Fault-tolerant Coverage in Dense Wireless Sensor Networks

Akshaye Dhawan and Magdalena Parks

Department of Mathematics and Computer Science, Ursinus College, 610 E Main Street, Collegeville, PA, U.S.A.

Keywords:

Wireless Sensor Networks, Coverage, Fault-tolerance.

Abstract:

In this paper, we present methods to detect and recover from sensor failure in dense wireless sensor networks.

In order to extend the lifetime of a sensor network while maintaining coverage, a minimal subset of the

deployed sensors are kept active while the other sensors can enter a low power sleep state. Several distributed

algorithms for coverage have been proposed in the literature. Faults are of particular concern in coverage

algorithms since sensors go into a sleep state in order to conserve battery until woken up by active sensors.

If these active sensors were to fail, this could lead to lapses in coverage that are unacceptable in critical

applications. Also, most algorithms in the literature rely on an active sensor that is about to run out of battery

waking up its neighbors to trigger a reshufﬂe in the network. However, this would not work in the case of

unexpected failures since a sensor cannot predict the occurrence of such an event. We present detection and

recovery from sensor failure in dense networks. Our algorithms exploit the density in the recovery scheme to

improve coverage by 4-12% in the event of random failures. This fault tolerance comes at a small cost to the

network lifetime with observed lifetime being reduced by 6-10% in our simulation studies.

1 INTRODUCTION

Wireless Sensor Networks (WSNs) have attracted a

lot of research interest due to their applicability in se-

curity, monitoring, disaster relief and environmental

applications. WSNs consist of a number of low-cost

sensors scattered in a geographical area of interest and

connected by a wireless RF interface. Sensors gather

information about the monitored area and send this

information to an external node known as the base

station. The radio on board these sensor nodes has

a limited range and allows the node to transmit over

short distances. In most deployment scenarios, it is

extremely expensive for each node to communicate

directly to the sink and hence, the model of commu-

nication is to transmit over short distances to other

peers.

In order to keep their cost low, sensors are

equipped with limited energy and computational re-

sources. The energy supply is typically in the form of

a battery and once the battery is exhausted, the sensor

is considered to be dead. Sensor nodes are also lim-

ited in terms of memory and processing capabilities.

Hence, harnessing the potential of these networks in-

volves tackling a myriad of different issues from al-

gorithms for network operation, programming mod-

els, architecture and hardware to more traditional net-

working issues. For a more detailed survey on the var-

ious computational research aspects of Wireless Sen-

sor Networks, see the survey papers (Akyildiz et al.,

2002; Chong and Kumar, 2003; Iyengar and Brooks,

2004a; Schmid and Wattenhofer, 2006; R

omer and

Mattern, 2004).

In this paper, we examine the problem of sensor

coverage. Many intended applications of Wireless

Sensor Networks involve having the network moni-

tor a region or a set of targets. To ensure that the

area or targets of interest can be covered, sensors

are usually deployed in large numbers by randomly

dropping them in this region. Deployment is usu-

ally done by ﬂying an aircraft over the region and

air dropping the sensors. Since the cost of deploy-

ment far exceeds the cost of individual sensors, many

more sensors are dropped than needed to minimally

cover the region. This leads to a dense network and

gives rise to an overlap in the monitoring regions of

individual sensors. A simplistic approach to meet the

coverage objective would be to turn on all sensors af-

ter deployment. But this needlessly reduces the life-

time of the network since the overlap between moni-

toring regions implies that not all sensors need to be

on at the same time. This can also lead to a very

lossy network with several collisions happening in the

medium access control (MAC) layer due to the den-

sity of nodes. In order to extend the lifetime of a sen-

sor network while maintaining coverage, a minimal

133

Dhawan A. and Parks M..

Fault-tolerant Coverage in Dense Wireless Sensor Networks.

DOI: 10.5220/0004273501330137

In Proceedings of the 2nd International Conference on Sensor Networks (SENSORNETS-2013), pages 133-137

ISBN: 978-989-8565-45-7

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

subset of the deployed sensors are kept active while

the other sensors can enter a low power sleep state.

Several distributed algorithms for coverage have been

proposed in the literature (Lu and Suda, 2003; Prasad

and Dhawan, 2007; Dhawan and Prasad, 2009; Cardei

and Du, 2005; Kumar et al., 2004; Cardei et al.,

2005).

