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Special Issue "Context Aware Environments and Applications"

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (20 September 2017)

Special Issue Editor

Guest Editor
Prof. Dr. Francisco Javier Falcone Lanas

Associate Professor, Department of Electrical Engineering, Universidad Publica de Navarra, UPNA, Spain
Website1 | Website2 | E-Mail
Interests: wireless networks; performance evaluation; distributed systems; context aware environments; IoT; next generation wireless systems

Special Issue Information

Dear Colleagues,

The evolution of communication systems, particularly within wireless systems, and the advent of the Internet of Things, is enabling the implementation of fully-interactive, context-aware environments. As a consequence, multiple applications, many of them related to Cyber Physical Systems, have been proposed and are currently under study, such as Intelligent Transportation Systems, Ambient Assisted Living, Smart Health, or Industrial/Factories of the Future.

The successful adoption and deployment of Context Aware Environments faces multiple challenges, related with communication network infrastructures (such as delay and fault tolerance), energy consumption, adaptability or device/system resilience. In this sense, novel solutions, such as IoT based systems, energy aware routing protocols, energy harvesting techniques or the integration of 4D/5G systems is envisaged as the path towards future developments.

This Special Issue aims to highlight advances in the development, testing, and modeling of Context Aware Scenarios and Applications, within the broad area of potential application of such systems. Topics include, but are not limited to:

  • Context Aware Testbeds: ITS, Smart Health, Smart City, etc.
  • System dynamics and modelling
  • Human/System Interaction
  • Wireless Sensor Network and device design, with focus on energy efficient operation
  • Cloud-based IoT platform development
  • Cyber Physical System development for Context Aware Environments

Dr. Francisco Falcone
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Ambient Assisted Living
  • Intelligent Transportation Systems
  • Cyber Physical Systems
  • Internet of Things
  • Wireless Sensor Networks
  • Smart Cities/Smart Regions

Published Papers (9 papers)

