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Novel Sensing Technologies for Environmental Systems and Sensing in the Wild

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

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 6331

Special Issue Editors

Prof. Dr. Li-Minn Ang

E-Mail Website
Guest Editor
School of Science and Engineering, University of the Sunshine Coast, Sippy Downs, QLD 4556, Australia
Interests: electrical and computer engineering; intelligent and smart sensor systems; IoT and data analytics for environmental sensing; innovative technologies for real world systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jasmine Kah Phooi Seng

E-Mail Website
Guest Editor
School of AI and Advanced Computing, Xian Jiaotong Liverpool University, Suzhou 215123, China
Interests: AI and intelligent systems; data science/analytics & machine learning; big data; Internet of Things (IoT); Internet of Vehicles (IoV); edge computing; multimodal information processing; embedded intelligence & systems; mobile software development
Special Issues, Collections and Topics in MDPI journals
Dr. Shanshan Zhao

E-Mail Website
Guest Editor
School of AI and Advanced Computing, Xian Jiaotong Liverpool University, Suzhou, China
Interests: VLPR; music representation; edge computing in AI
Dr. Kathy Townsend

E-Mail Website
Guest Editor
School of Science, Technology and Engineering, University of the Sunshine Coast, Sunshine Coast, QLD, Australia
Interests: marine conservation; human impacts on the marine environment

Special Issue Information

Dear Colleagues,

Recent novel and sensing technologies bring significant improvements for the design and implementation of real-time, reliable, and cost-efficient environmental systems and monitoring. We invite authors to contribute articles that present current trends and future perspectives for environmental systems and less explored domains.

Relevant topics include but are not limited to:

  1. Design of novel sensors, instrumentation, and devices for environmental systems and monitoring;
  2. Mobile, IoT, and crowdsensing systems for environmental sensing;
  3. Novel, intelligence-based, and wireless sensing approaches for environmental sensing;
  4. Unmanned vehicles and autonomous systems for environmental sensing;
  5. Ground penetration radar for environmental sensing;
  6. GPS/GNSS and spatial science approaches for environmental sensing;
  7. Environmental sensing in aquatic environments (e.g., marine, aquaculture, underwater sensor networks), underground environments (e.g., caves, underground sensor networks), volcanic environments, etc.;
  8. Camera and acoustic-based approaches for sensing in the wild (e.g., whales, dolphins, bees, bats);
  9. Energy harvesting and self-powered systems for environmental sensing;
  10. Trends in education with emphasis on the use of novel technologies and sensing systems for environmental science and/or engineering.

Prof. Dr. Li-minn (Kenneth) Ang
Prof. Dr. Jasmine Kah Phooi Seng
Dr. Shanshan Zhao
Dr. Kathy Townsend
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. 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 (3 papers)