Faults are of particular concern in coverage algo-

rithms since sensors go into a sleep state in order to

conserve battery until woken up by active sensors. If

these active sensors were to fail, this could lead to

lapses in coverage that are unacceptable in critical ap-

plications. Also, most algorithms in the literature rely

on an active sensor that is about to run out of battery

waking up its neighbors to trigger a reshufﬂe in the

network. However, this would not work in the case

of unexpected failures since a sensor cannot predict

the occurrence of such an event. In a denser network,

more sensors should be available to compensate for

the failing sensor. In this paper, we exploit the den-

sity to recover from failure. This robustness comes at

a small cost to the network lifetime.

The remainder of this paper is as follows. In Sec-

tion 2, we present related work in the literature on

both coverage and fault-tolerance in Wireless Sensor

Networks. In Section 3 we discuss our failure recov-

ery scheme. Section 4 presents our simulation set-up

and results. Finally, we conclude in Section 5.

2 RELATED WORK

Coverage Algorithms. In this section, we brieﬂy sur-

vey existing approaches to maximizing the lifetime

of sensor networks, while meeting certain coverage

objectives. (Cardei and Wu, 2006) gives a more de-

tailed survey on the various coverage problems and

the scheduling mechanisms they use. (Sahni and Xu,

2004) also surveys the coverage problem along with

other algorithmic problems relevant to sensor net-

works.

The coverage problem has been shown to be

NP-complete in (Abrams et al., 2004; Cardei and

Du, 2005). The approach taken in the literature

has evolved around algorithmic techniques commonly

used to work with NP-complete problems. This is

indicated by the use of solutions to related problems

such as that of ﬁnding domatic partitions, coloring, set

covers, etc, as applied to coverage. Initial approaches

to the problem in (Slijepcevic, 2001; Cardei and Du,

2005; Abrams et al., 2004) considered the problem of

ﬁnding the maximum number of disjoint covers. A

number of distributed heuristics are presented in (Lu

and Suda, 2003; Prasad and Dhawan, 2007; Dhawan

and Prasad, 2009; Cardei and Du, 2005; Kumar et al.,

2004; Cardei et al., 2005). We only discuss one spe-

ciﬁc algorithm below in the interest of space. Our al-

gorithm improves upon this algorithm by adding fail-

ure recovery to it.

Face-based Coverage. (Berman et al., 2004) present

a coverage algorithm that models the network as a

planar graph and then uses the faces of this graph to

determine which sensors should be active in order to

cover the area of interest. Each face represents a ge-

ographical area that can be sensed by one or more

sensors. These faces are found by tracing the outer

edge of the range of each sensor. The intersection

points of all of these ranges are found and placed as

nodes on a graph, with the lines along the edge of

each sensor becoming edges in the graph. A sensor

then simply needs to keep track of each of its faces

and which neighboring sensors are capable of cover-

ing those faces.

Each sensor can be in one of various states at any

given time. For every sensor, the basic states include

Active, Idle, Permanent, Vulnerable and Terminated.

Active sensors sense data for the faces they cover and

transmit that data to the base station. Idle sensors nei-

ther sense data nor transmit data but instead eneter a

low power sleep state. Permanent sensors, are a vari-

ation on active sensors and they also sense data and

transmit data, but are distinguished from active sen-

sors because they have at least one face for which they

are the only covering sensor. Vulnerable sensors are

sensors that are still in the process of deciding a state.

Finally, a Terminated state refers to sensors that are

no longer capable of contributing to the network.

Sensors periodically go through state transitions,

where they will change from one state to another. The

algorithm is broken into rounds and at the beginning

of each round, if an idle or active sensor has a vulner-

able neighbor, it will become vulnerable. This allows

a sensor to trigger a change in the cover set by becom-

ing vulnerable. This is called a reshufﬂe. Vulnerable

sensors become idle if all of its faces are covered by

either an active sensor, a permanent sensor, or a vul-

nerable sensor with a larger power supply. Vulnerable

sensors become active if they have a face not covered

by an active or vulnerable sensor. If a sensor’s battery

level goes low enough to only last a few more rounds,

it will transition to vulnerable to trigger a reshufﬂe so

that it may be replaced. Finally, any sensor becomes

terminated if its battery level goes to zero, and any

non-terminated sensor becomes permanent if it has a

face that is covered by no other non-terminated sen-

sor. See (Berman et al., 2004) for more details.

