Chantal Taconet

CA3M: A Runtime Model and a Middleware for Dynamic Context Management

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.

INCOME : multi-scale context management for the internet of things

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.

Building ubiquitous QoC-aware applications through model-driven software engineering

As every-day mobile devices can easily be equipped with multiple sensing capabilities, ubiquitous applications are expected to exploit the richness of the context information that can be collected by these devices in order to provide the service that is the most appropriate to the situation of the user. However, the design and implementation of such context-aware ubiquitous appplications remain challenging as there exist very few models and tools to guide application designers and developers in mastering the complexity of context information. This becomes even more crucial as context is by nature imperfect. One way to address this issue is to associate to context information meta-data representing its quality. We propose a generic and extensible design process for context-aware applications taking into account the quality of context (QoC). We demonstrate its use on a prototype application for sending flash sale offers to mobile users. We present extensive performance results in terms of memory and processing time of both elementary context management operations and the whole context policy implementing the Flash sale application. The cost of adding QoC management is also measured and appears to be limited to a few milliseconds. We show that a context policy with 120 QoC-aware nodes can be processed in less than 100 ms on a mobile phone. Moreover, a policy of almost 3000 nodes can be instantiated before exhausting the resources of the phone. This enables very rich application scenarios enhancing the user experience and will favor the development of new ubiquitous applications.

Trust-Based Context Contract Models for the Internet of Things

With the Internet of Things (IoT) paradigm, potentially private data could be made available on the Internet. Such data could then, be consumed by a growing number of applications. The acceptance and success of new pervasive applications depend on both the protection of privacy and the guarantee of quality of context (QoC). As in the IoT producers and consumers of context are decoupled, they are not aware of each other. Therefore, it is essential to provide them with means to express their guarantees and requirements concerning QoC and privacy. For this purpose, we propose meta-models to design context contracts defining privacy and QoC agreements, independently of the consumer and the producer sides. The contracts are key to an autonomous management of QoC and privacy in the IoT. Firstly, contracts may be modified at runtime to add, edit or remove clauses. Secondly, the middleware in charge of transmitting data from context producers to context consumers (e.g., context managers) will be able to match QoC/privacy requirements and guarantees. Finally, the matching process can adapt dynamically, for instance, to the current trust level between the two parties. These contracts will participate to build trust among IoT participants.

Context-awareness and model driven engineering: Illustration by an E-commerce application scenario

With the popularity of ubiquitous computing, context-aware applications become clearly necessary. This kind of applications allows mobile users to universally access services in respect to any user context including his computing environment. Challenges for these applications are to easily manipulate both context collection and analysis and to react dynamically to every relevant evolution in the execution context state. To face these issues, we propose in this article, a generic and extensible way to model context-awareness of any application using model-driven engineering (MDE) approach. We illustrate our solution by modeling a context-aware E-commerce application.

A QoC-Aware Discovery Service for the Internet of Things

The Internet of Things (IoT) is an emergent paradigm characterized by a plethora of smart objects connected to the Internet. An inherent characteristic of IoT is the high heterogeneity and the wide distribution of objects, thereby calling for ways to describe in an unambiguous and machine-interpretable way the resources provided by objects, their properties, and the services they offer. In this context, discovery services play a significant role as they allow clients (middleware platforms, end-users, applications) to retrieve available resources based on appropriate search criteria, such as resource type, capabilities, location, and Quality of Context (QoC) parameters. To cope with these concerns, we introduce QoDisco, a QoC-aware discovery service relying on multiple-attribute searches, range queries, and synchronous/asynchronous operations. QoDisco also comprises an ontology-based information model for semantically describing resources, services, and QoC-related information. In this paper, we describe the QoDisco architecture and information model as well as an evaluation of the search procedure in an urban air pollution monitoring scenario.

Towards QoC-aware location-based services

As location-based services on mobile devices are entering more and more everyday life, we are concerned in this paper with finding ways to master the level of quality of location information in order to take relevant decisions. Location being a typical example of context information, we manipulate it using the COSMOS framework that we develop for the management of context data and their associated quality meta-data or quality of context (QoC). We consider several QoC parameters that are important for location and determine how the QoC can help a location aggregator component to identify the current region where a user is located. The mechanisms we propose support a pragmatic approach in which application designers or deployers survey an area to demarcate regions surrounding locations, and application users are localized into these regions and are presented with the quality of the estimate. We report on the experimentation we performed on the campus of our institute collecting information from Wi-Fi, 3G networks and GPS signals, and show the accuracy we obtain at no additional infrastructure cost.

MuScADeL: A Deployment DSL Based on a Multiscale Characterization Framework

With the Internet of Things (IoT) paradigm, ambient systems move from locally distributed systems to Internet distributed systems. These systems become huge in term of number of devices and imply high heterogeneity (e.g., Of devices, of networks). They are continuously evolving with appearing and disappearing devices at runtime. The inner complexity of these systems, called multiscale systems, requires autonomic deployment middleware. Such middleware should deploy components where and when necessary, and adapt the architecture of the deployed systems considering the different scales of the systems. In this paper, we define MuScADeL, a domain-specific language (DSL) dedicated to multiscale and autonomic software deployment. MuScADeL allows designers to abstractly define deployment properties without exact knowledge of the devices and networks the system will be deployed on. This DSL is based on a scale-awareness framework, which helps designers to characterize the multiscale nature of a system from several viewpoints such as device, network, administration and geography. With MuScADeL, deployment designers may express multiscale properties of systems to deploy. MuScADeL is a building block for deployment middleware that targets multiscale distributed systems. We illustrate the possibilities of MuScADeL through a smart transport scenario.