Manuele Kirsch Pinheiro

Context inference of users' social relationships and distributed policy management

Inference of high-level context is becoming crucial in development of context-aware applications. An example is social context inference - i.e., deriving social relations based upon the users daily communication with other people. The efficiency of this mechanism mainly depends on the method(s) used to draw inferences based on existing evidence and sample information, such as a training data. Our approach uses rule-based data mining, Bayesian network inference, and user feedback to compute the probabilities of another user being in the specific social relationship with a user whose daily communication is logged by a mobile phone. In addition, a privacy mechanism is required to ensure the users personal integrity and privacy when sharing this users sensitive context data. Therefore, the derived social relations are used to define a users policies for context access control, which grant the restricted context information scope depending on the users current context. Finally, we propose a distributed architecture capable of managing this context information based upon these context access policies.

Service Discovery Mechanism for an Intentional Pervasive Information System

Pervasive Information System (PIS) provides a new vision of Information System available anytime and anywhere. The users of these systems must evolve in a space of services, in which several services are offered to him. However, PIS should enhance the transparency and efficiency of the system. We believe that a user-centric vision is needed to ensure a transparent access to the frequently changing space of services regardless of how to perform it. In this paper, we propose a new approach of PIS, both context-aware and intentional called IPIS. In this approach, services are proposed in order to satisfy users intention in a given context. Then, we propose a context-aware intentional service discovery mechanism. Such mechanism is based on an extension of OWL-S taking into account the notion of context and intention. We present in this paper IPIS platform. Then, we detail the proposed service discovery mechanism and present experimental results that demonstrate the advantage of using our proposition.

PER-MARE: Adaptive Deployment of MapReduce over Pervasive Grids

Map Reduce is a parallel programming paradigm successfully used to perform computations on massive amounts of data, being widely deployed on clusters, grid, and cloud infrastructures. Interestingly, while the emergence of cloud infrastructures has opened new perspectives, several enterprises hesitate to put sensible data on the cloud and prefer to rely on internal resources. In this paper we introduce the PER-MARE initiative, which aims at proposing scalable techniques to support existent Map Reduce data-intensive applications in the context of loosely coupled networks such as pervasive and desktop grids. By relying on the Map Reduce programming model, PER-MARE proposes to explore the potential advantages of using free unused resources available at enterprises as pervasive grids, alone or in a hybrid environment. This paper presents the main lines that orient the PER-MARE approach and some preliminary results.

Personalizing Web-Based Information Systems through Context-Aware User Profiles

In this paper, we propose context-aware profiles and a filtering process for personalising informational content that is delivered to mobile users by Web-based information systems (WIS). The context-aware profiles allow mobile users to express their personal preferences for particular situations they encounter when using a WIS. We argue that the preferences and the needs of a mobile user may vary according to the context in which he uses the system. By defining these profiles, we propose a filtering process that takes into account both the users current context and the users preferences for this context. This process selects, in a first step, the context-aware profiles that match the users current context, and then it filters the available informational content based on the selected profiles.

Service discovery and prediction on Pervasive Information System

Recent evolution of technology and its usages, such as Bring Your Own Device and Internet of Things, transformed the way we interact with Information Systems , leading to a new generation of IS, called the Pervasive Information Systems. These systems have to face heterogeneous pervasive environments and hide the complexity of such environment end-user. In order to reach transparency and proactivity necessary for successful PIS, new discovery and prediction mechanisms are necessary. In this paper, we present a new user-centric approach for PIS and propose new service discovery and prediction based on both user’s context and intentions. Intentions allow focusing on goals user wants to satisfy when requesting a service. Those intentions rise in a given context, which influence the service implementation. We propose a service discovery mechanism that observes user’s context and intention in order to offer him/her the most appropriate service satisfying her/his intention on the current context. We also propose a prediction mechanism that tries to anticipate user’s intentions considering the user’s history and the observed context. We evaluate both mechanisms and discuss advanced features future PIS will have to deal with.