Fault-tolerance. Fault Tolerance has been exten-

sively studied in the broader context of distributing

SENSORNETS2013-2ndInternationalConferenceonSensorNetworks

134

computing (Pradhan, 1996), and also in the context

of Wireless Sensor Networks. (Iyengar and Brooks,

2004b) examines the connection between classical

fault tolerance techniques and sensor networks and

provide two case studies. (Gupta and Younis, 2003)

examines fault tolerance in clustering, but only looks

at heterogeneous sensor networks where clustering is

performed by special high energy gateway nodes that

are much more powerful than regular sensor nodes.

(Kuhn, 2006) examines fault tolerant clustering by

formulating clustering as the k-fold dominating set

problem. They give a probabilistic algorithm for a

unit disk graph network. The authors go on to present

fast approximation algorithms for the special cases of

graphs with low arboricity in (Lenzen and Watten-

hofer, 2010). In (Raj and Ramesh, 2008), the authors

consider clustering in a sensor network to be deployed

in landslide detection applications. However, they de-

ﬁne a fault as a sensor that gives incorrect values as

opposed to a failed sensor.

3 FAILURE RECOVERY

We started with the basic face-based coverage algo-

rithm of (Berman et al., 2004) and looked at ways to

detect sensor failure in dense deployment scenarios.

Failures can cause this algorithm to break down since

the algorithm depends on sensors being able to trigger

a reshufﬂe before going to the terminated state. In the

case of sensor failure, this may not happen. A fail-

ing sensor does not know in advance that it is going

to fail, so it has no opportunity to trigger a reshufﬂe.

When an active or permanent sensor fails, the faces it

was responsible for will go uncovered until the next

reshufﬂe happens, which may be a while. An ideal

fault tolerant algorithm would be able to immediately

activate enough of those sensors to negate the effects

of the sensor failure.

In the case of dense deployments, the degree of

the graph representing the network (i.e., the num-

ber of neighbors a given sensor has) is larger than

that for less dense deployments. This leads to a net-

work organization wherein, as the density increases,

the number of active/permanent sensors within com-

munication range of each other also likely increases.

This coupled with the inherent broadcast property

of the wireless channel would allow neighboring ac-

tive/permanent sensors to detect the failure of other

active sensors since upon failing these sensors would

cease broadcasting. We modiﬁed the base algorithm

to detect failure by having each active sensor con-

struct a list of 1-hop neighbors that were either active

or permanent. The sensor then listened for broadcast

activity from these neighboring nodes. If at the expi-

ration of a timer, no activity was detected for one of

these neighboring, the sensor monitoring for activity

would trigger a reshufﬂe for the network by going to

a vulnerable state.

Without adding this mechanism for fault toler-

ance, the faces the failed sensor was responsible for

may go uncovered for a length of time depending

on the frequency of reshufﬂes in the system. If the

reshufﬂes are frequent, the sensors will take over the

failed sensor’s faces more quickly, but will not be

able to immediately recover from the failure. Faces

that the failed sensor was responsible for would be

uncovered until all neighboring sensors became vul-

nerable for the next reshufﬂe. In most cases, there

are some active or permanent neighboring sensors, re-

gardless of the number of vulnerable neighboring sen-

sors. Transitioning these sensors to vulnerable would

help ensure that recovery for the failed sensor hap-

pens more quickly. The biggest impact on the net-

work’s coverage after a sensor failure is immediately

after the sensor fails. Sensors responding to the fail-

ure would be able to respond immediately to cover

any faces that they can reach and the failed sensor was

responsible for. This allows the initial impact of the

failure to be minimized. It is worth mentioning that

there will always be at least one face that cannot be

immediately recovered in this way, since the failing

sensor was active or permanent because no other sen-

sor was covering one of its faces.

4 SIMULATION RESULTS

In order to study the performance of the proposed

fault-tolerance model, we have conducted some pre-

liminary simulations. We implement the face-based

algorithm as described in (Berman et al., 2004) in

C + + and then modify this implementation to include

the failure recovery process described in Section 3.

