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Link Probability Based Opportunistic Routing Metric in Wireless Network

Yanhua Li, Yuan-an Liu , Pengkui Luo

Abstract

Opportunistic routing is a new design trend of wireless

network routing protocol. It takes good advantages of the

broadcast nature of wireless network. The source can use

multiple potential paths to deliver the packets to the desti-

nation. The routing metric used for selecting the forwarder

lists is very important for designing the opportunistic rout-

ing scheme. In this paper, we propose a novel routing metricSTR (successful transmission rate) to choose the forwarder

list, which is based on total successful transmission rate.

It considers multi-links contribution, instead of one best

link information used in ETX. We also introduce the fair op-

portunistic routing with linear coding (FORLC) scheme us-

ing our STR metric. The extensive simulation results show

that the opportunistic routing with our STR metrics can al-

ways outperform ETX based ExOR scheme. The maximum

benefit of the throughput using the STR based FORLC can

be 30% more than the ETX based MORE. It also has at most10 times throughputs than traditional routing protocol.

Keywords: Opportunistic routing, routing metric, wire-less routing.

1. Introduction

The traditional routing protocols we used, such as

AODV, DSR and etc., are all based on single best path

to deliver packets. This would easily cause a lot of retrans-

missions and high frequent route rediscovery. Multi-path

routing protocol [5, 6] is an improvement to single path

routing protocol. It selects multi-paths to deliver packets.

It indeed increases the delivery ratio, but it increases the

duplicate transmissions of packets as well, since there is noefficient scheduling scheme for these paths. Opportunistic

routing [2, 3, 4, 8, 10, 9] uses multi-paths to deliver packets,

and do not increase the transmission cost. A forwarder list is

maintained for each flow. Any packet in the flow may use all

Yanhua Li and Yuan-an Liu are with Beijing U. of Posts &

Telecommunications, Beijing, China.(Email: yanhua2008@gmail.com,

yuliu@bupt.edu.cn)Pengkui Luo is with Dep. of CS, U. of Minnesota, Minneapolis, USA.

(Email: pengkui.luo@gmail.com.)

the nodes in the list to do the forwarding. And the nodes in

forwarder list are prioritized with some metric. Each node

only forwards the packets which havent been received by

any high priority node. This would efficiently reduce the

transmissions of duplicate packets. ETX is a routing met-

ric to choose the candidate set. ETX captures the minimum

number of total transmissions to send a packet from a cer-

tain node to the destination. ExOR [3, 1] uses the ETX to

choose a candidate forwarder set. It can provide better per-formances over traditional routing protocols shown in [7].

But there are still some problems in ExOR. After a transmis-

sion, all the nodes in the candidate set have to wait for the

forwarding of the nodes with higher priority in order. It is

not an efficient way to do the spatial reuse. Moreover mul-

ticast is not implemented. MORE (MAC-independent Op-

portunistic Routing Protocol) randomly mixes packets be-

fore forwarding them. This randomness ensures the routers

that overhear the same transmission will not forward the

same packets. In this way, MORE doesnt need special

scheduler to coordinate routers and can run directly on top

of802.11. In other words, MORE introduces network cod-

ing to Opportunistic Routing Protocol. It can also supportboth unicast and multicast. In Chachulskis paper [4], ETX

is used as a routing metric to select the forwarder list. Us-

ing ETX in MORE is not suitable because MORE is a fair

scheme, unlike ExOR which is an unfair scheme to use the

nodes based on their priorities. It does not need ETX to se-

lect candidate nodes with priorities, and treat them based on

the different priorities. MORE does not find a good way to

the process of forwarding in both unicast and multicast ei-

ther. Moreover, it does not introduce error control and rate

control schemes. ETX is not an optimal metric to choose

the forwarder list in opportunistic routing, because it only

considers one link with the lowest cost. When the node can

reach multiple neighbors, these multi-links can increase the

transmission rate and reduce the delivery cost. So the cost

depends on not only the single link probabilities, but also

the number of links it can leads to. The more number of

links it can use, the less cost it needs to take. Based on

the differences between ExOR and MORE, we suppose a

novel metric STR (successful transmission rate) for fair op-

portunistic routing protocols, and present a fair opportunis-

tic routing protocol (FORLC) which uses STR as a metric

2009 International Conference on Communications and Mobile Computing

978-0-7695-3501-2/09 $25.00 2009 IEEE

DOI 10.1109/CMC.2009.170

308

2009 International Conference on Communications and Mobile Computing

978-0-7695-3501-2/09 $25.00 2009 IEEE

DOI 10.1109/CMC.2009.170

308

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to select the forwarder list. The rest of the paper is orga-

nized as follows. Section 2 introduces the difference be-

tween fair scheme and unfair scheme of opportunistic rout-

ing protocols, using ExOR and MORE as examples. STR

is described in Section 3. In Section 4, we illuminate the

FORLC protocol. Section 5 gives the analysis of proposed

protocol and provides simulation results. We conclude ourpaper in Section 6.

