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<section id="abstract">
<p>
The Web of Things is applicable to multiple IoT domains,
including Smart Home, Industrial, Smart City, Retail, and Health
applications, where usage of the W3C WoT standards can simplify the
development of IoT systems that combine devices from multiple vendors
and ecosystems.
During the last charter period of the WoT Working Group several
specifications were developed to address requirements for
these domains.
</p>
<p>
This Use Cases and Requirements Document is created
to collect new IoT use cases from various domains
that have been contributed by various stakeholders.
These serve as a baseline for identifying requirements
for the standardization work in the W3C WoT groups.
</p>
</section>
<section id="sotd">
</section>
<section id="intro">
<h2>Introduction</h2>
<p>
The World Wide Web Consortium (W3C) has published the Web of Things
(WoT) Architecture and Web of Things (WoT) Thing Description (TD) as
official W3C Recommendations in May 2020. These specifications enable
easy integration across Internet of Things platforms and applications.
</p>
<p>The W3C Web of Thing Architecture [[wot-architecture]] defines an
abstract architecture, the WoT Thing Description [[wot-thing-description]]
defines a format to describes a broad spectrum of very different devices,
which may be connected over various protocols.
</p>
<p>
During the inception phase of the WoT 1.0 specifications in 2017-2018
the WoT IG collected use cases and requirements to enable
interoperability of Internet of Things (IoT)
services on a worldwide basis.
These released specifications have been created to address the
use cases and requirements for the first version of the WoT specifications,
which are documented in <a href="https://w3c.github.io/wot/ucr-doc/">
https://w3c.github.io/wot/ucr-doc/</a>
<p>
The present document gathers and describes new use cases and requirements
for future standardization work in the WoT standard.
</p>
<p>
This document contains chapters describing the use cases that were contributed by multiple authors,
functional and technical requirements on the Web of Things standards.
Additionally it contains a summary of the liaisons, where active collaboration is taking place at the time of
writing.
Since this document is a WG note, additional use cases will be added in future revisions of this document.
</p>
<section id="domains">
<h2>Domains</h2>
<p>
The collection of use cases can be separated into two categories:
<ul>
<li>Domain specific (vertical) use cases for a single application domain.
<ul>
<li>Agriculture</li>
<li>Smart City</li>
<li>Smart Buildings</li>
<li>Manufacturing</li>
<li>Retail</li>
<li>Health</li>
<li>Energy</li>
<li>Transportation</li>
<li>Automotive</li>
<li>Smart Home</li>
<li>Education</li>
</ul>
</li>
<li>Horizontal use cases that address multiple domains.
<ul>
<li>Discovery</li>
<li>Multi-Vendor System Integration</li>
<li>Digital Twin</li>
<li>Cross Protocol Interworking</li>
<li>Multimodal System Integration</li>
<li>Accessibility</li>
<li>Security</li>
<li>Lifecycle</li>
<li>VR/AR</li>
<li>Edge Computing</li>
</ul>
</li>
</ul>
Domain specific use cases are described in <a href="#sec-vertical-ucs"></a>,
horizontal use cases are described in <a href="#sec-horizontal-ucs"></a>
</p>
</section>
<section id="definitions">
<h2>Terminology, Stakeholders and Roles</h2>
<section id="terminology">
<h2>Terminology</h2>
The present document uses the terminology from WoT Architecture [[wot-architecture]].
</section>
<section id="stakeholders">
<h2>Stakeholders and Roles</h2>
<p>
The following stakeholders and actors were identified
when the use cases have been collected and requirements were identified.
Note that these stakeholders and roles may overlap in some use cases.
</p>
</section>
<ul>
<li>device owners</li>
<li>device user</li>
<li>cloud provider</li>
<li>service provider</li>
<li>device manufacturer</li>
<li>gateway manufacturer</li>
<li>identity provider</li>
<li>directory service operator</li>
</ul>
</section>
</section>
<!-- DOMAIN SPECIFIC USE CASES -->
<section id="sec-vertical-ucs">
<h2>Domain Specific Use Cases</h2>
<section id="agriculture">
<h2>Smart Agriculture</h2>
<section id="UC-greenhouse-horticulture-1"> <!-- old ID: smart-agriculture -->
<h2>Greenhouse Horticulture</h2>
<dl>
<dt>Submitter(s)</dt>
<dd>
Ryuichi Matsukura, Takuki Kamiya
</dd>
<dt>Target Users</dt>
<dd>
Agricultural corporation, Farmer, Manufacturers (Sensor, other facilities), Cloud provider
</dd>
<dt>Motivation</dt>
<dd>
Greenhouse Horticulture controlled by computers can create an optimal environment for growing plants. This
enables to improve productivity and ensure stable vegetable production throughout the year, independent of
the weather. This is the result of research on the growth of plants in the 1980s. For example, in
tomatoes, switching to hydroponics and optimizing the temperature, humidity and CO2 concentration required
for photosynthesis resulted in a five times increase in yield. The growth conditions for other vegetables
also have been investigated, and this control system is applied now.
