=Paper=
{{Paper
|id=Vol-2213/paper1
|storemode=property
|title=Ontological Requirement Specification for Smart Irrigation Systems: A SOSA/SSN and SAREF Comparison
|pdfUrl=https://ceur-ws.org/Vol-2213/paper1.pdf
|volume=Vol-2213
|authors=María Poveda Villalón,Quang-Duy Nguyen,Catherine Roussey,Christophe de Vaulx,Jean-Pierre Chanet
|dblpUrl=https://dblp.org/rec/conf/semweb/Poveda-Villalon18
}}
==Ontological Requirement Specification for Smart Irrigation Systems: A SOSA/SSN and SAREF Comparison==
https://ceur-ws.org/Vol-2213/paper1.pdf
?
Ontological Requirement Specification
for Smart Irrigation Systems:
a SOSA/SSN and SAREF Comparison
María Poveda-Villalón1, Quang-Duy Nguyen2, Catherine Roussey2,
Christophe de Vaulx3, and Jean-Pierre Chanet2
1
Ontology Engineering Group, Universidad Politécnica de Madrid, Spain
mpoveda@fi.upm.es
2
UR TSCF Irstea, Aubière, France
{quang-duy.nguyen, catherine.roussey, jean-pierre.chanet}@irstea.fr
3
Limos, UMR 6158, Aubière, France
christophe.devaulx@isima.fr
Abstract. Precision agriculture is nowadays getting more and more at-
tention in Europe. Due to the common water shortage problem, precision
irrigation could become a key activity to save and use water in a more
sustainable way. This paper builds upon an automatic irrigation system
implemented as a context-aware system in which context is acquired thanks
to a wireless sensor network. In such system, ontologies are used to solve inte-
gration problem of heterogeneous data provided by different types of sensors.
Moreover, ontologies enable reasoning over these data to enrich the context.
The automatic irrigation system will be installed on the pilot site of Irstea
called AgroTechnoPole, located in Montoldre. The main goal of this paper
is to analyze the SOSA/SSN and SAREF standard ontologies in regards to
the ontological requirements that arise from the AgroTechnoPole use case.
Keywords: Ontologies, Adaptive Context-Aware, Precision Irrigation
1 Introduction
In the 21st century, economical and environmental challenges force local governments
and citizens to improve processes and lifestyle habits to save precious and scarce
resources. One of the most critical resources is water. Agriculture consumes the most
water on a global scale [24]. There is a need to reduce water consumption while
maintaining the crop quality.
In the agricultural domain, farmers need to observe natural phenomenon to decide
appropriate activities to be carried out over the land. For example, farmers go to the
?
This research has been partially funded by the project “ConnecSens”, the “I-Site Clermont
- Programme WOW! Wide Open to the World - CAP20-25” (16-IDEX-0001) and the
ETSI STF534 “SAREF extensions” project. The CPER research project ConnecSens
is co-financed by the Auvergne Rhône-Alpes region in France via the program №1130,
and by the European Union via the FEDER fund.
2 M. Poveda-Villalón et al.
farmland to examine the crop growth and state of the soil before making irrigation
decisions. However, those activities are low-accuracy due to human lack of precision
in observations and also the rapid changes of weather. Therefore, this reduces the
yield and productivity.
Precision agriculture methods are intended to overcome this situation [21]: Digital
technologies are used to monitor farming environments and optimize agriculture
activities and production. Precision irrigation is one of the critical activities in pre-
cision agriculture. Accordingly, demonstration and experimental sites for precision
agriculture are emerging in the last years. For example, in the AgroTechnoPole
experimental farm located at Montoldre, a smart irrigation system is being developed.
The goal of such system is to capture the farm land data in order to activate an
irrigation system in a precise and accurate way. This type of system, also known
as adaptive context-aware systems, could benefit from semantic technologies like
ontologies to harmonize, integrate and reason over data. In the particular case of the
Montoldre site, an ontology is planned to be developed to semantically annotate the
data and provide inference over it.
The goal of this paper is two-fold: 1) to extract ontological requirements for an
irrigation context-aware system and 2) to analyze to what extent such requirements
are covered by two well-known standard ontologies for the Internet of Things (IoT)
domain. For extracting such requirements the Montoldre experimental-site set up,
and data has been taken into account. The ontologies selected for the analysis are
SOSA/SSN [9,10] (the new version of the Semantic Sensor Network ontology) and
SAREF [4] (Smart Appliances REFerence ontology). The study is limited to such
ontologies as they are well-known and adopted, available online, and overall due to
the fact that there are proposed and developed within standardization institutions.