2. Fair and Unfair opportunistic routing

scheme

In this section, we are in the position of introducing the

existing opportunistic routing schemes. Kindly, there are

two types scheme, unfair/fair opportunistic routing. Unfair

opportunistic routing scheme often builds a candidate for-

warder set, in which many forwarders are prioritized with

orders. The higher priority indicates that the node is closer

to the destination. So in ExOR, every node chooses thehighest prioritized node to forward the packets it received

first. And the nodes with lower priorities have to wait and

listen to the nodes with higher priorities, so that every node

only forwarder the packets that have not been received by

any higher priority node. Fair opportunistic routing scheme

also builds a candidate forwarder set. But all the nodes in

it are fair without any priority. The set just includes some

closer nodes to the destination than the source. MORE is

a typical fair opportunistic routing. Node forwards coded

packet, once it receives an innovative packet from others

and the credit value is positive. The node need neither a

special scheduler to coordinate with other routers, nor wait-

ing for other nodes. Fig. 1 shows an example topology, andETX values are marked for each node.

Figure 1. Connective Graph with ETX metricvalues marked.

In ExOR, the Source chooses the candidate forwarder

set as {B, A}, because B has lower ETX than A. Then,ExOR tries to send more packets through B to destination,

and the other nodes have to wait until B finishes its trans-

mission to decide which packets should be forwarded. Ob-

viously, in Fig. 1, if the source sends a batch of 125 pack-ets out, A and B would both receive 100 packets respec-tively as expected. Then A would send the 100 packet outto Dst along A C Dst and A D Dst. Dstwould receive totally 60 packets from both C and D re-

spectively. Now the question is how many distinguishedpackets would be received by Dst through As forward-

ing? We can easily get that this number of the different

packets obeys to the hyper-geometric distribution. Let X

denote the number of distinguished packets received by the

destination. Dst would receive E(X) = 84 distinguishedpackets through As transmissions, and will similarly re-

ceive 64 different packets from Bs transmissions. So, Acan send more packets to Bs in this particular case. This

also illustrates that ETX does not always make good de-

cision, since it does not consider the multiple links infor-

mation for each potential forwarder. There are also more

issues in ExOR, such as not supporting spatial reuse and

multi-cast. MORE makes some progress over ExOR. It cansupport spatial reuse and multi-cast, but it still uses ETX as

the routing metric to choose the candidate set. This would

be a big issue, since non-optimal forwarder list would re-

sults directly in many duplicate transmissions. In MORE,

because all the packets from the nodes are coded, just a few

coded packets which are not linearly independent with oth-

ers are considered as duplicate packets. Even though the

node A and node B may receive the same packets using

MORE, they would generate some new linear combinations

of these packets to send. This will reduce the number of

duplicate transmissions. Hence, opportunistic routing will

result in higher throughputs for each source destination pair,

since it takes advantage of multiple links to the destination.But if the source only uses this best route to deliver all the

packets, the total number of duplicate transmissions will be

the least in this graph. So, how to get lower number of du-

plicate transmissions from the source to the destination, as

well as getting higher throughput is a big challenge.

3. STR: Successful Transmission Rate

We introduce a novel routing metric, Successful Trans-

mission Rate (STR) to construct the forwarder lists. STR

denotes the expected successful transmission rate between

a certain node and the destination. Each node calculates itsSTR to the destination, and chooses some of the neighbors

with the higher STR values into its forwarder set. The exact

approach used to calculate the STR is showed below: (1) If

the node Xand the destination are within one hop (showed

in Fig. 2), the STR of node X is

STRX = PXD . (1)

(2) If there are two hops between node X and the desti-

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nation (showed in Fig. 3), the STR of node X is

STRX = PX1P1D +N

i=1

PXiPiD

i1

j=1

(1 PXj ). (2)

If there are more than 2 hops from node X to destination(showed in Fig. 4), the graph could always be transferred

to an equivalent graph in which the node X and Dst are

within 2 hops. We reserve the nodeXs one hop forwarders,and simply replace the subgraph between these one hop for-

warders andDst to a set of equivalent one hop probabilities.

These equivalent one hop probabilities are exactly the STR

values of each node. With this approach, we can calculate

STR for every node in a common graph. The STR of node

X is

STRX = PX1STR1D +N

i=1

PXiSTRiD

i1

j=1

(1 PXj ).

