Learning-based Coordination of Distributed Component Deployment

Self-organizing and resource-aware component deployment is an important feature of mobile pervasive systems. Distributed resources must be dynamically allocated to software components to ensure QoS demands and not distracting the user. In this paper, we propose a Reinforcement Learning technique to optimize distributed component deployment and migration. We argue that the approach meets some main requirements demanded by applications running on mobile systems. A motivating scenario is presented in which a distributed application server allows users to share content and run applications in mobile ad-hoc networks

Context-Aware Service Selection with Uncertain Context Information

The current evolution of Service-Oriented Computing in ubiquitous systems is leading to the development of context-aware services. These are services whose description is enriched with context information related to the service execution environment and adaptation capabilities. This information is often used for discovery and adaptation purposes. However, in real-life systems context information is naturally dynamic, uncertain and incomplete, which represents an important issue when comparing service description and user requirements. Uncertainty of context information may lead to an inexact match between provided and required service capabilities, and consequently to the non-selection of services. In order to handle uncertain and incomplete context information, we propose a mechanism inspired by graph-comparison for matching contextual service descriptions using similarity measures that allow inexact matching. Service description and requirements are compared using two kinds of similarity measures: local measures, which compare individually required and provided properties, and global measures, which take into account the context description as a whole. We show how the proposed mechanism is integrated in MUSIC, an existing adaptation middleware, and how it enables more optimal adaptation decision making

Modelling Embedded Systems with AADL: A Practical Study

In today’s world, embedded systems can be seen everywhere around us. These systems range from consumer electronics such as mobile phones, cameras and portable music players to sophisticated devices such as planes and satellite systems. In either form embedded systems are designed to perform specific tasks with constraints on their qualities and available resources. These constraints can either be soft or hard depending on the nature of the system: a satellite system, for example, has hard safety constraints. Some of the major constraints for embedded systems are high reliability, performance, safety and dependability, small memory size, low power and low processing capabilities. Designing systems with such constraints is a challenge. Developing system architectures during system development has gained importance as it helps in analyzing the system before its implementation. A system architecture is a formal description of a system that describes its building blocks, their properties and the interactions among them. System architectures can be used to analyze various properties of a system such as memory consumption and system safety. For embedded systems, this is of extreme importance since a well described system architecture allows us to predict whether any of the previously mentioned constraints can be met, without requiring the construction of an often expensive prototype implementation. Description of system architectures can be achieved using the formal notations offered by Architecture Description Languages (ADLs). Such ADLs often also provide tool support for the modelling and analysis of the system architecture. Many ADLs for embedded systems are available in both academic and industrial communities, such as Rapide, MetaH, AADL and Wright. Among the available ADLs, the best known and most actively used language is the Architecture Analysis and Design Language (AADL). Standardized by the Society of Automotive Engineers, AADL was originally developed for modelling and analysis of systems in the domain of avionics. However, because of its rich modelling and analysis capabilities, it is widely used for embedded systems in other domains as well. AADL provides a modelling formalism accompanied by a toolset to support modelling activities and system analyses. AADL models can be used to perform various analyses such as flow latency, resource consumption, real-time schedulability, security and safety analysis. Because of its history in the avionics domain, AADL does not address each and every modelling and analysis requirement of other embedded domains. However, during its design, it was foreseen that use of AADL in other domains could require additional modelling concepts and analyses. To meet potential needs AADL was designed as an extensible ADL. This chapter is intended to provide insight into the design needs of embedded systems and the formalisms available to address those needs.status: publishe

Middleware for the Internet of Things, Design Goals and Challenges

As the number of wireless devices increases and their size becomes smaller, there can be more interaction between everyday objects of our life. With advances in RFID chips and the introduction of new generations of these devices that are smaller and cheaper, it is possible to put a wireless interface on almost all everyday objects: vehicles, clothes, foodstuffs, etc. This concept is called the \textit{Internet of Things}. Interaction with thousands of wireless devices leads to a continuous and massive flow of events which are generated spontaneously. The question of how to deal with this enormous number of events is challenging and introduces new design goals for a communication mechanism. In this paper we argue that a middleware together with suitable linguistic abstractions is a proper solution. We also point out the challenges in developing this middleware. Moreover, we give an overview of recent related work and describe why they fail to address these challenges

Privacy in times of internet, social media and big data

The current use of the internet, social media and big data severely affects the privacy of ordinary users. This positioning paper is primarily aimed at the private user young and old who did not have special education or training regarding ICT but still uses these services intensively and who, whether or not, rightly worries about the hazards to which his or her privacy is exposed. This requires not only a better and deeper understanding of the technological possibilities and limitations, but also the commercial interests, and their relation to the constraints and threats of our personal privacy when using the many often valuable services. The speci c aspects of privacy as patients, or the privacy regulations for companies and institutions that track and process les with data from individuals, employees, students, or customers, is not dealt with but is referred to other reports. This positioning paper has been conceived by a working group of members of KVAB and external experts covering the different aspects of this interdisciplinary subject, that have met regularly over a period of one year.Since the ICT world is often overwhelmed with “jargon” words, the scope of which does not penetrate or because the newspapers sometimes describe very frightening lowly-backed situations, we rst discuss the main concepts both at the level of the machine learning, data extraction and the big data, as well as the privacy issues that arise, and nally the ways in which a better privacy can be acquired.In order to make this more concrete for the modal reader, we discuss important privacy hazards in a number of concrete situations, such as the digital life of a family, the big data police in passenger pro les, the internet of things, the context of smart cities, distributed information versus central collection, autonomous vehicles, and location information. Although this digital revolution is not over yet, the modal user can already modify his behavior.There is extensive scienti c literature on this subject, but there are also many widely accessible texts available recently, including websites, to which the interested reader is referred to in the bibliography.The ten recommendations mainly focus on various target groups and situations

EASY: Efficient semAntic Service discoverY in pervasive computing environments with QoS and context support

International audienceNo abstrac

Emerging professional skills: Insights and methods

In this workshop run by the Engineering skills SIG, attendees were given the opportunity to learn about emerging professional competencies, and strategies to overcome teaching barriers.The workshop format was “world cafe” with several tables for small groups to informally discuss these strategies within a time limit. Each table focussed on an emerging skill and/or scenario and participants each visited several tables. The session was informed by the engineering skills survey taken by SEFI 2021 conference attendees. It gave us views on new competencies, barriers to teaching them, and illustrations of good practice. Obstacles to teaching them include motivation, legitimacy, overloaded curriculums, student resistance, resource constraints, and pedagogical understandings.Ideally skills should be learned by students in contexts where they’re used. While many technical competencies are primarily developed in engineering practice, professional/soft abilities are often not. As a result, there ought to be some opportunity for the student to transfer, adapt and (re)learn them in an engineering degree. This report summarises the conference workshop outputs with sections for each table. Each section acknowledges the hosts/authors, a summary of the discussion, and any materials presented. Readers may find this paper useful when facilitating related discussions