ASP-based Knowledge Representations for IoT Configuration Scenarios

Workshop on Configuration 2017, Paris, France
Institute for Software Technology
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ASP-based Knowledge Representations for
IoT Configuration Scenarios
A. Felfernig1, A. Falkner2, M. Atas1,
S. Polat Erdeniz1, C. Uran1, P. Azzoni3, and S. Reiterer1
1Graz University of Technology, Austria
2Siemens, Austria
3Eurotech Group, Italy
Workshop on Configuration 2017

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Overview
•AGILE Project
•Example IoT Application Scenarios
•Ramp-up configuration
•ASP-based Knowledge Representations
•Runtime configuration
•Research Issues & Conclusions

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Horizon 2020 Project AGILE
• AGILE: an Adaptive & Modular Gateway for the IoT
• Duration: 2016 – 2018
• 17 Partners:
• Goal of TU Graz: recommendation & configuration for IoT

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Horizon 2020 Project AGILE
• Internet of Things (IoT): paradigm that envisions a
networked infrastructure enabling different devices
(things) to be interconnected at anyplace and anytime.
• Gateways: bridge devices to corresponding applications
on the basis of different protocols (e.g., temperature
sensor with weather app on Raspberry Pi).
• Ramp-up Configuration: initial determination of
gateway infrastructure (devices and applications).
• Runtime Configuration: reconfiguration of devices (e.g.,
sensors, drivers, and app settings).

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IoT Application Scenario:
Smart Homes
• Sensors: temperature,
body temperature,
heartrate, air pressure,
incoming light, motions
• Apps: personal trainer,
burglary protection,
automated watering, fall
detection, light
management,
personalized receipes

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Animal Monitoring
• Sensors: body
temperature, heartrate,
GPS data, ground photo-
graphs from drones, air
temperature data.
• Apps: disease detection, detection of forest fires,
observation of herd movements, fighting of poachers,
search for individual animals, remote treatments, “lost &
found” apps, mobile camera management, statistics (e.g.,
population sizes in herds).

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Enhanced Retail Services
• Sensors: own location,
item locations, items in
shopping cart, locations
of experts.
• Apps: personalized recommendations & special offers,
personalized content displays, gamification-based
shopping experiences, competitions, personalized item
information, retrieval of experts.

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Ramp-Up Configuration
• Initial determination of gateway infrastructure.
• Smart Homes: given information about rooms, levels,
etc., and the preferences of users, propose a
corresponding configuration per room (sensors, etc.).
• Animal Monitoring: given the topology of the area incl.
the types/amounts of animals, derive a configure-tion of
devices (e.g., drones, measurement units) needed.
• Enhanced Retail Services: given the size of the shop
and the different types of items, provide a configuration
that includes the needed devices and their positioning
(e.g., monitors and sensors).

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ASP based Knowledge Representation
• Intuitive approach to represent component structures,
generalizations, and corresponding constraints
• Open source (OS) based ASP solutions available, e.g.,
clingo (OS important for AGILE H2020 project)
• Chosen technology for ramp-up configuration in the
AGILE scenarios: ASP
• Performance issues (grounding, i.e., making an ASP
variable-free)
• Goal of the paper: intuitively summarize application of
ASP for building configuration knowledge bases

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Smart Home Model
Component types and attributes:
1{country(X,Y):domcountry(Y)}1:-
smarthome(X).
Associations:
1{smarthomeroom(X,Y): room(Y)}2:-
smarthome(X).
Requirement Relationships:
2{smarthomeroom(X,Y):room(Y)}2:-
smarthome(X), country(X,austria).
Incompatibiltiy Relationships:
:- roomappliance(X,Y), type(X,kitchen),
tv(Y).
Further aspects:
Symmetry breaking, integration of CSP
reasoning for optimization purposes

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Runtime Configuration & Recommendation
• Apps: given the set of currently installed apps and
sensors, recommend additional apps and devices. This
includes a reconfiguration of the current gateway settings.
• Collaborative Recommendation of Apps. Based on
gateway profile repositories and app repositories.
• Content-based Recommendation of Workflows. Based
on workflow repositories such as nodered.org (workflows
published by other gateways).
• Collaborative Recommendation of Sensors. Based on
gateway profile repositories and app repositories.

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Research Issues
• Improve performance and prediction quality of
solution search in ASP supporting the mentioned
ramp-up scenarios.
• Possibilities to improve test case generation and
management for complex configuration knowledge
bases (on the basis of existing SWE approaches).
• Increasing the performance of personalized
diagnosis (e.g., anytime and direct diagnosis).
• Knowledge compression techniques for configuration
processes on the gateway (runtime configuration).

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Conclusions
• Many existing IoT scenarios, for example, smarthomes,
animal monitoring, and enhanced retail services.
• Configuration plays a major role in IoT scenarios.
• Often combined with recommendation (e.g.,
recommendation of apps and gateway reconfiguration).
• Challenges in terms of algorithm efficiency on the
gateway (limited computational resources).
• Goal of this paper was to summarize in an intuitive
fashion how to build knowledge bases using ASP.

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Thank You!

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