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Special Issue "Select Papers from UCAmI & IWAAL 2013 - the 7th International Conference on Ubiquitous Computing and Ambient Intelligence & the 5th International Workshop on Ambient Assisted Living (UCAmI & IWAAL 2013: Pervasive Sensing Solutions"

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

Deadline for manuscript submissions: closed (15 March 2014)

Special Issue Editors

Guest Editor
Prof. Dr. Gabriel Urzaiz (Website)

Division of Engineering and Exact Sciences, Universidad AnáhuacMayab, Carretera Mérida-Progreso km.15.5, AP.96 Cordemex, 97310 Mérida, Yucatán, Mexico
Phone: +529999424800
Interests: heterogeneous and intelligent networks and systems to provide advanced services with application in Ubiquitous Computing and Ambient Intelligence
Guest Editor
Professor Chris Nugent (Website)

School of Computing and Mathematics, University of Ulster, Shore Road, Newtownabbey, County Antrim, Northern Ireland, BT37 0QB, UK
Interests: pervasive and mobile computing; smart environments, ambient assisted living

Special Issue Information

Dear Colleagues,

Ubiquitous Computing (UbiComp) and Ambient Intelligence (AmI) applications are receiving much attention. However, they still face many challenges. The main issues relate to the requirement of providing a wider variety of services, while at the same time providing increased levels of personalization. To compound this difficulty of balancing priorities, solutions have to provide increased levels of functionality while the complexity of use is expected to decrease. The development of new sensing solutions, coupled with their integration with mobile and pervasive computing, encapsulates some key topics being investigated in this Special Issue.

Authors are invited to submit the extended versions of their original papers and contributions, including but not limited to, the following topics of interest:

  • New hardware and software for UbiComp and AmI
  • New architectures for UbiComp and AmI
  • Middleware-based solutions for UbiComp and AmI
  • Software structures for UbiComp and AmI applications
  • Collaborative systems and operations for UbiComp and AmI solutions
  • Social networks in aUbiComp solution
  • Security considerations in aUbiComp solution
  • Analytical and modeling methods for Ubicomp and AmI solutions
  • New technical solutions for Ambient Assisted Living (AAL)
  • Advanced development methodologies for UbiComp/AmI solutions
  • Case Studies of deployed solutions within aUbiComp or AmI setting

Prof. Dr. Gabriel Urzaiz
Prof. Chris Nugent
Guest Editors

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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).


Published Papers (13 papers)

