The Experts below are selected from a list of 664527 Experts worldwide ranked by ideXlab platform
Sébastien Leriche - One of the best experts on this subject based on the ideXlab platform.
-
INCOME : multi-scale Context Management for the internet of things
2012Co-Authors: Jean-paul Arcangeli, Amel Bouzeghoub, Valérie Camps, Marie-françoise Canut, Sophie Chabridon, Denis Conan, Thierry Desprats, Romain Laborde, Emmanuel Lavinal, Sébastien LericheAbstract:Nowadays, Context Management solutions in ambient networks are well-known. However, with the IoT paradigm, ambient information is not anymore the only source of Context. Context Management solutions able to address multiple network scales ranging from ambient networks to the Internet of Things (IoT) are required. We present the INCOME project whose goal is to provide generic software and middleware components to ease the design and development of mass market Context-aware applications built above the Internet of Things. By revisiting ambient intelligence (AmI) Context Management solutions for extending them to the IoT, INCOME allows to bridge the gap between these two very active research domains. In this landscape paper, we identify how INCOME plans to advance the state of the art and we briefly describe its scientific program which consists of three main tasks: (i) multi-scale Context Management, (ii) Management of extrafunctional concerns (quality of Context and privacy), and (iii) autonomous deployment of Context Management entities.
-
AmI - INCOME – Multi-scale Context Management for the Internet of Things
Lecture Notes in Computer Science, 2012Co-Authors: Jean-paul Arcangeli, Amel Bouzeghoub, Valérie Camps, Sophie Chabridon, Denis Conan, Thierry Desprats, Romain Laborde, Emmanuel Lavinal, C. Marie-françoise Canut, Sébastien LericheAbstract:Nowadays, Context Management solutions in ambient networks are well-known. However, with the IoT paradigm, ambient information is not anymore the only source of Context. Context Management solutions able to address multiple network scales ranging from ambient networks to the Internet of Things (IoT) are required. We present the INCOME project whose goal is to provide generic software and middleware components to ease the design and development of mass market Context-aware applications built above the Internet of Things. By revisiting ambient intelligence (AmI) Context Management solutions for extending them to the IoT, INCOME allows to bridge the gap between these two very active research domains. In this landscape paper, we identify how INCOME plans to advance the state of the art and we briefly describe its scientific program which consists of three main tasks: (i) multi-scale Context Management, (ii) Management of extrafunctional concerns (quality of Context and privacy), and (iii) autonomous deployment of Context Management entities.
Denis Conan - One of the best experts on this subject based on the ideXlab platform.
-
INCOME : multi-scale Context Management for the internet of things
2012Co-Authors: Jean-paul Arcangeli, Amel Bouzeghoub, Valérie Camps, Marie-françoise Canut, Sophie Chabridon, Denis Conan, Thierry Desprats, Romain Laborde, Emmanuel Lavinal, Sébastien LericheAbstract:Nowadays, Context Management solutions in ambient networks are well-known. However, with the IoT paradigm, ambient information is not anymore the only source of Context. Context Management solutions able to address multiple network scales ranging from ambient networks to the Internet of Things (IoT) are required. We present the INCOME project whose goal is to provide generic software and middleware components to ease the design and development of mass market Context-aware applications built above the Internet of Things. By revisiting ambient intelligence (AmI) Context Management solutions for extending them to the IoT, INCOME allows to bridge the gap between these two very active research domains. In this landscape paper, we identify how INCOME plans to advance the state of the art and we briefly describe its scientific program which consists of three main tasks: (i) multi-scale Context Management, (ii) Management of extrafunctional concerns (quality of Context and privacy), and (iii) autonomous deployment of Context Management entities.
-
AmI - INCOME – Multi-scale Context Management for the Internet of Things
Lecture Notes in Computer Science, 2012Co-Authors: Jean-paul Arcangeli, Amel Bouzeghoub, Valérie Camps, Sophie Chabridon, Denis Conan, Thierry Desprats, Romain Laborde, Emmanuel Lavinal, C. Marie-françoise Canut, Sébastien LericheAbstract:Nowadays, Context Management solutions in ambient networks are well-known. However, with the IoT paradigm, ambient information is not anymore the only source of Context. Context Management solutions able to address multiple network scales ranging from ambient networks to the Internet of Things (IoT) are required. We present the INCOME project whose goal is to provide generic software and middleware components to ease the design and development of mass market Context-aware applications built above the Internet of Things. By revisiting ambient intelligence (AmI) Context Management solutions for extending them to the IoT, INCOME allows to bridge the gap between these two very active research domains. In this landscape paper, we identify how INCOME plans to advance the state of the art and we briefly describe its scientific program which consists of three main tasks: (i) multi-scale Context Management, (ii) Management of extrafunctional concerns (quality of Context and privacy), and (iii) autonomous deployment of Context Management entities.
