSR (Hierarchical State Routing) [14] andCGSR (the Center for Global Security Research) [15] are allusing table-driven strategy to establish routing of both intra-and interzone which is good to decrease routing updatingdelay but increase the cost of routing overhead unwillinglyMMWN (Multihop Mobile Wireless Networks) [16] is ahierarchical routing scheme proposed for ad hoc networksto achieve scalability However its location management isclosely tied with the network hierarchical topology whichmakes the location updating and location finding quite com-plex

In hierarchical routing protocols superior clusteringalgorithm can not only reduce the routing overhead but alsoincrease the scalability of the net Some have been proposedin literatures [17ndash20] and most of them are to satisfy the spe-cific demands such as the lowest ID cluster and the highestconnectivityTheDDVC(DynamicDopplerVelocityCluster-ing) and DLDC (Dynamic Link Duration Clustering) algo-rithms were proposed in [21] both of them are just applied topseudolinear highly dynamicMANETsTheMPBC (MobilityPrediction-Based Clustering) in [22] elects the node whichhas a low variance of the relative mobility values withrespect to its neighbors taking the cluster head responsibilityThe SCCR (Signal Characteristic Clustering Routing) usingSCBC (Signal Characteristic Based Clustering) algorithm in[23] elects the node which has a long link holding time totake the cluster head responsibility Although thismethod candecrease the possibility of reclustering but it cannot ensurethe elected cluster head is logically centric and more prof-itable to play the cluster head role and hence to increase thewhole network performance All of them somewhat ignoredthe scenario namely when a node moves out of its clusterhead transmission range but still has a link to another clustermember belonging to any cluster head whether the initialphrase of clustering will be reestablished or not And theconclusion in [24] reveals that frequent cluster changingconsumes lots of network resources and highly overlappedclusters decrease the efficiency of hierarchical structureThus

an efficient clustering algorithmmust maintain a more stableand less overlapping structure

Based on the analysis of the above a hybrid clusteringalgorithm (HCA) based on the cost metric which takes thelink expiration time and nodersquos relative degree into accountwas designed and a corresponding routing protocol (HCA-R) was proposed in Sections 2 and 3 respectivelyThe experi-ments and analysis are given in Section 4 Finally Section 5concludes the paper

2 Clustering Algorithm

21 Cost Metric for Clustering We assume that there are 119873mobile nodes roaming in a 119877times119877Km square and each movesaccording to the RRGM (Reference Region Group MobilityModel) [25] All nodes have same transmission range of 119903Km(119903 lt 119877) Each node can acquire their position and timeinformation through GPS and can also broadcast them totheir neighbors

The coordinates (1199091198941 1199101198941) and (119909

1198942 1199101198942) are the location

information sent by node 119894 to 119895 at two consecutive times 1199051

and 1199052 respectively Correspondingly the coordinates of node

119895 are (1199091198951 1199101198951) and (119909

1198952 1199101198952) For simplicity we assume that

node 119894 is fixed and node 119895 is moving but actually they are allmoving at all times

Figure 1 depicts the approaching and receding scenario ofnodes 119894 and 119895 The node 119894 is fixed in coordinate original (119874)but 119895moves along the vector 997888997888119860119861 We assume that nodes 119894 and119895 keep their velocity and direction at theΔ119905 = 119905

2minus1199051duration

and 119895 arrives at point 119861 at the moment 1199052

In Figure 1(a) there are two equations

119889119874119860

= radic(1199091198991minus 1199091198981)2+ (1199101198991minus 1199101198981)2

119889119874119861


(1)

Through the law of cosines there exists

119889119860119861

= radic119889119874119860

2+ 119889119874119861

2minus 2119889119874119860119889119874119861

cosang119860119874119861 (2)

where ang119860119874119861 = arcsin(1199101198991119889119874119860) minus arcsin(119910

1198992119889119874119861)

Thus the relative velocity between nodes 119894 and 119895 is

] =119889119860119861

Δ119905 (3)

We assume that the velocity and direction of nodes 119894 and119895 are not changing during Δ119905 So node 119895 leaves from thetransmission range of node 119894 need to track the segment 119861119862From the moment 119905

2 the holding time of the link between

nodes 119894 and 119895 is

119879119894119895=119889119861119862

]=(119889119862119863

minus 119889119861119863)

] (4)

where 119889119862119863

= radic1199032minus (119889119874119861

sinang119874119861119860)2 119889119861119863

= 119889119874119861

cosang119874119861119860minus119889119860119861 and ang119874119861119860 = arccos((119889

119860119861

2+ 119889119874119861

2minus 119889119874119860

2)2119889119874119861119889119860119861)

Mathematical Problems in Engineering 3

y

AB

C

xO

D

(a) Approaching scene

y

A

B

C

xO

D

(b) Receding scene

Figure 1 Sketch map of the approaching and receding scenario

The analysis of Figure 1(b) is similar to Figure 1(a) so

119879119894119895=119889119861119862

]=(119889119861119863

+ 119889119862119863)

]=(119889119861119863

+ 119889119862119863)

(119889119860119861Δ119905)

(5)

where the definition of 119889119874119860

and 119889119874119861

is the same as (1) 119889119860119861

=

radic119889119874119860

2+ 119889119874119861

2minus 2119889119874119860119889119874119861

cosang119860119874119861 ang119860119874119861 = arcsin(1199101198992

119889119874119861) minus arcsin(119910

1198991119889119874119860) 119889119861119863

= 119889119860119863

minus 119889119860119861

= 119889119874119860

cosang119874119860119861 minus119889119860119861 ang119874119860119861 = arccos((119889

119860119861

2+ 119889119874119860

2minus 119889119874119861

2)2119889119874119860119889119860119861) and

119889119862119863

= radic1199032minus (119889119874119860

sinang119874119860119861)2In a MANET of 119873 nodes the degree of node which is a

good indicator of node density can be easily denoted as

119889119894=

1 0 lt 119889119894119895lt 119877

0 otherwise(6)

where 119889119894119895

is the distance between nodes 119894 and 119895 119894 =

1 2 119873 119895 = 1 2 119873 When 119889119894is bigger node 119894 is more

nuclearThus the relative node degree of node 119894 can be calculated

as

119863119894=10038161003816100381610038161003816119889119894minus radic119873

10038161003816100381610038161003816 (7)

