Optimization of Energy-efficient Protocols with Energy-heterogeneity for Coverage

Preservation in Wireless Sensor Networks: An Empirical Study

Femi A. Aderohunmu, Jeremiah D. Deng, Martin K. Purvis

Department of Information Science, University of Otago

PO Box 56, Dunedin 9056, New Zealand{afemi,ddeng,mpurvis}@infoscience.otago.ac.nz

Abstract—The advances of wireless and sensing technologieshave opened up new doors for wide application of sensornetworks. To fully achieve the potentials of wireless sensornetworks, however, a few challenging issues have to be solved.While a majority of the research has focused on energypreservation, little attention has been paid to the coveragepreservation as a quality of services requirements. Anotherissue that is often overlooked is the inherent energy heterogene-ity among sensor nodes in the network. In this paper, we willconduct an empirical study on some improved heterogeneity-aware clustering protocols, and compare their performance onnetwork lifetime as well as coverage preservation under energyheterogeneity. We define a “full coverage time”, as well asa spatial uniformity measurement. Simulation results demon-strate the superiority of the new protocols in this respect. Ourconclusion is that the energy heterogeneity can be harnessedand it is promising to find optimized solutions for prolongednetwork lifetime and coverage preservation simultaneously.

Keywords-wireless sensor networks; clustering; coveragepreservation

I. INTRODUCTION

In recent years wireless sensor networks (WSN) have

gained much attention in the research communities due to

the fast advances in wireless communication technologies

that have paved the way for developing diverse applications

and services. The capabilities of sensor devices offers great

potential for use in different application domains ranging

from monitoring of our immediate environment to more

sophisticated industrial surroundings or production systems.

There is however a growing need for WSN nodes to handle

more complex tasks such as video streaming and biometric

recognition, and energy-saving remains a major requirement

for these battery-powered sensor nodes even though en-

ergy harvesting techniques are becoming available. Much

research attention has been given to energy conservation in

WSNs [3].

To provide robust functionalities in WSN applications,

another concern closely connected with energy consumption

is coverage maintenance. If the spatial distribution of live

nodes is not even during operation, the network can quickly

lose coverage to certain regions for monitoring. This is

highly undesirable for surveillance applications where a per-

sistent coverage is necessary for an application that requires

continuous surveillance in order to satisfy a specific quality

of service (QoS) constraint. Coverage maintenance has re-

ceived attention in recent studies [8], where a few coverage-

aware cost metrics are defined. In these approaches, sensors

that are important to the coverage task are less likely to be

selected as cluster leaders.

On the other hand, existing studies on clustered WSNs

have mainly focused on energy-homogeneous networks

where sensor nodes have the same energy capacities. In

realistic application scenarios, sensor nodes are prone to

failures and damages; on other occasions, nodes can be

replaced during network operation, and new nodes could

be deployed. These lead to energy heterogeneity among the

network nodes. So far, the effect of energy heterogeneity in

WSNs has not received much discussion. Overlooking such

a factor may result in not only reduced network lifetime, but

also speedier failure in maintaining network coverage.

Motivated by earlier works [7], [8], we investigate the

effect of energy heterogeneity in clustering protocol that

favors both energy preservation and coverage maintenance.

In a nutshell, we model the energy imbalance existing in

the network with a three-tier hierarchy and we analyze

the performances of the protocols with respect to coverage

preservation. We also investigate the spatial uniformity of

live node distribution and define a quantitative metrics for

performance evaluation.

The remainder of this paper is organized as follows.

We briefly review related work in Section II. New sensor

network models are introduced in Section III, followed by

our metrics defined for performance evaluation. Section IV

presents the simulation settings and the results obtained. Fi-

nally, in Section V we conclude the paper with a discussion

on future work.

