A User Conﬁgurable Metric for Clustering in Wireless Sensor Networks

Lina Xu, David Lillis, G. M. P. O’Hare and Rem Collier

Computer Science and Information,University College Dublin, Dublin, Ireland

Keywords:

User Conﬁgurable, Adaptation, Clustering, Voronoi.

Abstract:

Wireless Sensor Networks (WSNs) are comprised of thousands of nodes that are embedded with limited energy

resources. Clustering is a well-known technique that can be used to extend the lifetime of such a network.

However, user adaption is one criterion that is not taken into account by current clustering algorithms. Here,

the term “user” refers to application developer who will adjust their preferences based on the application

speciﬁc requirements of the service they provide to application users. In this paper, we introduce a novel

metric named Communication Distance (ComD), which can be used in clustering algorithms to measure the

relative distance between sensors in WSNs. It is tailored by user conﬁguration and its value is computed from

real time data. These features allow clustering algorithms based on ComD to adapt to user preferences and

dynamic environments. Through experimental and theoretical studies, we seek to deduce a series of formulas

to calculate ComD from Time of Flight (ToF), Radio Signal Strength Indicator (RSSI), node density and hop

count according to some user proﬁle.

1 INTRODUCTION

Clustering is a major approach to energy efﬁciency in

Wireless Sensor Networks (WSNs) (Heo and Varsh-

ney, 2005). However, most of the existing work

has focuses on energy saving, ignoring diverse re-

quirements from application developers (referred to

as “users” in this paper). They expect that the WSN

performs in a certain way that can beneﬁt their ap-

plications the most. For example, for some applica-

tions, the user may want an immediate response in a

clustered network rather than simply reducing power

consumption. In another case, a WSN application de-

veloper may demand transmission quality more than

any other criterion. Scenarios such as these involve

a tradeoff between competing demands. Power sav-

ing is no longer an overriding factor in the system.

An advanced WSN should be sufﬁciently intelligent

to understand users’ preferences and thereafter adapt

as those preferences change. Currently there is little

work showing any interest in adapting user preference

in a clustering algorithm. (Liu, 2012) concludes that

Quality of Service (QoS) is neglected in current re-

search and it needs to be addressed in the future. Be-

sides energy efﬁciency, transmission quality and net-

work latency are two additional basic metrics used to

evaluate the QoS of a system. Voronoi diagrams (Au-

renhammer, 1991) form a fundamental data structure

for many clustering algorithms. In Voronoi cluster-

ing, Euclidean distance needs to be calculated from

the coordinates of sensors or from some other mech-

anism for representing distance.

In this paper, we motivate a novel metric—

communication distance (ComD) to substitute the

concept of Euclidean distance. A brief implementa-

tion and analysis are provided. We also discuss the

usability and alternative for ComD in real systems.

Since ComD is user conﬁgurable, clustering algo-

rithms adopting this metric are also user-conﬁgurable.

In a network, if cluster heads (CHs) are already pre-

selected randomly or through some CH election al-

gorithm, each sensor can calculate its own ComD to

every CH by using a formula that is selected based on

user conﬁguration. Thus a sensor joining the closest

CH allows CHs to form locally optimal clusters. Us-

ing this method, ComD can evaluate and score every

single link between a sensor and a CH in the network

based on user preference. In the ideal model, every

sensor would join the CH that would render network

performance closer to the user’s preference. ComD

can overcome two particular limitations associated

with typical Voronoi-based clustering algorithms:

1. Euclidean distance cannot be used to reliably de-

termine the communication quality between sen-

sors. Even though two sensors are close to each

other geographically, their radio connection may

221

Xu L., Lillis D., M. P. O’Hare G. and Collier R..

A User Conﬁgurable Metric for Clustering in Wireless Sensor Networks.

DOI: 10.5220/0004800002210226

In Proceedings of the 3rd International Conference on Sensor Networks (SENSORNETS-2014), pages 221-226

ISBN: 978-989-758-001-7

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

prove inadequate due to obstacles.

2. Voronoi-based approaches based on Euclidean

distance do not accommodate user preferences.

ComD reﬂects user preferences. Clustering algo-

rithms using ComD therefore will gain adaptive

and conﬁgurable features.