In order to provide the reader with insights about context aware systems, Section
2 provides a description of such type of systems and their typical cycle of processes.
Then, the irrigation methodology to be applied in the Montoldre use case is presented
in Section 3 before listing the ontological requirements extracted in Section 4. Section
5 shows the analysis of covered requirements and discussion. Finally, related initiatives
are reviewed in Section 6 while Section 7 is devoted to the concluding remarks and
future lines of work.
2 Cycle of Processes for Smart Irrigation Systems
A context-aware system “uses context to provide relevant information and services to
users, where relevance depends on each user’s tasks” [1]. Moreover, an adaptive context-
aware system is a context-aware system that can “modify its behavior according to
changes in the application’s context” [5]. For example, a flood-aware system monitors
a watershed to take decisions to send alerts about flood risk. It becomes an adaptive
context-aware system, if it changes its monitoring schedule based on the flood risk [22].
The context of an adaptive context-aware system is considered as “any information
that can be used to characterize the situation of an entity. An entity could be a
person, a place, or an object that is considered relevant to the interaction between
a user and an application, including the user and applications themselves” [1]. Two
Ontological Requirement Specification for Smart Irrigation Systems 3
types of contexts can be defined: low-level context and high-level context [22]. The
low-level context contains quantitative data such as sensor measurements from WSN.
On the other hand, the high-level context contains qualitative data which is specified
according to the application that used this content. For example, the low-level context
"190 cbar" contains the quantitative value "190" produced by a soil sensor. In an
irrigation decision-making application, the low-level context is used to infer the high-
level context about the state of several entities. Based on the "190 cbar" the farming
plot state is inferred to be "dry". Rules are used to enrich the hight level context.
When the plot state is "dry" and the crop state is "10 leaf" then the irrigation state
is "On". Thanks to this high level context the application triggers irrigation actions.
In irrigation, an adaptive context-aware system has three specific components.
Firstly, a Wireless Sensor Network (WSN) plays the role of sensing and monitoring
the plot environment. Secondly, a Decision Support Systems (DSS) could: a) send
notifications to farmers to support them in the decision-making process; and b)
automatically make decisions and control the watering system. Thirdly, watering
devices are in charge of watering the soil in plots.
The processes of context-aware systems could be grouped and represented in a
cycle of processes. This cycle is divided into four phases: 1) context acquisition; 2)
context modeling; 3) context processing; and 4) context dissemination [17]. In adaptive
context-aware systems, adaptation process must be considered as an element of the
cycle of processes [22]. From our viewpoint, a fifth phase called "context exploitation"
that includes the adaptation process should be added to the cycle of processes. In this
sense, the adaptive context-aware cycle of processes for smart irrigation at Montoldre
is depicted in Figure 1 and its five phases are described as follows.
– Context acquisition: during this phase the context-aware system acquires raw
data from various sources. The primary source of data is raw measurement data
collected from sensors in the field. In addition, some weather data (forecast or
archive data provided by local weather station [20]) may be used to derive the crop
growth stage. Any knowledge about the plot or the crop, provided by farmers and
scientists, are also significant to improve the exactitude of the decision process.
– Context modeling: at this time raw data is automatically annotated and inte-
grated into the contextual system. These data will be structured and represented
in a correct format to become low-level context. Ontologies is considers as the best
candidate to model the low-level context including the concepts of deployment,
network, sensor, measurement, plot and crop.
– Context processing: along this phase, low-level context will be processed in order
to infer high-level context: this is a reasoning process. Rule-base or inference
engine can be applied to reason over the context.
– Context dissemination: at this step the high-level context previously generated
is distributed to external agents as other systems or users. Furthermore, when
external agents are humans, the high-level context must be transformed to a
human-readable format, for example by means of visualization techniques.
– Context exploitation: The high-level context is used by DSS process to run
irrigation actions and control the watering system. Adaptation processes are also
part of this phase. The contextual system can adjust the configuration of its
4 M. Poveda-Villalón et al.
Fig. 1: Phases of the Cycle of Processes for a Smart Irrigation System
components based on the context. For example, in a normal weather condition,
the WSN observes soil humidity each day. However during a rainy period, the
daily observation is unnecessary, then the WSN change its observation scheduling
once a week until the termination of the rainy period.