</dd>
<dt>Expected Devices</dt>
<dd>
Sensors (temperature, humidity, brightness, UV brightness, air pressure, and CO2)
Heating, CO2 generator, open and close sunlight shielding sheet.
</dd>
<dt>Expected Data</dt>
<dd>
Sensors values to clarify the gaps between conditions for maximizing photosynthesis and the current
environment.
Following sensors values at one or some points in the greenhouse: temperature, humidity, brightness, and
CO2.
</dd>
<dt>Dependencies</dt>
<dd>
WoT Architecture
<br>
WoT Thing Description
</dd>
<dt>Description</dt>
<dd>
Sensors and some facilities like heater, CO2 generator, sheet controllers are connected to the gateway via
wired or wireless networks. The gateway is connected to the cloud via the Internet. All sensors and
facilities can be accessed and controlled from the cloud.
To maximize photosynthesis, the temperature, CO2 concentration, and humidity in the greenhouse are mainly
controlled. When the sunlight comes in the morning and CO2 concentration inside decreases, the application
turns on the CO2 generator to keep over 400 ppm, the same as the air outside. The temperature in the
greenhouse is adjusted by controlling the heater and the sunlight shielding sheet.
The cloud gathers all sensor data and the status of the facilities. The application makes the best
configuration for the region of the greenhouse located.
<!--/dd>
<dt>Variants</dt>
<dd-->
</dd>
<dt>Gaps</dt>
<dd>
In the case of the wireless connection to the sensors, the gateway should keep the latest value of the
sensors since the wireless connection is sometimes broken. The gateway can create a virtual entity
corresponding to the sensor and allow the application to access this virtual entity having the actual
sensor status like sleeping.
</dd>
<!--dt>Existing Standards</dt>
<dd>
</dd>
<dt>Comments</dt>
<dd>
</dd-->
</dl>
</section>
<section id="UC-open-field-agriculture-1"> <!-- OLD: smart-agriculture-openfield -->
<h2>Open-field Agriculture</h2>
<dl>
<dt>Submitter(s)</dt>
<dd>
Cristiano Aguzzi
</dd>
<dt>Target Users</dt>
<dd>
Agricultural corporation, Farmer, Manufacturers (Sensor, other facilities), Cloud provider, Middleware
provider, Network providers, service provider.
</dd>
<dt>Motivation</dt>
<dd>
Water is vital for ensuring food security to the world’s population, and agriculture is the biggest consumer
amounting for 70% of freshwater. Field irrigation application methods are one of the main causes of water
wastage. The most common technique, surface irrigation, wastes a high percentage of the water by wetting
areas where no plants benefit from it. On the other hand, localized irrigation can use water more
efficiently and effectively, avoiding both under-irrigation and over-irrigation. However, in an attempt to
avoid under-irrigation, farmers feed more water than is needed resulting not only to productivity losses,
but also water wastages.
Therefore, technology should be developed and deployed for sensing water needs and automatically manage
water supply to crops. However, open field agriculture is characterized by a quite dynamic range of
requirements. Usually, solutions developed for one particular crop type cannot be reused in other
cultivations. Moreover, the same field can have different crop types or different sizes/shapes during the
years, meaning that technology to monitor the state of crop growth should be highly configurable and
adaptive. Even agriculture and irrigation methods can change and also they are very different depending on
the size of the field and its clime type.
Consequently, silos applications are deployed leveraging on IoT technologies to gather data about the crop
growth state and irrigation needs. The Web of Things may help to create a single platform where
cost-effective applications could adapt seamlessly between different scenarios, breaking the silos and
giving value both to the environment and the market.