II. RELATED WORK

The existing work related to clustering approach is so vast

that it would deserve an extensive literature review. Hence

we will only discuss a few related issues that is directly

2012 IEEE 14th International Conference on High Performance Computing and Communications

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2012 IEEE 14th International Conference on High Performance Computing and Communications

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DOI 10.1109/HPCC.2012.172

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connected with our work. Clustering is an effective approach

in handling energy management in WSNs. Low Energy

Adaptive Clustering Hierarchy (LEACH) [6] is a pioneering

work in this respect. In HEED [9] a hybrid criterion for

cluster-head selection was proposed based on residual energy

and a node’s proximity to its neighbors. Both LEACH and

HEED solutions assumed a homogeneous setup, which does

not conform to the real-world operation of a typical WSN.

Most likely, nodes will often have different energy levels as a

result of the terrain, recharging or new nodes deployment for

replacing dead ones. In Stable Election Protocol (SEP) [7],

it is demonstrated how energy heterogeneity can be better

utilized by adapting the cluster-head election probabilities

according to the sensors’ energy settings. The SEP protocol

uses two energy levels among sensor nodes to characterize

the heterogeneity problem. This is extended in the SEP-E

model [2], which proposed a three-node setup and further

adapted the cluster-head election probabilities to prolong the

network lifetime.

All the above works focused on a stochastic model to

improve the lifetime of WSN performance. Recently, a deter-

ministic model was proposed in [1], achieving a better per-

formance in network lifetime than the previous approaches

in both homogeneous and heterogeneous settings. This paper

is an extension to [1], by considering coverage preservation

and spatial distribution of energy as performance criteria.

On the other hand, the importance of network coverage

has been discussed along with network lifetime and energy

efficiency in [4]. Soro and Heinzelman proposed a set

of coverage-aware cost metrics and presented a cluster-

based network organization in [8]. They further proposed an

unequal clustering to force a balanced energy consumption.

A popular definition of functional network lifetime of

WSN is the time until the first node dies out [6], [7]. Others

defined the network lifetime to be the time in which the net-

work is able to perform sensing functions and transmit data

to the base station [4]. To our knowledge there is no network

lifetime definition related to coverage maintenance. Indeed,

spatial distribution of energy has hardly been quantitatively

investigated in the literature. In [6], the performance of the

LEACH protocol is visually compared with that of other

protocols, showing snapshots of live node distributions.

The contribution of this work is two-fold: 1) We define

new performance metrics that measure the coverage preser-

vance and coverage uniformity of WSNs; 2) we present an

empirical study that includes a number of recent clustering

protocols and evaluating their coverage performance under

energy heterogeneity settings.

III. THE WSN MODELS

A. The energy dissipation model

We consider the energy dissipation model as used in

a number of previous studies [6], [7]. Depending on the

transmission distance, two propagation modes are used: free

space and two-way. Details are given in [1].

B. Cluster formation in SEP-E and DEC

LEACH is based on the idea of using a random number

thresold to randomly decide whether a network node should

be elected as the cluster-head. SEP and SEP-E extended

this idea by adapting the threshold for different energy

capacities: normal, intermediate (not in SEP), and advanced

nodes. Details about the thresholding and random election

process are clearly outlined in [6], [7], [2].

The cluster formation process in DEC [1] is different

from the approaches used by LEACH, SEP and SEP-E.

DEC uses a deterministic model to elect cluster-heads (CH)

in each cluster, strictly depending on the residual energy

of each node as the network evolves. At the beginning

of the network, i.e., Round 1, the BS sets up the clusters

by choosing the nodes with the highest residual energy to

be the cluster-heads. In the subsequent rounds the clusters

self-elect the next cluster-head base on the residual energy

information which is piggy-backed during the exchange of

JOIN REQUEST sent by non-CHs to their respective CHs

at Round 1. This process continues until the sensor nodes

die out.

Once the cluster is formed, the CH node sets up a

TDMA schedule that governs the data transmission in the

cluster. The logical assignment of each non-CH node to a

cluster forms a logical Voronoi graph across the network.

