Special Issue "Machine Learning in WSN and IoT"

A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708). This special issue belongs to the section "Big Data, Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 1393

Special Issue Editors

Dr. Diogo Gomes
E-Mail Website
Guest Editor
Department of Electronics Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal
Interests: AI; service platforms; M2M; computer networks
Special Issues, Collections and Topics in MDPI journals
Dr. Mario Antunes
E-Mail Website
Guest Editor
Instituto de Telecomunicações, Universidade de Aveiro, Aveiro, Portugal
Interests: machine learning; text mining; time series; deep learning
Dr. Luis Miguel Contreras-Murillo
E-Mail Website
Guest Editor
Telefónica I+D / CTIO unit, Madrid, Spain
Interests: SDN; NFV; 5G; transport technologies; slicing; interconnection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

5G wireless technologies, together with IoT will foster the creation of large scale deployments of wireless sensor devices, leading to an unprecedented volume of unstructured data. The data gathered by these devices has little value in its raw state but creates a wealth of untapped source of data that can be used to analyse, improve and optimize several systems. This untapped source of data that can be explored by Machine Learning (ML) methods, which has become the de-facto tool to deal with massive datasets.

In order to further improve the state-of-the-art regarding the usage of ML with wireless sensor networks and IoT scenarios, numerous challenges need to be overcome. The lack of a unified data representation presents a challenge that breaks compatibility amongst the systems. Energy-efficient models are necessary for ML deployments close to the edge. Noise and gap resistant data analysis is a requirement for several scenarios. Data aggregation and dimensionality reduction is a necessary step to deal with the amount of data produced.

The Special Issue targets scientific contributions on the development, innovations, and implementations of ML applied to wireless sensor networks, deployed in real IoT scenarios. Topics include but are not limited to:

  • ML in Multi-Access Edge Computing (MEC)
  • Orchestration of ML services in 5G/6G environments
  • Distributed ML algorithms
  • Methods to deal with unstructured data
  • Uniformization/Normalization of sensor data
  • Energy efficient ML models
  • Noise and gap resistant data processing and modeling
  • ML transversal application in WSN
  • Testbed for ML evaluation for WSN or IoT

Dr. Diogo Gomes
Dr. Mario Antunes
Dr. Luis Miguel Contreras-Murillo
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. Journal of Sensor and Actuator Networks is an international peer-reviewed open access quarterly 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 1600 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

  • machine learning
  • deep learning
  • IoT
  • WSN

Published Papers (1 paper)

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Research

Article
A Feed-Forward Neural Network Approach for Energy-Based Acoustic Source Localization
J. Sens. Actuator Netw. 2021, 10(2), 29; https://doi.org/10.3390/jsan10020029 - 22 Apr 2021
Cited by 3 | Viewed by 1035
Abstract
The localization of an acoustic source has attracted much attention in the scientific community, having been applied in several different real-life applications. At the same time, the use of neural networks in the acoustic source localization problem is not common; hence, this work [...] Read more.
The localization of an acoustic source has attracted much attention in the scientific community, having been applied in several different real-life applications. At the same time, the use of neural networks in the acoustic source localization problem is not common; hence, this work aims to show their potential use for this field of application. As such, the present work proposes a deep feed-forward neural network for solving the acoustic source localization problem based on energy measurements. Several network typologies are trained with ideal noise-free conditions, which simplifies the usual heavy training process where a low mean squared error is obtained. The networks are implemented, simulated, and compared with conventional algorithms, namely, deterministic and metaheuristic methods, and our results indicate improved performance when noise is added to the measurements. Therefore, the current developed scheme opens up a new horizon for energy-based acoustic localization, a field where machine learning algorithms have not been applied in the past. Full article
(This article belongs to the Special Issue Machine Learning in WSN and IoT)
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