</dd>
<dt>Expected Devices</dt>
<dd>
<p>Sensors:</p>
<ul>
<li>Weather sensors (maybe collected together inside a <a
href="https://en.wikipedia.org/wiki/Weather_station">weather station</a>)
<ul>
<li>temperature</li>
<li>air humidity</li>
<li>air pressure</li>
<li>pluviometer</li>
<li>global solar radiation</li>
<li>anemometer (wind speed)</li>
<li> wind direction</li>
<li>global solar radiation and photosynthetically active radiation</li>
<li> gas/air quality sensor (i.e. CO2)</li>
</ul>
</li>
<li>Soil sensors (usually packed together in soil probes)
<ul>
<li>soil temperature</li>
<li>soil moisture/water content</li>
<li>soil conductivity (detecting salt levels in the soil)</li>
<li> water table sensor</li>
</ul>
</li>
<li>Drone sensors
<ul>
<li>camera</li>
<li>temperature sensitive camera</li>
<li>multispectral camera</li>
</ul>
</li>
</ul>
<p>Actuators:</p>
<ul>
<li>drones: used for data collection or pesticed/impollination</li>
<li>sprinklers</li>
<li>pumps</li>
<li>central pivot sprinklers</li>
<li>hose-reel irrigation machine</li>
</ul>
<p>Additional devices:</p>
<ul>
<li>Solar panels</li>
<li>Loggers: units that collect data from close sensors. </li>
<li>Gateways</li>
</ul>
</dd>
<dt>Expected Data</dt>
<dd>
Sensor data plays a central role in Smart Agriculture. In particular, it is critical that the information
sensed is associated with a timestamp. Common algorithms use *time series* to calculate the water needs of a
crop.
Furthermore, soil sensors usually are calibrated over a specific soil type (which may differ even in the
same geographic region). For example, the calibration data for a soil moisture sensor is represented by a
function that maps sensor output to soil water content. In literature, this function is knowns as a
*calibration curve*. Commercial sensors are precalibrated with a "standard" curve but on most occasions, it
fails to accurately measure the water content. Therefore, it can be configured during the installation phase
(which may happen every time the soil is plowed).
Finally, a crucial aspect is forecasting. Farmers use this information to actively change their management
procedures. Services exploit it to suggest irrigation schedule or change device settings to behave
accordingly to environmental changes.
To summarize here it is a list of most important expected data from Open field agriculture:
<ul>
<li>Calibration curve</li>
<li>Time series</li>
<li>Forecast data</li>
<li>Geolocations: sensor data must be contextualized in geolocation. Also, geolocation is critical in
massive open fields to localize instrument position.</li>
<li>Weather data</li>
<li>Unit of measure: commercial soli sensor may output their value in a different unit of measures (i.e.
volts or % water in an m^3 of soil)</li>
<li>Relative values</li>
<li>Depth position: geolocation is not sufficient to describe the parameters of the soil. Depth is an
additional context that should be added to an observed value. </li>
<li>Device owner information</li>
<li>Battery level and energy consumption</li>
</ul>
</dd>
<dt>Dependencies</dt>
<dd>
WoT Architecture, WoT Thing Description
</dd>
<dt>Description</dt>
<dd>
In open-field agriculture, the IoT solutions leverage on different radio protocols and devices. Usually,
radio protocols should cover long distances (even kilometers) and be energy efficient. Devices too need to
be energy saving as they are deployed for months and sometimes even years in harsh environments. A
sleeping-cycle is one mechanism they use to save energy usually coordinated by *loggers/gateways* or
preprogrammed. *Loggers* are deployed closed to sensor devices and have more storage space. They serve as
buffers between sensors and higher services. Often *loggers* and sensors are embedded in the same board,
otherwise, they are connected using cables or close-ranged radio protocols. On the other hand, *gateways*
serve as a collection point for data of an entire field or farm. They are much more capable devices and
usually are more energy-consuming. In some deployment scenarios, they host a full operating system with
multiple software facilities installed. Otherwise, gateways only serve as relays of data sent from the
loggers and sensors to cloud services and vice-versa. The cloud services may be partially hosted in edge
servers to preserve data privacy and responsiveness of the whole IoT solution. Possible cloud services are:
<ul>
<li>Weather forecasting/local weather forecasting</li>
<li>Soil digital twin to simulate and predict water content</li>
