Resolving and Mediating Ambiguous Contexts in Pervasive Environments

2011 ◽  
pp. 122-147 ◽  
Author(s):  
Nirmalya Roy ◽  
Sajal K. Das ◽  
Christine Julien
Keyword(s):  
State Estimation ◽  
Pervasive Computing ◽  
Independent Living ◽  
Vital Signs ◽  
Context Information ◽  
Context Aware ◽  
Information Theoretic ◽  
Significant Challenge ◽  
Environmental Measures ◽  
Optimal State Estimation

Pervasive computing applications envision sensor rich computing and networking environments that can capture various types of contexts of inhabitants of the environment, such as their locations, activities, vital signs, and environmental measures. Such context information is useful in a variety of applications, for example to manage health information to promote independent living in “aging-in-place” scenarios. In reality, both sensed and interpreted contexts are often ambiguous, leading to potentially dangerous decisions if not properly handled. Thus, a significant challenge facing the development of realistic and deployable context-aware services for pervasive computing applications is the ability to deal with these ambiguous contexts. In this chapter, the authors discuss a resource optimized quality assured ontology-driven context mediation framework for resource constrained sensor networks based on efficient context-aware data fusion and information theoretic sensor parameter selection for optimal state estimation. It has the ability to represent contexts according to the applications’ ontology and easily composable ontological rules to mediate ambiguous contexts.

Resolving and Mediating Ambiguous Contexts in Pervasive Environments

2012 ◽  
pp. 630-654
Author(s):  
Nirmalya Roy ◽  
Sajal K. Das ◽  
Christine Julien
Keyword(s):  
State Estimation ◽  
Pervasive Computing ◽  
Independent Living ◽  
Vital Signs ◽  
Context Information ◽  
Context Aware ◽  
Information Theoretic ◽  
Significant Challenge ◽  
Environmental Measures ◽  
Optimal State Estimation

Pervasive computing applications envision sensor rich computing and networking environments that can capture various types of contexts of inhabitants of the environment, such as their locations, activities, vital signs, and environmental measures. Such context information is useful in a variety of applications, for example to manage health information to promote independent living in “aging-in-place” scenarios. In reality, both sensed and interpreted contexts are often ambiguous, leading to potentially dangerous decisions if not properly handled. Thus, a significant challenge facing the development of realistic and deployable context-aware services for pervasive computing applications is the ability to deal with these ambiguous contexts. In this chapter, the authors discuss a resource optimized quality assured ontology-driven context mediation framework for resource constrained sensor networks based on efficient context-aware data fusion and information theoretic sensor parameter selection for optimal state estimation. It has the ability to represent contexts according to the applications’ ontology and easily composable ontological rules to mediate ambiguous contexts.

Research on Proactive Services Based on Context Rules in Virtual Environment

2014 ◽  
Vol 10 (02) ◽  
pp. 177-185
Author(s):  
Ming-Zhi Chen ◽  
Bo-Gang Lin ◽  
Shui-Li Chen
Keyword(s):  
Virtual Environment ◽  
Pervasive Computing ◽  
Context Information ◽  
Context Aware ◽  
Decision Tree Algorithm ◽  
Context Aware Computing ◽  
Ontology Language ◽  
Research Findings ◽  
Machine Interface ◽  
Application Requirements

The issues of Human–Machine Interface (HMI) of Virtual Environment (VE) are discussed in view of users' potential application requirements in VE. The Human-centered HMI containing rich context information is proposed by taking the advantage of the abundant research findings of artificial intelligence and pervasive computing. The technology of context-aware computing is introduced in this study for providing initiative services mode as well. First, in view of the characteristics of VE, the Web Ontology Language (OWL) is adopted to model the context information. Second, the modified ID3 decision-tree algorithm is applied to automatically generate the context rules of VE. Third, the polymorphic proactive services model based on context rules is designed. Finally, an example is given to illustrate how to apply context rule-inference to realize proactive services. The experiment demonstrates the correctness and effectiveness of the above mentioned algorithm and model.

scholarly journals Context-Aware Multi-layered Ontology for Composite Situation Model in Pervasive Computing

2020 ◽  
Vol 25 (5) ◽  
pp. 543-558
Author(s):  
Abderrahim Lakehal ◽  
Adel Alti ◽  
Philippe Roose
Keyword(s):  
Pervasive Computing ◽  
Real Life ◽  
Situation Model ◽  
Context Information ◽  
Mobile Service ◽  
Context Aware ◽  
Smart Systems ◽  
Services Delivery ◽  
Iot Devices ◽  
Smart Mobile

With the rapid advancement of technologies and analysis tools in the smart systems, enabling real-time context monitoring of user's living conditions and quality services delivery is increasing. Current studies in this area are focused on developing mobile applications with specific services, based on toolkit that allow developers to obtain context information from sensors. However, there exists a notable lack of ontology able to represent all the necessary context information starting from distributed users, and constantly changing environment. The modeling of user’s domains to represent diverse mobile and IoT devices, and finalizing with the description of user’s composite situations in smart-*(health, home, cities, car, office, etc.) domains. Considering interoperability, reusability, and flexibility, a new context composite situation ontology for smart systems is proposed with better representation of heterogeneous context. The ontology enables to sense, reason, and infer composite situations in various smart domains, prioritizes critical situations and facilitates the delivery of smart mobile service. Proposed ontology is formalized and validated on different smart environments with different user’s situations. Several experiments were carried out with a real-life motivating scenario. Experimental results showed that the proposed approach has reduced queries times and improved flexibility.

scholarly journals Context-Aware Human Activity Recognition in Industrial Processes

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 134
Author(s):  
Friedrich Niemann ◽  
Stefan Lüdtke ◽  
Christian Bartelt ◽  
Michael ten Hompel
Keyword(s):  
Activity Recognition ◽  
Time Management ◽  
Recognition Performance ◽  
Context Information ◽  
Context Aware ◽  
Recognition Model ◽  
Movement Data ◽  
Information Activity ◽  
The Subject ◽  
Motion Capturing

The automatic, sensor-based assessment of human activities is highly relevant for production and logistics, to optimise the economics and ergonomics of these processes. One challenge for accurate activity recognition in these domains is the context-dependence of activities: Similar movements can correspond to different activities, depending on, e.g., the object handled or the location of the subject. In this paper, we propose to explicitly make use of such context information in an activity recognition model. Our first contribution is a publicly available, semantically annotated motion capturing dataset of subjects performing order picking and packaging activities, where context information is recorded explicitly. The second contribution is an activity recognition model that integrates movement data and context information. We empirically show that by using context information, activity recognition performance increases substantially. Additionally, we analyse which of the pieces of context information is most relevant for activity recognition. The insights provided by this paper can help others to design appropriate sensor set-ups in real warehouses for time management.

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