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IoT Based Environmental Monitoring Systems
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IoT Based Environmental Monitoring Systems

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 14480

Special Issue Editors

Prof. Dr. Min-Shiang Hwang

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan
Interests: mobile computing security; blockchain technology; cryptography; steganography; network and communication security
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Cheng-Ying Yang

E-Mail Website
Guest Editor
Department of Computer Science, University of Taipei, Taipei 10066, Taiwan
Interests: communication system; signal processing; information security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Because of increasing environmental change, people face the prospect of living in variant environments. For example, climate change leads to higher temperatures, and the decreasing green space affects both physical and mental health. From a range of studies that have been conducted, it is known that these climate changes have adverse effects on human life. Hence, it is widely accepted that humans are responsible for environmental monitoring.

The current state-of-the-art practices of environmental monitoring are focused on systems with the properties of energy efficiency, low cost, short response time, good accuracy, acceptable signal-to-noise ratio, radio frequency interference interference rejection, and user-friendly interface. These systems are able to continuously collect data and work with computers to produce accurate predictions. The Internet of Things (IoT) is expected to provide the function of environmental protection by enabling the monitoring and control of vital changes in the environment. Devices with IoT functionality are capable of sensing data such as temperature, pressure, humidity, noise, pollution, object detection, patient vitals, etc. Additionally, through these devices, it is possible to process and wirelessly transmit the data collected. With effective short-term measures and long-term data analysis, we are able to present these data in a useful form.

Due to the growing needs of environmental monitoring, a great deal of related works have to be analyzed, designed, and implemented. This Special Issue attempts to link the science and the technology for IoT-based environmental monitoring systems in preparation for the framework of environment protection. Topics include, but are not limited to, the following:

  • Energy Efficiency of IoT;
  • Security and Privacy of IoT;
  • Healthcare IoT Systems;
  • Deployments and Testbeds;
  • AI technology of IoT (AIoT);
  • IoT Architectures;
  • IoT Communication Technologies;
  • IoT-enabled Smart City;
  • IoT-enabled application;
  • IoT-enabled Supply Chain;
  • Integrated IoT + 5G Systems.

Prof. Dr. Min-Shiang Hwang
Prof. Dr. Cheng-Ying Yang
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.

Keywords

  • Internet of Things (IoT)
  • environmental monitoring
  • Security IoT Application
  • Communication Technologies
  • Smart City

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Published Papers (4 papers)

