Smart-M3 Techniques

2017 ◽  
pp. 82-104
Keyword(s):  
Information Management ◽  
Semantic Information ◽  
Design Principles ◽  
Context Aware ◽  
Smart Space ◽  
Smart Spaces ◽  
Personalized Service ◽  
Heterogeneous Devices ◽  
Modeling Techniques ◽  
Smart Service

The previous chapters elaborated the design principles that guide the development of smart spaces-based applications using the Smart-M3 platform. The principles aim at such properties for applications as (i) interoperability for a multitude of participated heterogeneous devices, services, and users localized in the physical surrounding and (ii) context-aware, situational, and personalized service construction and delivery. In this chapter, we present selected ontology-oriented modeling techniques for applying the principles. The aspect of shared semantic information management becomes essential for service construction. We describe techniques how implement this management in a smart space. A question of what is a smart service compared with regular service is still debatable. We describe techniques how implement various intelligence attributes in services constructed and delivered in M3 spaces.

scholarly journals Enabling Semantic Technology Empowered Smart Spaces

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Jussi Kiljander ◽  
Arto Ylisaukko-oja ◽  
Janne Takalo-Mattila ◽  
Matti Eteläperä ◽  
Juha-Pekka Soininen
Keyword(s):  
Interaction Model ◽  
Context Aware ◽  
Semantic Technologies ◽  
Smart Space ◽  
Smart Spaces ◽  
Semantic Web Technologies ◽  
Space Applications ◽  
Semantic Technology ◽  
Context Aware Computing ◽  
Semantic Data

It has been proposed that Semantic Web technologies would be key enablers in achieving context-aware computing in our everyday environments. In our vision of semantic technology empowered smart spaces, the whole interaction model is based on the sharing of semantic data via common blackboards. This approach allows smart space applications to take full advantage of semantic technologies. Because of its novelty, there is, however, a lack of solutions and methods for developing semantic smart space applications according to this vision. In this paper, we present solutions to the most relevant challenges we have faced when developing context-aware computing in smart spaces. In particular the paper describes (1) methods for utilizing semantic technologies with resource restricted-devices, (2) a solution for identifying real world objects in semantic technology empowered smart spaces, (3) a method for users to modify the behavior of context-aware smart space applications, and (4) an approach for content sharing between autonomous smart space agents. The proposed solutions include ontologies, system models, and guidelines for building smart spaces with the M3 semantic information sharing platform. To validate and demonstrate the approaches in practice, we have implemented various prototype smart space applications and tools.

Efficient Ontology Integration Model for Better Inference in Context Aware Computing

2011 ◽  
Vol 268-270 ◽  
pp. 841-846
Author(s):  
Soo Mi Yang
Keyword(s):  
Video Data ◽  
Smart Devices ◽  
Collaborative Network ◽  
Context Aware ◽  
Integration Model ◽  
Smart Cameras ◽  
Ontology Integration ◽  
Context Aware Computing ◽  
Heterogeneous Devices ◽  
Distributed Ontology

In this paper, we describe efficient ontology integration model for better context inference based on distributed ontology framework. Context aware computing with inference based on ontology is widely used in distributed surveillance environment. In such a distributed surveillance environment, surveillance devices such as smart cameras may carry heterogeneous video data with different transmission ranges, latency, and formats. However even smart devices, they generally have small memory and power which can manage only part of ontology data. In our efficient ontology integration model, each of agents built in such devices get services not only from a region server, but also peer servers. For such a collaborative network, an effective cache framework that can handle heterogeneous devices is required for the efficient ontology integration. In this paper, we propose a efficient ontology integration model which is adaptive to the actual device demands and that of its neighbors. Our scheme shows the efficiency of model resulted in better context inference.

Sensor Information Management Mechanism for Context-aware Service in Ubiquitous Home

2007 ◽  
Vol 53 (4) ◽  
pp. 1393-1400 ◽  
Author(s):  
Seung-ho Baek ◽  
Eun-chang Choi ◽  
Jae-doo Huh ◽  
Kwang-roh Park
Keyword(s):  
Information Management ◽  
Context Aware ◽  
Management Mechanism ◽  
Sensor Information ◽  
Context Aware Service

The Gamut of E-Government Research, Design, and Implementation

2015 ◽  
pp. 1-20
Author(s):  
Kelvin Joseph Bwalya ◽  
Stephen M. Mutula
Keyword(s):  
Information Management ◽  
Mobile Devices ◽  
Research Design ◽  
Design Principles ◽  
Research And Practice ◽  
Design And Implementation ◽  
Government Project ◽  
Government Research ◽  
Contemporary Technology ◽  
Key Questions

E-Government research and practice has changed over the years to incorporate recent and contemporary technology developments and unique in evolving contextual environments. Further, the emerging conceptualization of service and applications interactions is slowly defining the gamut of e-Government research and practice. On another front, there has been a dynamic transition of e-Government being implemented on Web3.0 from the original Web2.0 platforms and advanced e-Government applications accessible on mobile devices i.e. ubiquitous or mobile government. Web3.0 presents a semantic platform allowing responsive man-machine interfaces and applications integration facilitating advanced information management possibilities. The chapter explores the contemporary issues in e-Government and articulates the pertinent factors that need to be interrogated for successful and sustainable e-Government development. Key questions of e-Government and the design principles that need to be taken into consideration in any e-Government project are explored.

