Evaluation of a Fault-tolerant WSN Routing Algorithm Based on Link

Quality

Unai Burgos, Iratxe Soraluze and Alberto Lafuente

Department of Computer Architecture and Technology, University of the Basque Country UPV/EHU

20018 San Sebasti

an, Spain

Keywords:

Wireless Sensor Networks, Routing, Fault Tolerance.

Abstract:

In this paper we propose a fault-tolerant routing algorithm for WSN. Our approach is based on link quality as

the main criteria to build an initial routing tree, although additional criteria, such as node reachability and path

diversity, are also considered. The routing tree is built using only local information (two-hop neighbourhood).

This information is also used to reconﬁgure locally the routing tree when a fault is detected. The routing

algorithm has been implemented using the OMNeT++ simulator and a preliminary performance evaluation

has been carried out. Results show that our algorithm reach comparable delivery rates than a standard ﬂooding

algorithm, being much more efﬁcient.

1 INTRODUCTION

Wireless Sensor Networks, WSN, are a promising

technology that have been used successfully for envi-

ronment monitoring, health care applications, trans-

portation, ubiquitous home networks and others.

WSN consist of one or more sinks and a huge num-

ber of small devices, called motes, with sensors, wire-

less communication and small computation capabil-

ity. Sensors are used to gather information to be sent

to a sink node. The sink node is used to process the

received data and to connect the wireless sensor net-

work with the Internet. The sink node is usually a

more powerful device with no practical limitations.

Note that current technology enables the implementa-

tion of the sink on a mobile device.

As motes are small, work unattended in the real

world, and are powered with very limited batteries,

energy constraints are usually severe and affect all the

aspects in the system design. Therefore, instead of

relaying on brute-force message forwarding (ﬂood-

ing), communication protocols should be designed

carefully in order to trade-off transmission power and

message retransmissions and forwarding. Reducing

transmission power, which increases energy waste

quadratically with respect to signal range, results in

more message loses and more hops to reach the sink

node. Furthermore, in the search of the optimality, it

should be also taken into account that message loses

also depends on phenomena as interferences and mul-

tipath fading (Doherty et al., 2012). Finally, a node

can crash, due to battery exhaustion or many other

reasons.

Therefore, a WSN needs an efﬁcient routing pro-

tocol and a fault management mechanism that reacts

by reconﬁguring the network upon failures and en-

sure a sufﬁcient quality of service (Yu et al., 2007). A

good solution should provide reliable and fault toler-

ant communication, scalability, low latency and quick

reconﬁguration with minimum energy consumption.

To face failures in routing paths, two general ap-

proaches are used, namely replication and retransmis-

sion (Alwan and Agarwal, 2009). The most common

replication mechanisms consist on transmitting mul-

tiple copies of the same data to the sink over multiple

paths (Ye et al., 2005). Note that when the same data

packet is sent along two fully node-disjointed paths

the packet delivery ratio is almost doubled (Tian and

Georganas, 2003). However, the transmission of mul-

tiple copies increases the energy consumption, and

the extra work to construct and maintain disjoint paths

introduces control message overhead and a lack of

scalability (Challal et al., 2011). On the other hand,

in retransmission techniques usually only one path is

used between the source node and the sink. As a

consequence, a broken path needs to be partially re-

constructed or completely discarded. Also in this ap-

proach network trafﬁc and hence energy consumption

increase due to end-to-end retransmissions.

Hop-by-hop recovery seems to be more reliable

Burgos U., Soraluze I. and Lafuente A..

Evaluation of a Fault-tolerant WSN Routing Algorithm Based on Link Quality.

DOI: 10.5220/0005330300970102

In Proceedings of the 4th International Conference on Sensor Networks (SENSORNETS-2015), pages 97-102

ISBN: 978-989-758-086-4

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

than end-to-end recovery (Kim, 2004). These rout-

ing algorithms consider a partial reconstruction of the

routing path due to a link or node failure. The main

beneﬁt is that failure detection is managed locally by

the nodes without global exchange of information, a

key issue in order to avoid messages.

