Special Issue "Data Science for Internet of Things"

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 30 April 2019

Special Issue Editors

Guest Editor
Prof. Dr. Antonio Pescapè

Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università di Napoli Federico II, Italy
Website | E-Mail
Interests: Networking, Internet Monitoring, Measurements and Management; Network Security
Guest Editor
Dr. Valerio Persico

Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università di Napoli Federico II, Italy
Website | E-Mail
Phone: +39 081 768-3821
Interests: networking, cloud-network monitoring, IP measurements, IP topology discovery, traffic classification

Special Issue Information

Dear Colleagues,

The availability of pervasive sensing and actuating devices, as well as of new wireless standards, at lower and lower prices is fueling the rise of the Internet of Things (IoT) paradigm, with a huge impact on several aspects of everyday life. Indeed, IoT has the potential to revolutionize the behaviors of both private and business users, dramatically impacting several application scenarios in both domestic and working fields, such as domotics, enhanced learning, e-health, and smart cities, as well as automation, industrial manufacturing, agriculture, logistics, and intelligent transportation systems.

Due to the increasing set of application scenarios leading to the pervasive deployment of sensing devices, IoT is among the most important sources of big data, as they generate unprecedented issues related to the acquisition, aggregation, storage, transmission, integration, and analyses needed for understanding and extracting value from such data.

Therefore, IoT data management and analysis is providing novel research challenges—either dictated by the specific application domains or crossing several of them—at sensing, network, and application layers, for both industry and academia. Often, novel architectural solutions involving advanced cloud- or fog-based architectures are imperative to cope with both the limited IoT processing capabilities and the huge data volumes.

To address the aforementioned challenges, this Special Issue is dedicated to the wide field of data science for IoT. We encourage original and high-quality contributions, addressing both theoretical and systems research.

Topics of interest include, but are not limited to:

  • IoT data mining and analytics
  • Heterogeneous IoT data integration
  • IoT data and platforms Interoperability
  • IoT scalability of storage and analysis based on AI
  • edge/fog/cloud-based architectures for data science
  • IoT communication platforms' bottlenecks
  • IoT data privacy and security issues
  • IoT-based e-health/mobile-health
  • IoT for Smart home and smart cities
  • Data management solutions for manufacturing, logistics, and Industry 4.0
  • Mobile Applications for Crowd-sensing
  • Semantic technologies for IoT
  • Web platforms for IoT
  • Machine learning techniques for big data
  • Interfaces to exploit big data
  • IoT services built on big data

Prof. Dr. Antonio Pescapé
Dr. Valerio Persico
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. Future Internet 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 1000 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

  • Internet of Things (IoT)
  • Data science
  • Big Data
  • Data management and analysis
  • Data Integration
  • Cloud Computing
  • E-health
  • Industry 4.0

Published Papers (1 paper)

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Research

Open AccessArticle A Personalized Recommendation Algorithm Based on the User’s Implicit Feedback in E-Commerce
Future Internet 2018, 10(12), 117; https://doi.org/10.3390/fi10120117
Received: 28 October 2018 / Revised: 23 November 2018 / Accepted: 27 November 2018 / Published: 29 November 2018
PDF Full-text (2700 KB) | HTML Full-text | XML Full-text
Abstract
A recommendation system can recommend items of interest to users. However, due to the scarcity of user rating data and the similarity of single ratings, the accuracy of traditional collaborative filtering algorithms (CF) is limited. Compared with user rating data, the user’s behavior [...] Read more.
A recommendation system can recommend items of interest to users. However, due to the scarcity of user rating data and the similarity of single ratings, the accuracy of traditional collaborative filtering algorithms (CF) is limited. Compared with user rating data, the user’s behavior log is easier to obtain and contains a large amount of implicit feedback information, such as the purchase behavior, comparison behavior, and sequences of items (item-sequences). In this paper, we proposed a personalized recommendation algorithm based on a user’s implicit feedback (BUIF). BUIF considers not only the user’s purchase behavior but also the user’s comparison behavior and item-sequences. We extracted the purchase behavior, comparison behavior, and item-sequences from the user’s behavior log; calculated the user’s similarity by purchase behavior and comparison behavior; and extended word-embedding to item-embedding to obtain the item’s similarity. Based on the above method, we built a secondary reordering model to generate the recommendation results for users. The results of the experiment on the JData dataset show that our algorithm shows better improvement in regard to recommendation accuracy over other CF algorithms. Full article
(This article belongs to the Special Issue Data Science for Internet of Things)
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