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IoT Sensor Networks for Environment Monitoring: From Sensor to Cloud
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IoT Sensor Networks for Environment Monitoring: From Sensor to Cloud

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

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 13380

Special Issue Editors

Dr. Vincent Breton

E-Mail Website
Guest Editor
Laboratoire de Physique de Clermont, Université Clermont Auvergne, CNRS/IN2P3, 63000 Clermont-Ferrand, France
Interests: Internet of Things; environmental radioactivity sensors; integration from sensor to cloud; cloud computing
Prof. Dr. Emmanuel Bergeret

E-Mail Website
Guest Editor
Laboratoire de Physique de Clermont, Université Clermont Auvergne, 63001 Clermont-Ferrand, ‎France
Interests: electronics; radio frequency identification device (RFID)

Special Issue Information

Dear Colleagues,

The Internet of Things is a major contributor to the digital transformation of our society. In the foreseeable future, the objects of everyday life will be equipped with microcontrollers, transceivers for digital communication, and suitable protocol stacks that will enable them to communicate not only with one another but also with the users. The data collected can be used, among other things, to protect the environment and to make cities environment friendly, but their construction and operation at a very large scale has a considerable cost in terms of energy and natural resources consumption. Reducing the ecological impact of environmental monitoring, sensor design, manufacturing, deployment and operation is critical, as well as the data treatment chain all the way from the field to the users.

This Special Issue calls for articles about developments and indoor and outdoor applications of Wireless Sensor Networks to monitor the environment, including the data processing chain from sensor to cloud. It aims to also address innovative approaches to improve data quality using edge computing or smart sensors, to take advantage of future internet infrastructures (5G), to increase sensor autonomy, and generally to reduce IoT’s ecological impact.

Dr. Vincent Breton
Prof. Dr. Emmanuel Bergeret
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

  • environmental sensors
  • smart cities
  • resource-constrained devices
  • edge computing
  • smart sensors
  • IoT ecological impact
  • data streaming

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Published Papers (4 papers)