2 RELATED WORK

A wide body of research on clustering algorithms for

WSNs is available. In particular, three notable sur-

vey papers have been published (Abbasi and Younis,

2007; Boyinbode et al., 2010; Liu, 2012). Clustering

algorithms tend to focus on speciﬁc objectives, for ex-

ample, energy efﬁciency, load balancing or increased

scalability. However, the implications of clustering

strategies for adapting user preference have received

little attention. Individual clustering algorithms use

different metrics to decompose the network into con-

nected clusters. Examples of such metrics include

distance, hop count and cluster size (Boyinbode et al.,

2010). The metrics are pre-ﬁxed in the clustering al-

gorithms and only fulﬁl certain requirements.

LEACH (Heinzelman et al., 2000) uses Radio Signal

Strength Indicator (RSSI) as a metric to cluster sen-

sors. In general, RSSI can be seen as a Link Quality

Estimator (LQE) to estimate the transmission relia-

bility. However, rather than using RSSI to guarantee

link quality, LEACH uses it from an energy reduction

perspective. Although AWARE (Urteaga et al., 2011)

concludes that it can achieve a better Packet Receive

Rate (PRR), it is not clear why this is the case. Ad-

ditionally, their clustering algorithm design does not

explicitly address the issue of reliable transmission.

(Tang and Li, 2006) provide a QoS control scheme on

a cluster-based WSN by changing data transmission

rates rather than clustering the network from a QoS

perspective. (Akkaya and Younis, 2003) introduce

a routing protocol that can ﬁnd a least cost, delay-

constrained path based on a clustered WSN. (Saukh

et al., 2006) presents a generic metric for tree routing

protocols by combing two QoS metrics: 1) end-to-end

success rate and 2) resource demand. Experimental

results show that it provides considerable energy sav-

ing with equivalent end-to-end packets success rate

comparing to other metrics.

3 MOTIVATION

Clustering algorithms can organise the sensors into

clusters to achieve speciﬁc objectives. However, lit-

tle existing work incorporates a user adaptive feature.

Since different application developers have their own

preferences on the performance of a WSN, support-

ing user conﬁgurable network clustering becomes es-

sential and should be the focus of the future research.

Users view the performance of a network from three

perspectives: network latency (L), transmission relia-

bility (R) and energy consumption (C). L, R and C are

normally treated as independent measurements of a

network. However, they are intimately related to each

other and optimising for one perspective can impact

on the others. For example, if the network reliability

is improved, retransmission may be alleviated, so the

energy cost and the network latency may correspond-

ingly be reduced.

Our goal is to develop a new metric ComD that ac-

counts for all three perspectives both individually and

jointly. It measures the logical distance between sen-

sors and can be used in Voronoi clustering to substi-

tute the concept of physical distance (Euclidean dis-

tance). Each user conﬁguration constructs a unique

non-Euclidean space. The ComD of the same link in

different spaces can have different values. The value

of ComD determines how close the performance of

a given connection can be to the user’s requirements.

If a link between two sensors is close to the user re-

quirement, the logical distance is short and vice versa.

While the physical sensor network is ﬁxed, the log-

ical structure transforms in different spaces accord-

ing to user preference. This metric allows a WSN

to dynamically re-cluster itself as user requirements

change. This will be achieved by designing ComD to

be a user-aware measure of the quality of a link be-

tween two sensor nodes. In order to achieve this, we

must ﬁrst decompose L, R and C and understand their

underlying interrelationships in more detail.

4 ANALYSIS AND EXPERIMENT

ON ComD

4.1 Overview of ComD

Since the value of ComD is entirely determined by

user conﬁguration on the QoS criteria (L stands for la-

tency, R stands for reliability and C stands for power

consumption), different calculation formulae should

be considered for different cases, as shown in Table 1.

L can be set to be low to indicate a design for low la-

tency. If there are no constraints on L, it will be set

to 0. It is the same situation with R and C. 7 dif-

ferent conﬁgurations are available. The conﬁguration

(0,0,0) is not considered since it means there is no

constraint on the network performance. ComD for

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Table 1: User conﬁgurations (ComD column reﬂects the

formulae in the following sections).

