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

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.

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 Empowering Advanced Driver-Assistance Systems from Topological Data Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 634
Author(s):  
Tarek Frahi ◽  
Francisco Chinesta ◽  
Antonio Falcó ◽  
Alberto Badias ◽  
Elias Cueto ◽  
...  
Keyword(s):  
Data Analysis ◽  
The State ◽  
Sensor Data ◽  
Topological Data Analysis ◽  
Motion Sensors ◽  
Driver Assistance Systems ◽  
The Road ◽  
Topological Features ◽  
Recent Developments ◽  
Topological Data

We are interested in evaluating the state of drivers to determine whether they are attentive to the road or not by using motion sensor data collected from car driving experiments. That is, our goal is to design a predictive model that can estimate the state of drivers given the data collected from motion sensors. For that purpose, we leverage recent developments in topological data analysis (TDA) to analyze and transform the data coming from sensor time series and build a machine learning model based on the topological features extracted with the TDA. We provide some experiments showing that our model proves to be accurate in the identification of the state of the user, predicting whether they are relaxed or tense.

scholarly journals Kernel Sparse Representation with Hybrid Regularization for On-Road Traffic Sensor Data Imputation

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2884 ◽  
Author(s):  
Xiaobo Chen ◽  
Cheng Chen ◽  
Yingfeng Cai ◽  
Hai Wang ◽  
Qiaolin Ye
Keyword(s):  
Data Analysis ◽  
Sparse Representation ◽  
Intelligent Transportation Systems ◽  
Missing Values ◽  
Road Traffic ◽  
Linear Representation ◽  
Transportation Systems ◽  
Sensor Data ◽  
L1 Norm ◽  
L2 Norm

The problem of missing values (MVs) in traffic sensor data analysis is universal in current intelligent transportation systems because of various reasons, such as sensor malfunction, transmission failure, etc. Accurate imputation of MVs is the foundation of subsequent data analysis tasks since most analysis algorithms need complete data as input. In this work, a novel MVs imputation approach termed as kernel sparse representation with elastic net regularization (KSR-EN) is developed for reconstructing MVs to facilitate analysis with traffic sensor data. The idea is to represent each sample as a linear combination of other samples due to inherent spatiotemporal correlation, as well as periodicity of daily traffic flow. To discover few yet correlated samples and make full use of the valuable information, a combination of l1-norm and l2-norm is employed to penalize the combination coefficients. Moreover, the linear representation among samples is extended to nonlinear representation by mapping input data space into high-dimensional feature space, which further enhances the recovery performance of our proposed approach. An efficient iterative algorithm is developed for solving KSR-EN model. The proposed method is verified on both an artificially simulated dataset and a public road network traffic sensor data. The results demonstrate the effectiveness of the proposed approach in terms of MVs imputation.

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.

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