Moeiz Miraoui

Dynamic Context-Aware Service Adaptation in a Pervasive Computing System

Pervasive computing requires that devices must be context-aware in order to provide proactively adapted services to both user and applications according to the current context. Most of the proposed approaches of service adaptation in a pervasive computing system are either not context-aware or based on an inappropriate definition of context. In this paper we propose a context-aware dynamic service adaptation approach for a device operating in a pervasive computing system. The approach is then modeled using colored Petri Nets and simulated using the CPN-Tools in order to validate it.

Dynamic context-aware and limited resources-aware service adaptation for pervasive computing

A pervasive computing system (PCS) requires that devices be context aware in order to provide proactively adapted services according to the current context. Because of the highly dynamic environment of a PCS, the service adaptation task must be performed during device operation. Most of the proposed approaches do not deal with the problem in depth, because they are either not really context aware or the problem itself is not thought to be dynamic. Devices in a PCS are generally hand-held, that is, they have limited resources, and so, in the effort to make them more reliable, the service adaptation must take into account this constraint. In this paper, we propose a dynamic service adaptation approach for a device operating in a PCS that is both context aware and limited resources aware. The approach is then modeled using colored Petri Nets and simulated using the CPN Tools, an important step toward its validation.

Dynamic context-aware adaptation of mobile phone incoming call indication using context similarity

Mobile phones are the most ubiquitous communication devices. The incoming call indication service is sometimes provided in an inappropriate form (e.g. ring tone when user is in a classroom, when driving, sleeping, etc.). Users context changes very often because of the mobility. It is very inconvenient to always keep the settings of the mobile device up to date, to the current context. In this paper, we propose a context-aware incoming call indication adaptation based on context similarity. The approach consists for a particular context to look for the nearest known context and adapt the service indication of incoming calls in the same way.

Modeling and simulation of a multiagent service oriented architecture for pervasive computing systems

Design and implementation of pervasive computing systems require new software tools such as architectures to support their development. In order to provide adapted services to user and applications according to the global context, devices in a pervasive computing system are context-aware. In this paper, we present the modeling and the simulation of multiagent service oriented architecture for pervasive computing system. An important step toward the validation of the architecture is made. We used colored Petri nets as modeling formalism and CPN Tools for the simulation of scenarios.

Context-aware services adaptation for a smart living room

Pervasive or ubiquitous computing was developed thanks to the technological evolution of embedded systems and computer communication means. Ubiquitous computing has given birth to the concept of smart spaces that facilitate our daily life and increase our comfort where devices provide proactively adpated services. In spite of the significant previous works done in this domain, there still a lot of work and enhancement to do in particular the taking into account of current users context when providing adaptable services. In this paper we propose an approach for context-aware services adaptation for a smart living room using two machine learning methods.

Contextual location prediction using spatio-temporal clustering

Purpose The prediction of a context, especially of a user’s location, is a fundamental task in the field of pervasive computing. Such predictions open up a new and rich field of proactive adaptation for context-aware applications. This study/paper aims to propose a methodology that predicts a user’s location on the basis of a user’s mobility history. Design/methodology/approach Contextual information is used to find the points of interest that a user visits frequently and to determine the sequence of these visits with the aid of spatial clustering, temporal segmentation and speed filtering. Findings The proposed method was tested with a real data set using several supervised classification algorithms, which yielded very interesting results. Originality/value The method uses contextual information (current position, day of the week, time and speed) that can be acquired easily and accurately with the help of common sensors such as GPS.

A Hybrid Modular Context-Aware Services Adaptation for a Smart Living Room

Smart spaces have attracted considerable amount of interest over the past few years. The introduction of sensor networks, powerful electronics and communication infrastructures have helped a lot in...

Contextual case-based reasoning applied to a mobile device

Purpose This paper aims to apply a contextual case-based reasoning (CBR) to a mobile device. The CBR method was chosen because it does not require training, demands minimal processing resources and easily integrates with the dynamic and uncertain nature of pervasive computing. Based on a mobile user’s location and activity, which can be determined through the device’s inertial sensors and GPS capabilities, it is possible to select and offer appropriate services to this user. Design/methodology/approach The proposed approach comprises two stages. The first stage uses simple semantic similarity measures to retrieve the case from the case base that best matches the current case. In the second stage, the obtained selection of services is then filtered based on current contextual information. Findings This two-stage method adds a higher level of relevance to the services proposed to the user; yet, it is easy to implement on a mobile device. Originality/value A two-stage CBR using light processing methods and generating context aware services is discussed. Ontological location modeling adds reasoning flexibility and knowledge sharing capabilities.

Spatiotemporal Context Modelling in Pervasive Context-Aware Computing Environment: A Logic Perspective

Pervasive context-aware computing, is one of the topics that received particular attention from researchers. The context, itself is an important notion explored in many works discussing its: acquisition, definition, modelling, reasoning and more. Given the permanent evolution of context-aware systems, context modeling is still a complex task, due to the lack of an adequate, dynamic, formal and relevant context representation. This paper discusses various context modeling approaches and previous logic-based works. It also proposes a preliminary formal spatiotemporal context modelling based on first order logic, derived from the structure of natural languages.

Ontology-Based Context Modeling and Reasoning for a Smart Space

The recent development on pervasive computing, sensors network and smart appliances has motivated the appearance of a new technological domain called smart spaces. Such spaces are defined as a physical space rich in equipment and software services that is capable of interacting with people in order to provide intelligent services to the user for improved comfort (quality of life), energy saving, security, and tremendous benefits for an elderly person living alone. Providing intelligent services requires the equipment to be sensitive to the context of use or context-awareness. The concept of context is a key enabling factor in such environment and understand it, establishing its components and modeling it are basic and important steps for the development of smart spaces. Previous works in such environments were unable to deal efficiently with context-awareness. In this paper we present both an ontology-based context modeling approach and context reasoning for a smart living room.