Some of the fault-tolerant routing algorithms pro-

posed in the literature consider only crash node fail-

ures and permanent link failures, i.e., once a failure

is detected the node is considered dead (Boukerche

et al., 2006) (ALMomani et al., 2011). In (Bouk-

erche et al., 2006) just one path is used for transmit-

ting data and node failures are handled by a retrans-

mission mechanism. If a sender does not receive an

ack message from the receiver node in a predeﬁned

timeout, the routing path is partially changed and the

packet is retransmitted using the new links in the path.

In this solution, ﬂooding is used to construct an initial

cost ﬁeld, as well as for node subscription. In (AL-

Momani et al., 2011) the routing path reconstruction

starts in a node that detects that its energy level is be-

low a speciﬁc threshold, and therefore it is going to

dead.

Nevertheless, the permanent failure assumption is

not realistic in many scenarios where link failures

or message looses in WSN are commonly due to

transient situations like temporal obstacles or inter-

ferences. In these cases enabling temporal alterna-

tive paths is an approach usually found in the liter-

ature (Nelakuditi et al., 2007) (Tian and Georganas,

2003).

Most of the routing protocols used for WSN are

reactive (Al-Karaki and Kamal, 2004), i.e., the routes

are built on demand after a ﬂooding started on the sink

node. Apart from the cost associated to the ﬂooding

and a high latency of the reconstruction of paths when

failures occur, a drawback of this approach is that it

does not manage efﬁciently the mobility of the sink

node, because the routing path is built with the sink

as root. In consecuence, it is convenient to design a

proactive routing algorithm as in (Heinzelman et al.,

2000) when the sink can move.

In the present work we assume that failures are

transient. Besides we consider that a link can be

characterized by a feature that we call the quality of

the link. Our aim is to select the most reliable links

to form a routing tree in order to reduce the num-

ber of retransmissions that would cause a further in-

crease in channel contention and more packet losses

(Li et al., 2005) (Youseﬁ et al., 2009) (Zhang et al.,

2007), as well as a decrease in the message delivery

rate (Zhang et al., 2007). System reconstruction is

carried out locally in the neighbourhood of the fault,

in order to ﬁnally reduce the energy consumption.

In this paper we describe a routing algorithm

based on link quality as the main criteria, as well

as a preliminary performance evaluation. Our strat-

egy is based on the construction of a routing tree in a

proactive manner. Besides link quality, we use addi-

tional criteria, such as node reachability and diversity

(Boettcher et al., 2003). The routing tree is built us-

ing only local information (two-hop neighbourhood).

This information is also used to reconﬁgure the rout-

ing tree when a fault is detected.

Although, performance on sink mobility scenar-

ios has not been evaluated in the scope of the present

work, our routing algorithm has been designed to be

ﬂexible enough to efﬁciently manage the dynamism

associated to sink mobility. In our protocol, if the sink

moves, only the routing information of a small area in

the neighbourhood of the sink needs to be updated.

2 ROUTING ALGORITHM

BASED ON LINK QUALITY

We will divide the description of our routing algo-

rithm in two parts. In the ﬁrst one we describe how an

initial routing tree is built after a node and link qual-

ity discovery period. In the second one, we describe

how the routing of data to the sink starts and how the

routing tree will be reconﬁgured due to link or node

failures.

The WSN architecture we consider consists of

a ﬁnite (but unbounded) set of resource-constrained

static sensor nodes, which we will denote by

, p

, . . . , p

, . . . (or by p, q, . . . for short), and one

more powerful sink node. We model this distributed

system as a set V of n nodes. A node p communicates

directly only with a subset N

of nodes of V, the nodes

in its communication range. The nodes in N

are con-

nected with p by a bidirectional communication link.

All the sensor nodes transmit with the same power

level and henceforth all they have the same transmis-

sion range.

Concerning timing assumptions, we consider a

synchronous model in which there are bounds on

message transmission times. Message transmission

time bounds can be estimated using application pa-

rameters such as transmission latency between neigh-

bor nodes. We assume that every node has a local

clock that can measure real-time intervals.

