Denis Conan

Software Architecture Patterns for a Context-Processing Middleware Framework

Ubiquitous applications are characterized by variations in their execution context. Their correct operation requires some continual adaptations based on the observation of this context. The design and implementation of these observation policies is thus the cornerstone of any ubiquitous application. COSMOS is a component-based framework for managing context information in ubiquitous context-aware applications. COSMOS decomposes context observation policies into fine-grained units called context nodes, which it implements as software components. These units perform basic context-related operations, such as gathering data from a system or network probe and computing threshold or average values. The framework assembles these units with a set of well-identified architectural design patterns. A mobile computing scenario illustrates the frameworks benefits.

Disconnected operations in mobile environments

The execution of distributed applications involving mobile terminals and fixed servers connected by wireless links raises the need for handling network disconnections, both involuntary during unexpected connection breakdowns, and voluntary when the user wants to save money and energy. In this paper, we investigate how standard CORBA mechanisms (Objects By Value and Portable Interceptors) can be used for enhancing legacy CORBA-based distributed applications in order to support voluntary and involuntary disconnections. We show that the first mechanism can be used for handling easily voluntary disconnections by copying on the terminal all the data necessary for running the application in a disconnected mode. The second mechanism allows also to handle involuntary disconnections; a switch between connected mode and disconnected mode can be performed transparently to the user.

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.

Complementarity of process-oriented and ontology-based context managers to identify situations

One issue for context-aware applications is to identify without delay situations requiring reactions. The identification of these situations is computed from both dynamic context information and domain specific knowledge. This identification is the output of a process involving context interpretation, aggregation and deduction. In smart environments, these treatments have to be efficient since they may be partly performed on constrained mobile devices. Two main approaches exist in the literature: process-oriented and ontology-based context management. In this paper, we claim that they are complementary and we propose an architecture which integrates the two approaches. We show in a scenario how context-aware applications can benefit from this architecture both to scale to numerous mobile users and to identify complex situations.

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.

OSIF: a framework to instrument, validate, and analyze simulations

In most existing simulators, the outputs of a simulation run consist either in a simulation report generated at the end of the run and summarizing the statistics of interest, or in a (set of) trace file(s) containing raw data samples produced and saved regularly during the run, for later post-processing. In this paper, we address issues related to the management of these data and their on-line processing, such as: (i) the instrumentation code is mixed in the modeling code; (ii) the amount of data to be stored may be enormous, and often, a significant part of these data are useless while their collect may consume a significant amount of the computing resources; and (iii) it is difficult to have confidence in the treatment applied to the data and then make comparisons between studies since each user (model developer) builds its own ad-hoc instrumentation and data processing. In this paper, we propose OSIF, a new component-based instrumentation framework designed to solve the above mentioned issues. OSIF is based on several mature software engineering techniques and frameworks, such as COSMOS, Fractal and its ADL, and AOP.

Failure, Disconnection and Partition Detection in Mobile Environment

In mobile environment, nodes can move around and voluntarily leave or join the network. Furthermore, they can crash or be disconnected from the network due to the absence of network signals. Therefore, failure, disconnection and mobility may create partitions in wireless networks which should be detected for fault and disconnection tolerance reasons. We present in this article an architecture of local and distributed detectors for mobile networks that detect failures, disconnections, and partitions. It is basically composed of three unreliable detectors: a heartbeat failure detector, a vector-based disconnection detector, and an eventually perfect partition detector.

COSMOS, composition de noeuds de contexte

Les applications ubiquitaires evoluent dans une grande diversite de contextes dutilisation. Or, cette diversite requiert une adaptation continuelle afin de preserver le bon fonctionnement des applications. Des lors, lobservation du contexte joue un role preponderant. Si les approches actuelles « centree utilisateur » et « systeme » ont prouve leur pertinence dans ce domaine, elles souffrent neanmoins de certaines limitations liees a lexpressivite des compositions des inferences et au passage a lechelle. Par consequent, nous proposons de reorganiser les fonctionnalites traditionnelles dun gestionnaire de contexte de maniere systematique en cycles « collecte / interpretation / identification de situations ». Cette approche repose sur la definition du concept de noeuds de contexte composes dans un graphe (hierarchie avec partage), et lexpression du concept en composant et architecture logicielle pour faciliter la definition et la gestion des politiques de gestion de contexte.

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.