Context-aware Middleware Research Articles

A Semantic-Based Middleware for Supporting Heterogeneity and Context-Awareness Within IoT Applications

A Semantic-Based Middleware for Supporting Heterogeneity and Context-Awareness Within IoT Applications

Enhancing user interaction with context-awareness in cultural spaces

The emergence of the Internet of Things has fueled a proliferation of smart things in many fields, including cultural spaces. Context-awareness addresses the production of large volumes of context by analyzing raw data and adding a meaning to them. Middleware systems have emerged, which perform context modelling and reasoning, supporting context-aware applications. The services provided by such applications can be personalized, automated and adapted to the current situation, thus enhancing the user interaction with the devices and the digital environment. In this work, a context-aware middleware system is presented, based on a hybrid reasoning schema, which combines multiple techniques to efficiently address each problem. The proposed middleware system is evaluated in a cultural space, where scenarios were designed and tested, using a mixture of real and artificial data. The experiments measured the accuracy, performance in terms of reaction time and scalability and the interactivity enhancement, achieved by the proposed middleware.

Modified Context Aware Middleware Architecture for Precision Agriculture

The opportunities for researchers are enhanced with the progression in the technology of communication and computing. The ease in life of many people like farmers, educators, administers, managers, etc., is increased with more inventions of the researchers using this new technology. The progressive technology for data management is providing more amount of information. However, is the user able to access the needed information when required? This question rises to the more questions like, how to identify whether the information is as per the requirement? Whether a user is authorized or not? The answer for all the questions is to make the support aware of the context. Therefore, the present technology needs to be modified to make the system aware about the context. The process of demonstrating the services based on the context using Wireless Sensor Networks (WSN) with the help of illustration on mango crop is emphasized in this paper. There are many serious problems like unsuitable fertilizer use, wrong selection of crops in wrong seasons, water waste, poor publicizing in the case of farming. These problems are addressed using the ubiquitous context aware middleware architecture for precision agriculture in mango crop.

A survey on the use of machine learning methods in context-aware middlewares for human activity recognition

A survey on the use of machine learning methods in context-aware middlewares for human activity recognition

A Context-Aware Middleware for Context Modeling and Reasoning: A Case-Study in Smart Cultural Spaces

The proliferation of smart things and the subsequent emergence of the Internet of Things has motivated the deployment of intelligent spaces that provide automated services to users. Context-awareness refers to the ability of the system to be aware of the virtual and physical environment, allowing more efficient personalization. Context modeling and reasoning are two important aspects of context-aware computing, since they enable the representation of contextual data and inference of high-level, meaningful information. Context-awareness middleware systems integrate context modeling and reasoning, providing abstraction and supporting heterogeneous context streams. In this work, such a context-awareness middleware system is presented, which integrates a proposed context model based on the adaptation and combination of the most prominent context categorization schemata. A hybrid reasoning procedure, which combines multiple techniques, is also proposed and integrated. The proposed system was evaluated in a real-case-scenario cultural space, which supports preventive conservation. The evaluation showed that the proposed system efficiently addressed both conceptual aspects, through means of representation and reasoning, and implementation aspects, through means of performance.

A Context-Aware Middleware Cloud Approach for Integrating Precision Farming Facilities into the IoT toward Agriculture 4.0

The adoption of Precision Farming (PF) practices involving ubiquitous computing advancements and conceptual innovations of “smart” agricultural production toward Agriculture 4.0 is a significant factor for the benefit of sustainable growth. In this context, the dynamic integration of PF facility systems into the Internet of Things (IoT) represents an excessive challenge considering the large amount of heterogeneous raw data acquired in agricultural environments by Wireless Sensor and Actuator Networks (WSANs). This paper focuses on the issue of facilitating the management, process, and exchange of the numerous and diverse data points generated in multiple PF environments by introducing a framework of a cloud-based context-aware middleware solution as part of a responsive, adaptive, and service-oriented IoT integrated system. More particularly, the paper presents in detail a layered hierarchical structure according to which all functional elements of the system cope with context, while the context awareness operation is accomplished into a cloud-based distributed middleware component that is the core of the entire system acting as a Decision Support System (DSS). Furthermore, as proof of concept, the functionality of the proposed system is studied in real conditions where some evaluation results regarding its performance are quoted.

