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Selected Papers from UCAmI 2018 –The 12th International Conference on Ubiquitous Computing and Ambient Intelligence

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (15 June 2019) | Viewed by 60284

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Dr. Macarena Espinilla-Estevéz

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Departament of Computer Science, University of Jaén, 23071 Jaén, Spain
Interests: smart environments; e-Health; intelligent systems; soft computing
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Dr. Vladimir Villarreal

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Guest Editor

Technological University of Panamá, Panamá, Republic of Panama
Interests: ambient assisted living; software architecture development; homecare; mobile patterns; medical ontologies design
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Dr. Ian McChesney

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Guest Editor

School of Computing, Ulster University, Jordanstown, Newtownabbey Co. Antrim, BT37 0QB, UK
Interests: process modelling; program comprehension; human activity recognition; open data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 12th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2018) will take place in Punta Cana (Dominican Republic), 4–7 December, 2018.

The UCAmI conference is an event that promotes the participation of specialists in different fields related to sensor technologies. In this Special Issue, the aim is to encourage leading, as well as new academic and industry practitioners, to engage in research relating to Ubiquitous Computing and Ambient Intelligence. Authors of the selected papers from the conference are invited to submit the extended versions of their original papers and contributions regarding the following topics:

Design and implementation of wearable sensors and embedded technologies in ambient assisted living contexts.
Sensors for environmental monitoring.
Location in ambient intelligence.
Security and privacy in ubiquitous computing and ambient intelligence.
Future internet and autonomous networking in ambient intelligence.
IoT, M2M and D2D communication in ambient intelligence.
Mobile Ad-Hoc and wireless sensor networks.
Use of sensors and actuator devices in ubiquitous environments and other technological solutions.
Implementation, deployment, and evaluation of sensor systems.
Tangible and natural user interfaces.
Internet of Things. Technological implementations using smart devices for sensing or actuation.
mHealth. Smart environments for health.
Applications of ubiquitous computing and ambient intelligence.
Edge/Fog Computing.
Cloud architecture and platform.

Dr. Macarena Espinilla
Dr. Vladimir Villarreal
Dr. Ian McChesney
Guest Editors

Manuscript Submission Information

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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 semimonthly 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 2600 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 Intelligence
Internet of things
Smart devices
Sensors and Actuators network
Smart Environments
Sensor-based Interactions
Ambient Assisted Living
Wearable Sensors
mHealth

Published Papers (12 papers)

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Editorial

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4 pages, 172 KiB

Open AccessEditorial

Ubiquitous Computing and Ambient Intelligence—UCAmI

by Macarena Espinilla, Vladimir Villarreal and Ian McChesney

Sensors 2019, 19(18), 4034; https://doi.org/10.3390/s19184034 - 19 Sep 2019

Cited by 5 | Viewed by 4867

Abstract

The Ubiquitous Computing (UC) idea envisioned by Weiser in 1991 [...] Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2018 –The 12th International Conference on Ubiquitous Computing and Ambient Intelligence)

Research

Jump to: Editorial

20 pages, 1223 KiB

Open AccessArticle

Activity Recognition for IoT Devices Using Fuzzy Spatio-Temporal Features as Environmental Sensor Fusion

by Miguel Ángel López Medina, Macarena Espinilla, Cristiano Paggeti and Javier Medina Quero

Sensors 2019, 19(16), 3512; https://doi.org/10.3390/s19163512 - 11 Aug 2019

Cited by 33 | Viewed by 5267

Abstract

The IoT describes a development field where new approaches and trends are in constant change. In this scenario, new devices and sensors are offering higher precision in everyday life in an increasingly less invasive way. In this work, we propose the use of spatial-temporal features by means of fuzzy logic as a general descriptor for heterogeneous sensors. This fuzzy sensor representation is highly efficient and enables devices with low computing power to develop learning and evaluation tasks in activity recognition using light and efficient classifiers. To show the methodology’s potential in real applications, we deploy an intelligent environment where new UWB location devices, inertial objects, wearable devices, and binary sensors are connected with each other and describe daily human activities. We then apply the proposed fuzzy logic-based methodology to obtain spatial-temporal features to fuse the data from the heterogeneous sensor devices. A case study developed in the UJAmISmart Lab of the University of Jaen (Jaen, Spain) shows the encouraging performance of the methodology when recognizing the activity of an inhabitant using efficient classifiers. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2018 –The 12th International Conference on Ubiquitous Computing and Ambient Intelligence)