-
CA3M : a runtime model and a middleware for dynamic Context Management
2009Co-Authors: Chantal Taconet, Zakia Imane Kazi-aoul, Mehdi Zaier, Denis ConanAbstract:In ubiquitous environments, Context-aware applications need to monitor their execution Context. They use middleware services such as Context managers for this purpose. The space of monitorable entities is huge and each Context-aware application has specific monitoring requirements which can change at runtime as a result of new opportunities or constraints due to Context variations. The issues dealt with in this paper are 1) to guide Context-aware application designers in the specification of the monitoring of distributed Context sources, and 2) to allow the adaptation of Context Management capabilities by dynamically taking into account new Context data collectors not foreseen during the development process. The solution we present, CA3M, follows the model-driven engineering approach for answering the previous questions: 1) designers specialised into Context Management specify Context-awareness concerns into models that conform to a Context-awareness meta-model, and 2) these Context-awareness models are present at runtime and may be updated to cater with new application requirements. This paper presents the whole chain from the Context-awareness model definition to the dynamic instantiation of Context data collectors following modifications of Context-awareness models at runtime
-
A framework for quality of Context Management
2009Co-Authors: Zied Abid, Sophie Chabridon, Denis ConanAbstract:Context-aware computing has to deal with a huge amount of Context data. Taking into account the quality of these data becomes a corner stone of an efficient Context Management solution. Information on the quality of Context helps taking appropriate decisions and allows to identify uncertain Context information saving processing time for deriving a pertinent description of the observed phenomenon. This paper presents a work in progress for integrating Quality of Context in COSMOS (Context entitieS coMpositiOn and Sharing) [4,13], a component-based framework for managing Context data in ubiquitous environments, and illustrates it throughout the example of the composition of Context information to implement a network connectivity vs energy adaptation situation
-
QuaCon - A framework for quality of Context Management
Lecture Notes in Computer Science, 2009Co-Authors: Zied Abid, Sophie Chabridon, Denis ConanAbstract:Context-aware computing has to deal with a huge amount of Context data. Taking into account the quality of these data becomes a corner stone of an efficient Context Management solution. Information on the quality of Context helps taking appropriate decisions and allows to identify uncertain Context information saving processing time for deriving a pertinent description of the observed phenomenon. This paper presents a work in progress for integrating Quality of Context in COSMOS (Context entitieS coMpositiOn and Sharing) [4,13], a component-based framework for managing Context data in ubiquitous environments, and illustrates it throughout the example of the composition of Context information to implement a network connectivity vs energy adaptation situation.
Sophie Chabridon - One of the best experts on this subject based on the ideXlab platform.
-
INCOME : multi-scale Context Management for the internet of things
2012Co-Authors: Jean-paul Arcangeli, Amel Bouzeghoub, Valérie Camps, Marie-françoise Canut, Sophie Chabridon, Denis Conan, Thierry Desprats, Romain Laborde, Emmanuel Lavinal, Sébastien LericheAbstract:Nowadays, Context Management solutions in ambient networks are well-known. However, with the IoT paradigm, ambient information is not anymore the only source of Context. Context Management solutions able to address multiple network scales ranging from ambient networks to the Internet of Things (IoT) are required. We present the INCOME project whose goal is to provide generic software and middleware components to ease the design and development of mass market Context-aware applications built above the Internet of Things. By revisiting ambient intelligence (AmI) Context Management solutions for extending them to the IoT, INCOME allows to bridge the gap between these two very active research domains. In this landscape paper, we identify how INCOME plans to advance the state of the art and we briefly describe its scientific program which consists of three main tasks: (i) multi-scale Context Management, (ii) Management of extrafunctional concerns (quality of Context and privacy), and (iii) autonomous deployment of Context Management entities.