When choosing cluster heads our clustering algorithmabsorbs the quintessence of some clustering algorithms (ieMPBC and SCBC) and takes the link expiration time andnodersquos relative degree into account not to take one of them asthe metric to select cluster heads In HCA long link expira-tion time can ensure the cluster holding a long time and tak-ing nodersquos relative degree into account can make the selectedcluster head in the center of its cluster as far as possibleTherefore we make a reasonable compromise based on theactual needs and operation environment to select clusterheads which can improve overall performance of MANETs

Each node is assigned a weight indicating whether the nodeis suitable to act as a cluster head So the cost metric (repre-sented as119872 for simplicity) of node 119894 can be expressed as

119872119894= 1199081119879119894119895+ 1199082119863119894 (8)

where 119894 = 1 2 119873 1199081and 119908

2are the weight factor of

parameters 119879119894119895and 119863

119894 respectively and if the parameter is

more important its weight factor will be greater 1199081and 119908

2

can be preestablished or adjusted adaptively according toreal-life network but 119908

1+ 1199082= 1 Here we set the initial

value of 1199081and 119908

2to 23 and 13 respectively and adjusted

them adaptively according to real-life network

22 Clustering Algorithm Clustering is a mechanism todynamically group nodes in MANETs into logically separat-ing or nonoverlapping entities called clusters In our schemethere are five possible states for nodes NULL cluster headcluster member gateway and cluster guest

In this paper we usually consider one-hop clustersbesides the only scenario namely when the cluster guestappears in this case the guest node is two hops away fromthe cluster head All the nodes in such a cluster are within therange of the cluster head but not necessarily within range ofeach other

221 Cluster Initialization The HCA algorithm used for theclustering initialization is described in Figure 2

Prior to the cluster initialization all nodes are in the stateof NULL Once started each node in the network broadcastsa HELLO message to have knowledge of its member nodeswhich can be used to calculate its cost metric Then eachnode broadcasts a CH ELECT message piggybacking thecost metric calculated by the algorithm in Section 21 to itsneighborUpon receiving it will compare the costmetricwithitself and the bigger one will be elected as a cluster head


Each node in the NULL state broadcasts a HELLO message in the form of

HELLO (node ID NULL) to its neighborswhich can be used to calculate its cost metric

Startalgorithm

If the cost metric is biggerthan othersrsquo

Change its role to cluster head and broadcastCH_CLAIM to its one-hop

neighbors

Yes

If received other nodersquosCH_CLAIM

Waiting for other nodesrsquo CH_CLAIM

No

Each node broadcasts a CH_ELECT messagepiggybacking the cost metric calculated by the algorithm in Section 21 to its neighbors

Yes

Change its role to cluster member

Change its role to cluster head

No

End algorithm

Compare the cost metric with itself

Figure 2 Cluster initialization

Finally the cluster head will broadcast the elected resultingmessage called CH CLAIM (cluster head claim) to its one-hop neighbors Upon receiving the neighbors will send RTJ(request to join) message to the cluster head and cluster headwill send ATJ (affirm to join) back when agreeing

After the above process some clusters will have beenformed But when a cluster member receives more than oneATJ message this denotes that the node lies in separate clus-ters but within transmission range of one another thereforeit will be elected as a gateway between these clusters

222 ClusterMaintenance Once the initial clustering phrasetakes place cluster heads and clustermembersmust exchangemessage to maintain the relationship periodically Namelythe cluster head periodically broadcasts CH CLAIM (clusterID) messages to its neighboring nodes And the clustermembers of the attached cluster broadcast cluster member(node ID cluster IDs) messages back to the cluster headperiodically where the node ID is the identifier of the broad-casting node and cluster ID is the list of clusters of which thenode is a member When topology changes we can deal withit according to the three cases as follows

(1) Deleting or Adding Nodes A cluster member would dis-sociate from the attached cluster if it does not hear periodicbroadcast from its cluster head Likewise the cluster headwillremove the cluster member from its list of members if it doesnot receive the periodic cluster member broadcasts

When a node including new coming or dissociated fromother clusters wants to join a cluster it should send RTJ toa cluster head and the cluster head will send a ATJ messageback only if the requesting node is allowed to join

Note thatwhen a nodemoves out of its cluster headrsquo trans-mission range but still has a link to another cluster memberbelonging to any cluster head it will become a cluster guest toavoid a new initial clustering formation taking place thoughthe cluster guestrsquos cost metric is bigger or not In this way itcan reduce the cluster head change rate and the ripple effectscaused by reclustering can be ignored Hence the routingoverhead will be decreased

(2) Replacing the Cluster Head Position Once a cluster headleaves its own cluster or is damaged the node belongingto this cluster would return to the NULL state Thus theyshould request joining other clusters or establish a newcluster Note that only when the node receives more than twoconsecutive RTJ messages from another certain node shouldthey establish a new cluster

(3) Merging Different Two Clusters When a cluster enters thetransmission range of another cluster and the variance of costmetric of the two cluster heads is small which denotes thatthe two clusters are worth merging If so the cluster headwhich has bigger metric will be reelected as the new clusterhead but the similar one must give up its cluster head roleto be a common member of the new cluster Otherwise it


Startalgorithm

End algorithm

If source anddestination are in the

same clusterYes No

If the distance between source and destination is smaller than

twice transmission range of source and destination

Source anddestination

communicate via relay


communicate directly

Source sends RREQ to neighbor

cluster head via gateway

If destinationis in the neighbor

clusterDestination

sends RREP to source along the coming

way

No

Yes No

Yes


communicate via intermediate

nodes

Figure 3 Flow diagram of HCA-R

denotes that the two clusters just incidentally pass by eachother in a short period and it is not worthy of merging Inthis way it can reduce the probability of cluster overlapping

3 Hierarchical Clustering Routing Protocol

In order to utilize the network resources efficiently HCA-R absorbs the quintessence of ZRP to use proactive strategybetween nodes within individual clusters and reactive strat-egy between clusters not like CBRP to use on-demand strat-egy between nodes of both intracluster and intercluster com-munication to purely decrease the routing overhead andHSRCGSR to use table-driven strategy to communicate in bothintra- and interzone to decrease average end-to-end delay butincrease the cost of routing overhead unwillingly In HCA-R unless necessary it will not activate the routing updateprocess as far as possible to avoid additional expenses inboth intracluster and intercluster communication and routemaintenance phrase Its flowdiagram is described in Figure 3

31 Intracluster Communication If the source and destina-tion are in the same cluster the data packet can be transmitteddirectly or relayed by cluster head Namely when the desti-nation is in the range of source source and destination cancommunicate with each other directly or relayed by clusterhead Otherwise source and destination must exchange datathrough cluster head