We consider that nodes can fail either by perma-

nently crashing (sensor nodes that fail do not recover)

or by omitting messages. Omission failures may oc-

cur either while sending or while receiving messages,

and these failures can be transient (a node may tem-

porarily omit messages and later on reliably deliver

SENSORNETS2015-4thInternationalConferenceonSensorNetworks

messages again), or permanent. At the same time, we

assume lossy links, i.e., messages can be lost tem-

porarily or permanently during its transmission in a

link. Henceforth nodes that have permanent message

omissions in all their outgoing links will be consid-

ered as crashed nodes. We also assume that there is

a maximum number of crash failures and permanent

omission failures in the system in such a way that the

system do not get partitioned. We assume that all the

nodes of the system that do not crash are able to re-

ceive and send messages along a path of lossy links

from every non-crashed nodes.

2.1 Building the Routing Tree

First of all, there is a discovery phase where each node

p sends periodical heartbeat messages during a period

of time in order to discover the nodes that are in its

transmission range, i.e., the subset N

⊂ V . In this

phase also the link quality for all the links is gath-

ered. Global knowledge about link quality is relevant

to build optimal routing paths, however, due to the

severe constrains in wireless sensor networks, broad-

casting link quality information to the whole network

is out of any practical consideration. In this algorithm

each node sets the link quality of nodes that are up to

two hop distance. A node p gathers the link quality

for all the nodes in N

and N

for all q ∈ N

, us-

ing neighbour information piggybacked on heartbeat

messages. We call N

to this set of nodes. Also some

other topological properties of the network such as

the reachability and diversity (Boettcher et al., 2003)

of each node are calculated.

We provide here a more formal description of the

properties:

• Link Quality

For any bidirectional link (p, q) = (q, p) between

nodes p and q, we represent the quality of link

(p, q) as W

. The value of W

is assigned on the

basis of the number of lost messages in the link

(p, q), as we explain next. Observe that, since we

are considering synchronous links, a message lose

can be detected at the receiving node by the time-

out expiration of a periodical message. Assume a

link (p, q) where p has sent M

p→q

messages and

q has sent M

q→p

messages. Assume that process

p has detected that l

q→p

messages from q have

been lost in the link (p, q), and that q has detected

that l

p→q

messages from p have been lost in the

link (p, q). Since we are considering bidirectional

communication, the quality of the link will be de-

termined by the most lossy direction of the link,

and it is represented by a real number in the range

(0, 1):

= 1 − max(

q→p

p→q

q→p

) (1)

• Reachability

The reachability property measures a brute-force

aspect of connectivity power of a node p. Con-

sider a node q such that p ∈ N

. We deﬁne the

reachability set of p from q, R

(q) as the nodes

connected to p that are not directly connected to

q. Henceforth,

(q)

provides a measure of

the reachability of p. In our example of Fig-

ure 1, observe that R

) = {p

} and R

) =

, p

}, thus

)

= 1 and

)

= 2,

which represents the fact that p

provides higher

reachability from p

than p

. The goal of consid-

ering this measure is to reduce the number of hops

of the routing tree.

• Diversity

Diversity refers to a much more subtle role of a

node p as a router in the network: the power of p

for reaching nodes that can not be reached from

other nodes. Note that diversity can be calculated

upon the information about the reachability sets

in a neighbourhood. In our example, p

and p

provides the highest possible value for diversity,

as both nodes are essential to maintain the graph

connected.

Figure 1: Collected data after information-gathering phase.

Once the information obtained about the quality

of links is statistically relevant, the construction of the

routing tree starts proactively. To proceed, an initial

EvaluationofaFault-tolerantWSNRoutingAlgorithmBasedonLinkQuality

decision is to identify a root node for the tree. Di-

verse criteria could be applied to select the root node,

depending on parameters such as network size, sink

location or sink mobility. In the case of static sink

node, as we are considering, the root of the tree will

be the sink node.

