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Global State Routing: A New Routing Scheme for Ad-hoc Wireless NetworksTsu-Wei Chen and Mario GerlaCom puter Science Department

University of California, Los Angeles{ suwei,gerla} @cs ucla.eduAbstract

In an ad -hoc environment with nowired comm unication infras-tructure, it is necessary that mobile hosts operate as routers inorder to maintain the information abou t connectivity. Howevel;with the presence of high mobility and low signallinterferenceratio (SIR), traditional routing schemes fo r wired networks arenot appropriate, as they either lack the ability to quickly re-flect the changing topology, or may cause excessive overhead,which degrades network perjbrmance. Considering these restric-tions, we propose a new scheme especially designed for rout-ing in an ad- ho c wireless environments. We call this schemeGlobal State Routing (GSlt),where nodes exchange vectors oflink states among their neighbors during routing information ex-change. Based on the link state vectors, nodes maintain a globalknowledge of the network topology and optimize their routing de-cisions locally. The pe ~o rm an ce f the algorithm, studied in thispaper through a series of simulations, reveals that this schemeprovides a better solution than existing approaches in a truly mo-bile, ad-hoc environment.I. Introduction

In an ad hoc wireless network whe re wired infrastructures arenot feasible, mobility and bandwidth are two key elements pre-senting research challenges. Mobility causes the life time of aconnection between two ho sts to vary greatly; and limited band-width makes a network to be easily congested by control sig-nalling. Routing schem es developed for wired networks seldomconsider restrictions of this type. Instea d, they assum e that thenetwork is mostly stable and the overhead for routing messagesis negligible. Considering the difference between wireless andwireline netw ork, we believe it is necessary to dev elop a wirelessrouting protocol that reacts quickly to changes of network topol-ogy but consumes only a reasonable am ount of bandwidth for thecontrol traffic.

In this paper, we propose a new routing scheme for ad-hocwireless networks. It is MAC (medium access control) layer ef-ficient because the overhead of con trol message is kept low. Itstill provides accurate solu tim s for finding optimal paths. Therest of this paper is o rgan ized as follow. In sectio n 11, we surveythe existing wireless routing protocols. Then w e propose a newrouting scheme in section I11 Section IV presen ts the complexityanalysis of this scheme and compares it with others. To verifythe effectiveness, we simulate a m obile environm ent and the re-port the performance results in section v. Lastly, we conclude ou r

work and propose issues for future research in section VI.11. Previous Work

Several routing schemes based on DBF (distributed Bellman-Ford) [ l ] or LS (link state) [2] have been proposed in the pastfor both wireline or wireless networks. The advantage s of DB Fare its simplicity and com putation efficiency due to its distributedcharacteristic. However, the slow convergence and the tendencyof creating routing loops make DBF not suitable for a wirelessnetwork with high mobility. Thou gh approaches were proposedin [3 , 4, 5, 61 to solve looping problem, none of them overcomethe problem of slow convergence.

In part for these reasons, LS is preferred and used in manymodern networks like Internet [7] or ATM [8]. In LS, a globalnetwork topology is maintained in all routers, and any link chan gewill be updated by flooding immediately. As a result, the time re-quired for a router to converge to the new topology is shorter thanin DBF. S ince global topology is maintained, preventing routingloop is a lso easier. Unfortunately, as LS relies o n flooding to dis-seminate the update information, exce ssive control overhead maybe generated, especially when the mobility is high. In addition,because of the large am ount of small packets, flooding is also in-efficient for the radio M AC layer.

A third routing sche me proposed recently for ad-hoc wirelessnetwork is called on-demand routing. Name ly, the route be-tween two nodes is computed when there is a need. Most on-demand routing are based on a querytresponse approach [9, 10,111. Since flooding is used for query packet dissemination androute maintenance, on-dema nd routing tends to becom e inefficientwhen traffic load and mobility increase.111. The Global State Routing