1Source

2

4

3

Destination

5

Cluster head

Cluster memberREQREPData

Figure 4 Example of intracluster

For example in Figure 4 source node 1 and destination5 are in the same cluster So node 1 can send data packet tonode 5 relayed by the cluster head (ie 1rarr2rarr5) or along theroute to node 5 (ie 1rarr4rarr5) directly if node 5 is within thetransmission range of node 1 If not node 1 must send datapacket to node 5 relayed by the cluster head (ie 1rarr2rarr5)

32 Intercluster Communication If the source and destina-tion are in the different clusters the source must take theintracluster strategy Namely at first the source sends a REQ(request) message to its attached cluster head and then thecluster head will broadcast this REQ to its adjacent cluster


1Source

3

4

5

Destination

7

69

8 10

Cluster head

Gateway

Cluster member

DataREQREP

2

Figure 5 Example of intercluster

head through gateway nodes and the process will continueuntil the REQ arrives at the cluster which belongs to thedestination node Finally the cluster head including thedestination sends a REP (responding)message back along thediscovered path Note that the cluster head in the discoveredpathwill transfer the REP along the local shortest routeThusthe source will get the shortest route to the destination

For example in Figure 5 source node 1 wants to send datato destination 9 It must send a REQ to its attached clusterhead 3 firstly and then node 3 sends the message to node 7through gateway nodes 5 and 6 Finally node 9 sends a REPalong the discovered path to node 1 Note that when node3 receives the REP it will send the message along the localshortest route (ie 5rarr1) to node 1 Thus node 1 gets theshortest route to node 9 (ie 1rarr5rarr6rarr7rarr9)

33 Route Maintenance Due to the frequent topology detec-tion an efficient and effective method of route maintenancein response to underlying network topology change is imper-ative because without routes validity the performance of arouting scheme in a dynamic mobile environment is affectedeven adversely

When an existing link is failure (such as the node on theexisting path that moves out of its one-hop neighbor or exitsfrom the network or the receiving node on the existing pathcannot receive message from sending node because of dete-rioration of the channel) the local repairing process wouldtake place Namely an existing shorter path will replace theoriginal route between the two nodes at which link is brokenMeanwhile aCLEARmessagewill be forwarded to the sourcenode which originates the packets to notify the change

Note that in order to ensure the validity and stability ofroute the local repairing process can take place only whenthe intercluster routes are invalidated And this mechanism isvery helpful to reduce the route reestablishing expenses andend-to-end delay

For example in Figure 6 source node 1 has an originalroute to destination 10 (ie 1rarr5rarr6rarr7rarr10) When the linkbetween gateway nodes 5 and 6 is broken gateway node 5

Source

3

4

5

Destination

7

610

8 9

Broken routeRepaired route

2

12

11

1413

15

1

times

Cluster head

Gateway

Cluster member

Figure 6 Example of route maintenance

Table 1 Simulation parameters

Parameter ValueNetwork area 300 times 300KmTransmission range 15 KmData packet size 512 bytesData transmission rate 5MbpsMAC TDMAEmission power 125WReceiver sensitivity minus2467 dBmPhysical layer character DSSS

will send a BROKEN message to its attached cluster head 3to launch the local repairing process Namely cluster head 3takes the intercluster strategy to build link with cluster head 7through gateway nodes 11 and 6 And then a CLEARmessageis forwarded to source node 1 and hence source 1 will get arepaired route to destination 10 (ie 1rarr5rarr11rarr6rarr7rarr10)

4 Performance Evaluation

To evaluate the clustering algorithm and clustering routingprotocol we implemented them in the simulator NS2 Thesimulation parameters are shown in Table 1 As indicated inthe table the simulation is done in 300 times 300Km area Bydefault each node has same transmission range of 15 Km andthe maximummoving speed of each is 20sim50ms The MAClayer used the TDMA (Time Division Multiple Access) pro-tocol with a channel bandwidth of 5Mbps Traffic sources are


CBR (constant bit rate) with the rate of 10 packets per secondand 512 bytes per packetThe physical layer uses the free spacemodel and takes the actual aeronautical communicationparameters into account such as transmitter power andreceiver sensitivityThe intracluster communication takes theOLSR (Optimized Link State Routing) proactive protocol butintercluster takes the AODV (Ad Hoc On-Demand DistanceVector) active routing protocol The simulation time is set to60min

41 Clustering Algorithm Evaluation In hierarchical routingprotocols network overhead mainly comes from clusteringprocess and information exchanges between clusters Thusthe less the clusters and control messages exchange the lessthe network overhead

In this paper we use four evaluation criteria to evaluatethe performance of the clustering algorithm the number ofclusters nodesrsquo switch times between clusters and averagecluster head holding time with respect to speed or transmis-sion range And the proposed clustering algorithm is simu-lated against other five existing clustering algorithms namelySCBC DDVC DLDC MPBC and MOBIC (Mobility-BasedClustering) For MOBIC the Cluster Connection Interval isset to infinity The nodes linearly move in a single directionwhere their direction is initially randomly chosen

Figure 7 shows the influence of the nodesrsquo number onthe number of clusters which indicates the overhead of thenetwork at the cluster head level FromFigure 7 it can be seenthat the number of clusters formed using HCA surpasses thenumber of clusters formed using the SCBC DDVC DLDCMPBC andMOBIC schemesTheHCA scheme outperformsthe others as it more accurately estimates link duration inaddition to taking nodersquos relative degree into account

With the increase of nodesrsquo density this will lead tofrequent topology changes and hence more establishing anddisconnecting of the links Figure 8 shows the results FromFigure 8 it can be seen that theHCA scheme outperforms theothers as it creates less clusters (hence more cluster memberswhen the nodesrsquo number is fixed) And because of theproposed role of cluster guest the HCA scheme avoids a newcluster formation process that took place therefore the nodesrsquoswitch times between clusters are decreased This indicatesthat the HCA performs more stability when the density ofnodes increases

Figure 9 demonstrates the average cluster head holdingtime with respect to nodesrsquo maximum moving speed FromFigure 9 it can be seen that with the speed increasingthe average cluster head holding time of all the schemes isdecreased This is because the network topology is changingfrequently when nodesrsquo speed grows up which leads to morereclustering As a result of it the cluster head holding timeis decreased But the HCA scheme outperforms the othersas it more accurately estimates link duration in addition totaking nodersquos relative degree into account So its cluster headholding time is longer than the others