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Research

Open AccessArticle mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification
Sensors 2017, 17(10), 2433; doi:10.3390/s17102433
Received: 20 September 2017 / Revised: 16 October 2017 / Accepted: 19 October 2017 / Published: 24 October 2017
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Abstract
The emerging research on automatic identification of user’s contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user’s contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications.
[...] Read more.
The emerging research on automatic identification of user’s contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user’s contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications. However, availability of cross-domain heterogeneous contexts provides a challenging opportunity for their fusion to obtain abstract information for further analysis. This work demonstrates extension of our previous work from a single domain (i.e., physical activity) to multiple domains (physical activity, nutrition and clinical) for context-awareness. We propose multi-level Context-aware Framework (mlCAF), which fuses the multi-level cross-domain contexts in order to arbitrate richer behavioral contexts. This work explicitly focuses on key challenges linked to multi-level context modeling, reasoning and fusioning based on the mlCAF open-source ontology. More specifically, it addresses the interpretation of contexts from three different domains, their fusioning conforming to richer contextual information. This paper contributes in terms of ontology evolution with additional domains, context definitions, rules and inclusion of semantic queries. For the framework evaluation, multi-level cross-domain contexts collected from 20 users were used to ascertain abstract contexts, which served as basis for behavior modeling and lifestyle identification. The experimental results indicate a context recognition average accuracy of around 92.65% for the collected cross-domain contexts. Full article
(This article belongs to the Special Issue Context Aware Environments and Applications)
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Open AccessArticle Estimation of the Driving Style Based on the Users’ Activity and Environment Influence
Sensors 2017, 17(10), 2404; doi:10.3390/s17102404
Received: 6 September 2017 / Revised: 17 October 2017 / Accepted: 19 October 2017 / Published: 21 October 2017
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Abstract
New models and methods have been designed to predict the influence of the user’s environment and activity information to the driving style in standard automotive environments. For these purposes, an experiment was conducted providing two types of analysis: (i) the evaluation of a
[...] Read more.
New models and methods have been designed to predict the influence of the user’s environment and activity information to the driving style in standard automotive environments. For these purposes, an experiment was conducted providing two types of analysis: (i) the evaluation of a self-assessment of the driving style; (ii) the prediction of aggressive driving style based on drivers’ activity and environment parameters. Sixty seven h of driving data from 10 drivers were collected for analysis in this study. The new parameters used in the experiment are the car door opening and closing manner, which were applied to improve the prediction accuracy. An Android application called Sensoric was developed to collect low-level smartphone data about the users’ activity. The driving style was predicted from the user’s environment and activity data collected before driving. The prediction was tested against the actual driving style, calculated from objective driving data. The prediction has shown encouraging results, with precision values ranging from 0.727 up to 0.909 for aggressive driving recognition rate. The obtained results lend support to the hypothesis that user’s environment and activity data could be used for the prediction of the aggressive driving style in advance, before the driving starts. Full article
(This article belongs to the Special Issue Context Aware Environments and Applications)
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Open AccessArticle AAL Platform with a “De Facto” Standard Communication Interface (TICO): Training in Home Control in Special Education
Sensors 2017, 17(10), 2320; doi:10.3390/s17102320
Received: 20 September 2017 / Revised: 6 October 2017 / Accepted: 9 October 2017 / Published: 12 October 2017
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Abstract
Framed within a long-term cooperation between university and special education teachers, training in alternative communication skills and home control was realized using the “TICO” interface, a communication panel editor extensively used in special education schools. From a technological view we follow AAL technology
[...] Read more.
Framed within a long-term cooperation between university and special education teachers, training in alternative communication skills and home control was realized using the “TICO” interface, a communication panel editor extensively used in special education schools. From a technological view we follow AAL technology trends by integrating a successful interface in a heterogeneous services AAL platform, focusing on a functional view. Educationally, a very flexible interface in line with communication training allows dynamic adjustment of complexity, enhanced by an accessible mindset and virtual elements significance already in use, offers specific interaction feedback, adapts to the evolving needs and capacities and improves the personal autonomy and self-confidence of children at school and home. TICO-home-control was installed during the last school year in the library of a special education school to study adaptations and training strategies to enhance the autonomy opportunities of its pupils. The methodology involved a case study and structured and semi-structured observations. Five children, considered unable to use commercial home control systems were trained obtaining good results in enabling them to use an open home control system. Moreover this AAL platform has proved efficient in training children in previous cognitive steps like virtual representation and cause-effect interaction. Full article
(This article belongs to the Special Issue Context Aware Environments and Applications)
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Open AccessArticle Multi-Robot Interfaces and Operator Situational Awareness: Study of the Impact of Immersion and Prediction
Sensors 2017, 17(8), 1720; doi:10.3390/s17081720
Received: 31 May 2017 / Revised: 18 July 2017 / Accepted: 19 July 2017 / Published: 27 July 2017
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Abstract
Multi-robot missions are a challenge for operators in terms of workload and situational awareness. These operators have to receive data from the robots, extract information, understand the situation properly, make decisions, generate the adequate commands, and send them to the robots. The consequences
[...] Read more.
Multi-robot missions are a challenge for operators in terms of workload and situational awareness. These operators have to receive data from the robots, extract information, understand the situation properly, make decisions, generate the adequate commands, and send them to the robots. The consequences of excessive workload and lack of awareness can vary from inefficiencies to accidents. This work focuses on the study of future operator interfaces of multi-robot systems, taking into account relevant issues such as multimodal interactions, immersive devices, predictive capabilities and adaptive displays. Specifically, four interfaces have been designed and developed: a conventional, a predictive conventional, a virtual reality and a predictive virtual reality interface. The four interfaces have been validated by the performance of twenty-four operators that supervised eight multi-robot missions of fire surveillance and extinguishing. The results of the workload and situational awareness tests show that virtual reality improves the situational awareness without increasing the workload of operators, whereas the effects of predictive components are not significant and depend on their implementation. Full article
(This article belongs to the Special Issue Context Aware Environments and Applications)
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Open AccessArticle An Ontology-based Context-aware System for Smart Homes: E-care@home
Sensors 2017, 17(7), 1586; doi:10.3390/s17071586
Received: 31 May 2017 / Revised: 27 June 2017 / Accepted: 4 July 2017 / Published: 6 July 2017
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Abstract
Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and
[...] Read more.
Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home. Full article
