scholarly journals Elastic AI: system support for adaptive machine learning in pervasive computing systems

2021 ◽  
Author(s):  
Christopher Cichiwskyj ◽  
Stephan Schmeißer ◽  
Chao Qian ◽  
Lukas Einhaus ◽  
Christopher Ringhofer ◽  
...  
Keyword(s):  
Pervasive Computing ◽  
Lower Energy ◽  
Data Sources ◽  
Sensor Data ◽  
System Support ◽  
Dynamic Changes ◽  
Computing Systems ◽  
Runtime Adaptation ◽  
Pervasive Systems ◽  
Application Requirements

AbstractArtificial intelligence (AI) is an important part of today’s pervasive computing systems. Still, there is no end-to-end system platform that allows to deploy, update, manage and execute AI models in pervasive systems. We propose such a system platform in this paper. Most importantly, we reuse concepts and techniques from twenty years of pervasive computing research on how to enable runtime adaptation and apply it to AI. This allows to specify adaptive AI models that are able to react to a multitude of dynamic changes, e.g. with respect to available devices, networking conditions, but also application requirements and sensor data sources. Developers can optimise their applications iteratively, starting with a generic setup and refining it step by step towards their specific pervasive computing scenario. To show the applicability of our platform, we apply it to two pervasive use cases and evaluate them, achieving up to four times faster inference and three times lower energy consumption compared to a classical AI deployment.

Ontology-based models in pervasive computing systems

2007 ◽  
Vol 22 (4) ◽  
pp. 315-347 ◽  
Author(s):  
JUAN YE ◽  
LORCAN COYLE ◽  
SIMON DOBSON ◽  
PADDY NIXON
Keyword(s):  
Pervasive Computing ◽  
Information Exchange ◽  
Next Generation ◽  
Computing Systems ◽  
Diverse Range ◽  
Shared Representations ◽  
Pervasive Systems ◽  
Structured Information ◽  
Compare And Contrast

AbstractPervasive computing is by its nature open and extensible, and must integrate the information from a diverse range of sources. This leads to a problem of information exchange, so sub-systems must agree on shared representations. Ontologies potentially provide a well-founded mechanism for the representation and exchange of such structured information. A number of ontologies have been developed specifically for use in pervasive computing, none of which appears to cover adequately the space of concerns applicable to application designers. We compare and contrast the most popular ontologies, evaluating them against the system challenges generally recognized within the pervasive computing community. We identify a number of deficiencies that must be addressed in order to apply the ontological techniques successfully to next-generation pervasive systems.

scholarly journals TRAP.NET: A REALIZATION OF TRANSPARENT SHAPING IN .NET

2009 ◽  
Vol 19 (04) ◽  
pp. 507-528 ◽  
Author(s):  
S. MASOUD SADJADI ◽  
FERNANDO TRIGOSO
Keyword(s):  
Wireless Networks ◽  
Grid Computing ◽  
Mobile Devices ◽  
Pervasive Computing ◽  
Autonomic Computing ◽  
Programming Model ◽  
Critical Systems ◽  
Dynamic Changes ◽  
Execution Environment ◽  
Application Requirements

We define adaptability as the capacity of software in adjusting its behavior in response to changing conditions. To list just a few examples, adaptability is important in pervasive computing, where software in mobile devices need to adapt to dynamic changes in wireless networks; autonomic computing, where software in critical systems are required to be self-manageable; and grid computing, where software for long running scientific applications need to be resilient to hardware crashes and network outages. In this paper, we provide a realization of the transparent shaping programming model, called TRAP.NET, which enables transparent adaptation in existing .NET applications as a response to the changes in the application requirements and/or to the changes in their execution environment. Using TRAP.NET, we can adapt an application dynamically, at run time, or statically, at load time, without the need to manually modify the application original functionality-hence transparent.

Situvis: A sensor data analysis and abstraction tool for pervasive computing systems

2010 ◽  
Vol 6 (5) ◽  
pp. 575-589 ◽  
Author(s):  
Adrian K. Clear ◽  
Thomas Holland ◽  
Simon Dobson ◽  
Aaron Quigley ◽  
Ross Shannon ◽  
...  
Keyword(s):  
Data Analysis ◽  
Pervasive Computing ◽  
Sensor Data ◽  
Computing Systems

A Security Architecture Based on Trust Management for Pervasive Computing Systems

2005 ◽  
Author(s):  
Lalana Kagal ◽  
Jeffrey Undercoffer ◽  
Filip Perich ◽  
Anupam Joshi ◽  
Tim Finin
Keyword(s):  
Pervasive Computing ◽  
Trust Management ◽  
Security Architecture ◽  
Computing Systems

scholarly journals Context-Aware User Modeling and Semantic Interoperability in Smart Home Environments

2015 ◽  
Vol 7 (3) ◽  
pp. 17-50
Author(s):  
Giorgos Siolas ◽  
George Caridakis ◽  
Phivos Mylonas ◽  
Giorgos Stratogiannis ◽  
Stefanos Kollias ◽  
...  
Keyword(s):  
Pervasive Computing ◽  
Smart Home ◽  
User Modeling ◽  
Collective Intelligence ◽  
Context Modeling ◽  
Home Environments ◽  
Computing Systems ◽  
Knowledge Based ◽  
Context Cues ◽  
Smart Home Environments

The current paper provides an overview on how user modeling, context awareness and content adaptation in Smart Home environments may be handled formally in order to capture the semantics that emerge from a newly introduced user experience: SandS is in fact a complete ecosystem of users within a social network, creating and exchanging content in the form of so-called recipes and developing a collective intelligence which adapts its operation through appropriate feedback provided by the user. The authors will approach SandS from the user's perspective and illustrate how users and their relationships can be modeled through a number of fuzzy stereotypical profiles. Additionally, context modeling in pervasive computing systems and especially in the Smart Home paradigm will be examined through appropriate representation of context cues in the overall interaction. Finally, the authors will investigate how users and system services although using languages of different semantic expressiveness can inter-operate successfully thanks to appropriate knowledge-based expert mappings.

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