Context aware self-optimisation scheduling in internet of things

The internet of things (IoT) consists of systems that are highly dynamic, homogenous and heterogeneous. These systems aspects bring in the automaticity resources management and scheduling that need to be addressed both at the design and runtime stage of systems development for a middleware. This paper looks at building support for middleware core components with self-optimisation and reconfiguration capabilities through the use of the exact match priority (EMP) algorithm to implement autonomic task scheduling of core components. The algorithm executes the scheduling process using dependent variables, context management tasks, number of configuration for the core components and number of components to minimise the waiting time for the reconfiguration of the system structures. The system supports the context-aware middleware in managing reconfigurable core components in an IoT environment. The experimental model indicates that autonomic task scheduling using EMP at the design stage of systems is important to achieve self-management.

Context Flow Graphs: Situation Modeling for Rule-Based Proactive Context-Aware Systems

A proactive context-aware system automatically adapts its user interface to the user’s situational needs. This is achieved by continuously capturing the environmental properties, reasoning upon the context, and detecting situations where unsolicited adjustments are helpful or notifications informative. If the characteristics of those situations are well known in advance, their occurrence can be detected at runtime by the rule-based processing of raw sensor data. However, rule-based context reasoning methods determine the user’s situation mostly based on present sensor signals instead of considering the situation to be likewise the product of the past context. This article introduces a graph-based situation modeling formalism for the specification of system-relevant environmental circumstances as context flow graphs. A directed cyclic graph represents thereby the distinct contextual characteristics a user’s situation is made of and the temporal order in which these appear and disappear during the evolution of the situation. Complex situations for rule-based proactive context-aware systems can then be expressed at a high level of abstraction and without the need to understand the underlying sensor-related signal processing mechanisms. The technical feasibility is demonstrated by a prototypical distributed proactive context-aware middleware that, in addition, comes up with a web-based user interface for the interactive graphical and logical modeling of situations as context flow graphs.

Context aware self-optimisation scheduling in internet of things

The internet of things (IoT) consists of systems that are highly dynamic, homogenous and heterogeneous. These systems aspects bring in the automaticity resources management and scheduling that need to be addressed both at the design and runtime stage of systems development for a middleware. This paper looks at building support for middleware core components with self-optimisation and reconfiguration capabilities through the use of the exact match priority (EMP) algorithm to implement autonomic task scheduling of core components. The algorithm executes the scheduling process using dependent variables, context management tasks, number of configuration for the core components and number of components to minimise the waiting time for the reconfiguration of the system structures. The system supports the context-aware middleware in managing reconfigurable core components in an IoT environment. The experimental model indicates that autonomic task scheduling using EMP at the design stage of systems is important to achieve self-management.

CAMeL: A Self-Adaptive Framework for Enriching Context-Aware Middlewares with Machine Learning Capabilities

Context-aware middlewares support applications with context management. Current middlewares support both hardware and software sensors providing data in structured forms (e.g., temperature, wind, and smoke sensors). Nevertheless, recent advances in machine learning paved the way for acquiring context from information-rich, loosely structured data such as audio or video signals. This paper describes a framework (CAMeL) enriching context-aware middlewares with machine learning capabilities. The framework is focused on acquiring contextual information from sensors providing loosely structured data without the need for developers of implementing dedicated application code or making use of external libraries. Nevertheless the general goal of context-aware middlewares is to make applications more dynamic and adaptive, and the proposed framework itself can be programmed for dynamically selecting sensors and machine learning algorithms on a contextual basis. We show with experiments and case studies how the CAMeL framework can (i) promote code reuse and reduce the complexity of context-aware applications by natively supporting machine learning capabilities and (ii) self-adapt using the acquired context allowing improvements in classification accuracy while reducing energy consumption on mobile platforms.