The root node is in charge of starting the tree con-

struction. Node p will select as its sons enough nodes

to reach all the nodes at two hop distance. Note that

diversity is necessarily the ﬁrst criteria to select some

of the son nodes. After that, p will select ﬁrst the

nodes with the best link quality and, among the nodes

with similar link quality, the nodes with the highest

reachability. This procedure is repeated by each of

the son nodes of p and propagated to the whole sys-

tem. Nodes with low reachability, diversity and qual-

ity of links will remain as leaf nodes in the tree and

henceforth will not participate in routing tasks.

In our example of Figure 1, p

is the root node

and N

= {p

, p

}. Observe that, based on

the criteria of link quality, node p

will chose p

its son node instead p

, in spite of the higher reacha-

bility provided by the latter. Figure 1 shows this de-

cision and the conﬁgurations of the reachability sets.

Observe also in the ﬁgure that p

is a better candi-

date than p

to be son node of p

because p

, despite

linked with a lower-quality link, has better diversity

(in fact p

is necessary to connect p

2.2 Routing and Managing Failures

In this section we describe the behaviour of the rout-

ing algortihm once the routing tree has been built.

To manage failures once the tree has been built,

the periodical heartbeat messages continue being sent

across the links that belong to the routing tree. The

only difference is that in this case the timers are set

depending on the link quality. Whenever a timer for

a heartbeat message of a link expires, it means that a

link failure has occurred. This failure might be tran-

sient, due to temporal interferences or obstacles, or

might be permanent due to a node crash. Note that

whether the message is omitted by a node or lost in the

channel is indistinguishable from the receiver point of

view. Anyway, whenever a message lose is detected

by q in a transmission from p to q, W

is updated,

and the link (p, q) is removed from the tree and re-

placed immediately to form an alternative route. This

decision is based on the fact that, although next mes-

sages sent from p to q could be received by q, in the

most common failure patterns, the probability of hav-

ing a sequence of losses is high. The same link (p, q)

could be part of the tree later on if the message lose

pattern of the link is benign and a failure occur in the

new links chosen for the tree. However, if the (p, q)

link failure is permanent, i.e, p has crashed, the W

link quality measure will decrease progressively, and

eventually link (p, q) will not belong to the tree any

more. To ﬁnd an alternative route when q detects a

failure in a link (p, q), only 2-hop local information

is needed. The criteria used to select the new link or

links are the same as the used to build the tree: link

quality, reachability and diversity. If p is a son of q,

q will replace p with some other son node(s). Other-

wise, if p is a parent of q, q will start a speciﬁc inverse

reconstruction mechanism to ﬁnd a new parent within

a two-hop distance.

Besides, to avoid crashes due to battery depletion,

when the battery level of a node goes beyond a tressh-

old, it results in a programmed decreasing of the node

functionality. Speciﬁcally, p will not be used as a

router so far, and consequently p should be excluded

of the routing tree by assigning a null value to the

quality of p’s links, i.e., W

= 0 for all node q node p

is connected to. We call this mode routing-off mode.

Note that, as a consequence of that, if some (p, q) was

in the routing tree, then the routing tree should be re-

constructed. Of course, in routing-off mode, p’s links

can still be used to communicate application mes-

sages, i.e., those generated by p as a source node in

the WSN.

3 ALGORITHM SIMULATION

AND EVALUATION

In order to carry out a preliminary evaluation, we have

implemented our routing algorithm using the OM-

NeT++ simulator with the MiXiM framework.

We focus the evaluation on the folowing parame-

ters:

• Start-up time: time that our protocol needs to

build the routing tree after the information gath-

ering phase.

• Message delivery rate: the percentage of mes-

sages that are delivered to the sink node among

the messages sent from the source nodes.

• Message load: number of messages created in

the network for each message created in a source

node. This also provides an estimation of energy

consumption.

• Latency: average time the messages created in a

source node need to be delivered to the sink node.

In order to provide a basis for the evaluation we

compare our algorithm to a basic ﬂooding algorithm.

