E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Data Science and Internet of Everything (IoE)"

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (15 March 2019)

Special Issue Editors

Guest Editor
Dr. Abir Jaafar Hussain

Liverpool John Moores University, Liverpool L3 5UA, UK
Website | E-Mail
Interests: Machine learning, Data Science, eHealth, Image Processing, Data Analysis, Signal Forcasting
Guest Editor
Prof. Dhiya Al-Jumeily

Liverpool John Moores University, Liverpool L3 5UA, UK
Website | E-Mail
Interests: AI-based clinical decision-making, medical knowledge engineering, human–machine interaction, wearable and intelligent devices and instruments, eSystem engineering
Guest Editor
Prof. Hissam Tawfik

Leeds Beckett University, Leeds LS1 3HE, UK
Website | E-Mail
Interests: eHealth, IoT, Time-series prediction, predictive analytics
Guest Editor
Prof. Panos Liatsis

Khalifa University, Abu Dhabi - United Arab Emirates
Website | E-Mail
Interests: Image processing, pattern recognition, machine learning, sensor systems

Special Issue Information

Dear Colleagues,

The 11th International Conference on Developments in eSystems Engineering will continue the success of the previous DeSE conferences. The main theme of DeSE2018 is Sensors, Data Science and Internet of Everything (IoE).

DeSE2018 will be held 2–5 September, 2018, in Cambridge, England, UK. It will provide a leading forum for disseminating the latest results in eSystem Development, Data Science and Big Data Research, IoT Development, and Applications, Big Data, Smart City, Smart Health, Smart Living and Smart Home, Health networking, Learning Analytics, Business Intelligence, Cloud Computing. Authors of the selected papers from DeSE 2018 are invited to submit the extended versions of their original papers and contributions regarding the following topics that related to the application in Sensors:

  • Internet of Everything and its applications
  • Advanced in Applications of AI
  • Biomedical intelligence and clinical data analysis
  • Bio-informatics, health informatics and bio-computing
  • Computational intelligence
  • Data mining, machine learning and expert systems
  • Image processing and medical imaging
  • Big data systems, big data algorithms, mining and management, toold and allpications
  • Deep learning methods and techniques

Dr. Abir Jaafar Hussain
Prof. Dhiya Al-Jumeily
Prof. Hissam Tawfik
Prof. Panos Liatsis
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 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 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.

Keywords

  • Sensors
  • Big data
  • Machine learning
  • Data science applications
  • Advanced AI

Published Papers (4 papers)

View options order results:
result details:
Displaying articles 1-4
Export citation of selected articles as:

Research

Open AccessArticle
A First Implementation of Underwater Communications in Raw Water Using the 433 MHz Frequency Combined with a Bowtie Antenna
Sensors 2019, 19(8), 1813; https://doi.org/10.3390/s19081813
Received: 14 March 2019 / Revised: 11 April 2019 / Accepted: 13 April 2019 / Published: 16 April 2019
PDF Full-text (1362 KB) | HTML Full-text | XML Full-text
Abstract
In 2016, there were 317 serious water pollution incidents in the UK, with 78,000 locations where businesses discharge controlled quantities of pollutants into rivers; therefore, continuous monitoring is vital. Since 1998, the environment agency has taken over 50 million water samples for water [...] Read more.
In 2016, there were 317 serious water pollution incidents in the UK, with 78,000 locations where businesses discharge controlled quantities of pollutants into rivers; therefore, continuous monitoring is vital. Since 1998, the environment agency has taken over 50 million water samples for water quality monitoring. The Internet of Things has grown phenomenally in recent years, reaching all aspects of our lives, many of these connected devices use wireless sensor networks to relay data to internet-connected nodes, where data can be processed, analyzed and consumed. However, Underwater wireless communications rely mainly on alternative communication methods such as optical and acoustic, with radio frequencies being an under-exploited method. This research presents real world results conducted in the Leeds and Liverpool Canal for the novel use of the 433 MHz radio frequency combined with a bowtie antenna in underwater communications in raw water, achieving distances of 7 m at 1.2 kbps and 5 m at 25 kbps. Full article
(This article belongs to the Special Issue Data Science and Internet of Everything (IoE))
Figures

