Guidelines and Challenges Towards the Implementation of Intelligent

Sensing Techniques in a Water Quality Prediction Application

Marcos X.

Alvarez, Mois

es S

anchez, Olga Zlydareva, Gregory M.P. O’Hare and Michael J. O’Grady

CLARITY: Centre for Sensor Web Technologies, School of Computer Science and Informatics,

University College Dublin, Belﬁeld, Dublin 4, Ireland

Keywords:

Environmental Wireless Sensor Networks (EWSNs), Water Quality Forecast, AFME, Crowdsourcing.

Abstract:

SmartCoasts is an INTERREG 4A project aimed at providing novel solutions for real-time monitoring and

forecasting of coastal water quality. The intended predictive system relies on freely available online weather

forecasts and a suite of real-time meteorological data measured across a river catchment. In a preliminary

stage, a prototype has been developed taking the real-time data from GPRS loggers deployed at strategically

located stations according to a centralised architecture. Even though such system has proven its suitability

providing accurate predictions, certain pitfalls that hamper usability have been detected. Adding intelligent

capabilities to the sensing nodes might help to overcome such situation. This paper presents a general overview

of the current situation and discusses some of the major challenges and difﬁculties that need to be faced in

order to set up a really smart Environmental Wireless Sensor Network.

1 INTRODUCTION

For more than a decade, Wireless Sensor Networks

(WSNs) have been widely adopted both in indoor

and outdoor monitoring applications. While indoor

locations are usually less exposed to external inter-

ferences and weather hazards, which eases durabil-

ity, maintenance and repeatability of the underlying

hardware and software, outdoor applications are more

prone to be affected by the surrounding conditions.

Nevetherless, applications where broad coverage area

is a must, such as climate monitoring (Arampatzis

et al., 2005), water quality monitoring (Le Dinh et al.,

2007) and precision agriculture (Hwang et al., 2010),

to cite some, are becoming nowadays increasingly

popular.

The initial achievements in deploying Environ-

mental Wireless Sensor Networks (EWSNs) as a reli-

able solution for data collection and observation were

mostly based on centralised architectures where sen-

sor nodes simply act as data collectors and transmit-

ters towards one or more base stations and lack any

processing capabilities, as this happens in a single

server (Martinez et al., 2004). Such approach is sim-

ple and robust, but lacks ﬂexibility on scaling and

software upgrading. Moreover, maintenance is com-

plicated due to the remote location of the nodes.

On the other hand, distributed EWSNs are use-

ful in many scenarios, such as water quality moni-

toring and forecasting, where wide area continuous

monitoring and minimum human intervention are re-

quired. To achieve such goal, the nodes in the net-

work should collaborate in a self-managed manner,

taking autonomous decisions about gathering, pro-

cessing and transmitting useful data at minimum en-

ergy cost.

This paper is a case study on the deployment of an

EWSN aimed at feeding a marine water quality fore-

casting system for short and mid-term predictions. It

is our goal to outline the main lessons learnt from

the work conducted until now, providing some gen-

eral ideas that could contribute to enable the reliable

implementation of generic environmental monitoring

applications. In particular, we consider that upgrading

from a centralised architecture, based on stand-alone

data loggers, to a distributed one might contribute to

improve the general performance of the forecasting

system.

The rest of the paper is organised as follows. In

Section 2 the currently deployed system and its main

components are described. Section 3 identiﬁes the

drawbacks which we faced during the exploitation of

SmartCoasts, while Section 4 introduces the proposed

solutions, ﬁnishing with the conclusions in Section 5.

428

X. Álvarez M., Sánchez M., Zlydareva O., M. P. O’Hare G. and O’Grady M..

Guidelines and Challenges Towards the Implementation of Intelligent Sensing Techniques in a Water Quality Prediction Application.

DOI: 10.5220/0004902104280431

In Proceedings of the 3rd International Conference on Sensor Networks (MOEOD-2014), pages 428-431

ISBN: 978-989-758-001-7

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

Figure 1: SmartCoasts deployment area map. Measurement

locations: - River level; - Weather station; - Rain

gauge; - Microbial sampling; - ADCP buoy; - Water

quality validation point.