Our goal is to design a routing scheme that is MAC efficientin ad-hoc wireless radio networks. That is, the control packetsize should be able to achieve optimized MAC throughput, andthe number of control packet should be controllable. We preferto maintain the knowledge of full network topology as in linkstate routing, but wish to avoid the inefficient flooding mecha-nism. Therefore, we develop our schem e based on LS, which hasthe advantage of routing accuracy, and we adopt the dissemina-tion method used in DBF, which has the a dvantag e of no flooding.This scheme is called Global State Routing (GSR), and moredetailed description is given below.
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A. Network ModelThe ad-hoc w ireless network is modeled as an undirected graphG = (V ,E ) , here V is a set of IVI nodes and E is a set of /Elundirected links connecting nodes in V . Each node has a uniqueidentifier and represents a mobile host with a wireless communi-

cation device with transmission range R, and an infinity storagespace. Nodes may m ove around and change their speed and di-rection independently. An undirected link (i , ) connecting twonodes i an d j is formed w hen the distance between i and j be -com e less than or equal to R. Link ( i , ) s removed from E whennode i and j mov e apart, and out of their transmission ranges.

For each node i , on e list and three tables are maintained. Theyare: a neighbor list A,, a topology table TTi, a next hop tableNEXTi and a distance table D i. Ai is defined as a set of nodesthat are adjacent to node i . Each destination j has an entry in ta-ble TTi which contains two parts: TTi.LS(j) nd TTi.SEQ(j) .TTi .LS(j) enotes the link state information reported by node j ,an d TTi.SEQ(j) enotes the timestam p indicating the time nodej has gen erated this link state information. Sim ilar, for every des-tination j ,N E X T i ( j ) denotes th e next hop to forward packets des-tined to j on the shortest path, while Di ( j ) enotes the distancesof the shortest path from i to j .Additionally, a weight function, weight: E -+ Zof, is used tocomp ute the distance of a link. Since min-hop shortest path is theonly objective in this paper, this weight function simply returns1 if two nodes have direct connection, otherwise, it returns cm.This weight function may also be replaced with other functions forrouting with different metrics. For instan ce, a bandwidth functioncan be used to realize a QoS routing.B. Algorithm

The d etails of GSR protocol are listed in Fig. 1. At the begin-ning, each node i starts with an empty neighbor list Ai, and anempty topology table TTi. After node i initializes its local vari-ables with proper values as described in procedure NodeInit(i), itlearns about its neighbors by examining the sender field of eachpacket in its inbound queue, PktQueue. That is, assuming that allnodes can be heard by i are is neighbors, node i adds all routingpacket senders to its neighbor list, Ai.

Node i then invokes PktProcess(i)to process the received rout-ing messages, which contain link state information broadcastedby it neighbors. PktProcess(i) makes sure that only the most upto date link state information is used to comp ute the best route bycomparing the embedded sequence number, pkt.SEQ(j),withthe ones stored in node i s local storage, for each destinationj . If any entry in the incoming message has a newer sequencenumber regarding destination j , TT;.LS(j)will be replaced bypkt.LS(j) , nd TT,.SEQ(j)will be replaced by pkt .SEQ(j) .After the routing m essages are examined, node i rebuilds therouting table based on the newly computed topology table andthen broadcasts the new information to its neighbors. Such pro-cess is periodically repeated.C. Information Dissemination

The key difference between our GSR and traditional LS is theway routing information is disseminated. In LS, link state packetsare generated and flooded into the network whenever a node de-

tects topology changes. GSR doesn t flood the link state pacInstead, nodes in GSR maintain the link state table based onup to date information received from neighboring nodes, and podically exchange it with their local neighbors o nly. Informais disseminated as the link state with larger sequence num berplaces the on e with smaller sequen ce numb ers. In this respec tsimilar to DBF (or more precisely, the DSDV [4])wh ere the vof distances is replaced according to the time stamp of sequnumber.D. Shortest Path Computation

FindSP(i) creates a shortest path tree rooted at i . In princany existing shortest path algorithm can b e used to create the In this paper, however, the procedure listed in Fig. 1is basethe Dijkstras algorithm [12] with modifications so that the hop table (NEXT,) and the distance tables(Di) are computeparallel with the tree reconstruction.At node i , FindSP(i) initiates with P = { i } , hen it iteuntil P = V . In each iteration, it searches for a node jthat node j minimizes the value of ( D , ( k )+ w e i y h C ( k , j )al l j and k , where j E V - P , k E A, an d weight(k , j )#Once node j is found, P is augmented w ith j , D( j ) s assi

to D ( k ) + w e i g h t ( k , j ) and N E X T i ( j ) is assigned to nestThat is, as the shortest path from i to j has to go through ksuccessor fo ri to j is the same successor fo ri to k .1V. Complexity

In this section, we analyze the complex ity of the GS R schand compare it with two other routing schemes: DB F and LScomplexity is studied und er five aspects:

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2 .3.

