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Ambient intelligence Simulations and Smart Sensors: Ubiquitous services and social simulations
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Ambient intelligence Simulations and Smart Sensors: Ubiquitous services and social simulations

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

Deadline for manuscript submissions: closed (1 September 2019) | Viewed by 8119

Special Issue Editors

Dr. Diego Martín

E-Mail Website
Guest Editor
ETSI de Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040 Madrid, Spain
Interests: Internet of Things; blockchain technologies; cyber physical systems; knowledge management; information retrieval
Special Issues, Collections and Topics in MDPI journals
Dr. Ramon Alcarria

E-Mail Website
Guest Editor
School of Geomatic Engineering, Technical University of Madrid, 28040 Madrid, Spain
Interests: service composition; prosumer; VGI; machine learning; Internet of Things; blockchain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Ambient intelligence (AmI) is applied to many scenarios, ranging from smart cities to emergency situations, including health care, social learning, and so on. In these scenarios, users consume AmI services with context awareness features, enabling interactions with sensors and embedded systems, context recognition, and communications.

Research in services for AmI over the past 15 years have focused on the need for smart sensors and actuators that interact with the environment and users. With the advances in the field, AmI is pursuing growingly ambitious goals in terms of the size and integration of its smart spaces, the number of users, and the level of adaptation to them. The proliferation of technologies for the communication of sensors is making the necessary advances in the field of simulations, showing not only the disposition and movement of the sensors, but also the interaction of the devices with users or agents following a predefined or dynamic behavior.

This Special Issue seeks scientific works that contribute to facilitating the development, interaction, composition, identification, monitoring, and communication with smart sensors, as well as reflecting the participation of the users or agents.

Authors are invited to submit papers in any of the following areas (not limited to):

Ubiquitous Services in Ambient Intelligence Scenarios:

  • Ubiquitous and pervasive services, networks, and applications
  • Prosumers or end-user service or data provision
  • Location-based services
  • Cyber-physical systems
  • Service reconfiguration and self-adaptation
  • Home automation
  • AmI scenarios’ trustworthiness using blockchain
  • Awareness scenarios for emergency situations

Ambient Intelligence and Social simulations:

  • Co-simulation
  • Event prediction in simulations
  • Agent-based social simulation
  • Agent-based simulation techniques and methodologies
  • Ambient intelligence (AmI) simulations
  • Network and smart sensors simulation

Dr. Diego Martín
Dr. Ramón Alcarria
Guest Editors

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 submissions that pass pre-check are 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 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
  • ubiquitous services
  • social simulations
  • smart sensors

Published Papers (2 papers)

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Research

13 pages, 19837 KiB  
Article
Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise Using Wearable Devices
by Kyle Ross, Pritam Sarkar, Dirk Rodenburg, Aaron Ruberto, Paul Hungler, Adam Szulewski, Daniel Howes and Ali Etemad
Sensors 2019, 19(19), 4270; https://doi.org/10.3390/s19194270 - 01 Oct 2019
Cited by 16 | Viewed by 4012
Abstract
Simulation-based training has been proven to be a highly effective pedagogical strategy. However, misalignment between the participant’s level of expertise and the difficulty of the simulation has been shown to have significant negative impact on learning outcomes. To ensure that learning outcomes are [...] Read more.
Simulation-based training has been proven to be a highly effective pedagogical strategy. However, misalignment between the participant’s level of expertise and the difficulty of the simulation has been shown to have significant negative impact on learning outcomes. To ensure that learning outcomes are achieved, we propose a novel framework for adaptive simulation with the goal of identifying the level of expertise of the learner, and dynamically modulating the simulation complexity to match the learner’s capability. To facilitate the development of this framework, we investigate the classification of expertise using biological signals monitored through wearable sensors. Trauma simulations were developed in which electrocardiogram (ECG) and galvanic skin response (GSR) signals of both novice and expert trauma responders were collected. These signals were then utilized to classify the responders’ expertise, successive to feature extraction and selection, using a number of machine learning methods. The results show the feasibility of utilizing these bio-signals for multimodal expertise classification to be used in adaptive simulation applications. Full article
(This article belongs to the Special Issue Ambient intelligence Simulations and Smart Sensors: Ubiquitous services and social simulations)
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22 pages, 535 KiB  
Article
A Bayesian Argumentation Framework for Distributed Fault Diagnosis in Telecommunication Networks
by Álvaro Carrera, Eduardo Alonso and Carlos A. Iglesias
Sensors 2019, 19(15), 3408; https://doi.org/10.3390/s19153408 - 03 Aug 2019
Cited by 5 | Viewed by 3605
Abstract
Traditionally, fault diagnosis in telecommunication network management is carried out by humans who use software support systems. The phenomenal growth in telecommunication networks has nonetheless triggered the interest in more autonomous approaches, capable of coping with emergent challenges such as the need to [...] Read more.
Traditionally, fault diagnosis in telecommunication network management is carried out by humans who use software support systems. The phenomenal growth in telecommunication networks has nonetheless triggered the interest in more autonomous approaches, capable of coping with emergent challenges such as the need to diagnose faults’ root causes under uncertainty in geographically-distributed environments, with restrictions on data privacy. In this paper, we present a framework for distributed fault diagnosis under uncertainty based on an argumentative framework for multi-agent systems. In our approach, agents collaborate to reach conclusions by arguing in unpredictable scenarios. The observations collected from the network are used to infer possible fault root causes using Bayesian networks as causal models for the diagnosis process. Hypotheses about those fault root causes are discussed by agents in an argumentative dialogue to achieve a reliable conclusion. During that dialogue, agents handle the uncertainty of the diagnosis process, taking care of keeping data privacy among them. The proposed approach is compared against existing alternatives using benchmark multi-domain datasets. Moreover, we include data collected from a previous fault diagnosis system running in a telecommunication network for one and a half years. Results show that the proposed approach is suitable for the motivational scenario. Full article
(This article belongs to the Special Issue Ambient intelligence Simulations and Smart Sensors: Ubiquitous services and social simulations)
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