WORK DONE ON MIDDLEWARE INFRASTRUCTURE FOR PREDICATE DETECTION

Context-awareness is an essential feature of pervasive applications, and runtime detection of contextual properties is one of the primary approaches to enabling context awareness. However, existing context-aware middleware does not provide sufficient support for detection of contextual properties in asynchronous environments. The contextual activities usually involve multiple context collecting devices, which are fully-decentralized and interact in an asynchronous manner. However, existing context consistency checking schemes do not work in asynchronous environments, since they implicitly assume the availability of a global clock or relay on synchronized interactions. To this end, we present the Middleware Infrastructure for Predicate detection in Asynchronous environments (MIPA), which supports context awareness based on logical time. Design and Structure of MIPA are explained in detail.

Model of a Context-Aware Middleware for Mobile Workers

With the development of Internet of things and Web of things, computing becomes more pervasive, invisible and present everywhere. In fact, in our environment, we are surrounded by multiple devices that deliver (web) services which meet the needs of the users. However, the mobility of these devices as the users has important repercussions that challenge software design of these applications because the variability of the environment cannot be anticipated at the design time. Thus, it will be interesting to dynamically discover the environment and adapt the application during its execution to the new contextual conditions. We therefore, propose a model of a context-aware middleware that can address this issue through a monitoring service which is capable of reasoning and observation channels capable of calculating the context during the runtime. The monitoring service evaluates the pre-defined X-Query predicates in the context manager and uses Prolog to deduce the services needed to respond back. An independent observation channel for each different predicate is then dynamically generated by the monitoring service depending on the current state of the environment. Each channel sends its result directly to the context manager which consequently calculates the context based on all the predicates’ results while preserving the reactivity of the self-adaptive system.

A smart mobile cloud environment for modelling and simulation of mobile cloud applications

Mobile cloud computing (MCC) has been introduced as an optimal solution to the constrained mobile devices. MCC takes mobile devices to a wholly new level where the latter rely on the cloud to store and process data. Since mobile cloud applications (MC-Apps) encompass more complicated features, they are considered as the next generation of mobile applications. Thereby new challenges arise in their development phase, such as exploiting the concept of mobility and contextual information. This new kind of applications requires new methods, of development, modelling and simulation. Addressing this lack in MCC, we propose in this paper a context-aware-based framework that allows: the description of MC-Apps' architecture with Mobile Cloud Architecture Description Language (MC-ADL); the simulation of MC-Apps' behaviour using mobile cloud simulation toolkit (MC-SIM); and the provision of a context-aware middleware: smart cloud gate (SCG) to ensure contextual configuration of the MC-Apps, and contextual evolution of its architecture.

User-oriented seamless service handoff in the ubiquitous computing environment

Purpose User-oriented seamless service handoff is prosperous in the ubiquitous computing environment. This paper aims to present an open architecture for the user-oriented seamless service handoff across heterogeneous platforms. The migration of service environment is the key to implement the user-oriented service handoff. A distributed service handoff scheme based on the binary sensor network and context-aware middleware has been proposed. Design/methodology/approach To validate this scheme, a test-bed has been constructed in the lab environment. Findings The experimental results show that the proposed scheme can reduce the service handoff delay compared to the centralized scheme. Originality/value The experimental results show that the scheme can reduce the delay of service handoff compared to the traditional centralized scheme.