4.

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Computation Complexity (CC): the number of computsteps for a node to perform a routing computation afteupdate message is received;Memory Co mplexity (MC): the memory space requirstore the routing inform ation;Data Complexity (DC): the aggregate size of control pets exchanged by a node in each time slo t;Packet Complexity (PC): the average number of roupackets exchange d by a node in each time slot;Convergence Time (CT): the times requ ires to detect achange.

Table I . Complexity Comparison

Table 1 shows the results of our comparison. In the tabldenotes the number of nodes in network (IVl),D denotes the imum hop distance, the diameter, in the network, d and I de

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the degree of node connectivity and the routing update interval,respectively.GSR and LS have sam e memory complexity and com putation

complexity as both maintain the topology for the whole networkand use Dijkstras algorithm to compu te shortest path routes. Di-jkstras algorithm requires typically O ( N 2 ) teps to compute theshortest paths from one source to all destinations, although it ispossible to reduce it to O(NlogN) 12]. O ( N 2 ) emory space isrequired to store the network topology represented by a connec-tion matrix. As f or DBF, it ha s complexity of O ( N ) or comput-ing and memory, as it only keeps the distance information for eachdestination, and co mp utes shortest paths in a distributed fashion .

For the data complexity, in GSR each node broadcasts infor-mation for N x d link s on average, and the complexity is dividedby I, he update interval. LS , on the other hand, has similar ac-cumulated da ta size for each link up date, but its update interval Imay become extremely small when mobility increase. This issue,to be addressed shortly, was verified through simulation.

In addition, as LS transm its one short packet for each link up-date, its packet comp lexity can be as high as O (N )when the mo-bility is high. On the other hand, both GSR and D BF transmit afixed numb er of update tables using longe r packets to optimize theMAC throughput.Lastly, the con vergence time f or GSR is also superior than thatfor DBF. In fact, if shorter update interval is used, GSR can con -verge as fast as LS.V. Simulation

Unlike in [5,4, 9, 101,where w ireless network is simulated bystatic network with higher link failure rate, we used a truly mobileenvironment in our simulator to determ ine the connectivity amongmobile hosts. The simulation is programmed in C++ to simulatean environm ent of 500 x 5CO unit2. Arbitrary numbers of nodes,representing the mobile hosts, move independently on their owntrajectories within this virtual space. The maximu m movin g speedand the number of nodes are given at run time.(1 ) no node failure during si mulation;(2) node number is always constant in the run time of simulation;(3) a time slotted system;(4) radio transmission range is fixed at R,which is specified at thebeginning of the simulation;( 5 ) two nodes can hear each o ther if they are within the transmis-sion range, that is, open space chann el model is used.

Three routing schemes: DBF, LS and GSR are used exclusivelyin the simulation. Th e DB F and LS are based on the schemesdescribed in [13]. Both DI3F and GSR can be executed with arouting update interval ( I ) specified at run time. By default, Iis set to 3 (one update per three time slots), while in LS, nodesflood link state packets whenever they de tect changes in their localconnectivities. Also, the num ber of nodes in our simulation is setto 60.A. Performance Measuirements

Additional assump tions used in our simulations includes:

Two metrics are used to evaluate the routing performances:routing inaccuracy and confrol overhead. Using them, we exam-ine the impact to the performances of different mobility, update

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interval and radio transmission rang e.A.l. Routing Inaccuracy Routing inaccuracy is checked bycompa ring the next hop table of each node with the tables g ener-ated by an off-line algorithm. T his off-line algorithm has knowl-edges of the exact network topology to compute the optimal so-lution for each node at each time slot. For a destination w hich isstill far away, an incorrect value in the next ho p table is less crit-ical than nodes that are close by. Considering this, w e define therouting inaccuracy for node i, Ai, as:

then the overall routing inaccuracy is com puted by averaging Ai,for all i E N , where N , nezt i () , opi(),D are defined in section111, and nezt2n,() s the next hop table computed by the off-linealgorithm.A.2. Control Overhead The control overhead is evaluatedby examining the average number of routing control packets ex-changed on each link. Th e reason for using the number of con-trol packets instead of the total control bits exchanged is due tothe characteristic of radio devices and MAC lay er protocol. It isknown that a radio device spends considerable time to sw itch fromreceive to transmit mode. T his typically exceeds the time used forsending a small packet. If spread sp ectrum is used, the acquisitiontime may beco me even more significant.