<li>Plant digital twin (growth and water needs prediction)</li>
<li>Irrigation advice service: combining the previous services and knowing the irrigation system topology
is possible to advise farms with the best times to irrigate a crop. </li>
<li>Pesticide and fertilize planning</li>
</ul>
The complete deployment topology of an open field agriculture solution is described in the diagram below:
<br>
<br>
<div class="resize"><img src="./images/Agriculture.svg"
alt="deployment topology of an open field agriculture solution"></div>
<br>
<br>
</dd>
<dt>Variants</dt>
<dd>
Open-field agriculture varies a lot between geographical location and methods. For example in the <a
href="http://swamp-project.org/">SWAMP project</a> there three different pilots with different
requirement/constraints:
<ul>
<li><a href="http://swamp-project.org/cbec/">Italian pilot</a> (Reggio Emilia region):
<ul>
<li>Relative small field size</li>
<li>Multiple connectivity solutions available: 4G, LPWAN, and WiFi</li>
<li>Variance in crop types, sometimes even inside the same farm</li>
<li>Small soil type variance</li>
<li>Precise model soil behavior</li>
<li>A great influence of the water table</li>
<li>Variance in the irrigation system</li>
<li>Channel-based water distribution</li>
<li>The main goal is to optimize water consumption</li>
</ul>
</li>
<li><a href="http://swamp-project.org/matopiba/">Brazilian pilot</a> (Matopiba and Guaspari location):
<ul>
<li>Huge field size</li>
<li>Centra pivot irrigation systems: need to optimize each sprinkler output</li>
<li>Soil type variance within the same field</li>
<li>A low number of connectivity options: no 4G, only radio communication base on LPWAN</li>
<li>Low crop type variance</li>
<li>the main goal is to optimize energy consumption</li>
</ul>
</li>
<li><a href="http://swamp-project.org/intercrop/">Spain pilot</a>:
<ul>
<li>Efficient localized irrigation and application of the right amount of water to the crop</li>
<li>arid location</li>
<li>The goal is to minimize water consumption but maintaining a good field yield.</li>
</ul>
</li>
</ul>
</dd>
<dt>Gaps</dt>
<dd>
Currently, there is no specification on how to model device status (i.e. connected/disconnected)
Examples of how to handle a device calibration phase may help developers to use a standardized approach.
Possibly define standard links types to define the relation between loggers and sensors
Handle both geographical position and depth information.
Ontology class for battery and energy consumption
Model historical and forecast data
</dd>
<dt>Existing Standards</dt>
<dd>
<ul>
<li>LPWAN [[rfc8376]]</li>
<li><a href="http://www.sdi-12.org/current_specification/SDI-12_version-1_4-Jan-10-2019.pdf">SDI 12</a>
</li>
<li>CoAP [[rfc7252]]</li>
<li>MQTT [[MQTT]]</li>
</ul>
</dd>
<dt>Comments</dt>
<dd>
This use case is designed using the experience gained in the European-Brazil Horizon 2020 SWAMP project.
Please follow the
<a href="http://swamp-project.org/">link</a> for further information. Since SWAMP is heavily oriented to
optimize water consumption,
this document just mentioned issues like plant feeding, fertilizing, pollination, yield prediction, crop
quality measurement, etc.
Nevertheless, WoT technologies may be employed also in these scenarios.
</dd>
</dl>
</section>
<section id="UC-irrigation-in-outdoor-environment-1"> <!-- old id: Agricultural-irrigation -->
<h2>Irrigation in Outdoor Environment</h2>
<dl>
<dt>Submitter(s)</dt>
<dd>
<ul>
<li>Catherine Roussey
<!--, INRAE [1], France-->
</li>
<li>Jean-Pierre Chanet
<!--, INRAE [1], France-->
</li>
</ul>
</dd>
<dt>Target Users</dt>
<dd>
<ul>
<li>device users: farmers</li>
<li>service provider</li>
</ul>
</dd>
<dt>Motivation</dt>
<dd>
Depending on the type of crops (e.g. maize), cultivated plots may need specific irrigation processes
in outdoor environments. Depending on the country there exist some specific pedo-climatic conditions
and some water consumption restrictions. Thus an irrigation system is installed on the plot. It is used
on a several days basis (e.g. every 7 days), for each plot. The goal is to optimize the irrigation decision
based on the crop development stage and the quantity of rain that has already fallen down on the plot.
For example an important rain may postpone the irrigation decision.
<br>
<br>
This use case aims to evaluate the number of days to delay the irrigation system, in addition to the basis
irrigation frequency (e.g. 2 delay days means 9 days between two irrigations).
