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Feature Papers in Environmental Sensing and Smart Cities
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Feature Papers in Environmental Sensing and Smart Cities

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 2269

Special Issue Editor

Prof. Dr. Kah Phooi Seng

E-Mail Website
Guest Editor
1. School of AI & Advanced Computing, Xi’an Jiaotong Liverpool University, Suzhou 215123, China
2. School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
Interests: artificial intelligence; deep learning; machine learning and data analytics; intelligent systems; Internet of Things and edge computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that the section on Environmental Sensing is now compiling a collection of papers in the area in “Sensing and Smart Cities” submitted by the Editorial Board Members (EBMs) of our section and outstanding scholars in this research field. We welcome contributions as well as recommendations from the EBMs.

The purpose of this Special Issue is to publish a set of papers that typify the very best insightful and influential original articles or reviews where our Section’s EBMs discuss key topics in the field. We expect these papers to be widely read and highly influential within the field. All papers in this Special Issue will be collected into a printed edition book after the deadline and will be well promoted.

We would also like to take this opportunity to call on more scholars to join the section on Environmental Sensing so that we can work together to further develop this exciting field of research. Potential topics include but are not limited to the following:

  • Sensing technology and IoT in smart cities;
  • Sensing technology in agriculture and food security;
  • Urban environmental monitoring, sensing and smart cities;
  • Industrial sensing and smart cities;
  • Artificial Intelligence in sensing and smart cities;
  • Internet of Things sensing and smart cities;
  • Marine sensing and smart cities;
  • Remote sensing focused on environmental monitoring and smart cities;
  • Remote sensing applications in geo-information and geophysics;
  • Sensing technology in oceans, coastal zones, and inland waters;
  • Natural disasters monitoring, warning, and response;
  • Ecological monitoring sensing and smart cities;
  • In situ techniques in environmental monitoring and smart cities;
  • Big data processing and dissemination, sensing and smart cities;
  • Image processing, sensing and smart cities;
  • Sensing and smart cities applications.

Prof. Dr. Kah Phooi Seng
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 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. 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 2600 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)

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Research

17 pages, 4997 KiB  
Article
Research on Sea State Signal Recognition Based on Beluga Whale Optimization–Slope Entropy and One Dimensional–Convolutional Neural Network
by Yuxing Li, Zhaoyu Gu and Xiumei Fan
Sensors 2024, 24(5), 1680; https://doi.org/10.3390/s24051680 - 05 Mar 2024
Viewed by 495
Abstract
This study introduces a novel nonlinear dynamic analysis method, known as beluga whale optimization–slope entropy (BWO-SlEn), to address the challenge of recognizing sea state signals (SSSs) in complex marine environments. A method of underwater acoustic signal recognition based on BWO-SlEn and one-dimensional convolutional [...] Read more.
This study introduces a novel nonlinear dynamic analysis method, known as beluga whale optimization–slope entropy (BWO-SlEn), to address the challenge of recognizing sea state signals (SSSs) in complex marine environments. A method of underwater acoustic signal recognition based on BWO-SlEn and one-dimensional convolutional neural network (1D-CNN) is proposed. Firstly, particle swarm optimization–slope entropy (PSO-SlEn), BWO-SlEn, and Harris hawk optimization–slope entropy (HHO-SlEn) were used for feature extraction of noise signal and SSS. After 1D-CNN classification, BWO-SlEn were found to have the best recognition effect. Secondly, fuzzy entropy (FE), sample entropy (SE), permutation entropy (PE), and dispersion entropy (DE) were used to extract the signal features. After 1D-CNN classification, BWO-SlEn and 1D-CNN were found to have the highest recognition rate compared with them. Finally, compared with the other six recognition methods, the recognition rates of BWO-SlEn and 1D-CNN for the noise signal and SSS are at least 6% and 4.75% higher, respectively. Therefore, the BWO-SlEn and 1D-CNN recognition methods proposed in this paper are more effective in the application of SSS recognition. Full article
(This article belongs to the Special Issue Feature Papers in Environmental Sensing and Smart Cities)
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14 pages, 2190 KiB  
Article
A Data-Driven Approach for Building the Profile of Water Storage Capacity of Soils
by Jiang Zhou, Ciprian Briciu-Burghina, Fiona Regan and Muhammad Intizar Ali
Sensors 2023, 23(12), 5599; https://doi.org/10.3390/s23125599 - 15 Jun 2023
Viewed by 1215
Abstract
The soil water storage capacity is critical for soil management as it drives crop production, soil carbon sequestration, and soil quality and health. It depends on soil textural class, depth, land-use and soil management practices; therefore, the complexity strongly limits its estimation on [...] Read more.
The soil water storage capacity is critical for soil management as it drives crop production, soil carbon sequestration, and soil quality and health. It depends on soil textural class, depth, land-use and soil management practices; therefore, the complexity strongly limits its estimation on a large scale with conventional-process-based approaches. In this paper, a machine learning approach is proposed to build the profile of the soil water storage capacity. A neural network is designed to estimate the soil moisture from the meteorology data input. By taking the soil moisture as a proxy in the modelling, the training captures those impact factors of soil water storage capacity and their nonlinear interaction implicitly without knowing the underlying soil hydrologic processes. An internal vector of the proposed neural network assimilates the soil moisture response to meteorological conditions and is regulated as the profile of the soil water storage capacity. The proposed approach is data-driven. Since the low-cost soil moisture sensors have made soil moisture monitoring simple and the meteorology data are easy to obtain, the proposed approach enables a convenient way of estimating soil water storage capacity in a high sampling resolution and at a large scale. Moreover, an average root mean squared deviation at 0.0307m3/m3 can be achieved in the soil moisture estimation; hence, the trained model can be deployed as an alternative to the expensive sensor networks for continuous soil moisture monitoring. The proposed approach innovatively represents the soil water storage capacity as a vector profile rather than a single value indicator. Compared with the single value indicator, which is common in hydrology, a multidimensional vector can encode more information and thus has a more powerful representation. This can be seen in the anomaly detection demonstrated in the paper, where subtle differences in soil water storage capacity among the sensor sites can be captured even though these sensors are installed on the same grassland. Another merit of vector representation is that advanced numeric methods can be applied to soil analysis. This paper demonstrates such an advantage by clustering sensor sites into groups with the unsupervised K-means clustering on the profile vectors which encapsulate soil characteristics and land properties of each sensor site implicitly. Full article
(This article belongs to the Special Issue Feature Papers in Environmental Sensing and Smart Cities)
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