The document discusses how eco-aware linked data devices promote sustainability, focusing on the integration of Internet of Things (IoT) technologies to optimize energy use. It highlights a case study of a sustainable coffee maker that uses linked data principles to minimize energy waste by adapting to usage patterns and providing feedback to users. The presentation emphasizes the importance of cooperation between humans and devices to enhance energy efficiency and environmental awareness.

1. Promoting Sustainability through
Energy-aware Linked Data Devices
Dr. Diego López-de-Ipiña
16:00-16:30, 18 October 2013, Essent.be, Veldkant 7, Kontich, Belgium
dipina@deusto.es
http://www.morelab.deusto.es
http://paginaspersonales.deusto.es/dipina
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2. Agenda
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IoT need for Linked Data
Eco-aware devices: why and what for?
Eco-aware Linked Data Devices
A practical case: Sustainable Linked Data
Coffee Maker
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3. IoT Promise
• There will be around 25 billion devices connected to the
Internet by 2015, 50 billion by 2020
– A dynamic and universal network where billions of identifiable
“things” (e.g. devices, people, applications, etc.) communicate
with one another anytime anywhere; things become contextaware, are able to configure themselves and exchange
information, and show “intelligence/cognitive” behaviour
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4. IoT Enabling Technologies
• Low-cost embedded computing and communication
platforms, e.g. Arduino or Rapsberry PI
• Wide availability of low-cost sensors and sensor networks
• Cloud-based Sensor Data Management Frameworks:
Xively, Sense.se
 Democratization of Internet-connected Physical Objects
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5. IoT impulse: Smart Cities, consumer
objects, mobile sensing, smart metering
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6. Current Hype: Health-promoting
Personal Data Devices
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7. Nature of Data in IoT
• Heterogeneity makes IoT devices hardly interoperable
• Data collected is multi-modal, diverse, voluminous
and often supplied at high speed
• IoT data management imposes heavy challenges on
information systems
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8. Avoiding Data Silos through
Semantics
• Cut-down semantics is applied to enable machineinterpretable and self-descriptive interlinked data
– Integration – heterogeneous data can be integrated or one
type of data combined with other
– Abstraction and access – semantic descriptions are
provided on well accepted ontologies such as SSN
– Search and discovery – resulting Linked Data facilitates
publishing and discovery of related data
– Reasoning and interpretation –new knowledge can be
inferred from existing assertions and rules
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9. Actionable Knowledge from
Sensorial Data
• Don’t care about the sensors, care about knowledge
extracted from their data correlation & interpretation!
– Data is captured, communicated, stored, accessed and shared
from the physical world to better understand the surroundings
– Sensory data related to different events can be analysed,
correlated and turned into actionable knowledge
– Application domains: e-health, retail, green energy,
manufacturing, smart cities/houses
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10. Example: Waste-related Linked
Statistics
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11. Linked Data Devices
• Devices that apply the Linked Data principles to publish information:
– Use URIs as names for things
– Use HTTP URIs so that people can look up those names
– When someone looks up a URI, provide useful information, using the standard
(RDF, SPARQL)
– Include links to other URIs, so that they can discover more things
• Devices are becoming BIG PROSUMERS of Linked Data:
– Progressively more dynamic Linked Data coming from increasing amounts of
Internet-connected devices
• About the environment but also about the people themselves, due to trend
towards continuous people-centric enriched data
– Personal Linked Data Cloud vs. Space’s Linked Data Clouds vs. LOD Cloud
• Humongous potential on correlating all these data (BIG LINKED DATA):
energy saving, improve health monitoring, better human energy balance
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12. Where do we spend Energy?
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13. Eco-Aware Devices
• Society wastes much more energy than it should  tons of
unnecessary CO2 emissions
– Due to inadequate use of electrical devices given the
intangible and invisible nature of energy
• Particularly in shared spaces, e.g. workplaces, where occupants
do not pay the bills directly
– Some studies reveal the enterprises could easily save 20% of
energy waste if measures were taken
• Solution: Eco-aware Devices
– Technological approach: Embedding intelligence in shared
appliances so that they react and adapt upon energy wasting
– Human behavioral change: Team-up with users to reduce
energy wasted through feedback and persuasion
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14. Eco-Aware Devices in Action
• They help and motivate people to reduce energy waste by:
– Informing persuasively to concerned users about the misuse of
electronic appliances
– Customizing the operating mode of everyday electrical
appliances as a function of their real usage pattern
– Enabling behaviour export by enabling cooperation among
devices
• How do we ally devices and humans?