A ﬂooding algorithm works inefﬁciently in its goal to

SENSORNETS2015-4thInternationalConferenceonSensorNetworks

100

obtain high delivery rates and low latencies and de-

lays. Henceforth, the goal of any well-designed tree-

construction algorithm should be to provide a routing

quality comparable to ﬂooding while improving efﬁ-

ciency parameters. Note also that a simple ﬂooding

algorithm does not require any initial conﬁguration

effort.

We have carried out three different experiments.

For each one of them we have measured the afore-

mentioned performance parameters in order to com-

pare our algorithm to the standard ﬂooding algorithm:

• Scalability. We study the performance of the sys-

tem for 25, 49, 81, 121, 169 and 225 nodes.

• Root location. Two possibilities are considered:

at a corner of the grid network and in the middle

of the network.

• The inﬂuence of link quality. We analyze the per-

formance of the algorithms for four different sce-

narios with different message lose probabilities.

To evaluate the performance, we consider that

there is only one source node that sends 30 data mes-

sages to the sink, one message every 0.2 seconds.

This source node is located as far as possible from

the sink node. We consider a node layout on a grid

and a transmission range in the same order than the

distance between nodes. Instead of relaying on the

failures induced by the simulator, we generate chan-

nel failures with a lose probability of 0.01. Finally, to

get a fair comparison with the ﬂooding algorithm, we

have used an implementation of our algorithm with-

out retransmissions.

3.1 Evaluation Results

In this subsection we summarized the result obtained

from the experiments.

3.1.1 Scalability

We have obtained that for our algorithm start-up times

increase linearly with the size of the network, from 2

seconds for 25 nodes to 6 seconds for 225 nodes.

Ours algorithm outperforms the ﬂooding algo-

rithm regarding message delivery, message load and

latency, as shown in Figure 2, Figure 3 and Figure 4

respectively.

3.1.2 Root Location

We have obtained that start-up times of our algorithm

do not depend signiﬁcantly on the location of the sink.

Regarding the rest of the performance parameters, in

general a centered location is beneﬁcial. Speciﬁcally,

Figure 2: Proposed algorithm versus ﬂooding algorithm re-

garding message delivery rate.

Figure 3: Proposed algorithm versus ﬂooding algorithm re-

garding message load.

Figure 4: Proposed algorithm versus ﬂooding algorithm re-

garding latency.

the latency is reduced to less than one half when the

sink is located in the middle of the network with re-

spect to a corner location.

3.1.3 The Inﬂuence of Link Quality

We generate different link qualities based on the dis-

tance between nodes and the transmission range. We

have evaluated our algorithm and the ﬂooding algo-

rithm with base link lose probabilities of 0.01, 0.1 and

0.2 which increases quadratically with the distance

between nodes.

We have obtained that start-up times, latencies

and message loads do not depend signiﬁcantly on the

link quality. On the contrary, message delivery rates

in both algorithms are signiﬁcantly affected by link

EvaluationofaFault-tolerantWSNRoutingAlgorithmBasedonLinkQuality

101

quality. Speciﬁcally, in our algorithm we have ob-

tained that message delivery rate decreases linearly

from near 90% to 65% as base lose probability in-

creases from 0.01 to 0.2.

4 DISCUSSION

As we have seen, our algorithm outperforms the ref-

erence ﬂooding algorithm in every evaluation crite-

ria. These results are as expected, since we have com-

pared our algorithm to a force-brute algorithm with

no optimization. A ﬂooding algorithm should be very

good in message delivery rate. However, when data

generation rates are high, as it is the case of our ex-

periments, the fowarding of messages results in a net-

work collapse and the message delivery rate (and pos-

sibly latencies) drop.

On the other hand, an algorithm as the proposed in

this paper will optimize the network trafﬁc (and other

parameters, as battery waste). The results we have

obtained conﬁrm this fact.

Currently we are carrying out more experimenta-

tion in order to (a) determine the key parameters to

be tuned in order to improve the performance of our

algorithm, and (b) compare our algorithm to similar

approaches, as the RPL (Winter et al., 2012) algo-

rithm.

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