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Research

Open AccessArticle Integration of Multisensor Hybrid Reasoners to Support Personal Autonomy in the Smart Home
Sensors 2014, 14(9), 17313-17330; doi:10.3390/s140917313
Received: 11 April 2014 / Revised: 5 June 2014 / Accepted: 2 September 2014 / Published: 17 September 2014
Cited by 2 | PDF Full-text (7767 KB) | HTML Full-text | XML Full-text
Abstract
The deployment of the Ambient Intelligence (AmI) paradigm requires designing and integrating user-centered smart environments to assist people in their daily life activities. This research paper details an integration and validation of multiple heterogeneous sensors with hybrid reasoners that support decision making [...] Read more.
The deployment of the Ambient Intelligence (AmI) paradigm requires designing and integrating user-centered smart environments to assist people in their daily life activities. This research paper details an integration and validation of multiple heterogeneous sensors with hybrid reasoners that support decision making in order to monitor personal and environmental data at a smart home in a private way. The results innovate on knowledge-based platforms, distributed sensors, connected objects, accessibility and authentication methods to promote independent living for elderly people. TALISMAN+, the AmI framework deployed, integrates four subsystems in the smart home: (i) a mobile biomedical telemonitoring platform to provide elderly patients with continuous disease management; (ii) an integration middleware that allows context capture from heterogeneous sensors to program environment´s reaction; (iii) a vision system for intelligent monitoring of daily activities in the home; and (iv) an ontologies-based integrated reasoning platform to trigger local actions and manage private information in the smart home. The framework was integrated in two real running environments, the UPM Accessible Digital Home and MetalTIC house, and successfully validated by five experts in home care, elderly people and personal autonomy. Full article
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Open AccessArticle A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors
Sensors 2014, 14(9), 16181-16195; doi:10.3390/s140916181
Received: 6 April 2014 / Revised: 22 August 2014 / Accepted: 26 August 2014 / Published: 2 September 2014
Cited by 4 | PDF Full-text (2953 KB) | HTML Full-text | XML Full-text
Abstract
Activity recognition for the purposes of recognizing a user’s intentions using multimodal sensors is becoming a widely researched topic largely based on the prevalence of the smartphone. Previous studies have reported the difficulty in recognizing life-logs by only using a smartphone due [...] Read more.
Activity recognition for the purposes of recognizing a user’s intentions using multimodal sensors is becoming a widely researched topic largely based on the prevalence of the smartphone. Previous studies have reported the difficulty in recognizing life-logs by only using a smartphone due to the challenges with activity modeling and real-time recognition. In addition, recognizing life-logs is difficult due to the absence of an established framework which enables the use of different sources of sensor data. In this paper, we propose a smartphone-based Hierarchical Activity Recognition Framework which extends the Naïve Bayes approach for the processing of activity modeling and real-time activity recognition. The proposed algorithm demonstrates higher accuracy than the Naïve Bayes approach and also enables the recognition of a user’s activities within a mobile environment. The proposed algorithm has the ability to classify fifteen activities with an average classification accuracy of 92.96%. Full article
Open AccessArticle Evaluation of Prompted Annotation of Activity Data Recorded from a Smart Phone
Sensors 2014, 14(9), 15861-15879; doi:10.3390/s140915861
Received: 15 April 2014 / Revised: 31 July 2014 / Accepted: 5 August 2014 / Published: 27 August 2014
Cited by 2 | PDF Full-text (1369 KB) | HTML Full-text | XML Full-text
Abstract
In this paper we discuss the design and evaluation of a mobile based tool to collect activity data on a large scale. The current approach, based on an existing activity recognition module, recognizes class transitions from a set of specific activities (for [...] Read more.
In this paper we discuss the design and evaluation of a mobile based tool to collect activity data on a large scale. The current approach, based on an existing activity recognition module, recognizes class transitions from a set of specific activities (for example walking and running) to the standing still activity. Once this transition is detected the system prompts the user to provide a label for their previous activity. This label, along with the raw sensor data, is then stored locally prior to being uploaded to cloud storage. The system was evaluated by ten users. Three evaluation protocols were used, including a structured, semi-structured and free living protocol. Results indicate that the mobile application could be used to allow the user to provide accurate ground truth labels for their activity data. Similarities of up to 100% where observed when comparing the user prompted labels and those from an observer during structured lab based experiments. Further work will examine data segmentation and personalization issues in order to refine the system. Full article
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Open AccessArticle Modeling IoT-Based Solutions Using Human-Centric Wireless Sensor Networks
Sensors 2014, 14(9), 15687-15713; doi:10.3390/s140915687
Received: 25 April 2014 / Revised: 6 August 2014 / Accepted: 15 August 2014 / Published: 25 August 2014
Cited by 2 | PDF Full-text (2211 KB) | HTML Full-text | XML Full-text