Case L R C ComD

0 0 Low (3)

0 High 0 1/(2)

0 High Low TBC

Low 0 0 (1)

Low 0 Low TBC

Low High 0 TBC

Low High Low TBC

conﬁguration C

, C

and C

are guaranteed ﬁxed for-

mulae that will be presented in the following sections

based on experiments. Other cases requiring com-

binations of criteria are marked “TBC” and will be

addressed in future work. Equation (1) (2) and (3)

present the calculations for L, R and C. The calcu-

lation formula of ComD for conﬁguration C

is the

reciprocal of Equation ( 2). The reason is that the

higher transmission quality between two sensors, the

shorter ComD should be. For other cases, since there

is a tradeoff between different criteria, to simplify our

problem, we set equal priority to each of them.

We believe that L, R and C are determined by ser-

val common factors: hop count, radio signal strength

(RSSI), time of ﬂight (ToF) and node density. RSSI

can indicate link quality between sensors. Since net-

work latency is measured at the user end, ToF in this

paper speciﬁcally refers to application level commu-

nication time rather than hardware signal level. ToF

reﬂects the time delay on a link. A communication

path may be formed by several links, therefore hop

count is also considered. To reveal the relationship

between the three metrics, we need to discover how

these factors inﬂuence each of the QoS metrics.

4.2 Network Assumptions

Several assumptions are made:

1. The Base Station (BS) is placed far from the sens-

ing ﬁeld. It is assumed to exhibit 100% reliability.

2. Each sensor has a ﬁxed location and only belongs

to a single cluster at any point. Sensors can con-

nect to their CHs through single or multi-hop.

3. All the sensors, including the CHs, adopt a ﬁrst-

come-ﬁrst-served (FCFS) processing pattern.

4. When the WSN starts, every sensor broadcasts its

own information to its neighbours and records the

RSSI, ToF from its neighbours.

Since limited work is focusing on application level

ToF, it needs careful investigation. In contrast, a lot of

research has examined the relationship between RSSI,

transmission power and link quality, so our analysis

for this area is partially founded on the existing work

(Baccour et al., 2012).

4.3 Network Latency

Network latency depends on the communication de-

lay between two sensors, which can be inﬂuenced

by several factors (e.g. distance or sensor process-

ing ability). Application-level ToF not only reveals

the communication time spent over the air, but also

the processing time on a sensor. Therefore it is highly

related to network latency.

To examine network latency, experiments were con-

ducted using SunSPOT nodes (Smith, 2007) embed-

ded with the CC2420 radio chip. All the nodes are

time synchronised to simplify the test. Otherwise,

round-trip time would be required. To investigate the

relationship between application level ToF and dis-

tance in an ofﬁce area, experiments were performed

by varying the transmitter’s location between 1 me-

tre and 35 metres. The transmitter created a connec-

tion with the receiver at 1 m. it was then moved

in 1 m interval, with the average ToF value for 10

packets recorded each time. The results from two re-

peated experiments are shown in Figure 1. This il-

lustrates that there are no major differences for ToF if

distance < 21m. When distance > 21m, ToF is unpre-

dictable and unstable. This is because the link quality

is weak and retransmission frequently occurs. Due to

the establishment of the connection, the ToF of the

ﬁrst packet is slightly longer. As we can see, when

retransmission does not occur, distance will not af-

fect application level ToF. The major reason is that the

time spent over air is too slight to be reﬂected on ap-

plication level ToF. We conclude therefore that physi-

cal distance is not an important factor in determining

network latency (L).

Figure 1: Single hop application level ToF for different dis-

tance.

In the above experiments, the measured ToF is based

on a single hop. However, a sensor may need more

than one hop to communicate with a certain CH. This

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Figure 2: Experiment 1: S

, S

and S

10m

need single hop to the receiver, S

20m

needs 2 hops with the transit from S

Figure 3: Experiment 2: S

10m

was changed from single hop to 2 hops with transit from S

. Other conditions keep the same

as in experiment 1.

motivates a further experiment in order to discover

the relationship between the number of hops and ToF.

