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Pervasive Intelligence and Computing
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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) | Viewed by 17609

Special Issue Editors

Prof. Dr. Jianhua Ma

E-Mail Website
Guest Editor
Faculty of Computer and Information Science, Hosei University, 2 Chome-17-1 Fujimi, Chiyoda, Tokyo 102-8160, Japan
Interests: ubiquitous/pervasive computing and smart environment; u-Things, u-Intelligence and u-Science; cyber space, science and sociology; service and social computing; mobile multimedia and wireless network; IoT/iThings and Wisdom Web of Things (W2T); location and context-aware application; autonomic, trusted and ubisafe computing; hyperspace/hyperworld and cyber-I (digital colone)
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Laurence T. Yang

E-Mail Website
Guest Editor
Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
Interests: parallel and distributed computing; embedded and ubiquitous/pervasive computing
Special Issues, Collections and Topics in MDPI journals
Dr. Flavia Delicato

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

grade E-Mail Website
Guest Editor
Computer Engineering, DIMES-Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, 87036 Rende, Italy
Interests: body area networks; Internet of Things; agent-based computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pietro Manzoni

E-Mail Website
Guest Editor
Department of Computer Engineering (DISCA), Universitat Politècnica de València, 46022 Valencia, Spain
Interests: mobile networks and systems; internet of things
Special Issues, Collections and Topics in MDPI journals

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 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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

22 pages, 4435 KiB  
Article
BL0K: A New Stage of Privacy-Preserving Scope for Location-Based Services
by Abdullah Albelaihy and Vijey Thayananthan
Sensors 2019, 19(3), 696; https://doi.org/10.3390/s19030696 - 8 Feb 2019
Cited by 4 | Viewed by 4273
Abstract
Location-based services present an inherent challenge of finding the delicate balance between efficiency when answering queries and maintaining user privacy. Inevitable security issues arise as the server needs to be informed of the query location to provide accurate responses. Despite the many advancements [...] Read more.
Location-based services present an inherent challenge of finding the delicate balance between efficiency when answering queries and maintaining user privacy. Inevitable security issues arise as the server needs to be informed of the query location to provide accurate responses. Despite the many advancements in localization security in wireless sensor networks, servers can still be infected with malicious software. It is now possible to ensure queries do not generate any fake responses that may appear real to users. When a fake response is used, there are mechanisms that can be employed so that the user can identify the authenticity of the query. For this reason, this paper proposes Bloom Filter 0 Knowledge (BL0K), which is novel phase privacy method that preserves the framework for location-based service (LBS) and combines a Bloom filter and the Zero knowledge protocol. The usefulness of these methods has been shown for securing private user information. Analysis of the results demonstrated that BL0K performance is decidedly better when compared to the referenced approaches using the privacy entropy metric. Full article
(This article belongs to the Special Issue Pervasive Intelligence and Computing)
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26 pages, 1150 KiB  
Article
Fog Architectures and Sensor Location Certification in Distributed Event-Based Systems
by Fátima Castro-Jul, Rebeca P. Díaz-Redondo, Ana Fernández-Vilas, Sophie Chabridon and Denis Conan
Sensors 2019, 19(1), 104; https://doi.org/10.3390/s19010104 - 29 Dec 2018
Cited by 3 | Viewed by 3820
Abstract
Since smart cities aim at becoming self-monitoring and self-response systems, their deployment relies on close resource monitoring through large-scale urban sensing. The subsequent gathering of massive amounts of data makes essential the development of event-filtering mechanisms that enable the selection of what is [...] Read more.
Since smart cities aim at becoming self-monitoring and self-response systems, their deployment relies on close resource monitoring through large-scale urban sensing. The subsequent gathering of massive amounts of data makes essential the development of event-filtering mechanisms that enable the selection of what is relevant and trustworthy. Due to the rise of mobile event producers, location information has become a valuable filtering criterion, as it not only offers extra information on the described event, but also enhances trust in the producer. Implementing mechanisms that validate the quality of location information becomes then imperative. The lack of such strategies in cloud architectures compels the adoption of new communication schemes for Internet of Things (IoT)-based urban services. To serve the demand for location verification in urban event-based systems (DEBS), we have designed three different fog architectures that combine proximity and cloud communication. We have used network simulations with realistic urban traces to prove that the three of them can correctly identify between 73% and 100% of false location claims. Full article
(This article belongs to the Special Issue Pervasive Intelligence and Computing)
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16 pages, 7188 KiB  
Article
Indoor Localization Based on Weighted Surfacing from Crowdsourced Samples
by Junhong Lin, Bang Wang, Guang Yang and Mu Zhou
Sensors 2018, 18(9), 2990; https://doi.org/10.3390/s18092990 - 7 Sep 2018
Cited by 13 | Viewed by 3762
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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20 pages, 585 KiB  
Article
Synthesizing and Reconstructing Missing Sensory Modalities in Behavioral Context Recognition
by Aaqib Saeed, Tanir Ozcelebi and Johan Lukkien
Sensors 2018, 18(9), 2967; https://doi.org/10.3390/s18092967 - 6 Sep 2018
Cited by 16 | Viewed by 4316
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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