Abstract
The Internet of Things (IoT) has inspired solutions that are already available for addressing problems in various application scenarios, such as healthcare, security, emergency support and tourism. However, there is no clear approach to modeling these systems and envisioning their capabilities at [...] Read more.
The Internet of Things (IoT) has inspired solutions that are already available for addressing problems in various application scenarios, such as healthcare, security, emergency support and tourism. However, there is no clear approach to modeling these systems and envisioning their capabilities at the design time. Therefore, the process of designing these systems is ad hoc and its real impact is evaluated once the solution is already implemented, which is risky and expensive. This paper proposes a modeling approach that uses human-centric wireless sensor networks to specify and evaluate models of IoT-based systems at the time of design, avoiding the need to spend time and effort on early implementations of immature designs. It allows designers to focus on the system design, leaving the implementation decisions for a next phase. The article illustrates the usefulness of this proposal through a running example, showing the design of an IoT-based solution to support the first responses during medium-sized or large urban incidents. The case study used in the proposal evaluation is based on a real train crash. The proposed modeling approach can be used to design IoT-based systems for other application scenarios, e.g., to support security operatives or monitor chronic patients in their homes. Full article
Open AccessArticle Active In-Database Processing to Support Ambient Assisted Living Systems
Sensors 2014, 14(8), 14765-14785; doi:10.3390/s140814765
Received: 26 March 2014 / Revised: 28 July 2014 / Accepted: 1 August 2014 / Published: 12 August 2014
Cited by 2 | PDF Full-text (533 KB) | HTML Full-text | XML Full-text
Abstract
As an alternative to the existing software architectures that underpin the development of smart homes and ambient assisted living (AAL) systems, this work presents a database-centric architecture that takes advantage of active databases and in-database processing. Current platforms supporting AAL systems use [...] Read more.
As an alternative to the existing software architectures that underpin the development of smart homes and ambient assisted living (AAL) systems, this work presents a database-centric architecture that takes advantage of active databases and in-database processing. Current platforms supporting AAL systems use database management systems (DBMSs) exclusively for data storage. Active databases employ database triggers to detect and react to events taking place inside or outside of the database. DBMSs can be extended with stored procedures and functions that enable in-database processing. This means that the data processing is integrated and performed within the DBMS. The feasibility and flexibility of the proposed approach were demonstrated with the implementation of three distinct AAL services. The active database was used to detect bed-exits and to discover common room transitions and deviations during the night. In-database machine learning methods were used to model early night behaviors. Consequently, active in-database processing avoids transferring sensitive data outside the database, and this improves performance, security and privacy. Furthermore, centralizing the computation into the DBMS facilitates code reuse, adaptation and maintenance. These are important system properties that take into account the evolving heterogeneity of users, their needs and the devices that are characteristic of smart homes and AAL systems. Therefore, DBMSs can provide capabilities to address requirements for scalability, security, privacy, dependability and personalization in applications of smart environments in healthcare. Full article
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Open AccessArticle MobiPag: Integrated Mobile Payment, Ticketing and Couponing Solution Based on NFC
Sensors 2014, 14(8), 13389-13415; doi:10.3390/s140813389
Received: 15 March 2014 / Revised: 13 July 2014 / Accepted: 16 July 2014 / Published: 24 July 2014
Cited by 5 | PDF Full-text (1124 KB) | HTML Full-text | XML Full-text
Abstract
Mobile payments still remain essentially an emerging technology, seeking to fill the gap between the envisioned potential and widespread usage. In this paper, we present an integrated mobile service solution based on the near field communication (NFC) protocol that was developed under [...] Read more.
Mobile payments still remain essentially an emerging technology, seeking to fill the gap between the envisioned potential and widespread usage. In this paper, we present an integrated mobile service solution based on the near field communication (NFC) protocol that was developed under a research project called MobiPag. The most distinctive characteristic of Mobipag is its open architectural model that allows multiple partners to become part of the payment value-chain and create solutions that complement payments in many unexpected ways. We describe the Mobipag architecture and how it has been used to support a mobile payment trial. We identify a set of design lessons resulting from usage experiences associated with real-world payment situations with NFC-enabled mobile phones. Based on results from this trial, we identify a number of challenges and guidelines that may help to shape future versions of NFC-based payment systems. In particular, we highlight key challenges for the initial phases of payment deployments, where it is essential to focus on scenarios that can be identified as more feasible for early adoption. We also have identified a fundamental trade-off between the flexibility supported by the Mobipag solution and the respective implications for the payment process, particularly on the users’ mental model. Full article
