06 linked datadevices v1

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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

[email protected] http://www.morelab.deusto.es

http://paginaspersonales.deusto.es/dipina

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Agenda

• IoT need for Linked Data

• Energy-aware devices: why and what for?

• Energy-aware Linked Data Devices

• A practical case: Sustainable Linked Data Coffee Maker

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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 context-aware, are able to configure themselves and exchange information, and show “intelligence/cognitive” behaviour

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IoT Enabling Technologies

• Low-cost embedded computing 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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IoT impulse: Smart Cities, consumer objects, mobile sensing, smart metering

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Current Hype: Health-promoting Personal Devices

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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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Avoiding Data Silos through Semantics

• Cut-down semantics is applied to enable machine-interpretable 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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Actionable Knowledge from Sensorial Data

• Don’t care about the sensors, care about knowledge extracted from data correlation and 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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Example: Waste-related LinkedStats

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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 can become big prosumers of Linked Data:

– Progressively more dynamic 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: energy saving, improve health monitoring, better human energy balance

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Where do we spend Energy?

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• 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 within shared

appliances so that they react and adapt upon energy wasting

– Human behavioral change: Team-up with users to reduce unnecessary energy consumption through feedback and persuasion

Eco-Aware Devices

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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

• Promoting a sustainable behaviour change publishing info in Social Networks and LOD Cloud and through seductive interfaces

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The 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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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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Step 1: Social

• Making it report energy consumptions into Twitter account: @Social_Coffee

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Step 2: Sustainable

• Adding a new state to the coffee machine: auto switch-off which intelligently switches the machine off when it will not be used (ARIMA time series)

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Step 3: Persuasive

• Applying diverse persuasive interfaces to motivate its users to make a more responsible use

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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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Step 4: Cooperative

• Newly deployed coffee makers import intelligence from others deployed in similar public environments to avoid cold-start problem

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Step 5: Linked Data Device

• Modeling 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 – SSN, SWEET, SWRC, GeoNames, PROV-O vocabularies

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Step 5: Linked Data Device

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Linked Data Life Cycle

• Linked Data generated by devices passes through several stages to enhance exploitation:

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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 eco-behaviour

• Future work: exploiting energy-consumption datasets, personal and environmental LOD Clouds

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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

[email protected] http://www.morelab.deusto.es

http://paginaspersonales.deusto.es/dipina

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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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References

• Energy efficiency in the workplace: the solutions and the users that advocate them

– http://bit.ly/17OHeMy

• Linked Open Data as the fuel for Smarter Cities. Mikel Emaldi, Oscar Peña, Jon Lázaro, Diego López-de-Ipiña, book chapter under revision

• Reducing energy waste through eco-aware everyday things. Juan López-de-Armentia, 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. doi:10.4018/jswis.2012010101