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In this paper, we describe a novel approach for ontology distribution in ubiquitous environments. We present the Context Aware Explorer (CAE) framework we have developed which allows building context aware applications. Our approach, while built upon Web Ontology Language OWL and RDF Schema, is aiming at modeling context ontology for supporting context reasoning and the design of a Smart Rule Engine that maintains a shared model of context for all computing entities in the smart space. The CAE main contribution is its ability to distribute ontology based on abstract parameters related to situation and to user profile. The ultimate goal is to decrease the response time of the reasoning engine with regards to reasoning accuracy. We also describe the implementation of CAE, and its associated ontology, in a prototype demonstrator of smart environment handling indoor and outdoor context aware services. The validation of the ontology distribution approach was applied to assistive environment dedicated to people having motor disabilities and tested within laboratory conditions. Preliminary performance results are presented to highlight the impact of distributed ontology concept.

Context‐awareness is an essential property of any pervasive system perceiving its environment. Such a system captures and processes context, i.e. the features describing the relevant aspects of environment state and user behaviour. However, development of these systems still requires solving a number of research and engineering challenges. The purpose of this paper is to propose perception framework, a RESTful middleware which simplifies and accelerates the development of pervasive systems. Perception framework allows constructing services' application logic using rules and context. Moreover, it collects sensor data and produces the context information that is required for the rules. The authors present the architecture, design, complete implementation, and prototype‐based verification of perception framework.

Development of context‐aware services is achieved with a novel architecture supporting building of the logic of web services using rules which directly manipulate the available elementary context represented with the Web Ontology Language (OWL) ontology. These rules are described using the Rule Interchange Format (RIF) with support for different rule languages. The implementation of this framework is aligned with RESTful principles, providing a lightweight and flexible solution for large‐scale context‐aware systems.

The fully implemented prototype verifies the feasibility of constructing the logic of context‐aware web services with the rules supported by perception framework.

The contributions of this paper include: the requirement specification for a generic context‐aware pervasive middleware; and the design and implementation of the framework (i.e. perception framework) supporting the development of context‐aware web services. The perception framework includes a generic rule‐based reasoner allowing developers to use several RIF‐compliant rule description languages.

A context‐aware system is a system that uses context to provide relevant information or services to its users. While there has been a variety of context middleware infrastructures and context‐aware applications, little work has been done on integrating context into database management systems. The purpose of this paper is to consider a preference database system that supports context‐aware queries, that is, queries whose results depend on the context at the time of their submission.

The paper proposes using data cubes to store the dependencies between context‐dependent preferences and database relations and on‐line analytical processing techniques for processing context‐aware queries. This allows for the manipulation of the captured context data at various levels of abstraction, for instance, in the case of a context parameter representing location, preferences can be expressed, for example, at the level of a city, the level of a country or both. To improve query performance, the paper uses an auxiliary data structure, called context tree. The context tree stores results of past context‐aware queries indexed by the context of their execution. Finally, the paper outline the implementation of a prototype context‐aware restaurant recommender.

The use of context is important in many applications such as pervasive computing where it is important that users receive only relevant information.

Although there is much research on location‐aware query processing in the area of spatial‐temporal databases, integrating other forms of context in query processing is a rather new research topic.

Research on context‐aware systems design has received significant attention lately. One of the research directions on context‐aware systems is towards context‐aware frameworks adapted to domain‐specific requirements, aiming to improve their applicability in a variety of applications, which share common requirements. The purpose of this paper is to present the design of a domain‐specific context‐aware platform supporting context acquisition, presentation and rule‐based control.

The proposed platform uses a formal context model, based on ontologies description, aiming to provide a common representation of contextual information, facilitating thus integration and reusability in application domains, which embrace a common set of requirements. A context‐aware system has been built upon a well defined data model, which inherits a list of offered functionalities and/or services at the acquisition, presentation and reasoning level. The presented platform entails an event‐driven context‐based inference mechanism aiming to enable automated reasoning.

The proposed platform has been applied in two different case studies, aiming to provide a proof of concept towards the applicability of the proposed framework in different application areas through the offered integration and adaptation mechanisms. Evaluation results, in the frame of these case studies, show the ease of using the platform and its acceptable performance in practice.

As context‐aware applications within specific domains share common requirements, it becomes mandatory to offer solutions, like the proposed in this paper, which can be easily adapted to application semantics and can be reused with different levels of abstractions in order to meet specific requirements. The present paper proposes a domain‐specific platform, which applies on a variety of application types with a common set of requirements.

Context awareness is one of the key aspects of pervasive computing systems. In such systems, a plethora of dynamic context information needs to be constantly retrieved, soundly interpreted, rapidly processed, maintained in various repositories, and securely disseminated. Thus, a flexible, scalable and interoperable context representation scheme needs to be established and solid context management mechanisms need to be adopted, which will perform well in large‐scale distributed pervasive systems. This paper elaborates on the COMPACT context middleware that has been designed to cope with the issues above and saturate pervasive computing environments with context awareness functionality.