Figure 10 demonstrates the average cluster head holdingtime with respect to nodesrsquo maximum transmission rangeFrom Figure 10 it can be seen that the HCA schemersquos clusterhead holding time is longer than others And the gap between

0

5

10

15

20

25

Num

ber o

f clu

sters

200 300 400 500 600 700 800Number of nodes

HCASCBCDLDC

MPBCDDVCMOBIC

Figure 7 Clustersrsquo number with respect to nodesrsquo number

200 300 400 500 600 700 800Number of nodes

HCASCBCDLDC

MPBCDDVCMOBIC

0102030405060708090

Nod

esrsquo s

witc

h tim

es b

etw

een

cluste

rs

Figure 8 Nodesrsquo switch times between clusters with respect tonodesrsquo number

HCASCBCDLDC

MPBCDDVCMOBIC

20 30 40 500

100200300400500600700800900

Maximum moving speed (ms)

Aver

age c

luste

r hea

d ho

ldin

g tim

e (s)

Figure 9 Average cluster head holding time with respect to speed


HCASCBCDLDC

MPBCDDVCMOBIC

10 15 20 25 300

100200300400500600700800900

Maximum transmission range (km)

Aver

age c

luste

r hea

d ho

ldin

g tim

e (s)

Figure 10 Average cluster head holding time with respect to trans-mission range

all the schemes is small as when the transmission range isexpanding the influence of nodersquos mobility on the link isreducing

42 Routing Protocol Evaluation In this paper we use threeevaluation criteria to evaluate the performance of our routingprotocol average end-to-end delay packet acceptance ratioand normalized routing overhead

Average end-to-end delay includes route finding trans-mission time in MAC layer and physical channel whichindicates the connectivity and efficiency of the networkPacket acceptance ratio is the ratio of the received data packetnumber at source to the sent data packet number at destina-tionThis indicates the reliability of the network Normalizedrouting overhead is the ratio of the total number of controlmessage transmissions (the forwarding of a control messageat each hop is counted as one control transmission) to thetotal number of data packets received which indicates theefficiency of the routing protocol

This section will compare the HCA-R routing protocolwith other five protocols that is SCCR MPBC-R DLDC-RDVDC-R and MOBIC-R

It is obvious that more nodes will lead to bigger averageend-to-end delay This is because the more the nodes partici-pate in the network communication themore the bandwidthwill be consumed and hencemore congestion will take placeso the average end-to-end delay will increase substantiallyFigure 11 demonstrates the average end-to-end delay withrespect to nodesrsquo number of all the schemes FromFigure 11 itcan be seen that theHCA-Routperforms others as it estimatesthe link duration more accurately and hence more stableclusters will be established Thus it will take little time tocommunicate with each other

Network routing overheadmainly comes from the controlmessage exchanges for example RTJ ATJ and CLEARpacket in the routing reestablishing and maintenance pro-cesses Figure 12 demonstrates the normalized routing over-head with respect to nodersquos number of all the schemes From

200 300 400 500 600 700 8000

100200300400500600700800

Number of nodes

Aver

age e

nd-to

-end

del

ay (m

s)

HCA-RSCCRDLDC-R

MPBC-RDDVC-RMOBIC-R

Figure 11 Average end-to-end delay with respect to nodesrsquo number

200 300 400 500 600 700 8000

102030405060708090

Number of nodes

Nor

mal

ized

rout

ing

over

head

HCA-RSCCRDLDC-R

DDVC-RMPBC-RMOBIC-R

Figure 12 Normalized routing overhead with respect to nodesrsquonumber

Figure 12 it can be seen that the HCA-R protocol outper-forms others as it uses proactive protocol between nodeswithin individual clusters and reactive protocol betweenclusters and hence restricts the flooding area effectively Andthe role of cluster guest reduces the new clustering processtaking place so little routing overhead will be obtained

Figure 13 demonstrates the packet acceptance ratio withrespect to maximummoving speed From Figure 13 it can beseen that with the speed growing up the packet acceptanceratio of all the schemes is going down It is due to the biggerspeed leading to more frequent topology changes and hencemore broken links or unreachable downstream nodes appearSo more data packets will be discarded The HCA-R routingprotocol outperforms others as it established more stableclusters Therefore the packet acceptance ratio of HCA-R isthe highest

5 Conclusion

Based on the analysis of existing clustering routing protocolsand clustering algorithms a clustering algorithmbased on the


20 30 40 500

20

40

60

80

100

120

140


Pack

et ac

cept

ance

ratio

()

HCA-RSCCRMPBC-R

DDVC-RDLDC-RMOBIC-R

Figure 13 Packet acceptance ratio with respect to speed

costmetric and a corresponding hierarchical routing protocolwere proposed Simulation results showed that the clusteringalgorithm improves clusterrsquos stability and the proposed hier-archical routing protocol provides superior performancewithseveral advantages over previous routing protocol

Competing Interests

The authors declare that they have no competing interests

References

[1] I Jawhar and J Wu ldquoQoS support in TDMA-based mobile adhoc networksrdquo Journal of Computer Science and Technology vol20 no 6 pp 797ndash810 2005

[2] S Kumar V S Raghavan and J Deng ldquoMedium access controlprotocols for ad hoc wireless networks a surveyrdquo Ad HocNetworks vol 4 no 3 pp 326ndash358 2006

[3] LGavrilovska andVAtanasovski ldquoAdHocnetworking towards4G challenges and QoS solutionsrdquo in Proceedings of the Inter-national Conference on Telecommunications in Modern SatelliteCable and Broadcasting Services pp 71ndash80 2005

[4] M S B Mahmoud and N Larrieu ldquoAn ADS-B based securegeographical routing protocol for aeronautical ad hoc net-worksrdquo in Proceedings of the IEEE 37th Annual ComputerSoftware and Applications ConferenceWorkshops (COMPSACWrsquo13) pp 556ndash562 IEEE Kyoto Japan July 2013

[5] C Perkins E Belding-Royer and S Das Ad hoc On-DemandDistance Vector (AODV) Routing IETF Network WorkingGroup 2003

[6] D Johnsson and W D Huyc RFC 4728 the Dynamic SourceRouting Protocol (DSR) for Mobile Ad Hoc Networks for IPv4IETF Network Work NG Grou 2007

