Transcript Achieving Autonomicity in IoT systems via Situational

18th Panhellenic Conference on Informatics
(PCI 2014) October 2-4 2014, Athens, Greece
Achieving Autonomicity in IoT systems via
Situational-Aware, Cognitive and Social Things
Orfefs Voutyras, Spyridon Gogouvitis, Achilleas Marinakis and
Theodora Varvarigou, National Technical University of Athens
Presenter: Orfefs Voutyras
Overview
Goal
Introduction
The concept of Knowledge
The concept of Experience
Types of learning
Learning through communication
Social properties of the Virtual Entities
Management components
Summary
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Goal
Our aim is to support knowledge flow between Things in
order to provide a system that acts in an autonomous
way, learns, observes and evaluates the usage and
communication patterns and generates new knowledge.
Our proposal focuses on the value of experience and
experience-sharing and investigates models and
principles designed for the social networks, which would
provide it with the potential to support novel applications
in more effective and efficient ways.
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Introduction (1/3)
The COSMOS project will provide a framework for the decentralized and
autonomous management of Things based on service-, interaction-,
location- and reputation-oriented principles, inspired by social media
technologies.
Achieving Autonomicity via Situational-Aware, Cognitive and Social Things
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Introduction (2/3)
The proposed approach follows the:
IoT-A reference model
– Virtual Entities (VEs) and Groups of Virtual Entities (GVEs).
Social Internet of Things (SIoT) paradigm
– it maps the social relations and interactions of the individuals to their VEs.
– it defines, monitors and exploits social relations and interactions between the VEs.
– it uses technologies and exploits services from the domain of the social media.
MAPE-K model
– self-management and
– autonomicity
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Introduction (3/3)
Monitor-Analyze-Plan-Execute (MAPE):
an autonomic control loop or autonomic
manager as proposed by IBM.
Situationalaware
Cognitive
SA
A
In addition to the MAPE components, an
autonomic manager also contains a
Knowledge block that is connected to all
four of the MAPE functional blocks,
producing a MAPE-K control loop.
We extend the MAPE-K loop by
introducing two new components, Social
Monitoring (SM) and Social Analysis
(SA).
Social
P
M
SM
E
Knowledge Component
The COSMOS MAPE-K loop
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The concept of Knowledge
Wisdom
know-Best
learning
Knowledge
know-How
planning
Information
analysis
know-What
Data
monitoring
raw-data collected through
IoT-services
.
know-Nothing
The COSMOS DIKW Pyramid
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The concept of Experience (1/2)
Experience can be:
a piece of Knowledge described by an ontology,
a Model resulting from Machine Learning or
contextual information
We focus mainly on the representation of experience through Cases
as defined in the Case Based Reasoning (CBR) technique.
A case can be considered as a combination of a problem with its
solution, whereas a problem consists of one or more events.
In other words, a case is a kind of rule for an actuation plan, which is
triggered when specific events are identified.
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The concept of Experience (2/2)
Ontologies are used for
the description of the VEs
Cases are one form of
Experience
Used as a means to
reason: cause and effect
(Problem – Solution)
Each VE may maintain its
own Case Base (CB)
locally as part of its KB
Classes of the COSMOS ontology
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Types of learning
Individual Learning, through self enrichment of local
CB.
Learning through communication, by using the
experience sharing (XP-sharing) mechanism.
Learning through a knowledge repository, when the
VE connects to the COSMOS platform.
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Individual Learning
A general CBR cycle may be
described by the following four
processes:
RETRIEVE the most similar
case or cases
REUSE the information and
knowledge in that case to
solve the problem
REVISE the proposed
solution
RETAIN the parts of this
experience likely to be
useful for future problem
solving
The CBR cycle (adapted from [Aamodt, 1994])
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Learning through communication
Forms of learning through
communication:
–
Demand driven or
–
Supply driven learning.
Influences overhead and hit
rate.
Dissemination options
–
Broadcasting
–
Narrow casting
–
Personal casting
Learning through communication
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Social properties of the virtual entities
Concept of Friends that act more like
Twitter’s Followers.
Used for greater versatility of communication
(decentralization) and knowledge acquisition.
Choice based on Relevance and
Dependability.
Relevance includes VE Domain and Physical
Entity matching (Homophily), as well as
Distance proximity through Location and
Geo-location measurements.
Example of VEs’ properties
Dependability measures social willingness
and usefulness of shared knowledge (Trust,
Reputation), as well as absence of
mechanical failures (Reliability).
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Management components
Profiling and Policy Management (PPM)
for assigning Unique VE IDs and
maintaining the openness factors of
individual VEs.
Friends Management (FM) for creating
and maintaining the Friend List, as well as
providing suggestions to the user.
Social Monitoring (SM) in order to
evaluate feedback on all social actions
concerning the VE.
Social Analysis (SA) so that the platform
can retrieve data from VEs’ SM
components and extract complex social
characteristics of the VEs as well as models
and patterns in intra VE communication.
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Summary
The COSMOS platform can be characterized as a SIoT platform since
it defines, monitors and exploits social relations and interactions
between the VEs and uses technologies from the domain of the social
media.
The social side of COSMOS improves the knowledge flow (distributed
knowledge) and introduces the concept of experience sharing
between Things, enabling Things to react in a more autonomous way.
However, one of the main concerns regarding the success of such an
architecture is its potential to maintain an opportunistic IoT system,
offering the human users motives to share the knowledge and IoTservices of their VEs.
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Thank you!
Orfefs Voutyras
Further Information:
http://iot-cosmos.eu
NTUA
[email protected]
The research leading to these results is
partially supported by the European
Community’s Seventh Framework Programme
under grant agreement n° 609043, in the
context of the COSMOS Project.
Autonomous Management
Managed system: The system collects and offers to the administrator
all the information needed to take decisions.
Predictive system: The system is able to recognise patterns, predict
the optimal configuration and make proposals to the administrator.
Adaptive system: The system is able, not only to “offer advice” for
certain actions, but can trigger on its own the right actions, based on
the information that it has gathered.
Autonomic system (real autonomy): The system’s actions are based
on business rules, models and goals. The users react with the system
only when some changes to these rules are needed.
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