Charles Gouin-Vallerand

Managing and Deployment of Applications with OSGi in the Context of Smart Home

The presence of a multitude of heterogeneous devices in a smart home environment create a whole puzzle when comes the time to deploy, update, or manage applications on these devices. The configuration and the updating of applications in that context can be expensive, as well as resource and time consuming, especially when there are several similar environments to implement. This paper considers those problems, brings a glimpse on possible solutions, and proposes the use of the OSGi framework to ease the deployment and the management of applications. Moreover this paper presents a summary of a distributed system currently in last phase of development that will resolve the proposed problematic. This system allows the remote managing and deployment in the context of a distributed and pervasive environment for cognitively impaired people.

A context-aware service provision system for smart environments based on the user interaction modalities

Ambient and pervasive technologies provide several ways to assist people with special needs in smart environments. However, the systems complexity and the size of the contextual information of these environments lead to several difficulties in deploying and providing the assistance services. A service provision mechanism which is aware of the environment context can simplify the deployment of assistance services on environment devices, by taking care of the decision processes. Moreover, the integration of the interaction modalities in the decision processes of such mechanisms allows deliveries of services to users based on their capabilities and preferences. In this paper, we present a context-aware service provision system for smart environment, which takes into account a whole set of contextual information: user profiles, device profiles, software profiles and environment topology. In regards to our previous work, this paper focuses on the modeling of the user interaction capabilities, built around the notion of interaction modalities. We also detail the integration of the model to the service provision reasoning process, as well as its implementation. Finally, we demonstrate the functionalities of this system through technical validations and scenarios carried out in a real smart apartment.

Online recognition of people's activities from raw GPS data: semantic trajectory data analysis

The widespread use of GPS devices leads to an increasing availability of people traces. The GPS trajectory of a moving object is a time stamped sequence of latitude and longitude coordinates. The analysis and extraction of knowledge from GPS trajectories is important for several applications domains, ranging from traffic management to advertisement and social studies. We present an approach capable of incrementally extracting semantic locations from peoples trajectories and inferring the activity done by users. We associate the places visited by people during their movements to a meaningful human activities using a novel algorithm that cluster incrementally users moves into different type of activities. Studies using GPS records from a confined spatio-temporal region demonstrate that the proposal is effective and is capable of inferring human activities without depleting the phone resources.

An analysis of the transitions between mobile application usages based on markov chains

By knowing more the usage of mobile application on smart phone, it is possible to predict which applications will be used next. This kind of information is particularly interesting from a recommendation system point of view. In this paper, we present an analysis of the transitions between application usages on the data collected by the University of Cambridges Device Analyzer project. Among other results, we conclude that the transition probabilities between application usages are distinct information from the probabilities of usage based on periods of time, such as application launches in a same hour and in a same day.

Analysis of a context-aware recommender system model for smart urban environment

Recommender systems, deployed on the web and in mobile devices, help users in finding services and products that are addressing their needs, based on their profile. On the other hand, such systems help also organizations in promoting their products or services to a targeted market. Users in mobility in smart urban spaces are surrounded with products and services, such as mobile device applications. In this paper, we are presenting the result of a survey, where participants had to give their opinions on recommendation modalities in several scenarios of context-aware service recommendation in urban environments. These results are followed by an analysis and a discussion.

Game Based Learning: A Case Study on Designing an Educational Game for Children in Developing Countries

Despite the efforts and global policies to achieve universal primary education, there are still 58 million children, roughly between the ages of 6 and 11, out of school globally. Although previous studies showed the value of serious games for learning, not enough efforts were made to apply serious games to reduce children illiteracy. Understanding the complexity of this problem, we put together a multidisciplinary research team, and an industrial partner, to work on the design of a serious game for children without access to school. In this paper, we present the preliminary results of our case study, showing the main design challenges faced to develop such tool and proposing solution to overcome those issues. We aim to call the attention of the research community about the urgent need of more studies in this field and we argue that our results could be used as starting point for taking forward future work in this direction.

Energy Optimization for Outdoor Activity Recognition

The mobile phone is no longer only a communication device, but also a powerful environmental sensing unit that can monitor a user’s ambient context. Mobile users take their devices with them everywhere which increases the availability of persons’ traces. Extracting and analyzing knowledge from these traces represent a strong support for several applications domains, ranging from traffic management to advertisement and social studies. However, the limited battery capacity of mobile devices represents a big hurdle for context detection, no matter how useful the service may be. We present a novel approach to online recognizing users’ outdoor activities without depleting the mobile resources. We associate the places visited by individuals during their movements with meaningful human activities using a novel algorithm that clusters incrementally user’s moves into different types of activities. To optimize the battery consumption, the algorithm behaves variably on the basis of users’ behaviors and the remaining battery level. Studies using real GPS records from two big datasets demonstrate that the proposal is effective and is capable of inferring human activities without draining the phone resources.

Prediction of next destinations from irregular patterns

Few decades ago, understanding human behaviors was considered as a mystery where predicting people’s future was impossible. Many changes have been noticed since that era. Thanks to current advances in location tracking technology and data mining techniques, predicting users’ behaviors has become possible. In this paper we present a new algorithm to online predict users’ next visited locations that not only learns incrementally the users’ habits, but also detects and supports the drifts in their patterns. Our original contribution includes a new algorithm for online association rules mining that supports the concept drift.

An Analysis Tool for the Contextual Information from Field Experiments on Driving Fatigue

Elderly drivers will be more present on the road in the next few years. Mobility is fundamental for the elderly because it allows them to maintain an active lifestyle. But the elderly may suffer from cognitive, physical or sensorial decline due to aging. To help them to drive, context-aware systems can assess the status of a driver and warn him or her about hazards. We present a data analysis tool for car driving context information that includes data mining and statistical evaluation algorithms. We applied our system to data collected by sensors into an instrumented vehicle in realistic driving conditions. Results show that our tool is able to store the contextual information collected and to enable an interactive visualization of the data collected. Thanks to this tool, it is easier to share information among the scientists working on the data. Moreover, it makes it convenient to store data in the cloud.

Toward a Self-Configuration Middleware for Smart Spaces

The self-configuration process can simplify the complexity and reduce the cost of the management and deployment of devices, applications and services in smart spaces. Mechanisms inspired from the autonomic computing and pervasive computing can be used to automate management of the heterogeneous spaces component. In this paper, we present our work on autonomic pervasive computing and mainly the self-configuration process. We also introduce our self-configuration middleware that is deployed at DOMUS Laboratory.