Organizing Contextual Data in Context Aware Systems

2016 ◽  
pp. 273-303 ◽  
Author(s):  
Umar Mahmud
Keyword(s):  
Service Discovery ◽  
Future Research ◽  
Context Aware ◽  
Qualitative Comparison ◽  
Contextual Data ◽  
Ontology Language ◽  
Extensible Markup ◽  
Current Context ◽  
Smart Service ◽  
Context Aware System

Context aware systems strive to facilitate better usability through advanced devices, interfaces and systems in day to day activities. These systems offer smart service discovery, delivery and adaptation all based on the current context. A context aware system must gather the context prior to context inference. This gathered context is then stored in a tagged, platform independent format using Extensible Markup Language (XML) or Web Ontology Language (OWL). The hierarchy is enforced for fast lookup and contextual data organization. Researchers have proposed and implemented different contextual data organizations a large number of which has been reviewed in this chapter. The chapter also identifies the tactics of contextual data organizations as evident in the literature. A qualitative comparison of these structures is also carried out to provide reference to future research.

Micro Context-Awareness for Autonomic Pervasive Computing

2011 ◽  
Vol 7 (2) ◽  
pp. 48-68 ◽  
Author(s):  
Bessam Abdulrazak ◽  
Patrice Roy ◽  
Charles Gouin-Vallerand ◽  
Yacine Belala ◽  
Sylvain Giroux
Keyword(s):  
Pervasive Computing ◽  
Context Awareness ◽  
Programming Model ◽  
Software Components ◽  
Context Aware ◽  
Smart Space ◽  
Global Knowledge ◽  
System Utilization ◽  
Pervasive System ◽  
Micro Level

Context-aware software provides adapted services to users or other software components. On the other hand, Autonomic Pervasive Computing uses context to reduce the complexity of pervasive system utilization, management and maintenance. This paper describes two context-awareness models, the macro and micro approaches, that define and integrate contextual views of individual pervasive components (micro level) and global knowledge of the system (macro level), and provides a more detailed overview of a micro Context-aware programming model for open smart space problems. These models are presented and compared with respect to their ability to meet the requirements of the Autonomic Pervasive Computing concept of the four selves.

Individual Client Strategies for Active Control of Information-Driven Service Construction in IoT-enabled Smart Spaces

2019 ◽  
Vol 10 (2) ◽  
pp. 20-36 ◽  
Author(s):  
Olga Bogoiavlenskaia ◽  
Andrey Vdovenko ◽  
Dmitry G. Korzun ◽  
Alexey Kashevnik
Keyword(s):  
Active Control ◽  
Performance Metrics ◽  
The Internet ◽  
Service Needs ◽  
Smart Space ◽  
Smart Spaces ◽  
Individual Strategy ◽  
Individual Client ◽  
The Internet Of Things ◽  
Information Broker

Smart spaces provide a platform for cooperative service construction by many devices in the Internet of Things (IoT) environments. When a service is constructed the service needs delivering to appropriate clients, which is typically implemented using the subscription operation (i.e., information-driven service construction). The passive form of subscription is ineffective in the IoT settings since the centralized solution—smart space information broker—needs to control all service construction updates and to notify all interested clients. This article considers the problem of active control for information-driven service construction when each client can use its own (individual) strategy to (additionally) control ongoing updates in the subscribed information. Five strategies for active control are selected for this study. For some simplified assumptions, analytical estimates are provided. For close-to-real evaluation of the strategies a simulation model is developed, based on which several performance metrics are experimentally studied.

scholarly journals Surveying Human Habit Modeling and Mining Techniques in Smart Spaces

2019 ◽  
Vol 11 (1) ◽  
pp. 23 ◽  
Author(s):  
Francesco Leotta ◽  
Massimo Mecella ◽  
Daniele Sora ◽  
Tiziana Catarci
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Literature Review ◽  
Visual Analysis ◽  
Future Research ◽  
Specific Method ◽  
Human Intervention ◽  
Smart Space ◽  
Smart Spaces ◽  
Research Questions

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field.

Semantic Information Management

2005 ◽  
pp. 593-600
Author(s):  
David G. Schwartz ◽  
Zvi Schreiber
Keyword(s):  
Information Management ◽  
Semantic Information ◽  
Sharp Focus

The need to manage enterprise data has been coming into increasingly sharp focus for some time. Years ago, data sat in silos attached to specific applications. Then came the network, with data becoming available across applications, departments, subsidiaries, and enterprises. Throughout these developments, one underlying problem has remained unsolved: Data resides in thousands of incompatible formats and cannot be systematically managed, integrated, unified, or cleansed.

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