A Framework for Context-Aware Applications

For Smart Spaces

Mohsin Saleemi, Natalia Díaz Rodríguez, Johan Lilius, Iván Porres ndiaz@abo.fi, msaleemi@abo.fi

Åbo Akademi University, Turku (Finland)

ruSmart 2011

Contents

• Introduction

• What is a Smart Space?

• Smart-M3 Platform

• Context Ontology Model

• System Architecture

• Context Inference Rules

• Application Development Tool for Smart-M3

• PythonRule Structure

• Python Programming of Smart Spaces with PythonRules

• Conclusions

• Future Work

Introduction

• Ubiquitous computing involving heterogeneous devices.

• Need for tackling Device Interoperability.

• Context : Any information that can be used to characterize the state of an entity.

• Focus on the User: A context system that adapts to the user’s preference.

• Against traditional context aware systems (based on sensors and static rules, requiring significant amount of human interactions to become adaptative) we chose Ontology based Context Modelling:

• Expressive models.

• Provides flexibility, genericity and extendibility.

• Smart-M3 Ontology based solution.

- E.g. PVR and mobile phone example

What is a Smart Space?

SMART SPACE:

An abstraction of space encapsulating both information from a physical space and access to this information allowing devices to join and leave the space.

Publish-subscribe methods are used in these dynamically changing environments.

Smart-M3

NOKIA’S SMART-M3 (An implementation of Smart Space): • A Multi device, Multi part and Multi vendor (M3) open source

cross-domain platform for independent agents to communicate.

• Semantic Information Broker (SIB): The central repository of RDF triples responsible for information storage, sharing and management through the Smart Space Access Protocol (SSAP).

• KNOWLEDGE PROCESSORS (KPs) entities implement functionality and interact with the Smart Space by inserting/retrieving/querying common information.

• An APPLICATION is constructed by aggregating KPs which perform tasks.

• COMMUNICATION happens not device to device but through the SIB.

Smart-M3 concept

Context Ontology Model

Inferred information causes the context ontology to be extended

Enabling the system to initiate adaptative decisions appropriate for a particular application

Context Ontology Model

System Architecture Context Providers

• Observed • Specified

Context Datatype Interpreter • Type conversion • OWL-S can be used to specify

functionality Context Reasoner/Rule Interpreter

• Infer high level context info. • Based on inference rules.

Ontologies • OWL ontologies define context

information in the SIB. Inference Rules

• Specific format • Domain specific • Can be provided as separate

libraries

Context Inference Rules

Since the end-user should not deal with the RDF store directly, a PythonRules module is presented to translate Python logic expressions to the SIB API (Query, Subscribe, Insert, Remove, Update). AIM: An independent PythonRules Module to allow easy definition of Rules to model Smart Spaces: •No need for learning Query languages or treat RDF data.

•Including Rule Reasoning.

Application Development tool for Smart-M3

• Ontology-Based application development

• Tool for rapid

application development

Tools

1. Ontology Library Generator

OWL-DL -> Python and C.

2. Middleware framework: Abstracts the communication with the SIB providing to the generated API handling of RDF triples and queries.

PythonRule Structure:

With()//When()>>Then()

• With clause: Assumptions, Assertions or

Declarations about existence of individuals.

• When clause: Conditions or events that must hold before the rule is triggered.

• Then clause: Actions to trigger, Conclusions

representing the inferred information.

Python Programming of Smart Spaces with PythonRules

Python Programming of Smart Spaces with PythonRules

Python Programming of Smart Spaces with PythonRules

Conclusions

• Smart Spaces: well suited for ambient applications to adapt to the user’s preferences.

• Information Sharing and Reusability allowed for diverse Dynamic Applications.

• PythonRules: Allows End-User to configure the behaviour of the Smart Space with no knowledge of Semantic Web technologies (query languages or RDF data).

• PythonRules aims at being independent of the RDF Store (other than Smart-M3 will be used).

And finally: Easy UI for non programmers.

Future Work

• Ongoing PythonRules module: Further development. • SIB consistency related issues (and

efficient subscriptions implementation). • Privacy Control. • Integrationg with OWL-S services. • Different Domain Applications:

• BioInformatics. • Office Domain, Home Automation. • Elderly Monitoring Systems, etc.

References

Smart-M3 approach and our Development Tools:

- Smart-M3 Software, Release 0.9.4 beta. Available: http://sourceforge.net/projects/smart-m3/

- Smart-M3 Ontology Library Generator OWL->Python API: http://sourceforge.net/projects/smart-m3/files/smart-m3-ontology\_to\_Python-API\_generator\_v0.9.1beta.tar.gz/

- Framework for Smart Space Application Development. Kaustell, Andre and Saleemi, M. Mohsin and Rosqvist, Thomas and Jokiniemi, Juuso and Lilius, Johan and Porres, Ivan. In Proceedings of the International Workshop on Semantic Interoperability, IWSI 2011

- End-User’s Service composition in Ubiquitous Computing using Smart Space approach. Saleemi, M. Mohsin and Lilius, Johan. Sixth International Conference on Internet and Web Applications and Services, IEEE, 2011.

- Ontology-Driven Smart Space Application Development. River Publishers Book Chapter (in revision).

MORE INFORMATION: Natalia Díaz, ndiaz@abo.fi

Mohsin Saleemi, msaleemi@abo.fi