Special Issue "Ambient Intelligence Environments"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 September 2018

Special Issue Editors

Guest Editor
Dr. Michael O'Grady

School of Computer Science, University College Dublin, Ireland
Website | E-Mail
Interests: Ambient Intelligence; Pervasive Computing; Ubiquitous Sensing
Guest Editor
Dr. Juan Ye

School of Computer Science, University of St Andrews, UK
Website | E-Mail
Interests: Privacy; Activity Recognition; Wearable computing
Guest Editor
Dr. Eleni Mangina

School of Computer Science, University College Dublin, Ireland
Website | E-Mail
Interests: Applied Artificial Intelligent; Robotics; Remotely Piloted Aircraft Systems; Virtual and Augmented Reality

Special Issue Information

Dear Colleagues,

Ambient intelligence has been the subject of significant effort by the research community over a number of years. Progress in the many domains necessary to realising the inherently interdisciplinary AmI vision has been extensively documented.  Nonetheless, AmI remains a niche research domain and its potential largely unfilled. This is despite the ever-increasing need for delivering transparent and intuitive interaction between people and their environments. Despite significant advances, bridging the gap between the promise of AmI and its pragmatic implementation remains a formidable challenge. The objective of this special issue to present the state-of-the-art in Ambient Intelligence, share experiences of long-term real-world AmI deployments, explore new exciting application areas, and introduce new research challenges and opportunities.

Original, high-quality contributions from both academic and industry are sought. Manuscripts submitted for review should not have been published or accepted for publication elsewhere; furthermore, submissions should not be under review by other journals or peer-reviewed conferences.

Topics of interest include, but are not limited to:

  • Novel architectures, platforms and technologies for AmI
  • Standardisation initiatives applicable to AmI
  • Internet of Things approaches for AmI
  • Cyber-Physical Systems
  • Privacy, security and data management within AmI
  • Sustainable and Green Computing models of AmI
  • Interaction design and novel user interfaces for AmI
  • Modelling computational and social intelligence within AmI

The editors would be particularly interested in receiving papers that address the following issues:

  • Ethical approaches to AmI
  • Open Science within the context of AmI
  • Methodologies for benchmarking AmI platforms and services
  • Unconventional applications of AmI

Dr. Michael O'Grady
Dr. Juan Ye
Dr. Eleni Mangina
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. Information 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 850 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

  • Intelligence systems
  • Context reasoning
  • Intelligent user interfaces
  • Smart environments
  • Activity recognition

Published Papers (1 paper)

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Research

Open AccessArticle Effective Intrusion Detection System Using XGBoost
Information 2018, 9(7), 149; https://doi.org/10.3390/info9070149
Received: 21 May 2018 / Revised: 15 June 2018 / Accepted: 19 June 2018 / Published: 21 June 2018
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Abstract
As the world is on the verge of venturing into fifth-generation communication technology and embracing concepts such as virtualization and cloudification, the most crucial aspect remains “security”, as more and more data get attached to the internet. This paper reflects a model designed
[...] Read more.
As the world is on the verge of venturing into fifth-generation communication technology and embracing concepts such as virtualization and cloudification, the most crucial aspect remains “security”, as more and more data get attached to the internet. This paper reflects a model designed to measure the various parameters of data in a network such as accuracy, precision, confusion matrix, and others. XGBoost is employed on the NSL-KDD (network socket layer-knowledge discovery in databases) dataset to get the desired results. The whole motive is to learn about the integrity of data and have a higher accuracy in the prediction of data. By doing so, the amount of mischievous data floating in a network can be minimized, making the network a secure place to share information. The more secure a network is, the fewer situations where data is hacked or modified. By changing various parameters of the model, future research can be done to get the most out of the data entering and leaving a network. The most important player in the network is data, and getting to know it more closely and precisely is half the work done. Studying data in a network and analyzing the pattern and volume of data leads to the emergence of a solid Intrusion Detection System (IDS), that keeps the network healthy and a safe place to share confidential information. Full article
(This article belongs to the Special Issue Ambient Intelligence Environments)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: A Machine Learning Solution for Privacy and Security in AmI Domains
Authors: Nayat Sanchez-Pi; Luis Martí and José Manuel Molina
Abstract: This research explores the capacity of Machine Learning techniques to detect anomalies related with intrusion in Ambient Intelligence (AmI) environment. The final goal is to incorporate this capacity to an Internet of Things (IoT) platform that allows to create an AmI environment using any hardware available on market. The intrusion detection system (IDS) is built using data from a specific AmI project. Using acquired data, anomalies detected in real time could be detected as intrusion.
Keywords: IDS; AmI; IoT; machine learning; predictive analisys; time series

 

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