[7] X Niu Z Tao G Wu C Huang and L Cui ldquoHybrid clusterrouting an efficient routing protocol for mobile ad hoc net-worksrdquo in Proceedings of the IEEE International Conference onCommunications (ICC rsquo06) pp 3554ndash3559 Istanbul TurkeyJuly 2006

[8] M Tao H Yuan W Wei Z Li and Y Qin ldquoGeographic infor-mation assisted routing flexibility control in hierarchical AdHoc networksrdquo in Proceedings of the 32nd Chinese Control

Conference (CCC rsquo13) pp 2190ndash2194 IEEE Press Xirsquoan ChinaJuly 2013

[9] J Zhou L Lei W Liu and J Tian ldquoA simulation analysisof nodes mobility and traffic load aware routing strategy inaeronautical Ad hoc networksrdquo inProceedings of the 9th Interna-tional Bhurban Conference on Applied Sciences and Technology(IBCAST rsquo12) pp 423ndash426 IEEE Islamabad Pakistan January2012

[10] S Hyeon and K-I Kim ldquoA new geographic routing protocolfor aircraft ad hoc networksrdquo in Proceedings of the IEEEAIAA29th Digital Avionics Systems Conference (DASC rsquo10) pp 2E2-1ndash2E2-8 IEEE Press Salt Lake City Utah USA October 2010

[11] X Zhang C Xu and J Xu ldquoHierarchical ZRPrsquos performancevs ZRPrsquos performance in MANETrdquo in Proceedings of the IEEEInternational Conference on Communication Software and Net-works (ICCSN rsquo15) pp 423ndash426 IEEE Press Chengdu ChinaJune 2015

[12] Asadinia S M K Rsfsanjani and A B Saeid ldquoA novel routingalgorithm based-on ant colony in Mobile Ad hoc Networksrdquoin Proceedings of the 3rd IEEE International Conference on Ubi-Media Computing (U-Media rsquo10) pp 77ndash82 IEEE Press JinhuaChina 2010

[13] S K Agarwa V Rishiwal and K V Arya ldquoFallout of differentrouting structures for differentmobility patterns in large ad-hocnetworksrdquo inConfluence 2013TheNext Generation InformationTechnology Summit (4th International Conference) pp 242ndash249IET Press Noida India 2013

[14] J Derenick C Thorne and J Spletzer ldquoOn the deploymentof a hybrid free-space opticradio frequency (FSORF) mobilead-hoc networkrdquo in Proceedings of the IEEERSJ InternationalConference on Intelligent Robots and Systems pp 3990ndash3996Bhopal India August 2005

[15] T Venkatesan P P Rajakumar and A Pitchaikkannu ldquoOver-view of proactive routing protocols in MANETrdquo in Proceedingsof the 4th International Conference on Communication Systemsand Network Technologies (CSNT rsquo14) pp 173ndash177 IEEE PressBhopal India 2014

[16] R Ramanathan and R Hain ldquoAn ad hoc wireless testbed forscalable adaptive QoS supportrdquo in Proceedings of the IEEEWireless Communications and Networking Confernce (WCNCrsquo00) vol 3 pp 998ndash1002 Chicago Ill USA September 2000

[17] J Y Yu and P H J Chong ldquoA survey of clustering schemesfor mobile ad hoc networksrdquo IEEE Communications Surveys ampTutorials vol 7 no 1 pp 32ndash48 2005

[18] M Abolhasan T Wysocki and E Dutkiewicz ldquoA reviewof routing protocols for mobile ad hoc networksrdquo Ad HocNetworks vol 2 no 1 pp 1ndash22 2004

[19] R Asokan ldquoA review of quality of service (QoS) routing pro-tocols for mobile ad hoc networksrdquo in Proceedings of the Inter-national Conference on Wireless Communication and SensorComputing (ICWCSC rsquo10) pp 1ndash6 IEEE Press Chennai IndiaJanuary 2010

[20] A Venkateswaran V Sarangan N Gautam and R AcharyaldquoImpact of mobility prediction on the temporal stability ofMANET clustering algorithmsrdquo in Proceedings of the 2nd ACMInternational Workshop on Performance Evaluation of Wirelessad hoc Sensor and Ubiquitous Networks (PE-WASUN rsquo05) pp144ndash151 IEEE Press Montreal Canada October 2005

[21] E Sakhaee and A Jamalipour ldquoStable clustering and communi-cations in pseudolinear highly mobile Ad hoc networksrdquo IEEETransactions on Vehicular Technology vol 57 no 6 pp 3769ndash3777 2008


[22] M Ni Z Zhong and D Zhao ldquoMPBC a mobility prediction-based clustering scheme for ad hoc networksrdquo IEEE Transac-tions onVehicular Technology vol 60 no 9 pp 4549ndash4559 2011

[23] T Yu M Linhua T Hong and Z Song ldquoA signal characteristicbased cluster scheme for aeronautical Ad Hoc networksrdquo IEICETransaction on Communications vol E97-B no 2 pp 441ndash4492014

[24] J Y Yu and P H J Chong ldquoAn efficient clustering scheme forlarge and densemobile ad hoc networks (MANETs)rdquoComputerCommunications vol 30 no 1 pp 5ndash16 2006

[25] J M Ng and Y Zhang ldquoA mobility model with group par-titioning for wireless ad hoc networksrdquo in Proceedings of the3rd International Conference on Information Technology andApplications (ICITA rsquo05) pp 289ndash294 Sydney Australia July2005

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of


Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of


Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of


Mathematical PhysicsAdvances in

Complex AnalysisJournal of


OptimizationJournal of


CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of


Operations ResearchAdvances in

Journal of


Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Algebra

Discrete Dynamics in Nature and Society



Decision SciencesAdvances in

Discrete MathematicsJournal of


Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of


In a word hierarchical-based routing not only plays downthe network spending by decreasing the number of nodeswhich participate in routing maintenance but also increasesthe network stability by dividing the network into easilycontrolled subnets