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23 pages, 1239 KiB

Open AccessArticle

Smartphone-Based Platform for Affect Monitoring through Flexibly Managed Experience Sampling Methods

by Carlos Bailon, Miguel Damas, Hector Pomares, Daniel Sanabria, Pandelis Perakakis, Carmen Goicoechea and Oresti Banos

Sensors 2019, 19(15), 3430; https://doi.org/10.3390/s19153430 - 05 Aug 2019

Cited by 9 | Viewed by 4922

Abstract

The identification of daily life events that trigger significant changes on our affective state has become a fundamental task in emotional research. To achieve it, the affective states must be assessed in real-time, along with situational information that could contextualize the affective data acquired. However, the objective monitoring of the affective states and the context is still in an early stage. Mobile technologies can help to achieve this task providing immediate and objective data of the users’ context and facilitating the assessment of their affective states. Previous works have developed mobile apps for monitoring affective states and context, but they use a fixed methodology which does not allow for making changes based on the progress of the study. This work presents a multimodal platform which leverages the potential of the smartphone sensors and the Experience Sampling Methods (ESM) to provide a continuous monitoring of the affective states and the context in an ubiquitous way. The platform integrates several elements aimed to expedite the real-time management of the ESM questionnaires. In order to show the potential of the platform, and evaluate its usability and its suitability for real-time assessment of affective states, a pilot study has been conducted. The results demonstrate an excellent usability level and a good acceptance from the users and the specialists that conducted the study, and lead to some suggestions for improving the data quality of mobile context-aware ESM-based systems. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2018 –The 12th International Conference on Ubiquitous Computing and Ambient Intelligence)

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20 pages, 3339 KiB

Open AccessArticle

Edge Computing, IoT and Social Computing in Smart Energy Scenarios

by Inés Sittón-Candanedo, Ricardo S. Alonso, Óscar García, Lilia Muñoz and Sara Rodríguez-González

Sensors 2019, 19(15), 3353; https://doi.org/10.3390/s19153353 - 31 Jul 2019

Cited by 85 | Viewed by 8849

Abstract

The Internet of Things (IoT) has become one of the most widely research paradigms, having received much attention from the research community in the last few years. IoT is the paradigm that creates an internet-connected world, where all the everyday objects capture data from our environment and adapt it to our needs. However, the implementation of IoT is a challenging task and all the implementation scenarios require the use of different technologies and the emergence of new ones, such as Edge Computing (EC). EC allows for more secure and efficient data processing in real time, achieving better performance and results. Energy efficiency is one of the most interesting IoT scenarios. In this scenario sensors, actuators and smart devices interact to generate a large volume of data associated with energy consumption. This work proposes the use of an Edge-IoT platform and a Social Computing framework to build a system aimed to smart energy efficiency in a public building scenario. The system has been evaluated in a public building and the results make evident the notable benefits that come from applying Edge Computing to both energy efficiency scenarios and the framework itself. Those benefits included reduced data transfer from the IoT-Edge to the Cloud and reduced Cloud, computing and network resource costs. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2018 –The 12th International Conference on Ubiquitous Computing and Ambient Intelligence)

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23 pages, 1154 KiB

Open AccessArticle

DNS/DANE Collision-Based Distributed and Dynamic Authentication for Microservices in IoT ^†

by Daniel Díaz-Sánchez, Andrés Marín-Lopez, Florina Almenárez Mendoza and Patricia Arias Cabarcos