(3)So, in this algorithm, node needs to know its on-hop for-

warders STR values to calculate its own STR value. The

STRs should be calculated from the nodes, which are closer

to destination, to the node farther away from the destina-

tion. For example, we calculate the STRs for the topology

shown in Fig. 1. Node C, D and Es STRs can simply be

calculated by using equation 1. Node A, Bs STRs can be

calculated with equation 2. When calculating the STR of

node Source, we use the As STR value to replace the sub-

graph between node A and node Dst, similarly for node B.

Then we can use equation 3 to get the STR value of node

source. All the STRs of nodes in Fig. 1 are listed in Table. 1.

Table 1. List of STR for each node in Fig. 1Node Src A B C D E Dst

STR 84% 84% 64% 80% 80% 80% 100%

4. FORLC: Fair Opportunistic Routing With

Linear Coding

In this section, we will introduce fair opportunistic rout-

ing with linear coding (FORLC) protocol, in which STR is

used to choose the forwarder set for every node. Every 20minutes, hello messages are broadcasted to exchange link

quality information between each node pair.

When a source requires a route to its destination, nodes

would use the algorithm below to select the local forwarder

sets. These forwarder sets include all the efficient nodes

which can be used as a potential forwarder of the data pack-

ets.

Like in MORE scheme, the source breaks up the file

into batches ofK packets, where batch size K may vary

Algorithm 1 For Node X Selecting the forwarder set.

1: /* Generate a preliminary forwarders Set R*/2: R := 3: for each vertex vi inN(X) do4: ifET X(vi, Des) < ETX(X,Des) then5: R := R {vi}6: end if7: end for

8: /*Choose useful forwarders C from R */9: ST Ro := 010: ST Rn := 011: C := 12: while TRUE do13: /*find the next best candidate v*/14: for each vertex vj in R do15: ifST Rn > ST RX(C {vj}) then16: v := vj17: ST Rn := ST RX(C {vj})18: end if19: end for20: if(ST Rn < Threshold) (R = ) then21: C := C {v}22: R := R {v}23: ST Ro := ST Rn24: else25: Break26: end if27: end while28: RETURNC

from one batch to another. These K uncoded packets are

called native packets. When the 802.11 MAC is ready tosend, the source creates a random linear combination of

the K native packets in the current batch and broadcasts

the coded packet. FORLC scheme uses STR as metric to

choose the forwarder list. Every node maintains a local for-

warder list. Before forwarding the packet, node would up-

date the packets forwarder list with its local one.

After receiving a packet, the node checks whether it is

in the packets forwarder set. If so, it would check whetherthe packet is linearly independent with the packets in its

cache. Then, it stores the innovative packet into its cache,

and makes a new linear combination of the packets it has.

The packets received by destination are all linearly coded

packets. When the destination receives a packet, it will

also check whether the packet is linear independent with

the packets in its cache. Destination only caches the pack-

ets, which are independent with each other. When destina-

tion receives K independent packets, it resumes the orig-

inal K packets and sends ACKs back to source along the

best route. An ACK packet indicates that the current batch

has been received successfully by the destination. Each for-

warder stops sending packets of the current batch, when itreceives the ACK. If source receives the ACK, it would stop

sending the current batch, and start the next batch.

In dense wireless network, not all of the neighbors of a

certain node would be involved into the forwarder set, be-

cause that will result in more duplicate transmissions and

more power consumptions. So, in FORLC, we introduce

a STR threshold to select the forwarder set. We always

choose the highest STR forwarder nodes into forwarder set,

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Figure 2. One hop nodepair.

Figure 3. Two hops nodepair

Figure 4. Substitution withSTR value.

until the nodes own STR is more than a certain threshold

or there is no qualified forwarders any more. We choose

95% as the threshold. This threshold is got by our simu-lations. We randomly changed the density of the network

many times, and varied the threshold with different values

in each topology to compare the performances, in terms of

total cost rate and duplicate transmission rate. The resultsshow that 95% is the best value as the threshold. When thethreshold is greater than 95%, the cost of transmissions willincrease rapidly. More detailed analysis is shown in sec-

tion 5.

5. Evaluation

In this section, we evaluate the performances of the STR

based FORLC protocol. In the simulations, we use 1 Mbpsas the transmission rate. Each simulation period is 300 sec-onds, and the payload size of each packet is 1 Kbytes. We

randomly generate the scenarios with a certain number ofintermediate nodes for the source-destination pair. The link

qualities between these nodes are all randomly generated.

At the beginning of each random scenario, nodes exchange

hello messages to collect the link qualities information of

the topology for 60 seconds. Then, the source sends thepackets to the destination using FORLC, MORE and AODV

protocols, respectively. The simulation in each scenario is

repeated 100 times, in order to reduce the effect of skewfrom a limited sample size. We choose the batch size as

100.