3 Human Decision System for Irrigation Scheduling: the
IRRINOV Use Case
In this section, we present the IRRINOV®4 method developed by Arvalis and its
partners. This method proposes a guide for farmers to make irrigation decisions
based on measurements of soil moisture sensors and pluviometers. The method is
designed to answer the following questions: (1) When to start the irrigation period
(i.e., when the watering devices should be installed on the plot)? (2) When to run
each irrigation? or What is the irrigation schedule ? (3) When to stop the irrigation
period (i.e., when farmers could withdraw the watering devices)?
The IRRINOV method provides a set of decision tables and recommendations that
allow farmers to schedule their irrigations on a single plot. Such method proposes nu-
merous variants depending on the soil, plot and crop types. We will use the IRRINOV
method of the region Limagne, this is, dedicated to maize crop plant on the clay-
limestone soil [2]. Following IRRINOV guidelines, the measuring equipment includes:
– An IRRINOV measuring station composed of 6 Watermark probes to measure
the soil water tension (tensiometer). Three Watermark probes should be placed
at 30 cm depth in the soil, and the other three should be placed at 60 cm depth
in the soil.
– A mobile pluviometer to measure the amount of water received by the crop
during an irrigation.
4
http://www.irrinov.arvalisinstitutduvegetal.fr/irrinov.asp
Ontological Requirement Specification for Smart Irrigation Systems 5
– A weather station with a pluviometer to measure the quantity of water received
by the crop during a rainfall.
The next sections describe some setups of the IRRINOV method that should be
taken into account for having a high-quality irrigation process.
3.1 Setup about Probes and Sensor Localization
The IRRINOV method specifies the localization of measuring equipment:
– The IRRINOV station should be located on a dominant soil (the main type of
soil of a plot) and accessible. The location of this station depends on the watering
system. The station should be placed between two sprinklers (i.e., watering devices
in the IRRINOV method of the region Limagne) and at least 60 m from the
edge of the plot. The Watermark probes should be located in two side-by-side
planting rows as in the schema presented in Figure 2.
– The mobile pluviometer should be close to the IRRINOV station. Its height
should be above the maximum height of crops and below the height of sprin-
klers. IRRINOV authors recommend that the pluviometer must be placed on
a telescopic standing foot to keep it higher than the crops.
– The agricultural weather station should be close to the plot, far away from
any buildings or trees, and at a specific height (below 2 meters which are the
maximum height of the crops).
3.2 Setup about Measurement Frequency
The IRRINOV station and the mobile pluviometer should be placed in the plot when
the crops reach the growth stage V25. The measurement could start 2 or 3 days after
the installation.
The watermark probes should be read once a week or every two or three days if
weather becomes dry. Moreover, a Watermark probe measurement should be carried
out:
– Before each planned irrigation, in order to confirm, delay or discard the irrigation.
– About 24 hours to 36 hours after the end of an irrigation round to evaluate how
effective the irrigation round has been (avoid measurements less than 24 h after
the end of irrigation round, because the measurements are unstable).
– After significant rains to evaluate their effects. Note that if rainfall amount is
under 10 mm then the irrigation should be run with no change.
The irrigation period should stop when the crops reach the growth stage R5.6
5
V2 is an ID defined in [15], and it is also named "5 leafs" according to the Arvalis growth
stage classification
6
R5 is an ID defined in [15], and it is also named "grain at 50% humidity" in Arvalis
classification system
6 M. Poveda-Villalón et al.
Fig. 2: Layout of Watermark Probes and Pluviometer in the Maize Rows
3.3 Setup about Validation of Measurement Value
To validate the measurement of Watermark probes, IRRINOV method establishes
that the difference between the values obtained from probes at the same depth should
not be more than 30 cbar. If the difference between probe measurements is above
30 cbar, it means that one of the probes is out of order and the farmer should go
in the field to correct the probe installation.
To obtain the voltage in cbar, the value read on the probe should be multiplied
by the correction coefficient which is specific for each batch of probes: For example,
probes from 2003 have a correction coefficient equal to 1.7.