The relationship helps to estimate the overall ToF of

an entire path based on the ToF of each single link

and hop counts. Four transmitters (S

, S

10m

and S

20m

) were placed at ﬁxed locations (1, 5, 10 and

20 meters) and each of them sent 100 packets to the

BS, within 1 or 2 hops (S

, S

and S

10m

were con-

ﬁgured for 1 hop, while S

20m

required 2 hops). In

experiment 1, as shown in Figure 2, S

and S

’s

ToF is mostly in the range [20,30] ms, while S

10m

’s

ToF has a somewhat larger range [20, 100] ms with a

standard deviation (STD) of 126.8ms because its con-

nection quality is not comparable with those at S

, which necessitates retransmission. However, for

single hop communication, the communication time

predominantly stays within [20,30] ms. S

20m

’s 2-hop

ToF is mostly in [30,40] ms range, longer than a sin-

gle hop but less than twice of it.

In experiment 2, S

10m

was changed from a single

hop to 2-hop with all other variables being held con-

stant. Following this, its ToF is increased to the range

[30,40] ms with the STD of 16.9ms. The reason for

lower STD is that the 2-hop connection is more reli-

able than the single hop and retransmission is allevi-

ated. The ToF of the other sensors stays in a similar

range as in experiment 1. If the link quality is poor,

retransmission will occur. In such a case, from the

user perspective, the required data is delayed. This

represents a further reason why we choose applica-

tion level ToF, since it is related to link quality and

can reﬂect the time cost on retransmission.

The multi-hop experiment conﬁrms the conclusion

drawn from the single link experiment namely that

distance has limited effect on application level ToF.

However, due to that the noise level changed in the

same environment, retransmission rate at 10 m is

higher than the results that are observed from the sin-

gle link experiment. The network delay is inﬂuenced

by multi-hop ToF that is determined by single hop

ToF and hop counts. In a real network, sensors can

only transmit data when the channel is clear, which

means that no other transmitter is using that channel.

As a result, the local density of the CH will also affect

the network latency (Kim et al., 2012). The network

delay between two sensors can be presented as a func-

tion of ToF, hop count and node density:

L = l (ToF,Hops,Density). (1)

4.4 Transmission Reliability

A packet can reach the receiver successfully only

when the over-air connection (the link) between two

sensors is reliable and the receiver’s buffer is not full

(Youseﬁ et al., 2010). RSSI has been proven to be a

good link quality estimator over a reasonable amount

of measurements (Baccour et al., 2012). It exhibits

a high correlation with PRR and it is more efﬁcient

than PRR. In our ofﬁce environment, the relation-

ship between RSSI and PRR is investigated through

experiments and the result is illustrated in Figure 4.

In ComD, we can use RSSI as a link quality metric.

Adopting the same idea as (Lin et al., 2006), if the

RSSI is over a speciﬁed threshold, we can assume that

the link is transmission reliable. This threshold is re-

lated to the environment, hardware and the required

quality. From the experiment in Section 4.3 we can

see that retransmission can be reﬂected in the applica-

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224

Figure 4: The relationship between RSSI and PRR.

tion level ToF. Meanwhile retransmission reveals poor

link quality. Therefore we believe that ToF can also

act as a proxy for link quality. Using the combination

of ToF and RSSI can address the link quality better. A

packet can be received only when the receiver’s buffer

is not full. The probability of overﬂow is determined

by data load, which depends on node density (Klein-

rock, 1975). However, the model is not practical in

real system since the receiving rate from all sources is

hard to measure before the network structure is ﬁnal

formed. The idea of predicting receiving rate from lo-

cal density is used. Hence in order to determine the

transmission reliability between two sensors, we use

a function that combines ToF, RSSI and node density:

R = r (RSSI, ToF, Density). (2)