ZRP (Zone Routing Protocol) [11] is typical hybridrouting which groups nodes into geographic zones It usesDVA (Distance Vector Algorithm) proactive routing betweennodes within individual zones and DSR (Dynamic SourceRouting) reactive routing between zones ZHLS (Zone-BasedHierarchical Link-State Routing) [1 12] protocol incorporateslocation information into a novel peer-to-peer hierarchicalrouting approach The ZHLS network is divided into non-overlapping zones and aggregating nodes into zones concealsthe detail of the network topology CBRP (Cluster BasedRouting Protocol) [13] uses an on-demand protocol betweennodes both intrazone and interzone communication As aresult it will cost much time in routing maintenance Theabove three routings have a similar shortcoming namelyrouting maintenance will consume a large portion of band-width and even may paralyze the network when the trafficis too large HSR (Hierarchical State Routing) [14] andCGSR (the Center for Global Security Research) [15] are allusing table-driven strategy to establish routing of both intra-and interzone which is good to decrease routing updatingdelay but increase the cost of routing overhead unwillinglyMMWN (Multihop Mobile Wireless Networks) [16] is ahierarchical routing scheme proposed for ad hoc networksto achieve scalability However its location management isclosely tied with the network hierarchical topology whichmakes the location updating and location finding quite com-plex

In hierarchical routing protocols superior clusteringalgorithm can not only reduce the routing overhead but alsoincrease the scalability of the net Some have been proposedin literatures [17ndash20] and most of them are to satisfy the spe-cific demands such as the lowest ID cluster and the highestconnectivityTheDDVC(DynamicDopplerVelocityCluster-ing) and DLDC (Dynamic Link Duration Clustering) algo-rithms were proposed in [21] both of them are just applied topseudolinear highly dynamicMANETsTheMPBC (MobilityPrediction-Based Clustering) in [22] elects the node whichhas a low variance of the relative mobility values withrespect to its neighbors taking the cluster head responsibilityThe SCCR (Signal Characteristic Clustering Routing) usingSCBC (Signal Characteristic Based Clustering) algorithm in[23] elects the node which has a long link holding time totake the cluster head responsibility Although thismethod candecrease the possibility of reclustering but it cannot ensurethe elected cluster head is logically centric and more prof-itable to play the cluster head role and hence to increase thewhole network performance All of them somewhat ignoredthe scenario namely when a node moves out of its clusterhead transmission range but still has a link to another clustermember belonging to any cluster head whether the initialphrase of clustering will be reestablished or not And theconclusion in [24] reveals that frequent cluster changingconsumes lots of network resources and highly overlappedclusters decrease the efficiency of hierarchical structureThus

an efficient clustering algorithmmust maintain a more stableand less overlapping structure

Based on the analysis of the above a hybrid clusteringalgorithm (HCA) based on the cost metric which takes thelink expiration time and nodersquos relative degree into accountwas designed and a corresponding routing protocol (HCA-R) was proposed in Sections 2 and 3 respectivelyThe experi-ments and analysis are given in Section 4 Finally Section 5concludes the paper

2 Clustering Algorithm

21 Cost Metric for Clustering We assume that there are 119873mobile nodes roaming in a 119877times119877Km square and each movesaccording to the RRGM (Reference Region Group MobilityModel) [25] All nodes have same transmission range of 119903Km(119903 lt 119877) Each node can acquire their position and timeinformation through GPS and can also broadcast them totheir neighbors

The coordinates (1199091198941 1199101198941) and (119909

1198942 1199101198942) are the location

information sent by node 119894 to 119895 at two consecutive times 1199051

and 1199052 respectively Correspondingly the coordinates of node

119895 are (1199091198951 1199101198951) and (119909

1198952 1199101198952) For simplicity we assume that

node 119894 is fixed and node 119895 is moving but actually they are allmoving at all times

Figure 1 depicts the approaching and receding scenario ofnodes 119894 and 119895 The node 119894 is fixed in coordinate original (119874)but 119895moves along the vector 997888997888119860119861 We assume that nodes 119894 and119895 keep their velocity and direction at theΔ119905 = 119905

2minus1199051duration

and 119895 arrives at point 119861 at the moment 1199052

In Figure 1(a) there are two equations

119889119874119860


119889119874119861


(1)

Through the law of cosines there exists

119889119860119861

= radic119889119874119860

2+ 119889119874119861

2minus 2119889119874119860119889119874119861

cosang119860119874119861 (2)

where ang119860119874119861 = arcsin(1199101198991119889119874119860) minus arcsin(119910

1198992119889119874119861)

Thus the relative velocity between nodes 119894 and 119895 is

] =119889119860119861

Δ119905 (3)

We assume that the velocity and direction of nodes 119894 and119895 are not changing during Δ119905 So node 119895 leaves from thetransmission range of node 119894 need to track the segment 119861119862From the moment 119905

2 the holding time of the link between

nodes 119894 and 119895 is

119879119894119895=119889119861119862

]=(119889119862119863

minus 119889119861119863)

] (4)

where 119889119862119863

= radic1199032minus (119889119874119861

sinang119874119861119860)2 119889119861119863

= 119889119874119861

cosang119874119861119860minus119889119860119861 and ang119874119861119860 = arccos((119889

119860119861

2+ 119889119874119861

2minus 119889119874119860

2)2119889119874119861119889119860119861)


y

AB

C

xO

D


y

A

B

C

xO

D

(b) Receding scene



119879119894119895=119889119861119862

]=(119889119861119863

+ 119889119862119863)

]=(119889119861119863

+ 119889119862119863)

(119889119860119861Δ119905)

(5)


and 119889119874119861

is the same as (1) 119889119860119861

=

radic119889119874119860

2+ 119889119874119861

2minus 2119889119874119860119889119874119861


119889119874119861) minus arcsin(119910

1198991119889119874119860) 119889119861119863

= 119889119860119863

minus 119889119860119861

= 119889119874119860


119860119861

2+ 119889119874119860

2minus 119889119874119861

2)2119889119874119860119889119860119861) and

119889119862119863

= radic1199032minus (119889119874119860



119889119894=

1 0 lt 119889119894119895lt 119877

0 otherwise(6)

where 119889119894119895




as

119863119894=10038161003816100381610038161003816119889119894minus radic119873

10038161003816100381610038161003816 (7)



119872119894= 1199081119879119894119895+ 1199082119863119894 (8)

where 119894 = 1 2 119873 1199081and 119908


parameters 119879119894119895and 119863



2



value of 1199081and 119908










Startalgorithm



neighbors

Yes



No


Yes



No

End algorithm












Startalgorithm

End algorithm


same clusterYes No










clusterDestination


way

No

Yes No

Yes



nodes






1Source

2

4

3

Destination

5

Cluster head






1Source

3

4

5

Destination

7

69

8 10

Cluster head

Gateway

Cluster member

DataREQREP

2








Source

3

4

5

Destination

7

610

8 9


2

12

11

1413

15

1

times

Cluster head

Gateway

Cluster member















0

5

10

15

20

25

Num

ber o

f clu

sters

200 300 400 500 600 700 800Number of nodes

HCASCBCDLDC

MPBCDDVCMOBIC


200 300 400 500 600 700 800Number of nodes

HCASCBCDLDC

MPBCDDVCMOBIC

0102030405060708090

Nod

esrsquo s

witc

h tim

es b

etw

een

cluste

rs


HCASCBCDLDC

MPBCDDVCMOBIC

20 30 40 500

100200300400500600700800900


Aver

age c

luste

r hea

d ho

ldin

g tim

e (s)