The TDMA schedule ensures that there are no collisions

among data packets and also allows turning off the radio

components of each non-CH node at all time except dur-

ing their transmission [6]. In order to reduce interference

among multiple cluster-heads, the communication with BS

is achieved using CDMA codes.

More details of DEC are available in [1].

C. Network Coverage

In previous work [4], [8], there are two basic coverage

problems associated with clustered WSN: point coverage

and area coverage. The former is normally used in appli-

cations where the deployment is deterministic, while the

latter is used more often in random deployments. In our

case we deal with area coverage, and we define the network

coverage as the summation of coverage of each individual

node modeled as a circular area of radius R with the sensor

node sitting at the center. Without any specific applications

in mind, we assume a scenario which is delay tolerant and

requires continuous data delivery, but it is mission critical

to maintain the full coverage. Here we further assume that

each sensor node does not move once deployed, and is

equipped with an omni-directional antenna that monitors

its own range. Note that due to the random deployment of

sensor nodes in the simulation, it is possible for the network

to start with a coverage less than 100%, but normally the

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uniform distribution of the nodes allow the coverage to be

above 95% at start in our simulations.

Our coverage definition has two implications. First, it

deviates from the assumption in LEACH that sensor nodes

of the same cluster detect the same event. In LEACH it was

assumed that the distance between nodes within a cluster

is small compared with the distance from which events are

sensed. The robustness of coverage is inherent under this

condition: one or two nodes’ death will not cause coverage

loss because other nodes in the same cluster can still sense

the same event. Under the new definition, however, even

though there may be overlapped areas between sensor nodes,

the death of one node will potentially cause coverage loss.

It is therefore a more stringent definition. And secondly, it

is likely that the sensor nodes will sense different events,

which therefore makes the new definition more appropriate

for modeling monitoring applications that demand localized

information, e.g., wild-fire monitoring or motion detection

in a vast open space.

This leads us to define a new metric called ‘full coverage

time’ (FCT), which integrates the coverage maintenance

with network lifetime: FCT is the time from the beginning of

the network operation till the network suffers from coverage

loss. This is different from the previous definition of network

lifetime (NLT), which is only indirectly related to coverage

preservation. As initial node death may or may not cause

coverage loss, we have NLT ≤ FCT.

D. Spatial Uniformity

In some application scenarios that tolerate sporadic sensor

deaths, strict coverage preservation is unnecessary. Live

nodes evenly distributed across the entire monitoring area

can roughly maintain an effective sensing coverage. It is

however possible for a large amount of nodes of a certain

monitoring region to all suddenly die out, and that region

becomes lost in coverage. Along with energy consumption

over time and therefore potential node deaths, how evenly

the network manages to maintain its sensors’ energy across

the monitoring area is also relevant to maintaining effective

coverage. Hence, this translates to measuring the spatial

uniformity of coverage as the network evolves. A good

protocol design is expected to maintain spatial uniformity

of coverage, which implies that energy consumption is more

even, and it is less likely for the network to suffer from

coverage loss due to early node deaths.

To evaluate how energy consumption occurs spatially over

time, we choose a cutting point during the simulation where

the amount of dead nodes reaches 50%. The entire sensing

area is split by a s × s grid, and live nodes within each of

the (M/s)2 cells can be counted. We use the variance of the

number of live nodes across all cells as a measurement of

the spatial uniformity of coverage. Obviously the smaller the

variance is, the more uniformly energy is consumed across

the network, resulting in live nodes more evenly spread

over the sensing area. This idea is shown in Fig.1. We can

then carry out F-test to verify whether the difference on the

variance measurement is significant or not across different

protocols.

0 20 40 60 80 1000

20

40

60

80

100A 5x5 grid at 50% network operation

livenode

deadnode

Figure 1: Live node distribution can be examined, e.g., by

using a 5× 5 grid.

IV. SIMULATION

A. Experiment Setup

Following the scenario used in [6], [7], we assume a

100m × 100m region with 100 sensor nodes scattered

randomly within. MATLAB is used to implement the sim-

ulations of four protocols: LEACH, SEP, SEP-E and DEC.