(This article belongs to the Special Issue Context Aware Environments and Applications)
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Open AccessArticle Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network
Sensors 2017, 17(7), 1486; doi:10.3390/s17071486
Received: 1 May 2017 / Revised: 13 June 2017 / Accepted: 14 June 2017 / Published: 23 June 2017
PDF Full-text (2653 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In the last three decades, researchers have examined extensively how context-aware systems can assist people, specifically those suffering from incurable diseases, to help them cope with their medical illness. Over the years, a huge number of studies on Chronic Obstructive Pulmonary Disease (COPD)
[...] Read more.
In the last three decades, researchers have examined extensively how context-aware systems can assist people, specifically those suffering from incurable diseases, to help them cope with their medical illness. Over the years, a huge number of studies on Chronic Obstructive Pulmonary Disease (COPD) have been published. However, how to derive relevant attributes and early detection of COPD exacerbations remains a challenge. In this research work, we will use an efficient algorithm to select relevant attributes where there is no proper approach in this domain. Such algorithm predicts exacerbations with high accuracy by adding discretization process, and organizes the pertinent attributes in priority order based on their impact to facilitate the emergency medical treatment. In this paper, we propose an extension of our existing Helper Context-Aware Engine System (HCES) for COPD. This project uses Bayesian network algorithm to depict the dependency between the COPD symptoms (attributes) in order to overcome the insufficiency and the independency hypothesis of naïve Bayesian. In addition, the dependency in Bayesian network is realized using TAN algorithm rather than consulting pneumologists. All these combined algorithms (discretization, selection, dependency, and the ordering of the relevant attributes) constitute an effective prediction model, comparing to effective ones. Moreover, an investigation and comparison of different scenarios of these algorithms are also done to verify which sequence of steps of prediction model gives more accurate results. Finally, we designed and validated a computer-aided support application to integrate different steps of this model. The findings of our system HCES has shown promising results using Area Under Receiver Operating Characteristic (AUC = 81.5%). Full article
(This article belongs to the Special Issue Context Aware Environments and Applications)
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Open AccessArticle Energy Efficiency in Public Buildings through Context-Aware Social Computing
Sensors 2017, 17(4), 826; doi:10.3390/s17040826
Received: 25 February 2017 / Revised: 6 April 2017 / Accepted: 7 April 2017 / Published: 11 April 2017
Cited by 1 | PDF Full-text (2415 KB) | HTML Full-text | XML Full-text
Abstract
The challenge of promoting behavioral changes in users that leads to energy savings in public buildings has become a complex task requiring the involvement of multiple technologies. Wireless sensor networks have a great potential for the development of tools, such as serious games,
[...] Read more.
The challenge of promoting behavioral changes in users that leads to energy savings in public buildings has become a complex task requiring the involvement of multiple technologies. Wireless sensor networks have a great potential for the development of tools, such as serious games, that encourage acquiring good energy and healthy habits among users in the workplace. This paper presents the development of a serious game using CAFCLA, a framework that allows for integrating multiple technologies, which provide both context-awareness and social computing. Game development has shown that the data provided by sensor networks encourage users to reduce energy consumption in their workplace and that social interactions and competitiveness allow for accelerating the achievement of good results and behavioral changes that favor energy savings. Full article
(This article belongs to the Special Issue Context Aware Environments and Applications)
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Open AccessArticle A Context-Aware S-Health Service System for Drivers
Sensors 2017, 17(3), 609; doi:10.3390/s17030609
Received: 13 January 2017 / Revised: 8 March 2017 / Accepted: 10 March 2017 / Published: 17 March 2017
PDF Full-text (7679 KB) | HTML Full-text | XML Full-text
Abstract
As a stressful and sensitive task, driving can be disturbed by various factors from the health condition of the driver to the environmental variables of the vehicle. Continuous monitoring of driving hazards and providing the most appropriate business services to meet actual needs
[...] Read more.
As a stressful and sensitive task, driving can be disturbed by various factors from the health condition of the driver to the environmental variables of the vehicle. Continuous monitoring of driving hazards and providing the most appropriate business services to meet actual needs can guarantee safe driving and make great use of the existing information resources and business services. However, there is no in-depth research on the perception of a driver’s health status or the provision of customized business services in case of various hazardous situations. In order to constantly monitor the health status of the drivers and react to abnormal situations, this paper proposes a context-aware service system providing a configurable architecture for the design and implementation of the smart health service system for safe driving, which can perceive a driver’s health status and provide helpful services to the driver. With the context-aware technology to construct a smart health services system for safe driving, this is the first time that such a service system has been implemented in practice. Additionally, an assessment model is proposed to mitigate the impact of the acceptable abnormal status and, thus, reduce the unnecessary invocation of the services. With regard to different assessed situations, the business services can be invoked for the driver to adapt to hazardous situations according to the services configuration model, which can take full advantage of the existing information resources and business services. The evaluation results indicate that the alteration of the observed status in a valid time range T can be tolerated and the frequency of the service invocation can be reduced. Full article
(This article belongs to the Special Issue Context Aware Environments and Applications)
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Open AccessArticle Holistic Context-Sensitivity for Run-Time Optimization of Flexible Manufacturing Systems
Sensors 2017, 17(3), 455; doi:10.3390/s17030455
Received: 28 December 2016 / Revised: 13 February 2017 / Accepted: 21 February 2017 / Published: 24 February 2017
Cited by 2 | PDF Full-text (3815 KB) | HTML Full-text | XML Full-text
Abstract
Highly flexible manufacturing systems require continuous run-time (self-) optimization of processes with respect to diverse parameters, e.g., efficiency, availability, energy consumption etc. A promising approach for achieving (self-) optimization in manufacturing systems is the usage of the context sensitivity approach based on data
[...] Read more.
Highly flexible manufacturing systems require continuous run-time (self-) optimization of processes with respect to diverse parameters, e.g., efficiency, availability, energy consumption etc. A promising approach for achieving (self-) optimization in manufacturing systems is the usage of the context sensitivity approach based on data streaming from high amount of sensors and other data sources. Cyber-physical systems play an important role as sources of information to achieve context sensitivity. Cyber-physical systems can be seen as complex intelligent sensors providing data needed to identify the current context under which the manufacturing system is operating. In this paper, it is demonstrated how context sensitivity can be used to realize a holistic solution for (self-) optimization of discrete flexible manufacturing systems, by making use of cyber-physical systems integrated in manufacturing systems/processes. A generic approach for context sensitivity, based on self-learning algorithms, is proposed aiming at a various manufacturing systems. The new solution encompasses run-time context extractor and optimizer. Based on the self-learning module both context extraction and optimizer are continuously learning and improving their performance. The solution is following Service Oriented Architecture principles. The generic solution is developed and then applied to two very different manufacturing processes. Full article
(This article belongs to the Special Issue Context Aware Environments and Applications)
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