For our simulation setup, we randomly create

dense networks of sensors. We varied the number of

sensors in a 30x30 area. We ran the simulation with

25, 50, 75, and 100 sensors scattered randomly in this

area. For any given network size, we created ﬁve

different randomly generated conﬁgurations. Addi-

tionally, each sensor was given a random battery level

between 10 and 20 units and a communication range

of 10 m. We used a reshufﬂe threshold of 2. The

reshufﬂe threshold is the maximum drop in battery

level before triggering a reshufﬂe. In order to inject

failure into the simulation, we randomly determined

how many sensors should fail each round based on

a failure chance of 5%, up to a maximum of 5% of

Fault-tolerantCoverageinDenseWirelessSensorNetworks

135

Table 1: Failing sensor statistics.

Sensor count Type Active neighbors Vulnerable neighbors Faces covered

25 basic 5.810 1.776 19.724

25 fault tolerant 5.879 7.690 17.672

50 basic 5.771 8.593 49.008

50 fault tolerant 5.372 15.947 39.796

75 basic 5.536 15.762 64.548

75 fault tolerant 5.205 22.217 58.843

100 basic 7.421 18.345 66.817

100 fault tolerant 6.528 27.549 53.724

the total sensors failing in one round. For every sen-

sor that failed, we made the sensor enter a terminated

stated immediately. In the fault tolerant version, any

non-terminated sensors immediately became vulnera-

ble. We ran this both with the normal version and the

fault tolerant version, and ensured that the same sen-

sors failed at the same time for both the normal ver-

sion and the fault tolerant version, in order to better

compare the two. The simulation was terminated once

the coverage dropped to below 80% of the maximum

possible coverage from the beginning of the simula-

tion.

Our ﬁrst goal was to track the number of active

sensors each failing sensor had as a neighbor. At the

time of each sensor’s failure, we recorded the number

of active neighbors, vulnerable neighbors, the number

of faces the sensor was covering, and the number of

faces with no other non-terminated sensor. The aver-

age over each of the ﬁve conﬁgurations can be seen

in Table 1. These active neighboring sensors become

vulnerable in reaction to the detection of the sensor

failure, triggering a reshufﬂe, and are very important

in the fault tolerant version. By design all these net-

works are extremely dense and hence we expect to see

multiple active neighbors for each failing sensor.

The implementation of the failure recovery algo-

rithm also led to an improvement of coverage as ex-

pected since reshufﬂes triggered by the detection of

a failed sensor improved the amount of coverage.

We tracked the total time for which the network was

100% covered and the fault tolerant version covered

between 4-12% more area than the basic algorithm.

This improved coverage came at a slight cost to the

network lifetime. We tracked the time by which net-

work coverage dropped below 80% and used this to

terminate the simulation. Figure 1 shows this data

for the different network sizes and for sensors with

different ranges. As can be seen from the ﬁgure,

the improved coverage gained through fault-tolerance

comes at a 6-10% decrease in lifetime in the worst

case (which happens at the higher range). At the

lower ranges, our fault tolerant algorithm provides

similar network lifetime to the basic algorithm.

Figure 1: Lifetime of the Network in rounds.

5 FUTURE WORK AND

CONCLUSIONS

In this paper, we present a fault-tolerance recovery

scheme for coverage in wireless sensor networks that

exploits the density of the network to detect and re-

cover from failures. The algorithm improved cov-

erage by 4-12% at the cost of reducing lifetime by

6-10%. Future work on fault-tolerant coverage so-

lutions includes exploring the use of a heterogeneous

collection of sensors, where some sensors have signif-

icantly more battery power than the rest. We are also

exploring a modiﬁed algorithm to determine which

sensor is best suited to cover certain faces based on

how many faces it would be responsible for, instead

of basing the decision entirely on battery levels.

REFERENCES

Abrams, Z., Goel, A., and Plotkin, S. (26-27 April 2004).

Set k-cover algorithms for energy efﬁcient monitoring

in wireless sensor networks. Information Processing

in Sensor Networks, 2004. IPSN 2004. Third Interna-

tional Symposium on, pages 424–432.

Akyildiz, I., Su, W., Sankarasubramaniam, Y., and Cayirci,

E. (2002). A survey on sensor networks. IEEE Com-

mun. Mag., pages 102–114.