-
AmI - INCOME – Multi-scale Context Management for the Internet of Things
Lecture Notes in Computer Science, 2012Co-Authors: Jean-paul Arcangeli, Amel Bouzeghoub, Valérie Camps, Sophie Chabridon, Denis Conan, Thierry Desprats, Romain Laborde, Emmanuel Lavinal, C. Marie-françoise Canut, Sébastien LericheAbstract:Nowadays, Context Management solutions in ambient networks are well-known. However, with the IoT paradigm, ambient information is not anymore the only source of Context. Context Management solutions able to address multiple network scales ranging from ambient networks to the Internet of Things (IoT) are required. We present the INCOME project whose goal is to provide generic software and middleware components to ease the design and development of mass market Context-aware applications built above the Internet of Things. By revisiting ambient intelligence (AmI) Context Management solutions for extending them to the IoT, INCOME allows to bridge the gap between these two very active research domains. In this landscape paper, we identify how INCOME plans to advance the state of the art and we briefly describe its scientific program which consists of three main tasks: (i) multi-scale Context Management, (ii) Management of extrafunctional concerns (quality of Context and privacy), and (iii) autonomous deployment of Context Management entities.
-
A framework for quality of Context Management
2009Co-Authors: Zied Abid, Sophie Chabridon, Denis ConanAbstract:Context-aware computing has to deal with a huge amount of Context data. Taking into account the quality of these data becomes a corner stone of an efficient Context Management solution. Information on the quality of Context helps taking appropriate decisions and allows to identify uncertain Context information saving processing time for deriving a pertinent description of the observed phenomenon. This paper presents a work in progress for integrating Quality of Context in COSMOS (Context entitieS coMpositiOn and Sharing) [4,13], a component-based framework for managing Context data in ubiquitous environments, and illustrates it throughout the example of the composition of Context information to implement a network connectivity vs energy adaptation situation
-
QuaCon - A framework for quality of Context Management
Lecture Notes in Computer Science, 2009Co-Authors: Zied Abid, Sophie Chabridon, Denis ConanAbstract:Context-aware computing has to deal with a huge amount of Context data. Taking into account the quality of these data becomes a corner stone of an efficient Context Management solution. Information on the quality of Context helps taking appropriate decisions and allows to identify uncertain Context information saving processing time for deriving a pertinent description of the observed phenomenon. This paper presents a work in progress for integrating Quality of Context in COSMOS (Context entitieS coMpositiOn and Sharing) [4,13], a component-based framework for managing Context data in ubiquitous environments, and illustrates it throughout the example of the composition of Context information to implement a network connectivity vs energy adaptation situation.
Jadwiga Indulska - One of the best experts on this subject based on the ideXlab platform.
-
PerCom Workshops - Context Management system for pervasive WMN
2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), 2011Co-Authors: Shivanajay Marwaha, Jadwiga IndulskaAbstract:Conventional layered protocols are not capable to handle the vagaries of Wireless Mesh Networks (WMN) such as rapidly changing wireless channel quality, battery power and network topology. Most WMN cross-layer proposals to overcome these problems utilize only a limited set of parameters from the concerned layers rendering them incapable of network and system wide optimizations as they do not consider global network Context. Additionally, their application is limited by the use-case and deployment scenarios as well as technologies considered and can also lead to destructive interaction due to the lack of a global view and also the lack of communication between the numerous cross-layer adaptations. To make WMNs truly pervasive, there is an urgent need to make the WMN protocol stack Context-aware, so that it can adapt dynamically to the deployment scenarios and operating conditions. Early designs for global network Context Management for WMN do not reflect WMN limitations such as scarce network capacity and CPU and RAM constraints of mobile devices. Most also do not consider higher-level Context and situation reasoning models and lack a simplified way of representing, managing and gathering Context information. Some of the current proposals utilize generic Context gathering concepts from Distributed Systems that could lead to excessively high communication overhead in WMN. This paper presents the requirements for developing a Middleware for managing WMN protocol and network Context, evaluates the state of the art and proposes a concrete, extensible, lightweight and comprehensive Context Management system for WMN named MESH-CMS which can overcome the aforementioned problems.
-
An Autonomic Context Management System for Pervasive Computing
2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom), 2008Co-Authors: Peizhao Hu, Jadwiga Indulska, Ricky RobinsonAbstract:Context-aware applications adapt to changing computing environments or changing user circumstances/tasks. Context information that supports such adaptations is provided by the underlying infrastructure, which gathers, pre-processes and provisions Context information from a variety of Context information sources. Such an infrastructure is prone to failures and disconnections that negatively impact on the ability of Context-aware applications to adapt (and therefore dramatically impact on their usability). This paper describes a model-based autonomic Context Management system (ACoMS) that can dynamically configure and reconfigure its Context information gathering and pre-processing functionality in order to provide fault tolerant provisioning of Context information. The approach uses standards based descriptions of Context information sources to increase openness, interoperability and scalability of Context-aware systems.