Design of a middleware for compound context-awareness on sensor-based mobile environments

In this paper, we design a middleware for context-awareness which provides compound contexts from diverse sensors on a mobile device. Until now, most of context-aware application developers have taken responsibility for context processing from sensing data. Such application-level context processing causes heavily redundant data processing and leads to significant resource waste in energy as well as computing. In the proposed scheme, we define primitive and compound context map which consists of relavant sensors and features. Based on the context definition, each application demands a context of interest to the middleware, and thus similar context-aware applications inherently share context information and procesing within the middleware. We show that the proposed scheme significantly reduces the resource amounts of cpu, memory, and battery, and that the performance gain gets much more when multiple applications which need similar contexts are running.

A smart brain: an intelligent context inference engine for context-aware middleware

Recently, 'activity' context draws increased attention from researchers in context awareness. Existing context-aware middleware usually employ the rule-based method to, which is easy to build and also intuitive to work with. However, this method is fragile, not flexible enough, and is inadequate to support diverse types of tasks. In this paper, we surveyed the related literature in premier conferences over the past decade, reviewed the main activity context recognition methods, and summarised their three main facets: basic activity inference, dynamic activity analysis, and future activity recommendation. Based on our previous work, we then proposed an intelligent inference engine for our context-aware middleware. Besides satisfying requirements for checking context consistency, our inference engine integrates the three methods for activity context recognition to provide a solution for all facets of activity context recognition based on our context-aware middleware.

A smart brain: an intelligent context inference engine for context-aware middleware

A smart brain: an intelligent context inference engine for context-aware middleware

MIDDLEWARE INFRASTRUCTURE FOR PREDICATE DETECTION IN ASYNCHRONOUS ENVIRONMENT

Context-awareness is an essential feature of pervasive applications, and runtime detection of contextual properties is one of the primary approaches to enabling context awareness. However, existing context-aware middleware does not provide sufficient support for detection of contextual properties in asynchronous environments. The contextual activities usually involve multiple context collecting devices, which are fully-decentralized and interact in an asynchronous manner. However, existing context consistency checking schemes do not work in asynchronous environments, since they implicitly assume the availability of a global clock or relay on synchronized interactions. To this end, we present the Middleware Infrastructure for Predicate detection in Asynchronous environments (MIPA), which supports context awareness based on logical time. Design and Structure of MIPA are explained in detail.

Context Aware Middleware Architectures: Survey and Challenges.

Context aware applications, which can adapt their behaviors to changing environments, are attracting more and more attention. To simplify the complexity of developing applications, context aware middleware, which introduces context awareness into the traditional middleware, is highlighted to provide a homogeneous interface involving generic context management solutions. This paper provides a survey of state-of-the-art context aware middleware architectures proposed during the period from 2009 through 2015. First, a preliminary background, such as the principles of context, context awareness, context modelling, and context reasoning, is provided for a comprehensive understanding of context aware middleware. On this basis, an overview of eleven carefully selected middleware architectures is presented and their main features explained. Then, thorough comparisons and analysis of the presented middleware architectures are performed based on technical parameters including architectural style, context abstraction, context reasoning, scalability, fault tolerance, interoperability, service discovery, storage, security & privacy, context awareness level, and cloud-based big data analytics. The analysis shows that there is actually no context aware middleware architecture that complies with all requirements. Finally, challenges are pointed out as open issues for future work.

A proposal of a classification model for the cognitive workload of human activity in a context-aware system

The skill level of a person in processing information, reacting to his/her surroundings and decision making for performing an activity is determined by the allocation of the mental resources demanded by such activity. When the allocation is inappropriate, there is a higher possibility for some accident to occur. Thus, one can notice that the cognitive workload spent by the person is an important variable that can take him to a risky situation. Since it is not possible to measure the cognitive workload spent by a person during the performance of an activity directly, we noticed the need to evaluate the level of his/her performance in order to be possible to infer the cognitive workload used. So, we propose the creation of a model to classify the cognitive workload based on the behavioral model skill-rule-knowledge and the relations of performance properties with the context surrounding the person. The evaluation of the model was made using a public dataset and the results showed a promising approach for the classification of human performances. Keywords: human activity, context-aware middleware, ubiquity, cognitive workload, human performance.