E-Mail Alert

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

Journal Browser

Journal Browser

Special Issue "Sensor Data Fusion for IoT and Industrial Applications"

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

Deadline for manuscript submissions: 31 May 2019

Special Issue Editor

Guest Editor
Dr. Natividad Duro Carralero

Department of Computer Sciences and Automatic Control, UNED, C/Juan del Rosal, 16, 28040 Madrid, Spain
Website | E-Mail
Interests: sensor data fusion, IoT environments, industry applications, machine learning, big data, deep learning

Special Issue Information

Dear Colleagues,

All devices and systems that we use nowadays have many different sensors. With these new technologies, we can collect all the information obtained by them and use all this data to enable more reliable and accurate decision-making without human intervention. Data fusion is an effective way for the optimum utilization of large volumes of data from multiple sources. This combination of multiple data sources usually yields more relevant, accurate, and useful information than is provided when using an individual data source. Computational intelligence plays a key role in the process of integrating and analyzing this information, since it is essential to the mathematical methods and techniques used for this.

There are some emerging areas that would greatly benefit from sensors data fusion such as Internet of Things (IoT), autonomous vehicles, deep learning for data fusion, smart cities, and many other industrial applications.

Internet of Things (IoT) is the new revolution of the last decade, being one of the most relevant trends in the software industry. The number of objects connected to IoT is growing progressively. The use of IoT implies a fusion between the digital and the physical world, so millions of things or devices of all types and sizes are interconnected between them. This combination of data can be also used in many interesting industrial applications.

This Special Issue encourages authors from academia and industry to submit new research results from the use of multiple sensor data fusion to generate IoT environments or other industrial applications. The Special Issue topics include, but are not limited to the following:

  • IoT environments using sensor data fusion
  • Industrial applications using sensor data fusion
  • Mathematical algorithms for sensor data fusion
  • Principles and techniques for sensor data fusion
  • Data preparation techniques for analysis in sensor data fusion

Dr. Natividad Duro Carralero
Guest Editor

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.

Published Papers (2 papers)

View options order results:
result details:
Displaying articles 1-2
Export citation of selected articles as:

Research

Open AccessArticle ANN-Based Soft Sensor to Predict Effluent Violations in Wastewater Treatment Plants
Sensors 2019, 19(6), 1280; https://doi.org/10.3390/s19061280
Received: 24 January 2019 / Revised: 1 March 2019 / Accepted: 6 March 2019 / Published: 13 March 2019
PDF Full-text (1447 KB) | HTML Full-text | XML Full-text
Abstract
Wastewater treatment plants (WWTPs) form an industry whose main goal is to reduce water’s pollutant products, which are harmful to the environment at high concentrations. In addition, regulations are applied by administrations to limit pollutant concentrations in effluent. In this context, control strategies [...] Read more.
Wastewater treatment plants (WWTPs) form an industry whose main goal is to reduce water’s pollutant products, which are harmful to the environment at high concentrations. In addition, regulations are applied by administrations to limit pollutant concentrations in effluent. In this context, control strategies have been adopted by WWTPs to avoid violating these limits; however, some violations still occur. For that reason, this work proposes the deployment of an artificial neural network (ANN)-based soft sensor in which a Long-Short Term Memory (LSTM) network is used to generate predictions of nitrogen-derived components, specifically ammonium ( S N H ) and total nitrogen ( S N t o t ). S N t o t is a limiting nutrient and can therefore cause eutrophication, while nitrogen in the S N H form is toxic to aquatic life. These parameters are used by control strategies to allow actions to be taken in advance and only when violations are predicted. Since predictions complement control strategies, the evaluation of the ANN-based soft sensor was carried out using the Benchmark Simulation Model N.2. (BSM2) and three different control strategies (from low to high control complexity). Results show that our proposed method is able to predict nitrogen-derived products with good accuracy: the probability of detecting violations of BSM2’s limits is 86–94%. Moreover, the prediction accuracy can be improved by calibrating the soft sensor; for example, perfect prediction of all future violations can be achieved at the expense of increasing the false positive rate. Full article
(This article belongs to the Special Issue Sensor Data Fusion for IoT and Industrial Applications)
Figures

Figure 1

Open AccessArticle A Soft–Hard Combination Decision Fusion Scheme for a Clustered Distributed Detection System with Multiple Sensors
Sensors 2018, 18(12), 4370; https://doi.org/10.3390/s18124370
Received: 6 November 2018 / Revised: 6 December 2018 / Accepted: 7 December 2018 / Published: 10 December 2018
PDF Full-text (3773 KB) | HTML Full-text | XML Full-text
Abstract
In the distributed detection system with multiple sensors, there are two ways for local sensors to deliver their local decisions to the fusion center (FC): a one-bit hard decision and a multiple-bit soft decision. Compared with the soft decision, the hard decision has [...] Read more.
In the distributed detection system with multiple sensors, there are two ways for local sensors to deliver their local decisions to the fusion center (FC): a one-bit hard decision and a multiple-bit soft decision. Compared with the soft decision, the hard decision has worse detection performance due to the loss of sensing information but has the main advantage of smaller communication costs. To get a tradeoff between communication costs and detection performance, we propose a soft–hard combination decision fusion scheme for the clustered distributed detection system with multiple sensors and non-ideal communication channels. A clustered distributed detection system is configured by a fuzzy logic system and a fuzzy c-means clustering algorithm. In clusters, each local sensor transmits its local multiple-bit soft decision to its corresponding cluster head (CH) under the non-ideal channel, in which a simple and efficient soft decision fusion method is used. Between clusters, the fusion center combines all cluster heads’ one-bit hard decisions into a final global decision by using an optimal fusion rule. We show that the clustered distributed system with the proposed scheme has a good performance that is close to that of the centralized system, but it consumes much less energy than the centralized system at the same time. In addition, the system with the proposed scheme significantly outperforms the conventional distributed detection system that only uses a hard decision fusion. Using simulation results, we also show that the detection performance increases when more bits are delivered in the soft decision in the distributed detection system. Full article
(This article belongs to the Special Issue Sensor Data Fusion for IoT and Industrial Applications)
Figures

Figure 1

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