A difference of 10 to 20 cbar of tension between two probes located at the same
depth is considered normal. Therefore, the IRRINOV method proposes to install
three probes per level of depth. The IRRINOV guidelines suggest farmers run an
irrigation when two probes out of three have reached the threshold value. The value
reached by two probes out of three is the one taken into account by the decision
method. The abnormal values are not considered. It is worth noting that when the
voltage value read from a Watermark probe is 199 cbar; there is a problem of contact
between the Watermark probe and the soil.
3.4 Setup about Decision Tables
The IRRINOV method [2] proposes several decision tables to determine when should
start the irrigation depending on soil type and crop growth stage.
The Table 1 determines when to run an irrigation for clay-limestone soil. This deci-
sion table is applied to maize crop when their growth stage is between V2 and V7.7 In
such table, cells contain the threshold of probes’ measurement as mention in section 3.3.
Let’s define two variables P robe30 (and P robe60) that represent the value reached
by two probes out of three probes at 30 cm depth (and at 60 cm depth).
7
V7 is an ID defined in [15], and it is also named "10 leafs" in the Arvalis growth stage
classification
Ontological Requirement Specification for Smart Irrigation Systems 7
Table 1: Duration of the Irrigation Round for Maize Growth Stage between V2 and V7
9 to 10 days 6 to 8 days below 5 days
P robe30 30 cbar 50 cbar 60 cbar
P robe60 10 cbar 20 cbar 20 cbar
total 40 cbar 70 cbar 80 cbar
The first column of a decision table should be read as “If the duration of irrigation
round is fixed between 9 and 10 days” and “when two probes at 30 cm depth out
of three (P robe30) are above the value 30 cbar and when two probes at 60 cm depth
out of three (P robe60) are above the value 10 cbar” or “when the total (P robe30
+ P robe60) is above 40 cbar”, then run an irrigation. Note that for this particular
decision table the two first lines are redundant with the last one due to the fact that
if P robe30>30 and P robe60>10, then P robe30+P robe60>40.
3.5 Automatic Irrigation System
Based on the IRRINOV human-oriented decision method, we would like to implement
an adaptive context-aware system for automatic irrigation. The automatic system
integrates the IRRINOV guidelines and will be deployed on the AgroTechnoPôle of
Irstea. The AgroTechnoPôle contains an experimental farm located at Montoldre
where researchers can test their prototypes such as robots, machinery, and wireless
sensor network. On this experimental farm, a weather station Davis Pro 2 is available
for air and weather measurements. Moreover, a significant number of sensors are
deployed in the field to monitor moisture and temperature of the soil.
The data about localization mentioned in section 3.1 should be acquired during
the context modeling phase to build the low-level context.
The frequency guidelines expressed in section 3.2 can be used to define the com-
munication and measurement frequencies of the network nodes, which are part of
our context-aware system. Note that detection of some rainfall or irrigation events
can trigger some measurement actions. Thus the output of the two pluviometers will
activate the measurement of the soil moisture nodes.
The guidelines proposed in 3.3 can be translated into rules to validate the data
stored during the context acquisition phase.
The decision table presented in section 3.4 could be translated by a set of rules
that look like:
If (8 days < IrrigationRoundDuration < 11 days) and (GrowthStage
< V7) and ((Probe30 + Probe60) >= 40), then (Irrigation state is true)
These rules are part of the context processing phase that will deduce from the
low-level context the high-level one. Note that the value of GrowthStage is a qual-
itative value that should be taken from a hierarchical list. Thus a specific function
should be defined to evaluate the “<” operator.
8 M. Poveda-Villalón et al.
4 Ontological Requirement Extraction
The goal of this section is to present ontological requirements of the adaptive context-
aware system in the Montoldre use case. To determine such requirements, we have
studied the IRRINOV method to identify its input and output data. We also analyze
all the information needed to evaluate the quality of an irrigation process. In addition,
several exemplary data gathered from the experimentations in Montoldre are taken
into consideration. The following ontological requirements have been extracted:
R1. Deployment: the devices involved in agricultural systems, for example in an
irrigation scenario, might be deployed in different ways depending on the station of
the agricultural year and the crop rotation due to a three-field system. In this sense,
the model needs to include deployment information. This requirement includes two
sub-requirements due to the spatiotemporal nature of agricultural deployments:
R1.1. Deployment Time: the temporal aspect of deployment needs to be
represented.
R1.2. Deployment Location: the geographical aspect of a deployment
needs to be represented.