HCASCBCDLDC

MPBCDDVCMOBIC

10 15 20 25 300

100200300400500600700800900


Aver

age c

luste

r hea

d ho

ldin

g tim

e (s)








200 300 400 500 600 700 8000

100200300400500600700800

Number of nodes

Aver

age e

nd-to

-end

del

ay (m

s)

HCA-RSCCRDLDC-R

MPBC-RDDVC-RMOBIC-R


200 300 400 500 600 700 8000

102030405060708090

Number of nodes

Nor

mal

ized

rout

ing

over

head

HCA-RSCCRDLDC-R

DDVC-RMPBC-RMOBIC-R




5 Conclusion



20 30 40 500

20

40

60

80

100

120

140


Pack

et ac

cept

ance

ratio

()

HCA-RSCCRMPBC-R

DDVC-RDLDC-RMOBIC-R



Competing Interests


References



































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










y

AB

C

xO

D


y

A

B

C

xO

D

(b) Receding scene



119879119894119895=119889119861119862

]=(119889119861119863

+ 119889119862119863)

]=(119889119861119863

+ 119889119862119863)

(119889119860119861Δ119905)

(5)


and 119889119874119861

is the same as (1) 119889119860119861

=

radic119889119874119860

2+ 119889119874119861

2minus 2119889119874119860119889119874119861


119889119874119861) minus arcsin(119910

1198991119889119874119860) 119889119861119863

= 119889119860119863

minus 119889119860119861

= 119889119874119860


119860119861

2+ 119889119874119860

2minus 119889119874119861

2)2119889119874119860119889119860119861) and

119889119862119863

= radic1199032minus (119889119874119860



119889119894=

1 0 lt 119889119894119895lt 119877

0 otherwise(6)

where 119889119894119895




as

119863119894=10038161003816100381610038161003816119889119894minus radic119873

10038161003816100381610038161003816 (7)



119872119894= 1199081119879119894119895+ 1199082119863119894 (8)

where 119894 = 1 2 119873 1199081and 119908


parameters 119879119894119895and 119863



2



value of 1199081and 119908










Startalgorithm



neighbors

Yes



No


Yes



No

End algorithm












Startalgorithm

End algorithm


same clusterYes No










clusterDestination


way

No

Yes No

Yes



nodes






1Source

2

4

3

Destination

5

Cluster head






1Source

3

4

5

Destination

7

69

8 10

Cluster head

Gateway

Cluster member

DataREQREP

2








Source

3

4

5

Destination

7

610

8 9


2

12

11

1413

15

1

times

Cluster head

Gateway

Cluster member















0

5

10

15

20

25

Num

ber o

f clu

sters

200 300 400 500 600 700 800Number of nodes

HCASCBCDLDC

MPBCDDVCMOBIC


200 300 400 500 600 700 800Number of nodes

HCASCBCDLDC

MPBCDDVCMOBIC

0102030405060708090

Nod

esrsquo s

witc

h tim

es b

etw

een

cluste

rs


HCASCBCDLDC

MPBCDDVCMOBIC

20 30 40 500

100200300400500600700800900


Aver

age c

luste

r hea

d ho

ldin

g tim

e (s)



HCASCBCDLDC

MPBCDDVCMOBIC

10 15 20 25 300

100200300400500600700800900


Aver

age c

luste

r hea

d ho

ldin

g tim

e (s)








200 300 400 500 600 700 8000

100200300400500600700800

Number of nodes

Aver

age e

nd-to

-end

del

ay (m

s)

HCA-RSCCRDLDC-R

MPBC-RDDVC-RMOBIC-R


200 300 400 500 600 700 8000

102030405060708090

Number of nodes

Nor

mal

ized

rout

ing

over

head

HCA-RSCCRDLDC-R

DDVC-RMPBC-RMOBIC-R




5 Conclusion



20 30 40 500

20

40

60

80

100

120

140


Pack

et ac

cept

ance

ratio

()

HCA-RSCCRMPBC-R

DDVC-RDLDC-RMOBIC-R



Competing Interests


References



































Volume 2014




Journal of











Journal of


Function Spaces






Algebra












Startalgorithm



neighbors

Yes



No


Yes



No

End algorithm












Startalgorithm

End algorithm


same clusterYes No










clusterDestination


way

No

Yes No

Yes



nodes






1Source

2

4

3

Destination

5

Cluster head






1Source

3

4

5

Destination

7

69

8 10

Cluster head

Gateway

Cluster member

DataREQREP

2








Source

3

4

5

Destination

7

610

8 9


2

12

11

1413

15

1

times

Cluster head

Gateway

Cluster member















0

5

10

15

20

25

Num

ber o

f clu

sters

200 300 400 500 600 700 800Number of nodes

HCASCBCDLDC

MPBCDDVCMOBIC


200 300 400 500 600 700 800Number of nodes

HCASCBCDLDC

MPBCDDVCMOBIC

0102030405060708090

Nod

esrsquo s

witc

h tim

es b

etw

een

cluste

rs


HCASCBCDLDC

MPBCDDVCMOBIC

20 30 40 500

100200300400500600700800900


Aver

age c

luste

r hea

d ho

ldin

g tim

e (s)



HCASCBCDLDC

MPBCDDVCMOBIC

10 15 20 25 300

100200300400500600700800900


Aver

age c

luste

r hea

d ho

ldin

g tim

e (s)








200 300 400 500 600 700 8000

100200300400500600700800

Number of nodes

Aver

age e

nd-to

-end

del

ay (m

s)

HCA-RSCCRDLDC-R

MPBC-RDDVC-RMOBIC-R


200 300 400 500 600 700 8000

102030405060708090

Number of nodes

Nor

mal

ized

rout

ing

over

head

HCA-RSCCRDLDC-R

DDVC-RMPBC-RMOBIC-R




5 Conclusion



20 30 40 500

20

40

60

80

100

120

140


Pack

et ac

cept

ance

ratio

()