Context-aware Scheduling for Apache Hadoop over Pervasive Environments☆

Abstract This article proposes to improve Apache Hadoop scheduling through the usage of context-awareness. Apache Hadoop is the most popular implementation of the MapReduce paradigm for distributed computing, but its design doesn’t adapt automatically to computing nodes’ context and capabilities. By introducing context-awareness into Hadoop, we intent to dynamically adapt its scheduling to the execution environment. This is a necessary feature in the context of pervasive grids, which are heterogeneous, dynamic and shared environments. The solution has been incorporated into Hadoop and evaluated through controlled experi- ments. The experiments demonstrate that context-awareness provides comparative performance gains, especially when part of the resources disappear during execution.

A New Approach for Service Discovery and Prediction on Pervasive Information System

Abstract Recent evolution of technology transformed the way we interact with Information Systems (IS), leading to a new generation of IS, the Pervasive Information Systems (PIS). These systems have to face heterogeneous pervasive environments, whose complexity they must hide from end-user. In order to reach transparency and proactivity necessary for successful PIS, new discovery and prediction mechanisms are necessary. In this paper, we propose a new user-centric approach for service discovery and prediction on PIS based on both users context and intentions. Intentions allow focusing on goals user wants to satisfy when requesting a service. Those intentions rise in a given context, which may condition the service implementation. We propose then a service discovery mechanism that observes users context and intention in order to offer him the service that may best satisfy her/his intention on the current context. We also propose a prediction mechanism that tries to anticipate users intentions considering the observed context and users history.

A Hybrid Peer-to-Peer Solution for Context Distribution in Mobile and Ubiquitous Environments

With the proliferation of mobile devices such as PDAs and smart-phones, users get accustomed to using them in their daily life. This raises the expectations for user-customized and environment-aware services. However, mobile context-aware systems inherently feature characteristics of distribution and heterogeneity which pose great challenges to their developers. In this chapter, we focus on context distribution in mobile and ubiquitous computing environments. After describing the requirements in such environments, we propose a hybrid peer-to-peer based context distribution approach, which is built on top of the JXTA framework, a standard for peer-to-peer systems. We categorize context-aware system entities into three types of peers according to their device capabilities and their roles in context distribution. The peers are able to dynamically discover each other along with their offered services, form groups, and communicate with each other. The proposed approach is evaluated against the derived requirements and illustrated through a motivating scenario.

When the Cloud Goes Pervasive: Approaches for IoT PaaS on a Mobiquitous World

Today, IoT applications are heavily dependent on public cloud computing services to perform data storage and analysis. Unfortunately, the cloud computing paradigm is unable to meet the requirements of critical applications that require low latency or enhanced privacy levels. The deployment of private cloud services on top of pervasive grids represent an interesting alternative to traditional cloud infrastructures, allowing the use of near-environment resources for IoT data analysis tasks. In this work we discuss the challenges associated with the deployment of IoT services over pervasive environments, and present a study case deployed over CloudFIT, a computing middleware for pervasive systems. Hence, we evaluate the behavior of a data-intensive application under volatility and heterogeneity constraints, bringing to light to the use of low-end devices that are usually located at the proximity to IoT sensors/actuators.

Context-Based Grouping and Recommendation in MANETs

We propose in this chapter a context grouping mechanism for context distribution over MANETs. Context distribution is becoming a key aspect for successful context-aware applications in mobile and ubiquitous computing environments. Such applications need, for adaptation purposes, context information that is acquired by multiple context sensors distributed over the environment. Nevertheless, applications are not interested in all available context information. Context distribution mechanisms have to cope with the dynamicity that characterizes MANETs and also prevent context information to be delivered to nodes (and applications) that are not interested in it. Our grouping mechanism organizes the distribution of context information in groups whose definition is context based: each context group is defined based on a criteria set (e.g. the shared location and interest) and has a dissemination set, which controls the information that can be shared in the group. We propose a personalized and dynamic way of defining and joining groups by providing a lattice-based classification and recommendation mechanism that analyzes the interrelations between groups and users, and recommend new groups to users, based on the interests and preferences of the user.