R2. Plot: a deployment might involve one or more platforms in which the devices
are placed. The description of the plot includes the geometry of the plot, the
irrigation round duration for this plot and the size of the plot.
R3. Network Configuration: the sensor network deployed in an agricultural
scenario follows a specific network configuration. As a side note, we defined a
node as any device connected to other devices of the network which possesses a
unique address. In our use case, the connections between sensor nodes and other
nodes in the network should be represented. This requirement could be split into
four sub-requirements:
R3.1. Network Topology: the connections between specific nodes should
be represented.
R3.2. Network Communication: the communication protocols used be-
tween nodes in a network should be described.
R3.3. Node Status: the status of a node such as active and inactive, should
be represented. This information is essential for example in the sensor data
acquisition quality control.
R3.4. Node Role: the role of a node such as an end node or a routing node,
should be represented.
R3.5. Node Location: the precise locations of sensors need to be represented.
This requirement is more detailed than R1.2 as in the case of the IRRINOV
method, specific guidance about the distance between nodes is provided. In
this sense, this requirement refers to the exact location of nodes so that the
distance between them could be calculated, rather than generic locations.
R4. Device: specific agricultural oriented devices as well devices used in other do-
main should be represented. These devices might be further classified as sensor or
actuators; however, it is not necessary for all the devices involved in the network to
belong to one of these types. The following list of devices corresponds to the Mon-
toldre use case and it does not intend to be exhaustive in the agricultural domain:
Ontological Requirement Specification for Smart Irrigation Systems 9
– Sensors: Weather station; Tensiometric probe (Watermark); Pluviometer.
– Actuators: Water inlet valve of the watering gun.
– Sink node: all sensor nodes send data to the sink node.
– Server: local server that support part of the DSS.
Taking all this into account, this requirement could be further specified leading
to the following sub-requirements:
R4.1. Sensor: sensors should be described.
R4.2. Actuator: actuators should be described.
R4.3. System Componency: componency relation between devices should
be represented.
R4.4. Domain Specific Devices: agricultural domain oriented devices are
needed to be represented, for example, soil moisture sensors, pluviometer or
IRRINOV station.
R5. Measurement: the observations made by sensors should be represented in
the model including values of the observation, units of measures and time-related
information about the observation, for example, when was it observed or to what
period does the observation corresponds to. In particular, the following units of
measurements need to be represented in the use case at hand: Millimetre (mm),
Centibar (cbar), and Watermark unit of measure (the Watermark soil moisture
measure ranges from 0 to 200.) which is transformed to cbar. In addition, Celsius
degrees (ºC), Decibel-milliwatts (dBm) and Millivolt (mV) are usually needed
in a broader scenario for data integration in irrigation systems.
Taking all this into account, this requirement could be further specified to
represent use case specific unit of measurement needed:
R5.1. Domain Specific Units of Measurement: concrete units of mea-
surement are needed for the use case at hand, for example, the Watermark
specific units.
R6. Property: specific agricultural oriented properties as well properties also
used in other domain should be represented. The following list of properties corre-
sponds to the Montoldre use case and it does not intend to be exhaustive in the
agricultural domain: Soil moisture; Water received during irrigation; Temperature
of the soil; Temperature of the air; Ambient humidity; Precipitation; Plant growth
stage.
Taking this into account, this requirement could be further specified to rep-
resent use case specific properties needed:
R6.1. Domain Specific Properties: concrete properties to be observed or
act upon are needed for the use case at hand, for example, the soil moisture
or the water received by the plot during the irrigation activity.
R7. Feature of Interest: in most of the cases, when observing a property it
is needed to represent the entity for what such property is observed. For example,
when measuring temperature, one might distinguish whether it is the temperature
of a room or a person. This entity is usually referred to as the feature of interest.
Considering IRRINOV method, this requirement could be further specified as
follows:
10 M. Poveda-Villalón et al.
R7.1. Feature of Interest Depth: in the specific case of IRRINOV method,
the depth of the plot part being observed is an essential factor. the specific
location of the feature of interest should be taken into account considering
not only a general location or geographical coordinates but including also the
depth. Please note that, according to IRRINOV method, the critical value
is about the depth of the soil, not the location of the sensors. While in some
scenarios the location of the sensor will coincide with the depth of the soil
is observed, it can not be taken for granted that will always be the case. For
example, if remote sensor observation are used to detect the crop growth stage,
the location of the sensor is different from the location of the observed feature
of interest (plot crop). Note that the separation of locations, and thus the
ability to support in-situ, ex-situ and remotely-sensed observations, is aligned
with the OGC Observations and Measurements standard8 (O&M) model for
features of interest description also represented in the SSN.