4.5 Energy Cost

A clustering algorithm normally can save power for a

network in two distinct ways: One way is through the

clustered structure. A CH can assemble the data from

each sensor in the cluster and then send the assem-

bled data to the BS. This saves energy through reduc-

ing the power consumption of transmission of each

individual sensor. However, it increases the overhead

of the CHs. In some extreme cases, some clusters

are much larger than others. The CHs of these larger

clusters will die earlier. As a result, the sensors in a

WSN should be distributed to each CH evenly, to bal-

ance the power usage. The node density surrounding

a CH will dictate the number of sensors that join the

cluster. Thus node density is a consideration when

determining energy cost. Additionally, (Wang et al.,

2006) indicates that multi-hop consumes more energy

than single hop under realistic circumstances. There-

fore sensors joining a CH through fewer hops can also

save more energy. The other way is through transmis-

sion power control. If the RSSI between two sensors

is higher than a threshold, the transmitter can lower

its transmission power to reduce the transmission cost

while maintaining the link quality. The threshold

erases the problem that when decreasing the transmis-

sion power, link quality becomes poor. This technique

Figure 5: Users view network latency (L), reliability (R)

and power consumption (C) as independent objectives, but

they are highly coupled.

is called transmission power control. More power can

be saved if the RSSI is higher (Lin et al., 2006). For

the above reasons, the energy cost for the communi-

cation between two sensors can be expressed as:

C = c (Density, Hops, RSSI). (3)

4.6 Multi-choice User Case

The relationship between network latency (L), reli-

ability (R) and power consumption (C) is shown in

Figure 5. Three of them are highly related to each

other and they can be determined by four factors in-

cluding local node density, ToF, hop count and RSSI.

However, how signiﬁcantly the four factors can affect

each of the three criteria is unequal. For conﬁgura-

tion C

, C

and C

in Table 1, the user only have one

single requirement. Therefore we can simply assign

ComD to C, 1/R (The higher the transmission relia-

bility is between two sensors, the shorter ComD ought

to be.) or L. For other multi-selection user conﬁgura-

tions, it is necessary to combine two or three metrics

to calculate ComD and this needs careful investiga-

tion. By default, we can assume that the user puts

equal weight on each criterion. One naive proposal

is to multiply the corresponding metrics. For exam-

ple, as C

combines C

(1/R), it can be

represented as ComD = C/R. The problem is that the

domains of function l, r, and c may demand the deter-

mination and application of some coefﬁcients for nor-

malisation. Furthermore, if RSSI affects C in a linear

manner while affecting R in a experimental manner,

when RSSI changes, R changes quicker than C. Con-

sequently, R may dominate the combination value.

Then the combination conﬁguration will have an un-

even priority on C and R. For the above reasons, the

multi-choice user cases still need further study.

5 DISCUSSION

In this paper, we choose four factors including ToF,

hop count, RSSI and node density to characterize net-

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225

work latency (L), reliability (R) and power consump-

tion (C). Although other factors can also be used,

there are several reasons supporting our choice:

1. The selected factors are easy to capture. Through

the broadcasting in the initial phase, ToF, RSSI

and local density can be known. The hop count

from a sensor to a CH is available from the packets

received from the CH.

2. The protocol only relies on communication infor-

mation. It is not necessary to know the physical

locations of the sensors, which cannot be easily

measured by the sensors themselves.

3. The measured values are obtained from real time

data, which makes ComD adaptive to a change-

able environment.

Currently the user conﬁguration of L, R and C is a bi-

nary choice. As we have mentioned in Section 4.1, for

the combination cases, we set equal priority to each

criterion. In the future, it will be implemented in a

manner that an application developer can put differ-

ent weight on the three metrics.

To support multi-hop communication in a cluster, not

only the CHs need to broadcast their information, but

also some other sensors. We call this process the

second-level broadcast. Deciding the number of sen-

sors that should perform second-level broadcasting is

a non-trivial problem. If there are not enough sen-

sors to broadcast, some sensors may be not able to

discover a multi-hop route.

6 CONCLUSIONS AND FUTURE

WORK

This paper provides a novel user-conﬁgurable met-

ric that facilitates user adaption in clustering algo-

rithms for WSNs. This metric is inﬂuenced by and

accommodates three performance objectives that nor-

mally exercise users in a WSN, namely: network la-

tency, transmission quality and energy consumption.

The underlying relationship between these three op-

erational parameters is revealed. In the future, more

work will be undertaken in the analysis of the inter-

relationship between these aspects so as to construct

a formula that balances them within a user conﬁgura-

tion. The performance of ComD will be evaluated in

both simulation and real time experiments.

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