Open AccessArticle Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes
Sensors 2014, 14(7), 12285-12304; doi:10.3390/s140712285
Received: 14 April 2014 / Revised: 26 June 2014 / Accepted: 30 June 2014 / Published: 10 July 2014
Cited by 4 | PDF Full-text (831 KB) | HTML Full-text | XML Full-text
Abstract
Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically [...] Read more.
Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks. Full article
Open AccessArticle Magnetic Field Feature Extraction and Selection for Indoor Location Estimation
Sensors 2014, 14(6), 11001-11015; doi:10.3390/s140611001
Received: 15 March 2014 / Revised: 31 May 2014 / Accepted: 13 June 2014 / Published: 20 June 2014
Cited by 6 | PDF Full-text (832 KB) | HTML Full-text | XML Full-text
Abstract
User indoor positioning has been under constant improvement especially with the availability of new sensors integrated into the modern mobile devices, which allows us to exploit not only infrastructures made for everyday use, such as WiFi, but also natural infrastructure, as is [...] Read more.
User indoor positioning has been under constant improvement especially with the availability of new sensors integrated into the modern mobile devices, which allows us to exploit not only infrastructures made for everyday use, such as WiFi, but also natural infrastructure, as is the case of natural magnetic field. In this paper we present an extension and improvement of our current indoor localization model based on the feature extraction of 46 magnetic field signal features. The extension adds a feature selection phase to our methodology, which is performed through Genetic Algorithm (GA) with the aim of optimizing the fitness of our current model. In addition, we present an evaluation of the final model in two different scenarios: home and office building. The results indicate that performing a feature selection process allows us to reduce the number of signal features of the model from 46 to 5 regardless the scenario and room location distribution. Further, we verified that reducing the number of features increases the probability of our estimator correctly detecting the user’s location (sensitivity) and its capacity to detect false positives (specificity) in both scenarios. Full article
Open AccessArticle Dealing with the Effects of Sensor Displacement in Wearable Activity Recognition
Sensors 2014, 14(6), 9995-10023; doi:10.3390/s140609995
Received: 21 March 2014 / Revised: 12 May 2014 / Accepted: 27 May 2014 / Published: 6 June 2014
Cited by 16 | PDF Full-text (6856 KB) | HTML Full-text | XML Full-text
Abstract
Most wearable activity recognition systems assume a predefined sensor deployment that remains unchanged during runtime. However, this assumption does not reflect real-life conditions. During the normal use of such systems, users may place the sensors in a position different from the predefined [...] Read more.
Most wearable activity recognition systems assume a predefined sensor deployment that remains unchanged during runtime. However, this assumption does not reflect real-life conditions. During the normal use of such systems, users may place the sensors in a position different from the predefined sensor placement. Also, sensors may move from their original location to a different one, due to a loose attachment. Activity recognition systems trained on activity patterns characteristic of a given sensor deployment may likely fail due to sensor displacements. In this work, we innovatively explore the effects of sensor displacement induced by both the intentional misplacement of sensors and self-placement by the user. The effects of sensor displacement are analyzed for standard activity recognition techniques, as well as for an alternate robust sensor fusion method proposed in a previous work. While classical recognition models show little tolerance to sensor displacement, the proposed method is proven to have notable capabilities to assimilate the changes introduced in the sensor position due to self-placement and provides considerable improvements for large misplacements. Full article
Open AccessArticle How can We Tackle Energy Efficiency in IoT BasedSmart Buildings?
Sensors 2014, 14(6), 9582-9614; doi:10.3390/s140609582
Received: 14 March 2014 / Revised: 14 May 2014 / Accepted: 21 May 2014 / Published: 30 May 2014
Cited by 7 | PDF Full-text (5384 KB) | HTML Full-text | XML Full-text
Abstract
Nowadays, buildings are increasingly expected to meet higher and more complex performance requirements. Among these requirements, energy efficiency is recognized as an international goal to promote energy sustainability of the planet. Different approaches have been adopted to address this goal, the most [...] Read more.
Nowadays, buildings are increasingly expected to meet higher and more complex performance requirements. Among these requirements, energy efficiency is recognized as an international goal to promote energy sustainability of the planet. Different approaches have been adopted to address this goal, the most recent relating consumption patterns with human occupancy. In this work, we analyze what are the main parameters that should be considered to be included in any building energy management. The goal of this analysis is to help designers to select the most relevant parameters to control the energy consumption of buildings according to their context, selecting them as input data of the management system. Following this approach, we select three reference smart buildings with different contexts, and where our automation platform for energy monitoring is deployed. We carry out some experiments in these buildings to demonstrate the influence of the parameters identified as relevant in the energy consumption of the buildings. Then, in two of these buildings are applied different control strategies to save electrical energy. We describe the experiments performed and analyze the results. The first stages of this evaluation have already resulted in energy savings of about 23% in a real scenario. Full article