2 GENERAL OVERVIEW OF THE

SMARTCOASTS PROJECT

Smart Coasts (SCs) (SmartCoasts, 2013) is an Irish

and Welsh INTERREG 4A project broadly motivated

by the EU Bathing Water Directive, which encour-

ages the development of information and communi-

cations technology (ICT) tools and real-time public

information systems in order to improve the main-

tenance of public health and increase the number of

beaches passing the new EU standards, thus quali-

fying for awards such as the Blue Flag. SCs is cur-

rently being validated in the Bray Bay (Ireland), and

across the catchment of the local River Dargle. Fig-

ure 1 shows a map of the area under study.

The development of a water quality monitoring

and prediction framework as the one described here

leans on contributions coming from a wide spectrum

of research ﬁelds, such as Microbiology, Civil Engi-

neering, Computer Science and Telecommunications.

This is an implicit consequence of the holistic ap-

proach followed in the SCs project. Thus, a rich va-

riety of sources act as system inputs, as Figure 1 sug-

gests. The core of the marine water quality forecast-

ing system comprises, on the one hand, an integrated

catchment-coastal model to simulate ﬂow and con-

taminant transport from the Dargle to the coast, and

on the other, a database that provides a simple way

to access and manage the large amounts of input and

output data required and generated by the system –a

detailed description of both components and the sys-

tem inputs can be found in (Bedri et al., 2013)–. Fig-

ure 2 traces out the architecture of the described sys-

tem as well as all the data ﬂows involved.

2.1 Main Features of the Deployed

Network Nodes

The primary meteorological phenomenon driving the

integrated model is forecasted rainfall, which is

amended by real-time observations when a signiﬁcant

deviation is detected. In-ﬁeld data across the catch-

ment are collected by commercial data loggers devel-

oped by Isodaq, known as Frogs (IsodaqFrog, 2013)

(see Figure 4). These devices run on an 8-bit micro-

controller and are equipped with a GSM/GPRS mo-

dem, which can be connected to an internal or exter-

nal antenna. Several digital, analogue (16-bit A/D)

and SDI-12 sensor inputs are provided.

A battery life of up to 7 years is possible but, under

the current conﬁguration –the Frogs are transmitting

the collected data on a daily basis–, the estimation is

reduced to two years. This ﬁgure will be dramatically

reduced should the sampling rate be increased, as de-

sired for example when big rainfall events occur. In

such situations, the integrated model should get up-

dates every 15 minutes, which would drop battery life

expectancy to only 3 months if the Frogs would keep

working continuously at this rate.

3 DRAWBACKS REVEALED BY

THE CURRENT NETWORK

IMPLEMENTATION

The network currently in operation cannot be actually

considered an EWSN, since the Frogs do not interact

with each other and follow a point-to-point commu-

nication schema through a GPRS base station, thus

completely relying on the public data network infras-

tructure. Such approach has demonstrated its validity

within the scope of the research work described here,

but also unveils a number of weaknesses.

First of all, some of the course sections of the

River Dargle and its tributaries are located in dark or

marginal areas where the GSM/GPRS signal coverage

is precarious, as Figures 3 illustrate. This has strongly

conditioned the ﬁnal location of the Frog data loggers,

as not the most suitable places in terms of data rele-

vance have been chosen, but those as close as pos-

sible where the base station was reachable. More-

over, signal fading events, unpredictable in the radio

propagation study made during the design phase of

the project, have been sporadically experienced as a

consequence of the changing surrounding conditions,

strongly affecting the radio link reliability.

Secondly, since the density of the deployed log-

gers is very low and their functionality is restricted

GuidelinesandChallengesTowardstheImplementationofIntelligentSensingTechniquesinaWaterQualityPrediction

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Figure 2: SmartCoasts architecture and data ﬂow schematic.

by the features that the commercial infrastructure of-

fers, the reactivity to the changes in the environment

conditions is very limited. This is because the Frogs

can only be scheduled to send the collected data on a

ﬁxed-period basis. On the contrary, it is desirable that

the network initiates a sequence of events, should a

certain event exceed a predeﬁned threshold. For ex-

ample, for the sake of our application, it would be

really interesting to increase the sampling rate down-

stream, when the rainfall at the river spring outnum-

bers a certain amount, in order to better capture the

ﬂow rise and the evolution of the faecal contamina-

tion washed off.