Their simulation results are then compared. Common pa-

rameters used in the simulation are shown in Table I. The

Table I: Common parameter settings for all experiments.

Parameter Values

Eelec 50nJ/bit

EDA 5nJ/bit/message

Eo 0.5J

k 4000

Popt 0.1

εfs 10pJ/bit/m2

εmp 0.0013pJ/bit/m4

R 10/15mn 100

heterogeneity configurations that is tested for the protocols is

m = 0.2, b = 0.3, α = 3 and μ = 1.5. Different settings will

affect the operation of the network. For example, we let 20%and 30% of the nodes be advanced nodes and intermediate

nodes with additional energy levels (3 and 1.5 times more

energy than the normal nodes, respectively).

LEACH was proposed for homogeneous energy settings.

For fair comparison, we initialize the network with the same

energy configuration and run LEACH so as to assess how

it copes with the heterogeneous setting. To run SEP simu-

lations, the setting is configured in a way that it maintains

the same total energy as that of other protocols. Despite the

difference on the internal settings of the four protocols, it is

reasonable to compare their performance as they start with

the same or similar initial conditions.

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B. Simulation Results

1) Performance metrics: The metrics below are used to

assess the performance of all clustering protocols involved:

1) Stability period: the period from the start of the

network operation to the first node death.

2) Instability period: the period between the first node

death and the last node death.

3) Full Coverage-Time (FCT): the period from the start

of the network operation till full coverage is no longer

maintained.

4) Number of live (and dead) nodes per round.

5) Half-life: the period from the start of the network to

time when any node uses up half of its energy.

6) Spatial uniformity of energy spread in the network.

As explained in [7] the larger the stability period and the

smaller the instability period are, the better the reliability of

the clustering process of the network is. However, we need to

note the trade-off between the reliability and lifetime of the

network system. In some cases the last alive node can still

provide feedback, but this could in most cases be unreliable.

So we expect a balance between these trade-offs, which is

reflected in both the stability and instability periods.

Multiple simulation runs are carried out for all four

protocols: LEACH, SEP, SEP-E and DEC. We first examine

network coverage performance using FCT under energy

heterogeneity conditions, then we investigate the network

lifetime with different load of extra energy, and finally

examine the spatial uniformity by using visual comparison

of node heat-maps, and using the evaluation of spatial

uniformity statistics.

Table II: Summary of Average lifetime of the sensors for 10

trials with Etotal = 102.5J , when the BS is located inside

the sensing region.

Protocols FCT Stability Half-life Instability

LEACH 1241 995 478 4585

SEP 1398 1385 631 5050

SEP-E 1624 1450 704 3751

DEC 2075 1839 945 640

2) Network lifetime under heterogeneity: Fig.2 shows the

coverage evolution over time measured in rounds during

one set of the simulations. Obviously DEC keeps the full

coverage for the longest time. This is very important for a

surveillance application with strict coverage requirements,

e.g., situation management in disaster response [5]. If the

coverage requirement is relaxed down to 80% coverage,

DEC still performs the best. In same manner, SEP-E per-

formed better than both SEP and LEACH up till about 75%.

Furthermore, LEACH seems to take over SEP-E after around

2000 rounds, while SEP stands out for coverage as low as

60% i.e at about 3000 rounds. One may note that LEACH

and SEP manages to keep around 20% coverage as long as

4500 rounds into simulation, however, the reliability of the

sensing result is already in question. When less coverage is

required for some application, down to 60% for instance,

it seems that SEP and LEACH then outruns both DEC and

SEP-E.

1000 2000 3000 4000 5000 6000 70000

10

20

30

40

50

60

70

80

90

100

Rounds

Cov

erag

e(%

)

LEACHSEP−EDECSEP

Figure 2: Comparing coverage performance of DEC, SEP-E,

LEACH (m = 0.2, b = 0.3, α = 3 and μ = 1.5) and SEP

(m = 0.3, b = 0, α = 3.5 and μ = 0), Etotal = 102.5J .