</dd>
<dt>Expected Devices</dt>
<dd>
<ul>
<li>6 tensiometers in the plot (soil moisture):
<ul>
<li> 3 tensiometers at 30 cm depth</li>
<li> 3 tensiometer at 60 cm depth</li>
</ul>
</li>
<li>1 weather station:
<ul>
<li>thermometer (outdoor temperature)</li>
<li>pluviometer (rain quantity)</li>
</ul>
</li>
<li>1 mobile pluviometer (quantity of water provided by the watering system)</li>
</ul>
</dd>
<dt>Expected Data</dt>
<dd>
To decide when to water a cultivated plot, we evaluate the crop growth stage, the root zone moisture level
and the number of delay days:
<ul>
<li>To evaluate the <b>Crop growth stage</b>, we need:
<ul>
<li>Min and max temperature per day: the <b>min temperature per day</b> is evaluated on the period
[d-1
18:00, d 18:00[. The <b>*max temperature per day</b> is evaluated on the period [d 06:00:00, d+1
06:00:00[.i</li>
<li><b><a href="https://en.wikipedia.org/wiki/Growing_degree-day">Growing degree day</a></b> values
uses
min and max temperature per day, the sowing day and the type of seed. The Growing degree day is
compared to some thresholds to evaluate the crop growth stage</li>
</ul>
</li>
<li>To evaluate the <b>Root zone moisture level</b>, we need:
<ul>
<li>Mean moisture per day per probe: in order to get reliable values, each tensiometer sends several
measurements of soil moisture, at fixed hours of the day (usually in the morning), that are
aggregated; their mean value is considered</li>
<li>For the set of 3 tensiometers localised at the same level of depth, the median value is evaluated
from their mean per day moisture measurements. One tensiometer may not provide accurate values (the
soil around the probe is too dry and the soil matter is not connected to the probe). The median
value
of three different tensiometers at the same depth will improve the accuracy of the moisture
measurement.</li>
<li>Then the sum of the two median values at two different depths is evaluated, to take into account
the
quantity of water available in the root zone volume. This aggregated value estimates the root zone
moisture level. </li>
<li>The root zone moisture level is compared to some thresholds (dependent on the crop growth stage)
to
evaluate if the crop needs water or not at the end of the basis irrigation period.</li>
</ul>
</li>
<li>To determine the <b>number of delay days</b>, we need:
<ul>
<li>The time period between two waterings of the same plot is dependent on the farm and known by the
farmer. When a watering is launched, no new watering should be planned during the basic irrigation
frequency. The quantity of rain that falls down on the plot may postpone the watering plan. The
total
quantity of rain per day is compared to some thresholds to determine the number of delay days.</li>
</ul>
</li>
</ul>
The mobile pluviometer is used to validate that the quantity of water received by the crop actually
corresponds to the quantity of water provided by the watering system.
<br>
<br>
At the end, the farmer may decide if they follow the irrigation recommendations or not. They could force the
watering for one of the next days.
<br>
</dd>
<dt>Affected WoT deliverables and/or work items</dt>
<dd>
<ul>
<li>WoT Architecture: wireless communication in outdoor environments presents some issues: communication
consumes lots of energy, sensor nodes have limited energy, weather conditions impact communication
quality</li>
<li>WoT Thing Description: the affordance should be precise enough to describe the soil at a specific
depth or the root zone volume or the min temperature per day</li>
</ul>
</dd>
<dt>Description</dt>
<dd>
To avoid Property right and consent management issues between farmers and cloud service providers on these
computed data, sensors are connected to the farm infrastructure and the services that evaluate aggregated
data are executed locally on this infrastructure.
<br>
<br>
The weather station may be located outside of the farm.
<br>
<br>
The tensiometers are located inside the farm. The tensiometers and the mobile pluviometer are connected
using wireless communication to the gateway. The gateway sends the measurements to the farm infrastructure.
</dd>
<dt>Variants:</dt>
<dd>
The crop growth stage may be observed by the farmer. In this case, they can force this value to update the
service inputs.
</dd>
<dt>Security Considerations</dt>
<dd>
The 6 tensiometers and 1 pluviometer are installed on the plot, but only the farmer should be able to change
their configurations (frequency of communication). Wireless communication should be used but the measurement
data should only be accessible through the farm network infrastructure.
</dd>
<dt>Privacy Considerations</dt>
<dd>
Data concerning quantity of water, type of seed, sowing day should be protected.
</dd>
<dt>Gaps</dt>
<dd>
The main potential issues come from tensiometers located in the plot, as they are known to be cheap and easy
to use probes but not always reliable. They can face multiple issues: if the soil gets too dry or the probe
is improperly installed, there may be air between the probe and the soil, therefore preventing the probe
from providing accurate conductivity measurements.