We recorder the total numbers of transmissions needed

for delivering all the packets successfully to the destination.

The average number of transmissions for each packet can becalculated easily by dividing the total numbers of transmis-

sions by the total number of data packets in each simulation.

This value will be referred to the Packet Transmission Cost

(PTR). The Fig. 5(a)and Fig. 5(b) show that the STR based

FORLC can outperform ETX based MORE and the tradi-

tional routing in all of the scenarios. Since the forwarder

lists selected by STR and ETX are the same for some node

pairs, we could see some points are located on the 45 degree

line (shown in Fig. 5(b)).

Since the linear coded packets received by destination

might not be independent with each other, these duplicate

transmissions are a huge waste of network resources. The

transmissions for the linear correlative packets are actually

the duplicate transmissions. The total number of duplicate

transmissions divided by the total number of packets will bereferred to the duplicate transmission rate, which captures

the number of duplicate transmissions for each data packet.

In the simulations, we varied the batch size from 20 packetsto 600 packets, we could see that the duplicate transmis-sion cost decreases as the batch size increases, and the STR

based FORLC always gets less duplicate transmission cost

than ETX based MORE, as shown in Fig. 5(c).

In this part, we generate a random topology with 50nodes in it, among them there are 10 source-destinationpairs. We still use 1 Mbps as the transmission rate. For thewhole simulation time, each pair completed 1 megabytesdata transmissions from the source to the destination. We

recorded the throughputs for each routing protocol. The

Fig. 5(d) shows that the comparison of throughputs between

traditional routing, MORE and FORLC protocol. Each bar

in the figure is the average value of100 simulations.

In Fig. 5(d), the bars are sorted by the values of the

throughputs of the FORLC protocol. The figure shows

that the opportunistic routing always has higher throughputs

than the traditional routing. For most of the node-pairs, the

FORLC has higher throughputs than the MORE protocol.

It has the same throughput as MORE for other node pairs,

because the forwarder sets selected by STR and ETX are

the same for these node pairs.

In dense network, each node may have lots of neighbors.Many nodes may be qualified for the forwarder set. If we

choose all these nodes in to the local forwarder set, it will

result in the more duplicate transmissions. So, we introduce

a STR threshold in the FORLC protocol. When the STR

value of the current node is larger than the threshold after

choosing some neighbors into forwarder set, the node will

stop introducing new forwarders. The results show that 95%is the best value as the threshold. When the threshold is

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0 10 20 30 40 50 60 700

10

20

30

40

50

60

70

The Packet Transmission Cost: Tranditional Routing

ThePacketTransmissionCost:FORLC

(a) Packet transmission cost: Traditional

routing vs. FORLC

0 10 20 30 400

10

20

30

40

The Packet Transmission Cost: ETX Based MORE

ThePacketTransmissionCost:FORLC

(b) Packet transmission cost: MORE vs.

FORLC

0 100 200 300 400 500 6000

0.1

0.2

0.3

0.4

Batch Size

DuplicateTransmissionCost

STR based FORLC

ETX based MORE

(c) Duplicate Transmission Cost:

FORLC vs. MORE

#1 #2 #3 #4 #5 #6 #7 #8 #9 #100

5

10

15

20

25

30

35

Node Pair ID

Throughputs(Kbytes/s)

Tranditional RoutingETX Based MORESTR Based FORLC

(d) Throughputs comparison

0~60%65%70%75%80%85%90%95%100% +Infinity0

0.05

0.1

0.15

0.2

0.25

STR Threshold

Duplicat

eTx.Cost(times/packet)

(e) Duplicate Transmissions Cost varies

with the STR threshold

0~60%65%70%75%80%85%90%95%100% +Infinity0

0.5

1

1.5

2

2.5

3

3.5

STR Threshold

Pac

ketTx.Cost(times)

(f) The Packet Transmission Cost varies

with the STR threshold

Figure 5. Simulation results.

greater than 95%, the cost of transmissions will increaserapidly.

6. Conclusion

In this paper, we propose a novel routing metric STR,

which is based on the successful transmission rate from

a node to the destination, to choose forwarder set for fair

opportunistic routing. We also propose FORLC protocol,

which uses the STR metric to select the forwarder set, and

do the linear coding for the data packets. It can improve

the network performances, in terms of throughputs and the

packet transmission cost. Comparing with the MORE, the

FORLC protocol can increase the throughput 30% more

than the MORE protocol at most, and 10 times than the tra-ditional routing at most in our simulations. The STR based

FORLC can also reduce the packet transmission cost.

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