– Giving a voice to smart everyday objects to participate in the
energy conservation promotion
• Publishing info in Social Networks and LOD Cloud and through
seductive interfaces
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15. Sustainable Linked Data
Coffee Maker
• Hypothesis: “the active collaboration of
people and Eco-aware everyday objects will
enable a more sustainable/energy efficient
use of the shared appliances within public
spaces”
• Contribution: An augmented capsule-based
coffee machine placed in a public spaces,
e.g. research laboratory
– Continuously collects usage patterns to offer
feedback to coffee consumers about the
energy wasting and also, to intelligently
adapt its operation to reduce wasted energy
• http://socialcoffee.morelab.deusto.es/
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16. Social + Sustainable + Persuasive +
Cooperative + Linked Data Device
1. Social since it reports its energy consumptions via social
networks, i.e. Twitter
2. Sustainable since it intelligently foresees when it should be
switched on or off
3. Persuasive since it does not stay still, it reports misuse and
motivates seductively usage corrections
4. Cooperative since it cooperates with other devices in order
to accelerate the learning process
5. Linked Data Device, since it generates reusable energy
consumption-related linked data interlinked with data from
other domains that facilitates their exploitation
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17. Step 1: Social
• Making it report energy consumptions into
Twitter account: @Social_Coffee
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18. Step 2: Sustainable
• Adding a new state to the coffee machine: auto switchoff which intelligently switches the machine off when it
will not be used (ARIMA time series)
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19. Step 2: Sustainable by Studying
Usage Patterns
• Predictive models (Heuristics, ARIMA and Bayessian) application
saves 13.56%, 14.02% and 15.18%, respectively, of the total
energy consumption
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20. Step 3: Persuasive
• Applying diverse persuasive interfaces to motivate its users
to make a more responsible use
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21. Step 3: Evaluating Persuasiveness
• The impact of eco-feedback was assessed in
an experiment with 3 coffee machines:
– Energy reduction was best in the device that was
instrumented with more suggestive interfaces
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22. Step 4: Cooperative
• Newly deployed coffee makers import intelligence from
others deployed in similar public environments to avoid
cold-start problem
– Giving place to ecosystem of cooperative Linked Data Devices
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23. Step 5: Linked Data Device
• Modelling not only the sensors but also their features of
interest: spatial and temporal attributes, resources that
provide their data, who operated on it, provenance and so on
– With SSN, SWEET, SWRC, GeoNames, PROV-O, … vocabularies
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24. Step 5: Linked Data Device
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25. Linked Data Life Cycle
• Data generated by devices must go through several
stages (several iterations on Linkage) before are ready
for exploitation:
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26. Conclusion: Energy-aware
Linked Data Devices
• Linked Data Devices allow us to intelligently combine their
machine interpretable data with domain-specific knowledge
• Eco-aware Devices persuade users and adapt upon energy
consumption
• Eco-aware Linked Data Devices communicate reports on energy
consumption to datasets to enable higher level abstractions
meaningful for human or automated decision making
– Our proof of concept, the Sustainable Linked Data Coffee
Maker reduces energy wasting and encourages a better ecobehaviour
• Future work: exploiting energy-consumption datasets, personal
and environmental LOD Clouds
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27. Promoting Sustainability through
Energy-aware Linked Data Devices
Dr. Diego López-de-Ipiña
16:00-16:30, 18 October 2013, Essent.be, Veldkant 7, Kontich, Belgium
dipina@deusto.es
http://www.morelab.deusto.es
http://paginaspersonales.deusto.es/dipina
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28. Acknowledgments
• Thanks to Diego Casado (@dieguich) and Juan López-de-Armentia
(@juanarmentia) for giving birth to the Social Sustainable
Persuasive Cooperative Linked Data-generating Coffee Maker !!!
– http://socialcoffee.morelab.deusto.es/
• Thanks to Mikel Emaldi (@memaldi) & Jon Lázaro (@jon_lazaro) for
the Linked Data support
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29. References
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Energy efficiency in the workplace: the solutions and the users that advocate them
– http://bit.ly/17OHeMy
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Linked Open Data as the fuel for Smarter Cities. Mikel Emaldi, Oscar Peña, Jon
Lázaro, Diego López-de-Ipiña, book chapter under revision
Fighting against Vampire Appliances through Eco-aware Things. Juan López-deArmentia, Diego Casado-Mansilla and Diego López-de-Ipiña. Proceedings of The
Sixth International Conference on Innovative Mobile and Internet Services in
Ubiquitous Computing (Extending Seamlessly to the Internet of Things - esIoT2012
- workshop), pp. 868-873. Palermo, Italy, July 2012.
http://dx.doi.org/10.1109/IMIS.2012.112
Reducing energy waste through eco-aware everyday things. Juan López-deArmentia, Diego Casado-Mansilla, Sergio López-Pérez, Diego López-de-Ipiña,
Mobile Information Systems, vol., no., pp.; http://dx.doi.org/10.3233/MIS-130172,
ISSN 1574-017x, July 2013.
Semantics for the Internet of Things: Early Progress and Back to the Future.
Barnaghi, P., Wang, W., Henson, C., & Taylor, K. (2012). International Journal on
Semantic Web and Information Systems (IJSWIS), 8(1), 1-21.
http://dx.doi.org/10.4018/jswis.2012010101
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