Open AccessArticle A Vision-Based System for Intelligent Monitoring: Human Behaviour Analysis and Privacy by Context
Sensors 2014, 14(5), 8895-8925; doi:10.3390/s140508895
Received: 11 March 2014 / Revised: 23 April 2014 / Accepted: 6 May 2014 / Published: 20 May 2014
Cited by 12 | PDF Full-text (2836 KB) | HTML Full-text | XML Full-text
Abstract
Due to progress and demographic change, society is facing a crucial challenge related to increased life expectancy and a higher number of people in situations of dependency. As a consequence, there exists a significant demand for support systems for personal autonomy. This [...] Read more.
Due to progress and demographic change, society is facing a crucial challenge related to increased life expectancy and a higher number of people in situations of dependency. As a consequence, there exists a significant demand for support systems for personal autonomy. This article outlines the vision@home project, whose goal is to extend independent living at home for elderly and impaired people, providing care and safety services by means of vision-based monitoring. Different kinds of ambient-assisted living services are supported, from the detection of home accidents, to telecare services. In this contribution, the specification of the system is presented, and novel contributions are made regarding human behaviour analysis and privacy protection. By means of a multi-view setup of cameras, people’s behaviour is recognised based on human action recognition. For this purpose, a weighted feature fusion scheme is proposed to learn from multiple views. In order to protect the right to privacy of the inhabitants when a remote connection occurs, a privacy-by-context method is proposed. The experimental results of the behaviour recognition method show an outstanding performance, as well as support for multi-view scenarios and real-time execution, which are required in order to provide the proposed services. Full article
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Open AccessArticle Ubiquitous Connected Train Based on Train-to-Ground and Intra-Wagon Communications Capable of Providing on Trip Customized Digital Services for Passengers
Sensors 2014, 14(5), 8003-8025; doi:10.3390/s140508003
Received: 17 March 2014 / Revised: 21 April 2014 / Accepted: 24 April 2014 / Published: 5 May 2014
Cited by 5 | PDF Full-text (1372 KB) | HTML Full-text | XML Full-text
Abstract
During the last years, the application of different wireless technologies has been explored in order to enable Internet connectivity from vehicles. In addition, the widespread adoption of smartphones by citizens represents a great opportunity to integrate such nomadic devices inside vehicles in [...] Read more.
During the last years, the application of different wireless technologies has been explored in order to enable Internet connectivity from vehicles. In addition, the widespread adoption of smartphones by citizens represents a great opportunity to integrate such nomadic devices inside vehicles in order to provide new and personalized on trip services for passengers. In this paper, a proposal of communication architecture to provide the ubiquitous connectivity needed to enhance the smart train concept is presented and preliminarily tested. It combines an intra-wagon communication system based on nomadic devices connected through a Bluetooth Piconet Network with a highly innovative train-to-ground communication system. In order to validate this communication solution, several tests and simulations have been performed and their results are described in this paper. Full article
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Open AccessArticle Using Massive Vehicle Positioning Data to Improve Control and Planning of Public Road Transport
Sensors 2014, 14(4), 7342-7358; doi:10.3390/s140407342
Received: 13 March 2014 / Revised: 15 April 2014 / Accepted: 17 April 2014 / Published: 23 April 2014
Cited by 2 | PDF Full-text (654 KB) | HTML Full-text | XML Full-text
Abstract
This study describes a system for the automatic recording of positioning data for public transport vehicles used on roads. With the data provided by this system, transportation-regulatory authorities can control, verify and improve the routes that vehicles use, while also providing new [...] Read more.
This study describes a system for the automatic recording of positioning data for public transport vehicles used on roads. With the data provided by this system, transportation-regulatory authorities can control, verify and improve the routes that vehicles use, while also providing new data to improve the representation of the transportation network and providing new services in the context of intelligent metropolitan areas. The system is executed autonomously in the vehicles, by recording their massive positioning data and transferring them to remote data banks for subsequent processing. To illustrate the utility of the system, we present a case of application that consists of identifying the points at which vehicles stop systematically, which may be points of scheduled stops or points at which traffic signals or road topology force the vehicle to stop. This identification is performed using pattern recognition techniques. The system has been applied under real operating conditions, providing the results discussed in the present study. Full article
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