Previous studies have shown that LEACH performs most

competitively when nodes are homogeneous [7]. This be-

havior is expected according to the optimized cluster-head

election in the homogeneous setup. The performance data of

the four protocols are summarized in Table II. On average,

the stability period as well as the half-life time of DEC and

SEP-E compared with LEACH and SEP are significantly

longer. Likewise the stability period of SEP is significantly

better than that of LEACH. The ranking of instability period

length is SEP, LEACH, SEP-E and DEC.

We further observe the performance of the protocols to

different load of heterogeneity as shown in Fig.3 where

the network lifetime (stability period) is plotted against the

percentage of total extra energy added to the network. It

is apparent that DEC significantly and increasingly outper-

forms the other protocols. Even though both SEP-E and SEP

are close in performance, SEP-E consistently outperforms

SEP. DEC, SEP-E and SEP continuously perform better as

energy heterogeneity increases in the network. On the other

hand, LEACH is majorly oblivious to the load of extra

energy heterogeneity. One of the advantages of the DEC

protocol is that it elects the cluster-heads based on their

respective residual energies hence it is able to cope well

with energy heterogeneity.

Further, FCT values are measured over ten simulation

runs respectively for the four protocols, and the statistics are

reported in Table II. Both DEC and SEP-E have a clear-cut

advantage over SEP and LEACH.

3) Spatial Uniformity: To visualize the energy distribu-

tion within the network over time, we plot out the ‘heat-

map’ of the sensor node energy at three points in time:

Round 1500 and Round 2200, roughly located at the stable

and instable periods respectively. The heat-maps are shown

in Fig.4 and Fig.5. A red pixel on the map indicates the

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9800

1000

1200

1400

1600

1800

2000

2200

Net

wor

k Li

fetim

e

Percentage of total extra energy (a*m +μ*b)

DECLEACHSEP−ESEP

Figure 3: Performance of DEC, SEP-E, LEACH and SEP as

the percentage energy load increases.

location is close to a live node. While the red fades away,

meaning the spot is still covered by a live node despite an

increase in distance. The blue color indicates the spot is not

covered by any live nodes.

From these figures, we observe that both DEC and SEP-

E offer better coverage compared with LEACH and SEP at

Round 1500, while SEP also outperforms LEACH. When

we examine the coverage in the early instability period at

Round 2200, DEC still shows a robust performance than

the other protocols. Further on, DEC is able to evenly

balance the energy consumption among the nodes resulting

to a prolonged FCT and hence the nodes die out closely at

the same time. In SEP-E, SEP and LEACH, the majority

of the normal nodes are dead but advanced nodes are

well preserved, and hence we see the prolonged instability

period, most obviously in SEP. It should be noted that these

behaviors of SEP-E, SEP and LEACH (in terms of coverage)

could be good for some applications with minimal coverage

requirements. But for a mission-critical application with full

coverage requirements, DEC is superior to SEP-E, SEP and

LEACH.

For quantitative analysis we use the spatial uniformity

defined in Section III-D. Grids of 4×4 and 5×5 granularities

are used to get the variance of live nodes in cells as the

spatial uniformity measure. For completeness, we have used

the same seed for the node distribution in order for good

comparisons among the protocols. The smaller the variances

are, the more uniform the live nodes distribute themselves

across the monitored region. To assess the difference on the

variance values obtained by different protocols, we resort to

the F-test, where the F-ratio (i.e., variance-ratio) indicates

the equality of two variances. If the the F-ratio is 1.00, it

indicates no statistical difference between the two values.