Berman, P., Calinescu, G., Shah, C., and Zelikovsky, A.

(2004). Power efﬁcient monitoring management in

sensor networks. In Wireless Communications and

SENSORNETS2013-2ndInternationalConferenceonSensorNetworks

136

Networking Conference, 2004. WCNC. 2004 IEEE,

volume 4, pages 2329–2334. IEEE.

Cardei, M. and Du, D.-Z. (May 2005). Improving wireless

sensor network lifetime through power aware organi-

zation. Wireless Networks, 11:333–340(8).

Cardei, M., Thai, M., Li, Y., and Wu, W. (2005). Energy-

efﬁcient target coverage in wireless sensor networks.

In IEEE Infocom, 2005.

Cardei, M. and Wu, J. (2006). Energy-efﬁcient coverage

problems in wireless ad hoc sensor networks. Com-

puter Communications, 29(4):413–420.

Chong, C.-Y. and Kumar, S. (2003). Sensor networks: evo-

lution, opportunities, and challenges. Proceedings of

the IEEE, 91(8):1247–1256.

Dhawan, A. and Prasad, S. K. (2009). A distributed algo-

rithmic framework for coverage problems in wireless

sensor networks. International Journal of Parallel,

Emergent and Distributed Systems(IJPEDS).

Gupta, G. and Younis, M. (2003). Fault-tolerant clustering

of wireless sensor networks. IEEE Wireless Commu-

nications and Networking, 2003, pages 1579–1584.

Iyengar, S. S. and Brooks, R. (2004a). Computing and

communications in distributed sensor networks. Spe-

cial Issue, Jr. of Parallel and Distributed Computing,

64(7).

Iyengar, S. S. and Brooks, R. (2004b). Computing and com-

munications in distributed sensor networks.

Kuhn, F. (2006). Fault-tolerant clustering in ad hoc and

sensor networks. In In 26th International Conference

on Distributed Computing Systems (ICDCS.

Kumar, S., Lai, T. H., and Balogh, J. (2004). On k-coverage

in a mostly sleeping sensor network. In MobiCom ’04:

Proceedings of the 10th annual international con-

ference on Mobile computing and networking, pages

144–158, New York, NY, USA. ACM.

Lenzen, C. and Wattenhofer, R. (2010). Minimum dominat-

ing set approximation in graphs of bounded arboric-

ity. In Distributed Computing, volume 6343 of Lec-

ture Notes in Computer Science, pages 510–524.

Lu, J. and Suda, T. (20-21 Oct. 2003). Coverage-aware self-

scheduling in sensor networks. Computer Communi-

cations, 2003. CCW 2003. Proceedings. 2003 IEEE

18th Annual Workshop on, pages 117–123.

Pradhan, D. K., editor (1996). Fault-tolerant computer sys-

tem design. Prentice-Hall, Inc., Upper Saddle River,

NJ, USA.

Prasad, S. K. and Dhawan, A. (2007). Distributed algo-

rithms for lifetime of wireless sensor networks based

on dependencies among cover sets. In HiPC ’07: Pro-

ceedings of the 14th International Conferenmce on

High Performance Computing. Springer.

Raj, R. and Ramesh, M. (2008). Fault tolerant clustering

approaches in wireless sensor network for landslide

area monitoring. In Proc. of the 2008 International

Conference on Wireless Networks (ICWN’08), pages

107–113.

omer, K. and Mattern, F. (2004). The design space of

wireless sensor networks. IEEE Wireless Communi-

cations, 11(6):54–61.

Sahni, S. and Xu, X. (2004). Algorithms for wireless sensor

networks. Intl. Jr. on Distr. Sensor Networks.

Schmid, S. and Wattenhofer, R. (2006). Algorithmic mod-

els for sensor networks. In Parallel and Distributed

Processing Symposium, 2006. IPDPS 2006. 20th In-

ternational, pages 11 pp.–.

Slijepcevic, S.; Potkonjak, M. (2001). Power efﬁcient or-

ganization of wireless sensor networks. Communi-

cations, 2001. ICC 2001. IEEE International Confer-

ence on, 2:472–476 vol.2.

Fault-tolerantCoverageinDenseWirelessSensorNetworks

137