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Special Issue "Pervasive Intelligence and Computing"

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

Deadline for manuscript submissions: closed (15 November 2018)

Special Issue Editors

Guest Editor
Prof. Dr. Jianhua Ma

Faculty of Computer & Information Sciences, Hosei University 3-7-2, Kajino-cho, Koganei-shi Tokyo 184-8584, Japan
Website | E-Mail
Phone: +81-42-387-4540
Fax: +81-42-387-6028
Interests: multimedia; networking, ubiquitous/pervasive computing; social computing; wearable technology; IoT; cyber life and cyber intelligence
Guest Editor
Prof. Laurence T. Yang

Department of Computer Science, St. Francis Xavier University, Antigonish, NS B2G 2W5, Canada
Website | E-Mail
Interests: parallel and distributed computing; embedded and ubiquitous/pervasive computing; big data; cyber–physical–social systems
Guest Editor
Dr. Flavia Delicato

Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
Website | E-Mail
Phone: +55-21- 994663303
Fax: +55-21- 39383938
Interests: distributed systems; ubiquitous/pervasive computing; Internet of Things; wireless sensor networks
Guest Editor
Prof. Dr. Giancarlo Fortino

Computer Engineering, DIMES-Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, Rende 87036, Italy
Website | E-Mail
Phone: +39.0984.494063
Fax: +39.0984.494713
Interests: Internet of Things; body area networks; agent-based computing
Guest Editor
Prof. Pietro Manzoni

Department of Computer Engineering, Universitat Politècnica de València, Camino de Vera, s/n 46022, Valencia, Spain
Website | E-Mail
Phone: +34-96.387.7007 Ext. 75726
Fax: +34-96.387.7579
Interests: Smart Mobile Systems; Intelligent Transport Systems (ITS); Opportunistic Networking; Smart Cities; Internet of Things

Special Issue Information

Dear Colleagues,

Over the last fifty years, computational intelligence has evolved from logic-based artificial intelligence, nature-inspired soft computing, and social-oriented agent technology to cyber-physical integrated ubiquitous intelligence towards Pervasive Intelligence (PI). This Special Issue aims to highlight the latest research results and advances focused on how to enable pervasive intelligence in everyday devices to learn and dynamically support our preferences and lifestyles at home, at work and on the move.

This Special Issue also cointans selected papers from the 2018 edition of the “International Conference on Pervasive Intelligence and Computing—PiCom 2018”. IEEE PICom 2018 will be held 12–15 August, 2018, in Athens, Greece, co-located with IEEE CyberSciTech 2018, IEEE DASC 2018 and DataCom 2018. This conference’s main objective is to bring together computer scientists and engineers, to discuss and exchange experimental and theoretical results, works-in-progress, novel designs, and test-environments or test-beds in the various areas of “Pervasive Intelligence and Computing”.

Potential topics include, but are not limited to:

  • Activity Recognition
  • Agent-based Computing
  • Big Data and Smart Data
  • Brain-inspired Computing
  • Cloud Computing
  • Cloud of Things and Cloud of Sensors
  • Context-Aware Computing
  • Crowd Souring and Intelligence
  • Cyber-Physical Computing
  • Deep Learning and Deep Computation
  • Device Virtualization
  • Edge and Fog Computing
  • Embedded HW, SW & Systems
  • HCI for Pervasive Computing
  • Intelligent Social Networking
  • Intelligent/Smart IoT
  • Middleware for Pervasive Computing
  • Mobile Data Mining
  • Mobile Data Modeling
  • Mobile Edge Computing (MEC)
  • Pervasive Devices and RFIDs
  • Pervasive Networks/Communications
  • Pervasive Technologies for ITS
  • Privacy, Security and Trust
  • Programming Abstractions for IoT
  • Semantic Analysis
  • Sensor Technology and Networks
  • Services for Pervasive Computing
  • Smart Cities and Smart Homes
  • Social Intelligence and Computing
  • The Internet of Things
  • Ubiquitous Data Mining
  • Ubiquitous Intelligence
  • Wearable Devices and Applications

Prof. Dr. Jianhua Ma
Prof. Dr. Laurence T. Yang
Dr. Flavia Delicato
Prof. Dr. Giancarlo Fortino
Prof. Dr. Pietro Manzoni
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 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.

Published Papers (2 papers)

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Research

Open AccessArticle Indoor Localization Based on Weighted Surfacing from Crowdsourced Samples
Sensors 2018, 18(9), 2990; https://doi.org/10.3390/s18092990
Received: 9 August 2018 / Revised: 28 August 2018 / Accepted: 3 September 2018 / Published: 7 September 2018
PDF Full-text (7188 KB) | HTML Full-text | XML Full-text
Abstract
Fingerprinting-based indoor localization suffers from its time-consuming and labor-intensive site survey. As a promising solution, sample crowdsourcing has been recently promoted to exploit casually collected samples for building offline fingerprint database. However, crowdsourced samples may be annotated with erroneous locations, which raises a
[...] Read more.
Fingerprinting-based indoor localization suffers from its time-consuming and labor-intensive site survey. As a promising solution, sample crowdsourcing has been recently promoted to exploit casually collected samples for building offline fingerprint database. However, crowdsourced samples may be annotated with erroneous locations, which raises a serious question about whether they are reliable for database construction. In this paper, we propose a cross-domain cluster intersection algorithm to weight each sample reliability. We then select those samples with higher weight to construct radio propagation surfaces by fitting polynomial functions. Furthermore, we employ an entropy-like measure to weight constructed surfaces for quantifying their different subarea consistencies and location discriminations in online positioning. Field measurements and experiments show that the proposed scheme can achieve high localization accuracy by well dealing with the sample annotation error and nonuniform density challenges. Full article
(This article belongs to the Special Issue Pervasive Intelligence and Computing)
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Open AccessArticle Synthesizing and Reconstructing Missing Sensory Modalities in Behavioral Context Recognition
Sensors 2018, 18(9), 2967; https://doi.org/10.3390/s18092967
Received: 12 July 2018 / Revised: 16 August 2018 / Accepted: 3 September 2018 / Published: 6 September 2018
PDF Full-text (585 KB) | HTML Full-text | XML Full-text
Abstract
Detection of human activities along with the associated context is of key importance for various application areas, including assisted living and well-being. To predict a user’s context in the daily-life situation a system needs to learn from multimodal data that are often imbalanced,
[...] Read more.
Detection of human activities along with the associated context is of key importance for various application areas, including assisted living and well-being. To predict a user’s context in the daily-life situation a system needs to learn from multimodal data that are often imbalanced, and noisy with missing values. The model is likely to encounter missing sensors in real-life conditions as well (such as a user not wearing a smartwatch) and it fails to infer the context if any of the modalities used for training are missing. In this paper, we propose a method based on an adversarial autoencoder for handling missing sensory features and synthesizing realistic samples. We empirically demonstrate the capability of our method in comparison with classical approaches for filling in missing values on a large-scale activity recognition dataset collected in-the-wild. We develop a fully-connected classification network by extending an encoder and systematically evaluate its multi-label classification performance when several modalities are missing. Furthermore, we show class-conditional artificial data generation and its visual and quantitative analysis on context classification task; representing a strong generative power of adversarial autoencoders. Full article
(This article belongs to the Special Issue Pervasive Intelligence and Computing)
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