R8. Action: in the case of the Montoldre use case the action “irrigation” is
needed to be represented.
R8.1. Domain Specific Actions: for the Montoldre use case, the action “ir-
rigation” is needed to be represented including the parameters such as the time
interval during which the action has to be carried out and the quantity of water
needed to be released. Note that the IRRINOV method does not provide guide-
lines on the amount of water that should be spread by the irrigation system.
R9. Crop: in farm irrigation methods, as in particular in the IRRINOV case,
the crop growing in the field to be irrigated represent essential information as
it affects the method calculations. Moreover, the date when the crops reach a
growth stage depends on the type of cultivar of the crop. This information should,
therefore, be represented.
5 Ontological Requirement Analysis
As the number of elements involved in the IoT landscape is constantly growing, new
solutions arise to cope with their heterogeneity and to ease interoperability between
platforms, ecosystems and devices. In this sense, numerous ontologies have been
defined to cover the IoT domain in many ways [8].
While there is a wide range of models for IoT ecosystems and devices description ,
this work focus on the coverage of the use case at hand by official ontologies proposed
by standardization bodies. For this reason, we choose the Semantic Sensor Network
Ontology (SOSA/SSN)9 and the Smart Appliances REFerence ontology (SAREF).
The SSN [3] was proposed by the World Wide Web Consortium (W3C) and
has been broadly adopted worldwide. SSN was developped as a core cross domain
ontology and borrow its vocabulary and modelisation from many operational standard
like the relational data model Observations and Measurements (o&M) standard from
OGC10. In oder address the omissions of original SSN, the joint W3C and OGC
8
http://www.opengeospatial.org/standards/om
9
https://www.w3.org/TR/vocab-ssn/
10
http://www.opengeospatial.org/standards/om
Ontological Requirement Specification for Smart Irrigation Systems 11
Spatial Data on the Web Working Group has developed a new version of the SSN
including a module called SOSA11 (Sensor, Observation, Sampler and Actuator),
among others, providing a new modular version of the SSN12 ontology. This new
version, called SOSA/SSN, extends the SSO Pattern (Stimulus Sensor Observation
Pattern), implemented in the previous version, by including classes and properties
for actuators and sampling. The three major components of SOSA are "sensors and
observations", "samplings and samples" and "actuators and actuations".
SAREF13 is an ontology for smart appliances that focuses on smart homes, and
provides an important contribution to enable semantic interoperability in the IoT
being adopted by European Telecommunication Standardization Institute (ETSI) as
a Technical Specification [6]. This ontology provides a core model for IoT that could
be extended and adapted to cover specific domains. SAREF focus on the represen-
tation of appliances and devices together with their functions, commands, services,
states and profiles. In the latest version, the ontology considers the representation
of measurements coming from sensors.
In what follows, a coverage analysis of the ontological requirements by the
SOSA/SSN (including all modules) and SAREF ontologies is presented providing
the mappings between requirements and ontology elements. Then, a discussion about
the requirements covered and open issues is drawn.
5.1 Ontological Requirement Coverage
Table 2 presents the relation between the requirements extracted in Section 4 and
the ontology elements defined in SOSA/SSN and SAREF ontologies. In this table,
each requirement is represented in a row where the first column from the left identify
the requirement itself and the information in the second and third columns represent
the coverage by the SOSA/SSN and SAREF ontologies respectively as follows: (a) an
empty cell indicates that the requirement is not addressed by the given ontology; (b) if
the requirement is covered, then the ontology elements from each ontology are included
in the cell using the prefix of the ontology and the identifier of the element (note
that these elements might be classes or properties); (c) an integer number between
parenthesis identifies side notes about the documentation provided in a given ontology
that could be useful for the requirement at hand, regardless whether it is covered or not.
As shown in Table 2, some requirements are not directly addressed by the analyzed
ontologies; however, their documentation include guidelines about how to address
them. In this sense, regarding R1.2, R3.5 and R7.1, it should be mentioned that
SOSA/SSN documentation suggests the use of geoSPARQL [18] to model geographical
information (Note (1) in the table). It is worth noting that the correct representation
of R3.5 and R7.1 are of particular importance as such information is taken into
account during the IRRINOV method calculations, as shown for example, in Figure 2.