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Research

19 pages, 404 KiB  
Article
Heuristic Resource Reservation Policies for Public Clouds in the IoT Era
by Omer Melih Gul
Sensors 2022, 22(23), 9034; https://doi.org/10.3390/s22239034 - 22 Nov 2022
Cited by 1 | Viewed by 1526
Abstract
With the advances in the IoT era, the number of wireless sensor devices has been growing rapidly. This increasing number gives rise to more complex networks where more complex tasks can be executed by utilizing more computational resources from the public clouds. Cloud [...] Read more.
With the advances in the IoT era, the number of wireless sensor devices has been growing rapidly. This increasing number gives rise to more complex networks where more complex tasks can be executed by utilizing more computational resources from the public clouds. Cloud service providers use various pricing models for their offered services. Some models are appropriate for the cloud service user’s short-term requirements whereas the other models are appropriate for the long-term requirements of cloud service users. Reservation-based price models are suitable for long-term requirements of cloud service users. We used the pricing schemes with spot and reserved instances. Reserved instances support a hybrid cost model with fixed reservation costs that vary with contract duration and an hourly usage charge which is lower than the charge of the spot instances. Optimizing resources to be reserved requires sufficient research effort. Recent algorithms proposed for this problem are generally based on integer programming problems, so they do not have polynomial time complexity. In this work, heuristic-based polynomial time policies are proposed for this problem. It is exhibited that the cost for the cloud service user which uses our approach is comparable to optimal solutions, i.e., it is near-optimal. Full article
(This article belongs to the Special Issue IoT Sensor Networks for Environment Monitoring: From Sensor to Cloud)
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16 pages, 1947 KiB  
Article
Low Power Environmental Image Sensors for Remote Photogrammetry
by Alpha Yaya Balde, Emmanuel Bergeret, Denis Cajal and Jean-Pierre Toumazet
Sensors 2022, 22(19), 7617; https://doi.org/10.3390/s22197617 - 8 Oct 2022
Cited by 6 | Viewed by 2759
Abstract
This paper aims to prove the feasibility of a 4D monitoring solution (3D modeling and temporal monitoring) for the sandbar and to characterize the species’ role in the landscape. The developed solution allows studying the interaction between the river dynamics and vegetation using [...] Read more.
This paper aims to prove the feasibility of a 4D monitoring solution (3D modeling and temporal monitoring) for the sandbar and to characterize the species’ role in the landscape. The developed solution allows studying the interaction between the river dynamics and vegetation using a network of low resolution and low power sensors. The issues addressed concern the feasibility of implementing a photogrammetry solution using low-resolution sensors as well as the choice of the appropriate sensor and its testing according to different configurations (image capture and storage on the sensor and/or image transmission to a centralization node) and also the detailed analysis of the different phases of the process (camera initialization, image capture, network transmission and selection of the most appropriate standby mode). We reveal that the tiny, low-cost board (ESP32-Cam) can perform a 3D reconstruction and propose using the camera’s UXGA (1600, 1200) resolution because of the quality rendering and energy consumption. A multi-node scenario based on a combined Wi-Fi and GSM relay is proposed in the study showing several years of autonomy for the system. Finally, to illustrate the energy cost of the module, we have defined a study process, where we have identified and quantified one by one the different phases of operation of the card for better energy optimization (setup, camera configuration, shooting, saving on SD card, or sending by Wi-Fi). The device is now operational for deployment on the Allier River (France). Full article
(This article belongs to the Special Issue IoT Sensor Networks for Environment Monitoring: From Sensor to Cloud)
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26 pages, 4562 KiB  
Article
CEBA: A Data Lake for Data Sharing and Environmental Monitoring
by David Sarramia, Alexandre Claude, Francis Ogereau, Jérémy Mezhoud and Gilles Mailhot
Sensors 2022, 22(7), 2733; https://doi.org/10.3390/s22072733 - 2 Apr 2022
Cited by 24 | Viewed by 4874
Abstract
This article presents a platform for environmental data named “Environmental Cloud for the Benefit of Agriculture” (CEBA). The CEBA should fill the gap of a regional institutional platform to share, search, store and visualize heterogeneous scientific data related to the environment and agricultural [...] Read more.
This article presents a platform for environmental data named “Environmental Cloud for the Benefit of Agriculture” (CEBA). The CEBA should fill the gap of a regional institutional platform to share, search, store and visualize heterogeneous scientific data related to the environment and agricultural researches. One of the main features of this tool is its ease of use and the accessibility of all types of data. To answer the question of data description, a scientific consensus has been established around the qualification of data with at least the information “when” (time), “where” (geographical coordinates) and “what” (metadata). The development of an on-premise solution using the data lake concept to provide a cloud service for end-users with institutional authentication and for open data access has been completed. Compared to other platforms, CEBA fully supports the management of geographic coordinates at every stage of data management. A comprehensive JavaScript Objet Notation (JSON) architecture has been designed, among other things, to facilitate multi-stage data enrichment. Data from the wireless network are queried and accessed in near real-time, using a distributed JSON-based search engine. Full article
(This article belongs to the Special Issue IoT Sensor Networks for Environment Monitoring: From Sensor to Cloud)
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15 pages, 2769 KiB  
Article
Low-Power Failure Detection for Environmental Monitoring Based on IoT
by Jiaxi Liu, Weizhong Gao, Jian Dong, Na Wu and Fei Ding
Sensors 2021, 21(19), 6489; https://doi.org/10.3390/s21196489 - 28 Sep 2021
Cited by 5 | Viewed by 2767
Abstract
Many environmental monitoring applications that are based on the Internet of Things (IoT) require robust and available systems. These systems must be able to tolerate the hardware or software failure of nodes and communication failure between nodes. However, node failure is inevitable due [...] Read more.
Many environmental monitoring applications that are based on the Internet of Things (IoT) require robust and available systems. These systems must be able to tolerate the hardware or software failure of nodes and communication failure between nodes. However, node failure is inevitable due to environmental and human factors, and battery depletion in particular is a major contributor to node failure. The existing failure detection algorithms seldom consider the problem of node battery consumption. In order to rectify this, we propose a low-power failure detector (LP-FD) that can provide an acceptable failure detection service and can save on the battery consumption of nodes. From simulation experiments, results show that the LP-FD can provide better detection speed, accuracy, overhead and battery consumption than other failure detection algorithms. Full article
(This article belongs to the Special Issue IoT Sensor Networks for Environment Monitoring: From Sensor to Cloud)
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