Finally, the determinism that the communication

scheme shows, allows a quite accurate estimation of

the power consumption in the long term, which is def-

initely a beneﬁt, but on the other hand, provides a sub-

optimal solution. The lack of processing capabilities

in the sensing nodes impedes taking autonomous de-

cisions about who, when and what to transmit, which

would improve the energy management and thus, the

Figure 3: GSM (left) and GPRS (right) signal coverage es-

timation for the River Dargle catchment area. The colour

bars indicate an increasing strength, meaning the left-most

colour a poor signal and the right-most one a good signal.

No signal areas are represented in white.

life expectancy of the network nodes. Given the rela-

tively high costs, both in economic and energy terms,

of transmitting to the base station at this stage, it is

desirable to enable nodes with short-range communi-

cation features when possible, since not always it is

necessary to transmit all the collected information to

the base station, should that transmission could be de-

livered to a neighbouring node.

4 PROPOSED SOLUTIONS

Until now, our research work has mainly focused on

developing a functional prototype that demonstrates

the feasibility of the intended water quality forecast-

ing system. This is why most of the attention has been

given to the integration aspect, with special focus on

data acquisition and management. The next step –

currently in progress– is aimed at ﬁnding an optimal

solution to the trade-off between energy consumption

and transmission rate. Some improvements both in

the hardware and software levels are proposed.

In the short term, the sensor nodes are being up-

graded from an 8- to a 32-bit microcontroller archi-

tecture. At this moment we are developing initial

lab tests with the ARM-Cortex M3 family, harness-

ing a SAM3S-EK2 evaluation board (see Figure 4).

The newly designed devices offer enough processing

power to deal with the computational requirements

in the future scenario, where intelligence will be dis-

tributed over the entire network, as the nodes are sup-

posed to react immediately when and where an unex-

pected event happens. That functionality is achieved

as a result of implementing the Java ME-based Agent

Factory Micro Edition (AFME) agent platform on all

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Figure 4: Isodaq’s Frog RX data logger (left) and SAM3S-

EK2 evaluation board (right).

the network nodes. The ﬁnal goal behind this deci-

sion is maximizing the network life expectancy, as

a consequence of diminishing the number and data

length of the costly transmissions to the base station,

which would be replaced when possible by cheaper

short-range communications between the nodes. Be-

sides, enabling the Java technology in the new nodes

opens the door to wider interoperable infrastructures

that will be able to run the same code on different

nature platforms, thus easing environmental data in-

terchange.

Finally, in the long-term it is expected to allow

the aggregation of crowdsourced data to the sensed

values. With the ubiquitous availability of mobile de-

vices, citizens have turned into a valuable source of

environmental data, as suggested by other state-of-

the-art research works (CobWeb, 2013). In our opin-

ion, such information would contribute to ﬁll the tem-

poral and spatial gaps that might arise in wide area

deployments like this. For instance, an angler by the

river could detect a hazardous discharge, take a geo-

referenced photo and upload it to the system, and this

would trigger the network to increase the sampling

rate in the area and advice the local authorities to take

safety measures. The database-centered approach that

has been already followed will facilitate a smooth in-

tegration of the crowdsourced inputs, as it works as

an abstraction layer that masks the complexity of han-

dling both the data uploaded by the human users and

the outputs from the different sensors involved. This

is a remarkable feature, given the fact that one of the

major bottlenecks in the deployment of environmen-

tal monitoring systems is the great number of differ-

ent proprietary data formats that the sensors can pro-

duce. Furthermore, such database will be the core

of an environmental information system that will pro-

vide short- and mid-term forecasts about the bathing

water quality conditions, as well as historical reports

delivered in open source formats.

5 CONCLUSIONS

In this paper the case study of a bathing water qual-

ity forecasting system based on stand-alone data log-

gers was presented. We consider the deployed sys-

tem as a starting point towards the implementation of

a fully-featured Environmental Wireless Sensor Net-

work with intelligent data managing and routing.

The main pitfalls encountered until now and the

solutions envisaged to overcome those difﬁculties

have been described. In our opinion, the ideas out-

lined here can beneﬁt several other general-purpose

outdoor environment monitoring applications.

ACKNOWLEDGEMENTS

Smart Coasts is supported by the European Re-

gional Development Fund (ERDF) through the Ire-

land Wales Program (INTERREG 4A) and by Sci-

ence Foundation Ireland (SFI) under the grant

07/CE/I1147.

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