Sensors 2019, 19(15), 3292; https://doi.org/10.3390/s19153292 - 26 Jul 2019

Cited by 9 | Viewed by 3901

Abstract

IoT devices provide real-time data to a rich ecosystem of services and applications. The volume of data and the involved subscribe/notify signaling will likely become a challenge also for access and core networks. To alleviate the core of the network, other technologies like fog computing can be used. On the security side, designers of IoT low-cost devices and applications often reuse old versions of development frameworks and software components that contain vulnerabilities. Many server applications today are designed using microservice architectures where components are easier to update. Thus, IoT can benefit from deploying microservices in the fog as it offers the required flexibility for the main players of ubiquitous computing: nomadic users. In such deployments, IoT devices need the dynamic instantiation of microservices. IoT microservices require certificates so they can be accessed securely. Thus, every microservice instance may require a newly-created domain name and a certificate. The DNS-based Authentication of Named Entities (DANE) extension to Domain Name System Security Extensions (DNSSEC) allows linking a certificate to a given domain name. Thus, the combination of DNSSEC and DANE provides microservices’ clients with secure information regarding the domain name, IP address, and server certificate of a given microservice. However, IoT microservices may be short-lived since devices can move from one local fog to another, forcing DNSSEC servers to sign zones whenever new changes occur. Considering DNSSEC and DANE were designed to cope with static services, coping with IoT dynamic microservice instantiation can throttle the scalability in the fog. To overcome this limitation, this article proposes a solution that modifies the DNSSEC/DANE signature mechanism using chameleon signatures and defining a new soft delegation scheme. Chameleon signatures are signatures computed over a chameleon hash, which have a property: a secret trapdoor function can be used to compute collisions to the hash. Since the hash is maintained, the signature does not have to be computed again. In the soft delegation schema, DNS servers obtain a trapdoor that allows performing changes in a constrained zone without affecting normal DNS operation. In this way, a server can receive this soft delegation and modify the DNS zone to cope with frequent changes such as microservice dynamic instantiation. Changes in the soft delegated zone are much faster and do not require the intervention of the DNS primary servers of the zone. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2018 –The 12th International Conference on Ubiquitous Computing and Ambient Intelligence)

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20 pages, 411 KiB

Open AccessArticle

Systematic Literature Review of Food-Intake Monitoring in an Aging Population

by Enrique Moguel, Javier Berrocal and José García-Alonso

Sensors 2019, 19(15), 3265; https://doi.org/10.3390/s19153265 - 24 Jul 2019

Cited by 21 | Viewed by 4309

Abstract

The dietary habits of people directly impact their health conditions. Especially in elder populations (in 2017, 6.7% of the world’s population was over 65 years of age), these habits could lead to important-nutrient losses that could seriously affect their cognitive and functional state. Recently, a great research effort has been devoted to using different technologies and proposing different techniques for monitoring food-intake. Nevertheless, these techniques are usually generic but make use of the most innovative technologies and methodologies to obtain the best possible monitoring results. However, a large percentage of elderly people live in depopulated rural areas (in Spain, 28.1% of the elderly population lives in this type of area) with a fragile cultural and socioeconomic context. The use of these techniques in these environments is crucial to improving this group’s quality of life (and even reducing their healthcare expenses). At the same time, it is especially challenging since they have very specific and strict requirements regarding the use and application of technology. In this Systematic Literature Review (SLR), we analyze the most important proposed technologies and techniques in order to identify whether they can be applied in this context and if they can be used to improve the quality of life of this fragile collective. In this SLR, we have analyzed 326 papers. From those, 29 proposals have been completely analyzed, taking into account the characteristics and requirements of this population. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2018 –The 12th International Conference on Ubiquitous Computing and Ambient Intelligence)

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14 pages, 830 KiB

Open AccessArticle

Protocol for Streaming Data from an RFID Sensor Network ^†

by Gentza Souto, Florian Muralter, Laura Arjona, Hugo Landaluce and Asier Perallos

Sensors 2019, 19(14), 3148; https://doi.org/10.3390/s19143148 - 17 Jul 2019

Cited by 6 | Viewed by 3321

Abstract

Currently, there is an increasing interest in the use of Radio Frequency Identification (RFID) tags which incorporate passive or battery-less sensors. These systems are known as computational RFID (CRFID). Several CRFID tags together with a reader set up an RFID sensor network. The reader powers up the tags’ microcontroller and their attached sensor using radio frequency waves, and tags backscatter, not only their EPC code but also the value of those sensors. The current standard for interrogating these CRFID tags is the EPC global Class 1 Generation 2 (EPC C1G2). When several tags are located inside the reader interrogation area, the EPC C1G2 results in very poor performance to obtain sensor data values. To solve this problem, a novel protocol called Sensor Frmed Slotted Aloha (sFSA) for streaming sensor data dealing with the tag collisions is presented. The proposed protocol increases the Sensor Read Rate (SRR), defined as the number of sensor data reads per second, compared to the standard. Additionally, this paper presents a prototype of an RFID sensor network to compare the proposed sFSA with the standard, increasing the SRR by more than five times on average. Additionally, the proposed protocol keeps a constant sensor sampling frequency for a suitable streaming of these tag sensors. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2018 –The 12th International Conference on Ubiquitous Computing and Ambient Intelligence)