Regarding R5, SOSA/SSN documentation proposes to link to the Quantities,
Units, Dimensions and Data Types Ontologies (QUDT) [11], the Ontology of Units
11
http://www.w3.org/ns/sosa
12
http://www.w3.org/ns/ssn
13
http://w3id.org/saref
12 M. Poveda-Villalón et al.
of Measure (OM) [19] or the RDF extension mechanism for Unified Code for Units
of Measure datatype (UCUM) [13] (Note (2) in Table 2). For the case of SAREF, the
OM ontology is used as a suggestion for covering this aspect (Note (3) in Table 2).
Table 2: Requirement Coverage by SOSA/SSN modules and SAREF.
Requirement SOSA/SSN SAREF
R1 Deployment ssn:Deployment
R1.1 Deployment time
R1.2 Deployment location (1)
R2 Plot sosa:Platform
R3 Network configuration
R3.1 Network topology
R3.2 Network communication
R3.3 Node status saref:State
R3.4 Node role saref:Task
R3.5 Node location (1)
R4 Device ssn:System saref:Device
R4.1 Sensor sosa:Sensor saref:Sensor
R4.2 Actuator sosa:Actuator saref:Actuator
R4.3 System componency ssn:hasSubSystem saref:consistsOf
R4.4 Domain specific devices
saref:Measurement
sosa:Observation
R5 Measurement (2)
saref:UnitOfMeasure
(3)
R5.1 Domain specific units of measure-
ment
R6 Property ssn:Property saref:Property
R6.1 Domain specific properties
R7 Feature of interest sosa:FeatureOfInterest
R7.1 Feature of interest depth (1)
saref:Function
R8 Action sosa:Procedure saref:Command
R8.1 Domain specific actions
R9 Crop
5.2 Discussion and Open Issues
In the following, the insights extracted from the ontological requirement coverage
analysis presented before are detailed. First of all, it should be mentioned that the two
analyzed ontologies have been defined as general ontologies covering top-level concepts
that should be specialized by other ontologies developed for specific use cases. In this
sense, it is not a criticism about the lack of coverage of requirements R4.4, R5.1, R6.1
and R8.1 as they refer to domain-specific knowledge. However, it is important to
define and take such requirements into account to drive the development of extensions
Ontological Requirement Specification for Smart Irrigation Systems 13
or new ontologies for the agricultural use case. More precisely, the requirements
provided in this work would represent a valuable input for the development of a
SAREF extension for the agricultural domain.
Main observation is that none of the analyzed ontologies allows the description
of network configuration (R3), topology (R3.1) and communication lines (R3.2).
For modelling different deployments (R1) in which devices might be involved the
SOSA/SSN ontology provide some coverage considering deployments, platforms as
well as system componency. However, while there is a recommendation to represent
geographical information linking to other ontologies, it does not address the temporal
characteristic of deployments needed to be represented in the agricultural domain.
SAREF on its side, do not consider the representation of different deployments.
Therefore, we can claim that none of the models fully address the spatial-temporal
deployment information (R1.1 and R1.2).
Focusing on the action that a device might carry out (R8), it should be mentioned
that each model provides a suggestion to model them. In this sense, SSN approach
is more related to algorithms, workflows, etc. including information about the input
needed and output generated. SAREF model is oriented to the functionality the
devices are designed for including practical information as the commands that could
be executed, for example, “open” and “close”. However, none of the models represents
web oriented information about where this action can be executed, or the data could
be retrieved, that is, no one allows the representation of web services or web thing
description.
The crop representation (R9) deserves a special mention for this study. While in
Table 2 it is indicated that this requirement is not covered by the analyzed ontologies,
some clarifications should be done for the SOSA/SSN ontology. In this case, one might
argue that this information could be modeled as a characteristic of a feature of interest,
where the feature of interest would be the plot being observed. However, it could be
also considered that this information is related to the platform that hosts the sensors,
that could also be a given plot. Taking all this into account, developers should first
decide to which entity is more accurate to attach such information and then, define
probably new domain properties to express the possible values of the crop types.
Related to the crop representation, we should mention the need for representing
plant growth state (R6.1). This information could change from plant types and from
different classifications systems. In this sense, resources for defining controlled vocab-
ularies and mapping between concepts, for example SKOS,14 should be considered
to model this issue.
