A Survey on Service Composition Middleware in Pervasive Environments

The development of pervasive computing has put the light on a challenging problem: how to dynamically compose services in heterogeneous and highly changing environments? We propose a survey that defines the service composition as a sequence of four steps: the translation, the generation, the evaluation, and finally the execution. With this powerful and simple model we describe the major service composition middleware. Then, a classification of these service composition middleware according to pervasive requirements - interoperability, discoverability, adaptability, context awareness, QoS management, security, spontaneous management, and autonomous management - is given. The classification highlights what has been done and what remains to do to develop the service composition in pervasive environments

Ontologies for context-aware applications

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Desing and Validation of a Light Inference System to Support Embedded Context Reasoning

Embedded context management in resource-constrained devices (e.g. mobile phones, autonomous sensors or smart objects) imposes special requirements in terms of lightness for data modelling and reasoning. In this paper, we explore the state-of-the-art on data representation and reasoning tools for embedded mobile reasoning and propose a light inference system (LIS) aiming at simplifying embedded inference processes offering a set of functionalities to avoid redundancy in context management operations. The system is part of a service-oriented mobile software framework, conceived to facilitate the creation of context-aware applications—it decouples sensor data acquisition and context processing from the application logic. LIS, composed of several modules, encapsulates existing lightweight tools for ontology data management and rule-based reasoning, and it is ready to run on Java-enabled handheld devices. Data management and reasoning processes are designed to handle a general ontology that enables communication among framework components. Both the applications running on top of the framework and the framework components themselves can configure the rule and query sets in order to retrieve the information they need from LIS. In order to test LIS features in a real application scenario, an ‘Activity Monitor’ has been designed and implemented: a personal health-persuasive application that provides feedback on the user’s lifestyle, combining data from physical and virtual sensors. In this case of use, LIS is used to timely evaluate the user’s activity level, to decide on the convenience of triggering notifications and to determine the best interface or channel to deliver these context-aware alerts.

Context constraint integration and validation in dynamic web service compositions

System architectures that cross organisational boundaries are usually implemented based on Web service technologies due to their inherent interoperability benets. With increasing exibility requirements, such as on-demand service provision, a dynamic approach to service architecture focussing on composition at runtime is needed. The possibility of technical faults, but also violations of functional and semantic constraints require a comprehensive notion of context that captures composition-relevant aspects. Context-aware techniques are consequently required to support constraint validation for dynamic service composition. We present techniques to respond to problems occurring during the execution of dynamically composed Web services implemented in WS-BPEL. A notion of context { covering physical and contractual faults and violations { is used to safeguard composed service executions dynamically. Our aim is to present an architectural framework from an application-oriented perspective, addressing practical considerations of a technical framework

MLContext: A Context-Modeling Language for Context-Aware Systems

Context awareness refers to systems that can both sense and react based on their environment. The complexity of these systems makes necessary to apply software engineering techniques in their development, such as Model-Driven Software development (MDD). One of the main difficulties that developers of context-aware systems must tackle is how to manage the needed context information. In this paper, we present MLContext, a textual Domain Specific Language (DSL) which is specially tailored for modeling context information and automatically generating software artefacts from context models. It has been designed to provide a high-level abstraction, to be an easy to learn, and to promote reuse of context models. We have built a toolkit including an editor and a parser to convert MLContext textual specifications into models. As a proof of concept, we have automatically generated ontologies and Java code for the OCP middleware. MLContext models can be reused in applications with the same context because they do not include details related to the platforms or the implementation. These context models can be specified by non-developers users because MLContext provides high-level abstractions of the domain

Edge-to-cloud sensing and actuation semantics in the industrial Internet of Things

There are billions of devices worldwide deployed, connected, and communicating to other systems. Sensors and actuators, which can be stationary or movable devices. These Edge devices are considered part of the Internet of Things (IoT) devices, which can be referred to as a tier of the Computing Continuum paradigm. There are two main concerns at stake in the success of this ecosystem. The interoperability between devices and systems is the first. Mainly, because most of them communicate uniquely and differently from each other, leading to heterogeneous data. The second issue is the lack of decision-making capacity to conduct actuations, such as communicating through different computing tiers based on latency constraints due to a certain measured factor. In this article, we propose an ontology to improve device interoperability in the IoT. In addition, we also explain how to ease data communication between Computing Continuum devices, providing tools to enhance data management and decision-making. A use case is also presented, using the automotive industry, where quickness in maneuver determination is key to avoid accidents. It is exemplified using two Raspberry Pi devices, connected using different networks and choosing the appropriate one depending on context-aware conditions.This work is partially funded by: Industrial Doctorates (2019 DI 001) from Generalitat de Catalunya. The SUDOQU project (PID2021-127181OB-I00) from MCIN/AEI. FEDER “Una manera de hacer Europa”; and project 2017-SGR-1749 from Generalitat de Catalunya. Also with the support of inLab FIB at UPC and Worldsensing.Peer ReviewedPostprint (published version

COCOA: COnversation-based service COmposition in pervAsive computing environments with QoS support

International audienceNo abstrac

C-Arc: A Novel Architecture for Next Generation Context- Aware Systems

Computing is becoming increasingly mobile and ubiquitous. This implies that applications and services must be aware and adapt to highly dynamic environments. However, building contextaware mobile services is currently a complex and time consuming task. The emergence of truly ubiquitous computing, enabled by the availability of mobile and heterogeneous devices and an increasing number of commercial off-the-shelf sensing technologies, is hampered by the lack of standard architectural support for the development of context-aware systems. In this paper, the common architecture principles of context-aware systems are presented and the crucial contextaware architecture issues to support the next generation context-aware systems which will enable seamless service provisioning in heterogeneous, dynamically varying computing and communication environments are identified and discussed. Furthermore, a novel architecture, CArc,is proposed to aid in the development of the next generation context-aware systems. A prototype implemented of C-Arc is also presented to demonstrate the architecture. C-Arc provides support for most of the tasks involved in dealing with context, namely acquiring context from various sources, interpreting context and disseminating context.Keywords: Context-aware architecture, context-aware systems, context-aware mobile services,mobile and ubiquitous computing