-
PerCom - An Autonomic Context Management System for Pervasive Computing
2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom), 2008Co-Authors: Jadwiga Indulska, Ricky RobinsonAbstract:Context-aware applications adapt to changing computing environments or changing user circumstances/tasks. Context information that supports such adaptations is provided by the underlying infrastructure, which gathers, pre-processes and provisions Context information from a variety of Context information sources. Such an infrastructure is prone to failures and disconnections that negatively impact on the ability of Context-aware applications to adapt (and therefore dramatically impact on their usability). This paper describes a model-based autonomic Context Management system (ACoMS) that can dynamically configure and reconfigure its Context information gathering and pre-processing functionality in order to provide fault tolerant provisioning of Context information. The approach uses standards based descriptions of Context information sources to increase openness, interoperability and scalability of Context-aware systems.
-
ATC - Towards a standards-based autonomic Context Management system
Lecture Notes in Computer Science, 2006Co-Authors: Jadwiga Indulska, Karen HenricksenAbstract:Pervasive computing applications must be sufficiently autonomous to adapt their behaviour to changes in computing resources and user requirements. This capability is known as Context-awareness. In some cases, Context-aware applications must be implemented as autonomic systems which are capable of dynamically discovering and replacing Context sources (sensors) at run-time. Unlike other types of application autonomy, this kind of dynamic reconfiguration has not been sufficiently investigated yet by the research community. However, application-level Context models are becoming common, in order to ease programming of Context-aware applications and support evolution by decoupling applications from Context sources. We can leverage these Context models to develop general (i.e., application-independent) solutions for dynamic, run-time discovery of Context sources (i.e., Context Management). This paper presents a model and architecture for a reconfigurable Context Management system that supports interoperability by building on emerging standards for sensor description and classification.
Jean-paul Arcangeli - One of the best experts on this subject based on the ideXlab platform.
-
INCOME : multi-scale Context Management for the internet of things
2012Co-Authors: Jean-paul Arcangeli, Amel Bouzeghoub, Valérie Camps, Marie-françoise Canut, Sophie Chabridon, Denis Conan, Thierry Desprats, Romain Laborde, Emmanuel Lavinal, Sébastien LericheAbstract:Nowadays, Context Management solutions in ambient networks are well-known. However, with the IoT paradigm, ambient information is not anymore the only source of Context. Context Management solutions able to address multiple network scales ranging from ambient networks to the Internet of Things (IoT) are required. We present the INCOME project whose goal is to provide generic software and middleware components to ease the design and development of mass market Context-aware applications built above the Internet of Things. By revisiting ambient intelligence (AmI) Context Management solutions for extending them to the IoT, INCOME allows to bridge the gap between these two very active research domains. In this landscape paper, we identify how INCOME plans to advance the state of the art and we briefly describe its scientific program which consists of three main tasks: (i) multi-scale Context Management, (ii) Management of extrafunctional concerns (quality of Context and privacy), and (iii) autonomous deployment of Context Management entities.
-
AmI - INCOME – Multi-scale Context Management for the Internet of Things
Lecture Notes in Computer Science, 2012Co-Authors: Jean-paul Arcangeli, Amel Bouzeghoub, Valérie Camps, Sophie Chabridon, Denis Conan, Thierry Desprats, Romain Laborde, Emmanuel Lavinal, C. Marie-françoise Canut, Sébastien LericheAbstract:Nowadays, Context Management solutions in ambient networks are well-known. However, with the IoT paradigm, ambient information is not anymore the only source of Context. Context Management solutions able to address multiple network scales ranging from ambient networks to the Internet of Things (IoT) are required. We present the INCOME project whose goal is to provide generic software and middleware components to ease the design and development of mass market Context-aware applications built above the Internet of Things. By revisiting ambient intelligence (AmI) Context Management solutions for extending them to the IoT, INCOME allows to bridge the gap between these two very active research domains. In this landscape paper, we identify how INCOME plans to advance the state of the art and we briefly describe its scientific program which consists of three main tasks: (i) multi-scale Context Management, (ii) Management of extrafunctional concerns (quality of Context and privacy), and (iii) autonomous deployment of Context Management entities.