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Research

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26 pages, 8373 KiB  
Article
Swarm Intelligence Internet of Vehicles Approaches for Opportunistic Data Collection and Traffic Engineering in Smart City Waste Management
by Gerald K. Ijemaru, Li-Minn Ang and Kah Phooi Seng
Sensors 2023, 23(5), 2860; https://doi.org/10.3390/s23052860 - 06 Mar 2023
Cited by 7 | Viewed by 2005
Abstract
Recent studies have shown the efficacy of mobile elements in optimizing the energy consumption of sensor nodes. Current data collection approaches for waste management applications focus on exploiting IoT-enabled technologies. However, these techniques are no longer sustainable in the context of smart city [...] Read more.
Recent studies have shown the efficacy of mobile elements in optimizing the energy consumption of sensor nodes. Current data collection approaches for waste management applications focus on exploiting IoT-enabled technologies. However, these techniques are no longer sustainable in the context of smart city (SC) waste management applications due to the emergence of large-scale wireless sensor networks (LS-WSNs) in smart cities with sensor-based big data architectures. This paper proposes an energy-efficient swarm intelligence (SI) Internet of Vehicles (IoV)-based technique for opportunistic data collection and traffic engineering for SC waste management strategies. This is a novel IoV-based architecture exploiting the potential of vehicular networks for SC waste management strategies. The proposed technique involves deploying multiple data collector vehicles (DCVs) traversing the entire network for data gathering via a single-hop transmission. However, employing multiple DCVs comes with additional challenges including costs and network complexity. Thus, this paper proposes analytical-based methods to investigate critical tradeoffs in optimizing energy consumption for big data collection and transmission in an LS-WSN such as (1) finding the optimal number of data collector vehicles (DCVs) required in the network and (2) determining the optimal number of data collection points (DCPs) for the DCVs. These critical issues affect efficient SC waste management and have been overlooked by previous studies exploring waste management strategies. Simulation-based experiments using SI-based routing protocols validate the efficacy of the proposed method in terms of the evaluation metrics. Full article
(This article belongs to the Special Issue Novel Sensing Technologies for Environmental Systems and Sensing in the Wild)
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12 pages, 1715 KiB  
Article
Multimodal Sensor-Input Architecture with Deep Learning for Audio-Visual Speech Recognition in Wild
by Yibo He, Kah Phooi Seng and Li Minn Ang
Sensors 2023, 23(4), 1834; https://doi.org/10.3390/s23041834 - 07 Feb 2023
Cited by 3 | Viewed by 2473
Abstract
This paper investigates multimodal sensor architectures with deep learning for audio-visual speech recognition, focusing on in-the-wild scenarios. The term “in the wild” is used to describe AVSR for unconstrained natural-language audio streams and video-stream modalities. Audio-visual speech recognition (AVSR) is a speech-recognition task [...] Read more.
This paper investigates multimodal sensor architectures with deep learning for audio-visual speech recognition, focusing on in-the-wild scenarios. The term “in the wild” is used to describe AVSR for unconstrained natural-language audio streams and video-stream modalities. Audio-visual speech recognition (AVSR) is a speech-recognition task that leverages both an audio input of a human voice and an aligned visual input of lip motions. However, since in-the-wild scenarios can include more noise, AVSR’s performance is affected. Here, we propose new improvements for AVSR models by incorporating data-augmentation techniques to generate more data samples for building the classification models. For the data-augmentation techniques, we utilized a combination of conventional approaches (e.g., flips and rotations), as well as newer approaches, such as generative adversarial networks (GANs). To validate the approaches, we used augmented data from well-known datasets (LRS2—Lip Reading Sentences 2 and LRS3) in the training process and testing was performed using the original data. The study and experimental results indicated that the proposed AVSR model and framework, combined with the augmentation approach, enhanced the performance of the AVSR framework in the wild for noisy datasets. Furthermore, in this study, we discuss the domains of automatic speech recognition (ASR) architectures and audio-visual speech recognition (AVSR) architectures and give a concise summary of the AVSR models that have been proposed. Full article
(This article belongs to the Special Issue Novel Sensing Technologies for Environmental Systems and Sensing in the Wild)
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Review

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16 pages, 1502 KiB  
Review
Sensors for Biomass Monitoring in Vegetated Green Infrastructure: A Review
by Farhad Jalilian, Caterina Valeo, Angus Chu and Rustom Bhiladvala
Sensors 2023, 23(14), 6404; https://doi.org/10.3390/s23146404 - 14 Jul 2023
Cited by 2 | Viewed by 1050
Abstract
Bioretention cells, or rain gardens, can effectively reduce many contaminants in polluted stormwater through phytoremediation and bioremediation. The vegetated soil structure develops bacterial communities both within the soil and around the vegetation roots that play a significant role in the bioremediative process. Prediction [...] Read more.
Bioretention cells, or rain gardens, can effectively reduce many contaminants in polluted stormwater through phytoremediation and bioremediation. The vegetated soil structure develops bacterial communities both within the soil and around the vegetation roots that play a significant role in the bioremediative process. Prediction of a bioretention cell’s performance and efficacy is essential to the design process, operation, and maintenance throughout the design life of the cell. One of the key hurdles to these important issues and, therefore, to appropriate designs, is the lack of effective and inexpensive devices for monitoring and quantitatively assessing this bioremediative process in the field. This research reviews the available technologies for biomass monitoring and assesses their potential for quantifying bioremediative processes in rain gardens. The methods are discussed based on accuracy and calibration requirements, potential for use in situ, in real-time, and for characterizing biofilm formation in media that undergoes large fluctuations in nutrient supply. The methods discussed are microscopical, piezoelectric, fiber-optic, thermometric, and electrochemical. Microscopical methods are precluded from field use but would be essential to the calibration and verification of any field-based sensor. Piezoelectric, fiber-optic, thermometric, and some of the electrochemical-based methods reviewed come with limitations by way of support mechanisms or insufficient detection limits. The impedance-based electrochemical method shows the most promise for applications in rain gardens, and it is supported by microscopical methods for calibration and validation. Full article
(This article belongs to the Special Issue Novel Sensing Technologies for Environmental Systems and Sensing in the Wild)
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