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32 pages, 8735 KiB

Open AccessArticle

by Gabriele Salvatore de Blasio, José Carlos Rodríguez-Rodríguez, Carmelo R. García and Alexis Quesada-Arencibia

Sensors 2019, 19(14), 3087; https://doi.org/10.3390/s19143087 - 12 Jul 2019

Cited by 15 | Viewed by 4660

Abstract

Indoor positioning systems (IPS) are used to locate people or objects in environments where the global positioning system (GPS) fails. The commitment to make bluetooth low energy (BLE) technology the leader in IPS and their applications is clear: Since 2009, the Bluetooth Special Interest Group (SIG) has released several improved versions. BLE offers many advantages for IPS, e.g., their emitters or beacons are easily deployable, have low power consumption, give a high positioning accuracy and can provide advanced services to users. Fingerprinting is a popular indoor positioning algorithm that is based on the received signal strength (RSS); however, its main drawbacks are that data collection is a time-consuming and labor-intensive process and its main challenge is that positioning accuracy is affected by various factors. The purpose of this work was to develop a semi-automatic data collection support system in a BLE fingerprinting-based IPS to: (1) Streamline and shorten the data collection process, (2) carry out impact studies by protocol and channel on the static positioning accuracy related to configuration parameters of the beacons, such as transmission power (Tx) and the advertising interval (A), and their number and geometric distribution. With two types of systems-on-chip (SoCs) integrated in Bluetooth 5 beacons and in two different environments, our results showed that on average in the three BLE advertising channels, the configuration of the highest Tx (+4 dBm) in the beacons produced the best accuracy results. However, the lowest Tx (−20 dBm) did not worsen them excessively (only 11.8%). In addition, in both scenarios, when lowering the density of beacons by around 42.7%–50%, the error increase was only around 8%–9.2%. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2018 –The 12th International Conference on Ubiquitous Computing and Ambient Intelligence)

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19 pages, 3498 KiB

Open AccessArticle

Semi-Automated Data Labeling for Activity Recognition in Pervasive Healthcare

by Dagoberto Cruz-Sandoval, Jessica Beltran-Marquez, Matias Garcia-Constantino, Luis A. Gonzalez-Jasso, Jesus Favela, Irvin Hussein Lopez-Nava, Ian Cleland, Andrew Ennis, Netzahualcoyotl Hernandez-Cruz, Joseph Rafferty, Jonathan Synnott and Chris Nugent

Sensors 2019, 19(14), 3035; https://doi.org/10.3390/s19143035 - 10 Jul 2019

Cited by 13 | Viewed by 5066

Abstract

Activity recognition, a key component in pervasive healthcare monitoring, relies on classification algorithms that require labeled data of individuals performing the activity of interest to train accurate models. Labeling data can be performed in a lab setting where an individual enacts the activity under controlled conditions. The ubiquity of mobile and wearable sensors allows the collection of large datasets from individuals performing activities in naturalistic conditions. Gathering accurate data labels for activity recognition is typically an expensive and time-consuming process. In this paper we present two novel approaches for semi-automated online data labeling performed by the individual executing the activity of interest. The approaches have been designed to address two of the limitations of self-annotation: (i) The burden on the user performing and annotating the activity, and (ii) the lack of accuracy due to the user labeling the data minutes or hours after the completion of an activity. The first approach is based on the recognition of subtle finger gestures performed in response to a data-labeling query. The second approach focuses on labeling activities that have an auditory manifestation and uses a classifier to have an initial estimation of the activity, and a conversational agent to ask the participant for clarification or for additional data. Both approaches are described, evaluated in controlled experiments to assess their feasibility and their advantages and limitations are discussed. Results show that while both studies have limitations, they achieve 80% to 90% precision. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2018 –The 12th International Conference on Ubiquitous Computing and Ambient Intelligence)

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28 pages, 6408 KiB

Open AccessArticle

Smart Management Consumption in Renewable Energy Fed Ecosystems

by Francisco Javier Ferrández-Pastor, Juan Manuel García-Chamizo, Sergio Gomez-Trillo, Rafael Valdivieso-Sarabia and Mario Nieto-Hidalgo

