EFFICIENT NETWORK DESIGN FOR ENVIRONMENTAL

MONITORING APPLICATIONS

A Practical Approach

Farooq Sultan and Salam A. Zummo

Electrical Engineering Department, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia

Keywords: Wireless Sensor Network, LEACH, Network Life Time, Latency.

Abstract: The deployment of a wireless sensor network is highly dependent on the target environment. Once the

characteristics of the desired area are know, the question of network size arises. Factors like transmission

power level, cost of network deployment and the coverage area directly affect the size of the network. This

paper analyzes the behaviour of a typical multi-hop wireless sensor network operating in an outdoor

environment. By considering two separate cases; fixed cost and fixed deployment area, we present best

network set-up statistics based on actual received power measurements.

1 INTRODUCTION

The concept of a wireless sensor network (WSN)

involves the integration of sensing, processing and

communication abilities to create highly autonomous

networks. In order to combine sensors, processors

and radio devices, a detailed study of the desired

application as well as the capabilities of the

available hardware has to be done. Wireless nodes

are the building blocks of WSNs; sensing,

processing and communicating abilities are

integrated to produce miniature devices that can

form and maintain a network. The origins of WSN

trace back to the distributed networks program

(Lacoss, 1986) launched by DARPA in the late 80s

using bulky wireless devices to convey information

to the end-user. The advancement in the CMOS

technology has led to extremely small devices with

exceptionably high processing abilities giving rise to

tiny wireless nodes (MicaZ, Telos etc. by Crossbow

Technologies).

The set-up and operation of a WSN is highly

dependent on the application. The main emphasis of

the work presented in this paper is towards

environmental monitoring scenarios. Environmental

data collection requires the collection of multiple

sensor readings from geographically different

locations over time. These readings need to be

transmitted to a base station, where analysis can be

done to deduce results (Sun, 2010). From the

network deployment perspective, a large number of

nodes need to be placed (randomly or

deterministically) having the ability to relay/forward

information to the sink node.

This paper presents a model for the efficient

deployment of a WSN in an outdoor environment.

By carrying out received power measurement

analysis for a specially designed general purpose

wireless sensor node in an outdoor environment,

simulations have been done for the cases of fixed

cost and fixed coverage area. The designed

application provides the required network size, the

transmission power requirement and the coverage

area statistics for different combinations of the input

parameters.

This paper is organized as follows; Section 2

presents an overview of the WSN architecture,

Section 3 has the procedure for received signal

strength measurements, Results and analysis is

presented in Section 4 and the paper concludes with

a summary in Section 5.

2 WSN ARCHITECTURE

2.1 Setup Requirements

Environmental monitoring usually requires periodic

data logging, giving rise to extremely low data rates.

Compared to traditional single hop networks, the

WSN has some unique features (Ye, 2009);

Sultan F. and A. Zummo S..

EFFICIENT NETWORK DESIGN FOR ENVIRONMENTAL MONITORING APPLICATIONS - A Practical Approach.

DOI: 10.5220/0003614900690072

In Proceedings of the International Conference on Wireless Information Networks and Systems (WINSYS-2011), pages 69-72

ISBN: 978-989-8425-73-7

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

1) All nodes can form a network based on

some protocol.

2) All nodes can sense and route the

information to nearby nodes.

3) Nodes can join or leave the network

without any management issues.

The above mentioned requirements enable the

nodes to operate in a highly collaborative manner

thereby preserving the energy supplies for the longer

operation.

2.2 Network Structure

As mentioned in Section 2.1, the network topology

of the WSN is governed by the protocols adopted by

the nodes. For the results presented in this paper, a

hierarchical network topology is assumed based on

the low energy adaptive clustering hierarchy

(LEACH) (Baghyalakshmi, 2009). According to this

approach, all the nodes in the network consider

themselves as either cluster heads (CH) or member

nodes (MN). Figure 1 shows a graphical

representation of such a network.

Figure 1: Network layout of a typical WSN for

environmental monitoring.

The CH has the responsibility of collecting the

data from its MNs and relaying it to the next-hop

CH until it reaches the base station. MNs only

communicate with their respective CH using the

least possible transmit power (P

), whereas CH can

adjust their transmit power levels based on the

requirement of the network topology.

3 RECEIVED SIGNAL

STRENGTH (RSS)

MEASUREMENTS

RSS measurements were carried out using the

custom designed wireless sensor node in an open

outdoor environment (University stadium). The

transceiver unit, CC2420 (Chipcon, 2005), used on

the used wireless sensor node, offers five different P

settings including 0dBm. Moreover, the CC2420 has

the ability to calculate the RSS of the received

packet fairly accurately.

The actual RSS of a packet is not considered to

be an accurate representation of the environmental

conditions for large WSNs deployments as packets

can be received with extremely low RSS (<90 dBm).

Instead a different metric, packet receive rate (PRR),

is taken into account to represent the fraction of

packets received. PRR enables a much closer

understanding of the channel conditions by

considering packet loss; which is vital in

environmental monitoring applications.

Figure 2: PRR with varying transmitter-receiver separation

for four different P

in an outdoor environment.

Measurements for PRR relative to the

transmitter-receiver separation were done for four P

levels as shown in Figure 2. For 0 dBm, the

measurements were inconclusive as a perfect

reception was achieved even up to a separation of 70

m. The data presented in Figure 2 was used as a base

for determining the signal quality at a given P

. All

the statistics presented in the following sections of

the paper are based on these measurements.