HCA-RSCCRMPBC-R

DDVC-RDLDC-RMOBIC-R



Competing Interests


References



































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










Startalgorithm

End algorithm


same clusterYes No










clusterDestination


way

No

Yes No

Yes



nodes






1Source

2

4

3

Destination

5

Cluster head






1Source

3

4

5

Destination

7

69

8 10

Cluster head

Gateway

Cluster member

DataREQREP

2








Source

3

4

5

Destination

7

610

8 9


2

12

11

1413

15

1

times

Cluster head

Gateway

Cluster member















0

5

10

15

20

25

Num

ber o

f clu

sters

200 300 400 500 600 700 800Number of nodes

HCASCBCDLDC

MPBCDDVCMOBIC


200 300 400 500 600 700 800Number of nodes

HCASCBCDLDC

MPBCDDVCMOBIC

0102030405060708090

Nod

esrsquo s

witc

h tim

es b

etw

een

cluste

rs


HCASCBCDLDC

MPBCDDVCMOBIC

20 30 40 500

100200300400500600700800900


Aver

age c

luste

r hea

d ho

ldin

g tim

e (s)



HCASCBCDLDC

MPBCDDVCMOBIC

10 15 20 25 300

100200300400500600700800900


Aver

age c

luste

r hea

d ho

ldin

g tim

e (s)








200 300 400 500 600 700 8000

100200300400500600700800

Number of nodes

Aver

age e

nd-to

-end

del

ay (m

s)

HCA-RSCCRDLDC-R

MPBC-RDDVC-RMOBIC-R


200 300 400 500 600 700 8000

102030405060708090

Number of nodes

Nor

mal

ized

rout

ing

over

head

HCA-RSCCRDLDC-R

DDVC-RMPBC-RMOBIC-R




5 Conclusion



20 30 40 500

20

40

60

80

100

120

140


Pack

et ac

cept

ance

ratio

()

HCA-RSCCRMPBC-R

DDVC-RDLDC-RMOBIC-R



Competing Interests


References



































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










1Source

3

4

5

Destination

7

69

8 10

Cluster head

Gateway

Cluster member

DataREQREP

2








Source

3

4

5

Destination

7

610

8 9


2

12

11

1413

15

1

times

Cluster head

Gateway

Cluster member















0

5

10

15

20

25

Num

ber o

f clu

sters

200 300 400 500 600 700 800Number of nodes

HCASCBCDLDC

MPBCDDVCMOBIC


200 300 400 500 600 700 800Number of nodes

HCASCBCDLDC

MPBCDDVCMOBIC

0102030405060708090

Nod

esrsquo s

witc

h tim

es b

etw

een

cluste

rs


HCASCBCDLDC

MPBCDDVCMOBIC

20 30 40 500

100200300400500600700800900


Aver

age c

luste

r hea

d ho

ldin

g tim

e (s)



HCASCBCDLDC

MPBCDDVCMOBIC

10 15 20 25 300

100200300400500600700800900


Aver

age c

luste

r hea

d ho

ldin

g tim

e (s)








200 300 400 500 600 700 8000

100200300400500600700800

Number of nodes

Aver

age e

nd-to

-end

del

ay (m

s)

HCA-RSCCRDLDC-R

MPBC-RDDVC-RMOBIC-R


200 300 400 500 600 700 8000

102030405060708090

Number of nodes

Nor

mal

ized

rout

ing

over

head

HCA-RSCCRDLDC-R

DDVC-RMPBC-RMOBIC-R




5 Conclusion



20 30 40 500

20

40

60

80

100

120

140


Pack

et ac

cept

ance

ratio

()

HCA-RSCCRMPBC-R

DDVC-RDLDC-RMOBIC-R



Competing Interests


References



































Volume 2014




Journal of











Journal of


Function Spaces






Algebra

















0

5

10

15

20

25

Num

ber o

f clu

sters

200 300 400 500 600 700 800Number of nodes

HCASCBCDLDC

MPBCDDVCMOBIC


200 300 400 500 600 700 800Number of nodes

HCASCBCDLDC

MPBCDDVCMOBIC

0102030405060708090

Nod

esrsquo s

witc

h tim

es b

etw

een

cluste

rs


HCASCBCDLDC

MPBCDDVCMOBIC

20 30 40 500

100200300400500600700800900


Aver

age c

luste

r hea

d ho

ldin

g tim

e (s)



HCASCBCDLDC

MPBCDDVCMOBIC

10 15 20 25 300

100200300400500600700800900


Aver

age c

luste

r hea

d ho

ldin

g tim

e (s)








200 300 400 500 600 700 8000

100200300400500600700800

Number of nodes

Aver

age e

nd-to

-end

del

ay (m

s)

HCA-RSCCRDLDC-R

MPBC-RDDVC-RMOBIC-R


200 300 400 500 600 700 8000

102030405060708090

Number of nodes

Nor

mal

ized

rout

ing

over

head

HCA-RSCCRDLDC-R

DDVC-RMPBC-RMOBIC-R




5 Conclusion



20 30 40 500

20

40

60

80

100

120

140


Pack

et ac

cept

ance

ratio

()

HCA-RSCCRMPBC-R

DDVC-RDLDC-RMOBIC-R



Competing Interests


References



































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










HCASCBCDLDC

MPBCDDVCMOBIC

10 15 20 25 300

100200300400500600700800900


Aver

age c

luste

r hea

d ho

ldin

g tim

e (s)








200 300 400 500 600 700 8000

100200300400500600700800

Number of nodes

Aver

age e

nd-to

-end

del

ay (m

s)

HCA-RSCCRDLDC-R

MPBC-RDDVC-RMOBIC-R


200 300 400 500 600 700 8000

102030405060708090

Number of nodes

Nor

mal

ized

rout

ing

over

head

HCA-RSCCRDLDC-R

DDVC-RMPBC-RMOBIC-R




5 Conclusion



20 30 40 500

20

40

60

80

100

120

140


Pack

et ac

cept

ance

ratio

()

HCA-RSCCRMPBC-R

DDVC-RDLDC-RMOBIC-R



Competing Interests


References



































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










20 30 40 500

20

40

60

80

100

120

140


Pack

et ac

cept

ance

ratio

()

HCA-RSCCRMPBC-R

DDVC-RDLDC-RMOBIC-R



Competing Interests


References



































Volume 2014




Journal of











Journal of


Function Spaces






Algebra





















Volume 2014




Journal of











Journal of


Function Spaces






Algebra









research article a clustering routing protocol for mobile...

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