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Research

17 pages, 10211 KiB  
Article
ICARUS—Very Low Power Satellite-Based IoT
by Marco Krondorf, Steffen Bittner, Dirk Plettemeier, Andreas Knopp and Martin Wikelski
Sensors 2022, 22(17), 6329; https://doi.org/10.3390/s22176329 - 23 Aug 2022
Cited by 10 | Viewed by 3724
Abstract
The ICARUS (International Cooperation for Animal Research Using Space) satellite IoT system was launched in 2020 to observe the life of animals on Earth: their migratory routes, living conditions, and causes of death. These findings will aid species conservation, protect ecosystem services by [...] Read more.
The ICARUS (International Cooperation for Animal Research Using Space) satellite IoT system was launched in 2020 to observe the life of animals on Earth: their migratory routes, living conditions, and causes of death. These findings will aid species conservation, protect ecosystem services by animals, measure weather and climate, and help forecast the spread of infectious zoonotic diseases and possibly natural disasters. The aim of this article is to explain the system design of ICARUS. Essential components are ‘wearables for wildlife’, miniature on-animal sensors, quantifying the health of animals and the surrounding environment on the move, and transmitting artificially intelligent summaries of these data globally. We introduce a new class of Internet-of-things (IoT) waveforms—the random-access, very-low-power, wide-area networks (RA-vLPWANs) which enable uncoordinated multiple access at very-low-signal power and low-signal-to-noise ratios. RA-vLPWANs used in ICARUS solve the problems hampering conventional low-power wide area network (LPWAN) IoT systems when applied to space communications. Prominent LPWANs are LoRA, SigFox, MIOTY, ESSA, NB-IoT (5G), or SCADA. Hardware and antenna aspects in the ground and the space segment are given to explain practical system constraints. Full article
(This article belongs to the Special Issue IoT Based Environmental Monitoring Systems)
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21 pages, 4496 KiB  
Article
Data Collection from Buried Sensor Nodes by Means of an Unmanned Aerial Vehicle
by Christophe Cariou, Laure Moiroux-Arvis, François Pinet and Jean-Pierre Chanet
Sensors 2022, 22(15), 5926; https://doi.org/10.3390/s22155926 - 8 Aug 2022
Cited by 10 | Viewed by 2926
Abstract
The development of Wireless Underground Sensor Networks (WUSNs) is a recent research axis based on sensor nodes buried a few dozen centimeters deep. The communication ranges are, however, highly reduced due to the high attenuation of electromagnetic waves in soil, leading to issues [...] Read more.
The development of Wireless Underground Sensor Networks (WUSNs) is a recent research axis based on sensor nodes buried a few dozen centimeters deep. The communication ranges are, however, highly reduced due to the high attenuation of electromagnetic waves in soil, leading to issues of data collection. This paper proposes to embed a data collector on an Unmanned Aerial Vehicle (UAV) coming close to each buried sensor node. The whole system was developed (sensor nodes, data collector, gateway) and experimentations were carried out in real conditions. In hovering mode, the measurements on the RSSI levels with respect to the position of the UAV highlight the interest in maintaining a high altitude when the UAV is far from the node. In dynamic mode, the experimental results demonstrate the feasibility of carrying out the data collection task while the UAV is moving. The speed of the UAV has, however, to be adapted to the required time to collect the data. In the case of numerous buried sensor nodes, evolutionary algorithms are implemented to plan the trajectory of the UAV optimally. To the best of our knowledge, this paper is the first one that reports experiment results combining WUSN and UAV technologies. Full article
(This article belongs to the Special Issue IoT Based Environmental Monitoring Systems)
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17 pages, 5337 KiB  
Article
A Demand-Centric Repositioning Strategy for Bike-Sharing Systems
by Ying-Chih Lin
Sensors 2022, 22(15), 5580; https://doi.org/10.3390/s22155580 - 26 Jul 2022
Cited by 3 | Viewed by 1930
Abstract
Transport-sharing systems are eco-friendly and the most promising services in smart urban environments, where the booming Internet of things (IoT) technologies play an important role in the smart infrastructure. Due to the imbalanced bike distribution, bikes and stalls in the docking stations could [...] Read more.
Transport-sharing systems are eco-friendly and the most promising services in smart urban environments, where the booming Internet of things (IoT) technologies play an important role in the smart infrastructure. Due to the imbalanced bike distribution, bikes and stalls in the docking stations could be unavailable when needed, leading to bad customer experiences. We develop a dynamic repositioning strategy for the management of bikes in this paper, which supports dispatchers to keep stations in service. Two open datasets are examined, and the exploratory data analysis presents that there is a significant difference of travel patterns between working and non-working days, where the former has an excess demand at rush hours and the latter is usually at a low demand. To evaluate the effect when the demand outstrips a station’s capacity, we propose a non-linear scaling technique to transform demand patterns and perform the clustering analysis for each of five categories obtained from the sophisticated analysis of the dataset. Our repositioning strategy is developed according to the transformed demands. Compared with the previous work, numerical simulations reveal that our strategy has a better performance for high-demand stations, and thus can substantially reduce the repositioning cost, which brings benefit to bike-sharing operators for managing the city bike system. Full article
(This article belongs to the Special Issue IoT Based Environmental Monitoring Systems)
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16 pages, 4963 KiB  
Article
A Novel Digital Twin Architecture with Similarity-Based Hybrid Modeling for Supporting Dependable Disaster Management Systems
by Seong-Jin Yun, Jin-Woo Kwon and Won-Tae Kim
Sensors 2022, 22(13), 4774; https://doi.org/10.3390/s22134774 - 24 Jun 2022
Cited by 10 | Viewed by 3406
Abstract
Disaster management systems require accurate disaster monitoring and prediction services to reduce damages caused by natural disasters. Digital twins of natural environments can provide the services for the systems with physics-based and data-driven disaster models. However, the digital twins might generate erroneous disaster [...] Read more.
Disaster management systems require accurate disaster monitoring and prediction services to reduce damages caused by natural disasters. Digital twins of natural environments can provide the services for the systems with physics-based and data-driven disaster models. However, the digital twins might generate erroneous disaster prediction due to the impracticability of defining high-fidelity physics-based models for complex natural disaster behavior and the dependency of data-driven models on the training dataset. This causes disaster management systems to inappropriately use disaster response resources, including medical personnel, rescue equipment and relief supplies, to ensure that it may increase the damages from the natural disasters. This study proposes a digital twin architecture to provide accurate disaster prediction services with a similarity-based hybrid modeling scheme. The hybrid modeling scheme creates a hybrid disaster model that compensates for the errors of physics-based prediction results with a data-driven error correction model to enhance the prediction accuracy. The similarity-based hybrid modeling scheme reduces errors from the data dependency of the hybrid model by constructing a training dataset using similarity assessments between the target disaster and the historical disasters. Evaluations in wildfire scenarios show that the digital twin decreases prediction errors by approximately 50% compared with those of the existing schemes. Full article
(This article belongs to the Special Issue IoT Based Environmental Monitoring Systems)
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