4 RESULTS AND ANALYSIS

4.1 Fixed Cost and PRR

For this case, the cost of the network, in the number

of nodes available, and the PRR are provided as the

input to the model. This case presents the situation

in which, the user has a fixed number of nodes and

he requires a certain PRR as a measure of the QoS.

Figure 3 shows the results of a network comprising

of 50 nodes operating at a PRR of 0.9.

WINSYS 2011 - International Conference on Wireless Information Networks and Systems

Figure 3: Network life (in hours) for different cluster sizes

for a network comprising of 50 nodes operating at a PRR

of 0.9 for an outdoor vicinity of 50 m

From Figure 3, it can be observed that, for a

fixed number of nodes, regardless of the transmit

power level used; a lightly packed cluster topology

results in the maximum possible life of the network.

When the deployment area is also fixed, this

condition implies a larger number of lightly packed

clusters operating fairly close to each other. Due to

less inter-cluster distance, low P

is required to fulfil

the communication requirements between adjacent

clusters, hence increasing the overall operating life

of the WSN. As the individual clusters become

populated, the separation between them must be

increased to ensure maximum coverage of the given

area, thereby increasing the requirement of P

which

results in lowering the network operating time.

Tables 1 and 2 present the network lifetime

statistics for the case presented in Figure 3 for two

different PRR requirements. The PRR is a measure

of the link quality and is not dependent on the

network life time as observed from the figures in

Table 1 and Table 2. The maximum inter-cluster

spacing is inversely proportional to the PRR and is

extracted from the measurement data presented in

Figure 2. As the desired PRR is decreased (0.5 for

worst case), the inter cluster spacing increases

enabling the network to span a much larger area as

compared to a PRR of 0.9.

4.1.1 End-to-End Latency Analysis

The total time required for a packet from its

generation to the delivery at the base station is

referred to as the end-to-end latency. For

environmental monitoring applications in critical

situations (hospitals etc.), the latency plays an

important role and must be minimized for the data to

be of any meaningful value. NS-2 simulations of the

network mentioned in Figure 3, give the stats

Table 1: Network life (hours) and corresponding

maximum inter-cluster separation for PRR=0.9.

Cluster Size

(nodes)

(dBm)

-5 -10 -15 -25

2 53 56 59 61

5 32 35.13 37 38.3

10 30 31 33 34

25 28 29 31 32

50 27 29 30 31

Inter-cluster

Spacing (m)

12.6 2.5 1.3 0.6

Table 2: Network life (hours) and corresponding

maximum inter-cluster separation for PRR=0.5.

Cluster Size

(

nodes

)

-5 -10 -15 -25

2 53 56 59 61

5 32 35.13 37 38.3

10 30 31 33 34

25 28 29 31 32

50 27 29 30 31

Inter-cluster

Spacing (m)

23.7 6.7 3.3 0.9

provided in Figure 4.

When small cluster sizes are adopted, a large

number of clusters are required to cover a given

region. Although this increases the network

operation life, but makes the packet reach the base-

station over multiple hops. Delay at individual hops

adds up and becomes significant due to the sleep

cycles and congestion conditions.

Figure 4: End-to-end latency for a 50 node network

operating at varying cluster sizes at a PRR of 0.9 for an

outdoor vicinity of 50 m

0 5 10 15 20 25 30 35 40 45 50

0.5

1.5

2.5

3.5

4.5

5.5

Cluster size (nodes)

End-to-end latency (s)

EFFICIENT NETWORK DESIGN FOR ENVIRONMENTAL MONITORING APPLICATIONS - A Practical Approach

As it can be observed from Figure 4, the latency

could be as high as 5.2 seconds.

4.2 Fixed Area and PRR

Practical situations might require the user to specify

the coverage area instead of the cost (as in Section

4.1). For a PRR of 0.9 and an area of 50 m

the

network size (in clusters) is presented in Figure 5.

Figure 5: Network size (clusters) at different inter-cluster

transmission powers with a PRR of 0.9 for an outdoor

vicinity of 50 m

As expected, for a low P

a larger number of

clusters are required. The number of nodes within a

cluster is dependent on the actual intra-cluster

transmission power levels selected which depends

on the implementation scenario.

5 CONCLUSIONS

WSN deployment is a tricky job unless handled

wisely. This paper presented a model based on

actual PRR measurements to study the relationship

between critical factors like network lifetime, end-

to-end latency and the coverage plan for a given

area. By analyzing the simulation results, a trade-off

between network life and the end-to-end delay was

observed. Based on the target environment and the

required parameters, this model tends to provide the

best deployment plan according to the desired PRR.

ACKNOWLEDGEMENTS

The authors would like to acknowledge King Fahd

University of Petroleum and Minerals (K.F.U.P.M.)

for providing a highly conducive environment for

research. In addition, we would like to acknowledge

King Abdulaziz City of Science and Technology

(KACST) for funding this project under project

number ط م6 -2 .

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Baghyalakshmi, D., Ebenezer, J., SatyamurtY, S. 2010.

Low latency and energy-efficient routing protocols for

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CC2420.

-25 -20 -15 -10 -5

Inter cluster transmit power (dBm)

Network size (clusters)

WINSYS 2011 - International Conference on Wireless Information Networks and Systems