The bigger the ratio is, the more likely the two variances are

different. Usually it is expected that the p-value should have

less than or equal to 0.05 for a high statistical significance

level. Because of the pair-wise nature of the test, we conduct

the pairwise comparisons of DEC versus other protocols. As

it makes little sense to compare the early stages of network

operation, we choose the 50% network operation time, i.e.,

10 20 30 40 50 60 70 80 90 100

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40

50

60

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100

(a)

20 40 60 80 100

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(b)

20 40 60 80 100

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(c)

20 40 60 80 100

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(d)

Figure 4: The heat-map at Round=1500 for (a) DEC

coverage, (b) SEP-E coverage, (c) SEP coverage, and (d)

LEACH coverage.

10 20 30 40 50 60 70 80 90 100

10

20

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(a)

10 20 30 40 50 60 70 80 90 100

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(b)

10 20 30 40 50 60 70 80 90 100

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(c)

10 20 30 40 50 60 70 80 90 100

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(d)

Figure 5: The heat-map at Round=2200 for (a) DEC

coverage, (b) SEP-E coverage, (c) SEP coverage and (d)

LEACH coverage.

when the network reaches the point with only 50% nodes

still alive, as the cutting point of the simulation.

We conduct the F-test on results from five independent

experiments for each protocol and collect the pair-wise F-

ratios, giving the box-plot in Fig.6. DEC has the highest vari-

ance ratios compared with the other protocols with medium

sitting well above 2. Overall the difference is however only

statistically significant on the p = 0.10 level.

To summarize, we have used two methods to assess the

spatial uniformity of coverage: visual assessment of cover-

age heat-maps, and F-test on the defined spatial uniformity

measurement. Overall, DEC and SEP-E report better spatial

uniformity among the sensor nodes than both LEACH and

SEP. DEC is able to best tackle the effect of energy-

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2.0

2.2

2.4

2.6

Protocols

Var

ianc

e R

atio

DEC vs. SEP DEC vs. LEACH DEC vs. SEP−E

2.0

2.2

2.4

2.6

2.0

2.2

2.4

2.6

Figure 6: Comparison of spatial uniformity of the protocols.

The boxplot is generated from pairwise F-ratios averaged

over 5 independent experiments.

heterogeneity. This is reasonable, as the DEC model guar-

antees that the nodes with the highest residual energy gets

elected as cluster-head.

V. DISCUSSIONS AND CONCLUSION

The WSN clustering scheme, originally inspired by [6]

and further enhanced by [7], was proposed to cope with

energy consumption in WSN. Following this approach, this

paper introduced an empirical study on energy consumption

and coverage preservation of two new protocols SEP-E

and DEC in heterogeneous settings. Nodes in SEP-E elect

themselves as cluster-heads stochastically, based on their

energy levels, retaining more uniformly distributed energy

among sensor nodes. Nodes in DEC use a more deterministic

approach to elect themselves as cluster-head, thus showing

a better performance than the other protocols. Our results

show that DEC and SEP-E is more robust with respect to

network lifetime and the full coverage time. In most of

the cases, DEC and SEP-E maintain a balanced and more

uniform spread of energy among nodes.

Both the DEC and SEP-E model design can be very

important for applications that require continuous re-

energization of nodes throughout the data retrieval process,

by deploying new nodes to replace dead ones, or it could

be useful when testing the effect of introducing updated

sensor nodes into an existing network. This provides a

justification for the different energy levels we have used in

our simulations.

It should be noted that we have not specifically attempted

any mathematical framework for coverage optimization. We

have however demonstrated empirically that it is possible to

take advantages of the energy heterogeneity in WSNs for

both energy and coverage preservation. We have not either

tried out different heterogeneity settings exhaustively and the

simulations are conducted in a limited application scenario.

Furthermore, we have focused on an architecture with single-

hop routing (via the cluster-head), but for larger networks a

more reasonable solution is to use relayed, multi-hop routing

among the CHs. This would have impact on the energy

consumption patterns and the coverage uniformity, and these

will be interesting directions for further investigation in light

of energy heterogeneity.

ACKNOWLEDGMENT

This work has been supported in part by the University

of Otago Postgraduate Scholarship.

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