<br>
<br>
To be sure of the quality of those measurements each tensiometer sends its measurements several times (3 to
5) per day. The tensiometer may send an inappropriate value due to the bad connection between the soil and
the probe, that is the reason why three tensiometers are used and the median value is computed. If the
gateway does not receive the value of one sensor during a whole day, an alert should be sent. To take an
irrigation decision, at least one measurement per sensor and per day should be provided.
<br>
<br>
The gateway can create a virtual entity corresponding to the sensor and allow the application to access this
virtual entity having the actual sensor status like sleeping.
<br>
<br>
Sensor nodes deployed in outdoor environments may take into account that their energy supply device
(battery, solar panel) constrains the lifetime of the device. Thus they should be able to alert that they
may not be able to provide a service due to lack of energy or they should be able to change their
configuration and switch communication protocols to save as much energy as possible.
<br>
<br>
Moreover wireless communication can be impacted by weather conditions or any outdoor conditions. For example
a tractor that comes too close to the sensor node may move the communication device and destroy some
components. Some kind of network supervision must be achieved (for instance by the gateway) to check node
availability.
</dd>
<dt>Existing Standards</dt>
<dd>
<ul>
<li>Semantic Sensor Network Ontology (SSN/SOSA) [[vocab-ssn]]</li>
<li>SAREF4AGRI ETSI Standard [[SAREF4AGRI]]</li>
<li><a href="https://www.w3.org/TR/prov-o/">PROV-O</a></li>
<li><a
href="https://irstea.github.io/caso/OnToology/ontology/caso.owl/documentation/index-en.html">CASO</a>
</li>
<li><a href="http://www.w3id.org/def/irrig">IRRIG</a></li>
</ul>
The CASO and IRRIG ontologies extend SSN, PROV-O and SAREF4AGRI to implement an irrigation expert system.
<br>
<br>
A thesaurus climate and forecast that describes the weather properties and associated phenomenon is
available at <a href="http://vocab.nerc.ac.uk/collection/P07/">http://vocab.nerc.ac.uk/collection/P07/</a>.
<br>
<br>
The weather measurements provided by the agricultural weather station of Agrotechnopole is available at <a
href="http://ontology.irstea.fr/weather/snorql/">http://ontology.irstea.fr/weather/snorql/</a>. [5]
</dd>
<dt>Comments</dt>
<dd>
This use case has been implemented in France, following local conditions and regulations. There exists an
open manual irrigation decision method called <a
href="http://www.irrinov.arvalisinstitutduvegetal.fr/irrinov.asp">IRRINOV®</a> developed by Arvalis [2]
and INRAE dedicated to France and some specific crops: maize, wheat and cereals, potatoes and beans.
<br>
<br>
IRRINOV® can be automated using wireless sensor networks and semantic web technologies. The considered
network is of star type: all sensors can communicate with a common gateway, which is connected to the
Internet. The IRRINOV® implementation was developed in [3]. This work presents an expert system for maize
using drools. It automates the irrigation decision for maize based on sensor measurements.
<br>
<br>
To measure weather properties, we use the recommendation provided by the French National Weather Institute:
Météo France[4]. Its web site defines how to evaluate the min and max temperatures per day in <a
href="http://www.meteofrance.fr/publications/glossaire/154123-temperature-minimale">http://www.meteofrance.fr/publications/glossaire/154123-temperature-minimale</a>
(in French, we found no equivalent description in English).
</dd>
<dt>References</dt>
<dd>
[1] <a href="https://www.inrae.fr/">https://www.inrae.fr/</a>
<br>
[2] <a href="https://www.arvalisinstitutduvegetal.fr/">https://www.arvalisinstitutduvegetal.fr/</a>
<br>
[3] Q-D. Nguyen, C. ROUSSEY, M. Poveda-Villalón, C. de Vaulx , J-P. Chanet. Development Experience of a
Context-Aware System for Smart Irrigation Using CASO and IRRIG Ontologies. Applied Science 2020, 10(5),
1803; <a href="https://doi.org/10.3390/app10051803">https://doi.org/10.3390/app10051803</a>
<br>
[4] <a href="http://www.meteofrance.fr/">http://www.meteofrance.fr/</a>
<br>
[5] C. ROUSSEY,S. BERNARD, G. ANDRÉ, D. BOFFETY. Weather Data Publication on the LOD using SOSA/SSN2022-11-07
Ontology. Semantic Web Journal, 2019 <a
href="http://www.semantic-web-journal.net/content/weather-data-publication-lod-using-sosassn-ontology-0">http://www.semantic-web-journal.net/content/weather-data-publication-lod-using-sosassn-ontology-0</a>
</dd>
</dl>