Figure 1

Open AccessArticle
Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images
Sensors 2019, 19(6), 1265; https://doi.org/10.3390/s19061265
Received: 24 January 2019 / Revised: 24 February 2019 / Accepted: 8 March 2019 / Published: 13 March 2019
Cited by 1 | PDF Full-text (4534 KB) | HTML Full-text | XML Full-text
Abstract
Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typically, these images suffer from low contrast, complex background, variations in lesion shape and color, which affect the accuracy of their segmentation and subsequent classification. This research proposes an automated [...] Read more.
Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typically, these images suffer from low contrast, complex background, variations in lesion shape and color, which affect the accuracy of their segmentation and subsequent classification. This research proposes an automated system for detection and classification of ulcers in WCE images, based on state-of-the-art deep learning networks. Deep learning techniques, and in particular, convolutional neural networks (CNNs), have recently become popular in the analysis and recognition of medical images. The medical image datasets used in this study were obtained from WCE video frames. In this work, two milestone CNN architectures, namely the AlexNet and the GoogLeNet are extensively evaluated in object classification into ulcer or non-ulcer. Furthermore, we examine and analyze the images identified as containing ulcer objects to evaluate the efficiency of the utilized CNNs. Extensive experiments show that CNNs deliver superior performance, surpassing traditional machine learning methods by large margins, which supports their effectiveness as automated diagnosis tools. Full article
(This article belongs to the Special Issue Data Science and Internet of Everything (IoE))
Figures

Figure 1

Open AccessArticle
Design and Application of Toxic and Harmful Gas Monitoring System in Fire Fighting
Sensors 2019, 19(2), 369; https://doi.org/10.3390/s19020369
Received: 15 November 2018 / Revised: 5 January 2019 / Accepted: 15 January 2019 / Published: 17 January 2019
PDF Full-text (1713 KB) | HTML Full-text | XML Full-text
Abstract
In recent years, fire accidents in petrochemical plant areas and dangerous goods storage ports in China have shown a trend of frequent occurrence. Toxic and harmful gases are diffused in the scenes of these accidents, which causes great difficulties for fire fighting and [...] Read more.
In recent years, fire accidents in petrochemical plant areas and dangerous goods storage ports in China have shown a trend of frequent occurrence. Toxic and harmful gases are diffused in the scenes of these accidents, which causes great difficulties for fire fighting and rescue operations of fire fighting forces, and consequently, casualties of firefighters often occur. In order to ensure the safety of firefighters in such places, this paper designs a monitoring system of toxic and harmful gases specially used in fire fighting and rescue sites of fire forces, and establishes the transmission network, monitoring terminal and data processing software of the monitoring system of toxic and harmful gases, establishing the danger model of the monitoring area of toxic and harmful gas-monitoring terminal, and the danger model of fire fighters’ working area, fusing the field toxic and harmful gas data, terminal positioning data, and field environmental data, designing the data structure of the input data set and the network structure of the RNN cyclic neural network model, and realizing the dynamic early warning of toxic and harmful gases on site. Full article
(This article belongs to the Special Issue Data Science and Internet of Everything (IoE))
Figures

Figure 1

Open AccessArticle
Quadrant-Based Minimum Bounding Rectangle-Tree Indexing Method for Similarity Queries over Big Spatial Data in HBase
Sensors 2018, 18(9), 3032; https://doi.org/10.3390/s18093032
Received: 12 July 2018 / Revised: 31 August 2018 / Accepted: 8 September 2018 / Published: 10 September 2018
PDF Full-text (3074 KB) | HTML Full-text | XML Full-text
Abstract
With the rapid development of mobile devices and sensors, effective searching methods for big spatial data have recently received a significant amount of attention. Owing to their large size, many applications typically store recently generated spatial data in NoSQL databases such as HBase. [...] Read more.
With the rapid development of mobile devices and sensors, effective searching methods for big spatial data have recently received a significant amount of attention. Owing to their large size, many applications typically store recently generated spatial data in NoSQL databases such as HBase. As the index of HBase only supports a one-dimensional row keys, the spatial data is commonly enumerated using linearization techniques. However, the linearization techniques cannot completely guarantee the spatial proximity of data. Therefore, several studies have attempted to reduce false positives in spatial query processing by implementing a multi-dimensional indexing layer. In this paper, we propose a hierarchical indexing structure called a quadrant-based minimum bounding rectangle (QbMBR) tree for effective spatial query processing in HBase. In our method, spatial objects are grouped more precisely by using QbMBR and are indexed based on QbMBR. The QbMBR tree not only provides more selective query processing, but also reduces the storage space required for indexing. Based on the QbMBR tree index, two query-processing algorithms for range query and kNN query are also proposed in this paper. The algorithms significantly reduce query execution times by prefetching the necessary index nodes into memory while traversing the QbMBR tree. Experimental analysis demonstrates that our method significantly outperforms existing methods. Full article
(This article belongs to the Special Issue Data Science and Internet of Everything (IoE))
Figures

Figure 1

Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top