Sensors 2019, 19(13), 2967; https://doi.org/10.3390/s19132967 - 05 Jul 2019

Cited by 8 | Viewed by 4691

Abstract

Advances in embedded electronic systems, the development of new communication protocols, and the application of artificial intelligence paradigms have enabled the improvement of current automation systems of energy management. Embedded devices integrate different sensors with connectivity, computing resources, and reduced cost. Communication and cloud services increase their performance; however, there are limitations in the implementation of these technologies. If the cloud is used as the main source of services and resources, overload problems will occur. There are no models that facilitate the complete integration and interoperability in the facilities already created. This article proposes a model for the integration of smart energy management systems in new and already created facilities, using local embedded devices, Internet of Things communication protocols and services based on artificial intelligence paradigms. All services are distributed in the new smart grid network using edge and fog computing techniques. The model proposes an architecture both to be used as support for the development of smart services and for energy management control systems adapted to the installation: a group of buildings and/or houses that shares energy management and energy generation. Machine learning to predict consumption and energy generation, electric load classification, energy distribution control, and predictive maintenance are the main utilities integrated. As an experimental case, a facility that incorporates wind and solar generation is used for development and testing. Smart grid facilities, designed with artificial intelligence algorithms, implemented with Internet of Things protocols, and embedded control devices facilitate the development, cost reduction, and the integration of new services. In this work, a method to design, develop, and install smart services in self-consumption facilities is proposed. New smart services with reduced costs are installed and tested, confirming the advantages of the proposed model. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2018 –The 12th International Conference on Ubiquitous Computing and Ambient Intelligence)

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18 pages, 2811 KiB

Open AccessArticle

Bus Travel Time Prediction Model Based on Profile Similarity

by Teresa Cristóbal, Gabino Padrón, Alexis Quesada-Arencibia, Francisco Alayón, Gabriel de Blasio and Carmelo R. García

Sensors 2019, 19(13), 2869; https://doi.org/10.3390/s19132869 - 28 Jun 2019

Cited by 18 | Viewed by 3848

Abstract

In road-based mass transit systems, travel time is a key factor in providing quality of service. This article proposes a method of predicting travel time for this type of transport system. This method estimates travel time by taking into account its historical behaviour, represented by historical profiles, and the current behaviour recorded on the public transport vehicle for which the prediction is to be made. The model uses the k-medoids clustering algorithm to obtain historical travel time profiles. A relevant feature of the model is that it does not require recent travel time data from other vehicles. For this reason, the proposed model may be used in intercity transport contexts in which service planning is carried out according to timetables. The proposed model has been tested with two real cases of intercity public transport routes and from the results obtained we may conclude that, in general, the average error of the predictions is around 13% compared to the observed travel time values. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2018 –The 12th International Conference on Ubiquitous Computing and Ambient Intelligence)

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14 pages, 2563 KiB

Open AccessArticle

Assisting Visually Impaired People in the Public Transport System through RF-Communication and Embedded Systems ^†

by Yessica Sáez, José Muñoz, Francisco Canto, Antony García and Héctor Montes

Sensors 2019, 19(6), 1282; https://doi.org/10.3390/s19061282 - 14 Mar 2019

Cited by 22 | Viewed by 5628

Abstract

For a significant number of people with visual impairments, public transport plays an important role in productivity, community participation, and independence, since it may be the only feasible mobility option to participate in their education, work, medical care, food, and to attend many other places in their community. To use the public bus system safely, effectively, and autonomously, these people need to collect information about their physical environment and visible information at stops and terminals, such as timetables, routes, etc. Unfortunately, most people who are blind or visually impaired experience difficulties in getting on the right bus or getting off at the right destination. These situations usually force them to depend on other people that assist them in activities close to their homes, or settle for simpler jobs, or simply stay at home. Therefore, our efforts should aim to develop a system where technology is used to empower people with visual disabilities, allowing them to navigate autonomously in the public transport system. This paper presents a system based on radio frequency (RF) communication proposed within the framework of the MOVIDIS (Mobility for Visually Disabled People) research project (funded by the National Secretariat of Science, Technology and Innovation-SENACYT, under Grants No. 109-2015-4-FID14-073 and No. 99-2018-4-FID17-031), which provides an alternative to assist people with visual disabilities with their mobility in the public transport system. The various modules of this system communicate with each other by means of radio frequency and allow users to interact with buses and their respective stops. The first experimental results show that RF communication represents a viable option to help people with visual disabilities in public transport services. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2018 –The 12th International Conference on Ubiquitous Computing and Ambient Intelligence)

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