Regarding open issues for the representation of the Montoldre use case, even
though it has not been defined as an ontological requirement, it is worth noting
that the SAREF description of saref:Profile about saref:Commodity could be
of interest to represent the water waste or saving achieved by the smart irrigation
system. In this case, water would be the saref:Commodity.
Finally, another issue to be covered, that could be represented in another level
of knowledge representation, for example using rules, is the definition of problems
14
http://www.w3.org/TR/skos-primer
14 M. Poveda-Villalón et al.
based on measurements. For example, an observation higher than 200 in a watermark
sensor is considered an indicator of functioning problems.
6 Related Work
There exist several context-aware systems using ontologies for representing data and
rules. For example, we can mention the work described in [7] where an ontology
supporting precision agriculture applications is presented. This ontology, developed in
OWL, intend to model the knowledge needed by a Decision Support System including
knowledge about plant characteristics, pant state, environmental parameters, sensor,
and actuators
Another application using ontologies in the crop cultivation domain is the approach
presented in [14]. The method presented in this work combines a domain ontology and
a task ontology. Both are implemented in OWL. The domain ontology represents soil,
seed and agricultural machines while the task ontology is about planting processes
such as soil selection, seed selection, fertilization, and irrigation. In this regard, we can
also mention the ontology presented in [23] for hilly citrus production. In this case,
authors present an agricultural ontology specialized in hilly citrus, also it reuses some
terms from AOS (Agricultural Ontology Service) ontology. The ontology includes
citrus nutrient imbalance modeling, hilly citrus fertilization modeling, and hilly citrus
irrigation and drainage modeling. It is worth noting that the ontologies mentioned
in such approaches are unavailable online, or at least the ontologies’ locations are not
provided in the papers. Therefore, it is impossible to: 1) reused them and 2) check
whether in their implementation standard ontologies are reused.
However, the goal of the present study is not to provide an ontology for irrigation
context-aware systems nor to review existing ontologies in the agricultural domain. The
objective of this work is to analyze standard ontologies in the IoT domain candidate
to be reused in a smart irrigation system. In this sense, we can mention the work
presented by [16] where ontology alignment between SSN and SAREF ontologies are
analyzed, proposed and applied to a specific device description. It is worth noting that
such analysis considers the first version of SSN instead of the new ontology SOSA/SSN.
Finally, the new SOSA/SSN and SAREF ontologies have been analyzed in the
work presented by [12]. This work presents a set of alignments between the SEAS
ontology and the above-mentioned ontologies. In this case, the objective is focused
on the adoption of best practices and patterns implemented in the SEAS ontology
in SAREF in order to ease its maintenance.
In summary, up to authors’ knowledge, there are no reference studies about the
use of standard IoT ontologies for the agricultural domain.
7 Conclusions and Future Work
The main contributions of this paper are: 1) a set of ontological requirements for
adaptive context-aware systems in the agricultural domain. The requirements are
extracted from the pilot site in Montoldre, and a specific irrigation methodology
knows as IRRINOV; and 2) a preliminary analysis of the requirements coverage by
Ontological Requirement Specification for Smart Irrigation Systems 15
two well-known standard ontologies: SOSA/SSN et SAREF. It is the first step for
designing an ontology to support the adaptive context-aware system to be developed
in Montoldre. This work also represents a valuable contribution for the ongoing
extension of SAREF for the agricultural domain (SAREF4AGRI).15 As already
shown, some requirements are not covered by SOSA/SSN and SAREF, as they are
too specific for the domain at hand. It is clear that the two analyzed ontologies are top
level and should be specialized for particular cases. The future lines of this work would
pass, on the one hand, by specializing the SOSA/SSN ontology in a new ontology,
on other hands, by contributing to the SAREF4AGRI extension. Moreover, there are
independent domain requirements not covered by none of the ontologies, for example,
the network characteristics. In this case, a search for existing ontologies representing
networks should be carried out. Other ontologies about IoT and service web should
also be considered such as the Web of Things16 and the oneM2M ontologies.17. Finally,
it is necessary to form a set of rules-based integrated into the adaptive context-aware
system for irrigation in agriculture. This rules-based is a critical element to transforms
the IRRINOV human-oriented decision guide into an automatic irrigation system.
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