Fo r LS, we acco unt for each link state packet that is g eneratedby a node either because it detects a topology change, or it re-ceives one from its neighbors and forwards it by flooding. Each ofpacket requires a transition fo r radio dev ice from receiving modeto transmitting mod e. For DB F type algorithm, a routing table up-date is counted as one packet. This is under the assumption thatthe routing table can be transmitted in a fixed num ber of consecu-tive MAC layer frames (w ithout transmitlreceive switching.)B. Simulation ResultsIn addition to routing accuracy and control overhead, we alsoexamine the impact to performance d ue to changes in mobility,update interval and radio transmission rang e. These results aresummarized below.B.l. Routing Inaccuracy Fig. 2 show s the inaccuracy of dif-ferent routing schemes at different node speeds. LS performsbest at all speed ranges, since it reacts the fastest to the topol-ogy changes. GSR performs less accurately than L S since it up-dates the routing information only every three time slots. How-ever, GSR still performs better than DB F.B.2. Control Overhead As shown in Fig. 3, both DBF andGSR algorithms have a flat distribution of pac ket overhead , whichmeans the overhead of both cases remains constan t regardless ofmobility. This is because nodes in both schem es exchange routinginformation periodically with only their adjacent neighbors. Onthe other hand, with LS schemes, the overhead is much worsethan DBF and GSR which means m ore packets ar c generated. Thefigures also show that as degree of mobility becomes higher, theoverhead for LS increases. This validates that LS is not suitablefor high mobility environment.


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B.3. Mobility Impa ct As Fig. 3shows, the control overheadfor LS increases rapidly as nodes m ove at higher speeds. An un-manageable flood of packets overwhelms the radio channel anddominates packet que ue in each node. On the other hand, mobil-ity has no effect on control overhead for DBF and GSR. This isreasonable because in LS , routing updates are event driven: a nodesends a link state packet into the network w henever changes in itsneighborhood aredetected. And a large amount of these link statepackets will then be generated due to the flooding mechanism.

The impact of mobility to routing inaccuracy is, however, in-dependen t of the impact to control overhead. O verall, higher mo-bility causes higher inaccuracy fo r all three schem es. LS performsthe best in every mobility value, as indicated by Fig. 2. LS sus-tains inaccuracy equal to or lower than 15% even at a node speedof 1 60 units per time slot, while DBF provides poorly acceptablerouting solutions. Our GSR performs between D BF and LS; inlow speed range, GSR is as accurate as LS; while in high speedrange, GSR b ecomes worse but is still better than DBF.B.4. Update Interval Upd ate interval also plays an importantrole for the routing overhead and inaccuracy. As we show ed ear-lier, LS achieves higher routing accuracy because update pack-ets are sent out immediately whenever a node detects topologychange. Thus the delay is bounded by the minimum time res-olution used by the system. Fig. 4 shows that GSR inaccuracyis degraded or improved by adjusting the routing interval up ordown. The sam e holds for DBF as shown in Fig. 5 , except thatthe improvem ent is not very significant as the accuracy is alreadypoor even in the low mobility conditions. As expected, we notethat more improvem ents can be observed when mobility is high.B.5. Radio Transmission Range The range of radio trans-mission determin es the degree of node connectivity. As shown inFig. 6, the larger the transm ission range th e larger the connectivitydegree and the larger the control packet size for GSR and LS.

