Context Aware Middleware Architectures: Survey and Challenges

Abstract: 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

Designing for user attention: a method for supporting unobtrusive routine tasks

NOTICE: this is the author’s version of a work that was accepted for publication in Science of Computer Programming. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Science of Computer Programming, [Volume 78, Issue 10, 1 October 2013, Pages 1987–2008] DOI 10.1016/j.scico.2013.03.002The automation of user routine tasks is one of the most important challenges in the development of Ambient Intelligence systems. However, this automation may be annoying since some tasks may grab users attention in inappropriate situations. Since user attention is a valuable resource, task automation must behave in a considerate manner demanding user attention only when it is required. To address this issue, this work presents a systematic method for supporting the design and automation of unobtrusive routine tasks that can adjust their obtrusiveness level at runtime according to the user attentional resources and context. This method proposes to design the routine tasks that the system must carry out and how they must interact with users in terms of obtrusiveness. The method also provides a software infrastructure that makes the execution of the tasks at the appropriate obtrusiveness degree a reality. Finally, the system has been validated by means of usefulness and performance tests and a practical case study that demonstrates the correctness and applicability of our approach without compromising system performance.This work has been developed with the support of MICINN under the project EVERYWARE TIN2010-18011 and co-financed with ERDF, in the grants program FPU, and it has also been supported by the Christian Doppler Forschungsgesellschaft and the BMWFJ, Austria.Gil Pascual, M.; Serral Asensio, E.; Valderas Aranda, PJ.; Pelechano Ferragud, V. (2013). Designing for user attention: a method for supporting unobtrusive routine tasks. Science of Computer Programming. 78(10):1987-2008. https://doi.org/10.1016/j.scico.2013.03.002S19872008781

Emerging research themes in services-oriented systems

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Achieving Autonomic Computing through the Use of Variability Models at Run-time

Increasingly, software needs to dynamically adapt its behavior at run-time in response to changing conditions in the supporting computing infrastructure and in the surrounding physical environment. Adaptability is emerging as a necessary underlying capability, particularly for highly dynamic systems such as context-aware or ubiquitous systems. By automating tasks such as installation, adaptation, or healing, Autonomic Computing envisions computing environments that evolve without the need for human intervention. Even though there is a fair amount of work on architectures and their theoretical design, Autonomic Computing was criticised as being a \hype topic" because very little of it has been implemented fully. Furthermore, given that the autonomic system must change states at runtime and that some of those states may emerge and are much less deterministic, there is a great challenge to provide new guidelines, techniques and tools to help autonomic system development. This thesis shows that building up on the central ideas of Model Driven Development (Models as rst-order citizens) and Software Product Lines (Variability Management) can play a signi cant role as we move towards implementing the key self-management properties associated with autonomic computing. The presented approach encompass systems that are capable of modifying their own behavior with respect to changes in their operating environment, by using variability models as if they were the policies that drive the system's autonomic recon guration at runtime. Under a set of recon guration commands, the components that make up the architecture dynamically cooperate to change the con guration of the architecture to a new con guration. This work also provides the implementation of a Model-Based Recon guration Engine (MoRE) to blend the above ideas. Given a context event, MoRE queries the variability models to determine how the system should evolve, and then it provides the mechanisms for modifying the system.Cetina Englada, C. (2010). Achieving Autonomic Computing through the Use of Variability Models at Run-time [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/7484Palanci

An Adaptive Cognitive Sensor Node for ECG Monitoring in the Internet of Medical Things

The Internet of Medical Things (IoMT) paradigm is becoming mainstream in multiple clinical trials and healthcare procedures. Cardiovascular diseases monitoring, usually involving electrocardiogram (ECG) traces analysis, is one of the most promising and high-impact applications. Nevertheless, to fully exploit the potential of IoMT in this domain, some steps forward are needed. First, the edge-computing paradigm must be added to the picture. A certain level of near-sensor processing has to be enabled, to improve the scalability, portability, reliability and responsiveness of the IoMT nodes. Second, novel, increasingly accurate data analysis algorithms, such as those based on artificial intelligence and Deep Learning, must be exploited. To reach these objectives, designers, and programmers of IoMT nodes, have to face challenging optimization tasks, in order to execute fairly complex computing tasks on low-power wearable and portable processing systems, with tight power and battery lifetime budgets. In this work, we explore the implementation of a cognitive data analysis algorithm, based on a convolutional neural network trained to classify ECG waveforms, on a resource-constrained microcontroller-based computing platform. To minimize power consumption, we add an adaptivity layer that dynamically manages the hardware and software configuration of the device to adapt it at runtime to the required operating mode. Our experimental results show that adapting the node setup to the workload at runtime can save up to 50% power consumption. Our optimized and quantized neural network reaches an accuracy value higher than 97% for arrhythmia disorders detection on MIT-BIH Arrhythmia dataset

04451 Abstracts Collection -- Future Generation Grids

The Dagstuhl Seminar 04451 "Future Generation Grid" was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl from 1st to 5th November 2004. The focus of the seminar was on open problems and future challenges in the design of next generation Grid systems. A total of 45 participants presented their current projects, research plans, and new ideas in the area of Grid technologies. Several evening sessions with vivid discussions on future trends complemented the talks. This report gives an overview of the background and the findings of the seminar

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period