Presence of innumerable sensors, complex deduction of contexts from sensor data, and reusability of contextual information impose the requirement of middleware for context aware computing. Smart applications, hosted in myriad devices (e.g. PDA, mobile, PCs), acquire different contexts from the middleware and act intelligently based on the available contexts in a context‐aware computing environment. As the system grows larger, scalable delivery of contexts from the middleware to numerous context‐aware applications will be inevitable. However, pure unicast based or pure broadcast‐based dissemination cannot provide high scalability as well as low‐average latency. The purpose of this paper is to present a scalable context delivery mechanism for the middlewares to facilitate the development of larger context‐aware computing systems.

The proposed scheme is based on hybrid data dissemination technique where the most frequently requested data (e.g. HOT contexts) are delivered through multicast and the rest (e.g. COLD contexts) are delivered through unicast to reduce network traffic. The paper dynamically prioritizes and classifies the HOT and COLD context data depending on the number of requests and longest waiting time. Moreover, the division of bandwidth between the delivery of HOT and COLD contexts reduces average latency. Polling traffic is decreased by incorporating leasing mechanism. Extensive simulation is conducted to evaluate the proposed scheme.

The mechanism dynamically prioritizes and classifies the hot and cold context data depending on the request rate and longest waiting time. The solution addresses the push popularity problem that occurs in the passive as the passive clients access data without sending explicit requests. The leasing mechanism is incorporated to reduce the periodical requests (polling) for better performance.

The paper is of value in presenting a scalable context delivery mechanism for the middlewares to facilitate the development of larger context‐aware computing systems and also in presenting implementation details of a prototype that is developed using Jini framework and Java reliable multicast service (JRMS) library.

The purpose of this paper is to contribute to the extant literature about the co-evolvement of Business Process Management (BPM) and the Internet of Things (IoT) by proposing the IoT-enabled Context-aware BPM (IoT-CaBPM) framework to bridge from the IoT infrastructure to context-aware business processes.

Motivated by the “Three Waves” of BPM research, IoT-enabled context-awareness is, therefore, expected to be achieved for enhancing the business process design, which pilots a new wave of BPR (Business Process Redesign/Reengineering) to enable the business process coevolve with IoT and analytics. This paper reports an illustrative case study of BPR in a Chinese bulk port, one of the hub seaports that widely adopted IoT technologies over the last few years.

The IoT implementation and data analytics has increased the efficiency and improve the monitoring effectively. The proposed IoT-CaBPM framework availably helps to identify and match nodes of IoT devices, business decisions and analytic models in order to redesign a business process towards context-aware variability. As IoT is rapidly becoming the new dominant IT paradigm is moving towards mature implementation in various industries, the corresponding BPR must be planned and executed strategically for achieving better benefits.

Despite some research extend BPM standard by integrating IoT devices as a sort of resources or report generically that the ports operations are affected by IoT, there is still a lack of layers from the IoT infrastructure to context-aware business processes. An industrial BPR case with business models in detail is also a lack for presenting the specific implications and effectiveness of the adoption of such technologies. This paper fills in this gap.

The purpose of this paper is to present a component framework for building visual interfaces for pervasive computing systems.

The proposed framework enables end‐users to build visual interfaces for their systems by using document‐editing manner.

The building and operating visual interfaces are useful for managing pervasive computing systems.

The framework is designed based on Java but programming language‐independent version is needed.

A component framework was implemented for building visual interfaces for pervasive computing.

A framework for visual interfaces for pervasive computing is unique.

The increasing number of context aware services, which depend on various multimodal sensing, processing and actuating techniques, technologies and formats ask for a physical framework that is able to handle their heterogeneity. Thereto, we propose a context model bridging the semantic gaps between context aware services. In addition we propose a simple system architecture of Distribution Servers and Transformation Servers that bridge semantic gaps among context aware services. Applying our framework we solve the heterogeneity problem existing for location services. Location is typically a form of context where heterogeneity is a problem.

Context‐aware mobile computing extends the horizons of the conventional computing model to a ubiquitous computing environment that serves users at anytime, anywhere. To achieve this, mobile applications need to adapt their behaviors to the changing context. The purpose of this paper is to present a generalized adaptive middleware infrastructure for context‐aware computing.

Owing to the vague nature of context and uncertainty in context aggregation for making adaptation decisions, the paper proposes a fuzzy‐based service adaptation model (FSAM) to improve the generality and effectiveness of service adaptation using fuzzy theory.

By the means of fuzzification of the context and measuring the fitness degree between the current context and the predefined optimal context, FSAM selects the most suitable policy to adopt for the most appropriate service. The paper evaluates the middleware together with the FSAM inference engine by using a Campus Assistant application.

The paper is of value in presenting a generalized adaptive middleware infrastructure for context‐aware computing and also comparing the performance of the fuzzy‐based solution with a conventional threshold‐based approach for context‐aware adaptation.