Fig. 7 sh ows the decrease in routing error rate as transmissionrange increases. Larger transmission range means mo re nodes canbe reached in one hop without requiring routing decision. How-ever, as indicated in [14], the spatial reuse is less efficient whentransmission range is large. It is interesting to note that, the worstcase doesnt happens when R = 80, which is the smallest rangein our simulation. Instead, it happens at about R = 150, regard-less of mobility. It is obvious that as transmission range increases,the hop distance between any two nodes also decreases, so lessrouting error may be formed. On the other hand, as transmissionrange decreases, the graph will become disjoint. To the extreme,when the transmission rang e equals zero, no two nodes can talk toeach other, and the next hop entries will becom es all empty.VI. ConclusionIn this paper, we introduce a new routing scheme, the GlobalState Routing, to provide an efficient routing solution for wireless,mobile networks. The routing accuracy of GSR is comparable toan ideal LS scheme and thus superior to the traditional DBF, al-though it doesn t require individual link state broadcasting whichmay cause serious consumption of wireless bandwidth. As a re-sult, GSR is more desirable for a mobile environment where mo-bility is high and band width is relatively low.

Our future plan includes the evaluation of MAC layer impto the routing efficiency. This requires enhancem ents to o ur simlators with different MAC layers such as MACA, M AC AP R aCluster/TDMA [14]. Qo S routing for multimedia sup port in wiless environmen t is also considered to be emb edded in GS R sinit provides higher routing accuracy w ithout sacrificing bandw idBesides simulation, implementing GSR in an IP wireless testbis also in progress.ReferencesD. Bertsekas and R. Gallager, Routing in Data Networchapter 5, Prentice Hall, second edition, 1 987.

J.M. McQuillan et al. , The new routing algorithm for ARPANET, IEEE Transactionof Communications,vol.no . 5, pp. 71 1,719, May 1980.S. Murthy and J.J. Garcia-Luna-Aceves, A routing protofor packet radio networks, in Proc. IEEE M obicom, N199 5, pp. 86-95.C.E. Perkins and P. Bhagwat, Highly dyn amic destinatiosequenced distance-vector routing (DSDV) for mobile coputers, in ACM SIGCOMM94, 1994 , pp. 234-244.S. Murthy and J.J. Garcia-Luna-A ceves, A routing protofor packet radio networks, in Proc. IEEE M obicom, N1995, pp. 86-95.C. Hedrick, Routing Information Protocol, in IETF R1058 , 1988 .J. Moy, O SPF Version 2, in IETF RFC 1583, 1994.The ATM Forum,Specification v l O, 1996.M . S. Corson and A. Ephrem ides, A destributed routinggorithm for mobile wireless networks, ACM-Baltzer Jonal of Wireless Netw orks, vol. 1, pp. 61-81, Jan . 1995.V. D. Park and M. S. Corson, A highly adaptive destriburouting algorithm for mobile wireless networks, IEEE focom, 1997.C.-K. Toh, A novel distributed routing protocol to sup pad-hoc mobile compu ting, in IEEE IPCCC, 1996.R. Sedgewick, Weighted Graphs, chapter 3 1, AddisioWesley, 1983.Andrew S. Tanenbaum, ComputerNetworks, ThirdEditiPrentice Hall, 1996.M. Gerla and J. T. Tsai, Multicluster, mobile, multimeradio network, ACM-Baltzer Journal of Wireless Netwo rvol. 1, no . 3 , pp . 255-265, 1995.

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Fig. 2. Inaccuracy: 60 nodesDBF

0.1'Co I I . . ,0 20 40 60 80 100 120 140 160Mobility (unlVlimesl0)

Fig. 5. Inaccuracy at different update intervals:DBF

=F in d S P ( i ) =Dijkstra's shortes t-path algorithlnD ; ( i )t :P t i } ;.~foreachz {j I (j V ) ( j # i ) }thenD i ( z )t e ig h t ( i ,2) ;N E X T . ( k l t :

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-dW P # V d oforeach k E V - P , 1 E PFind ( 1 . k ) such thatweighr(1,k) = m i n { D i ( l )+ weight(1,k ) } ;-dP t U { k } ;D i ( k )t , (Z)+ weight(1,k ) ;N E X T l ( k )t E X T i ( 1 ) ;-d= ktProcess(i,p k t )source c kt.source;

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50 100 150 2w 250 300 35 0 4w 450 500TX Range

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Fig. 6. Connectivity vs. TX. range Fig. 7. Inaccuracy vs. TX . range

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