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Sensor Fusion and Visualization in IoT Applications for Environmental Monitoring

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

Deadline for manuscript submissions: closed (31 January 2020) | Viewed by 23753

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Special Issue Editor

Prof. Dr. Bartolomeo Montrucchio

E-Mail Website
Guest Editor

DAUIN, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy
Interests: quantum computing; parallel computing; image processing; computer vision; wireless sensor networks

Special Issue Information

Dear Colleagues,

In environmental monitoring, many different kinds of sensors are used to collect information,

such as, obviously, temperature, humidity, particulate matter, pressure, and also gasses present in the atmosphere. These data can be collected using sensors able to gather unidimensional data in time.

However, the same data can be collected by means of sensors with two, three or even more dimensions. An example can be temperature, which can be collected using thermal imaging cameras.

Data can also be recorded remotely, eventually from a big distance.

In particular, with the spread of IoT linked sensors, it is now possible to collect data remotely and then also show them by means of advanced visualization techniques.

Here is a list of the main topics of interest in this Special Issue, also considering that the relevance is not limited to these issues but extended to all papers dealing with a fusion of multiple sensors in IoT environmental monitoring applications:

Databases and data fusion for multiple sensors management;
Sensors and technologies for rainfall and snowfall measurements in wide sensor networks management and visualization;
Augmented and virtual reality for sensor fusion;
Multivariate visualization of environmental data;
Scientific visualization of vector fields from multiple sensors in a 2D and 3D environment;
Fiber Bragg grating (FBG) sensors and complex data visualization;
LWIR and MWIR imaging;
RFID-based sensor networks and data visualization;
Environmental monitoring in mobility, in particular in urban contexts;
GPS, LIDAR and radar data fusion.

Prof. Dr. Bartolomeo Montrucchio
Guest Editor

Manuscript Submission Information

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Keywords

environmental monitoring
GPS
LIDAR
radar
data fusion
Fiber Bragg grating (FBG) sensors
LWIR and MWIR imaging
visualization
RFID-based sensor networks
mobile sensing

Published Papers (4 papers)

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Research

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21 pages, 7785 KiB

Open AccessArticle

Animal Movement Prediction Based on Predictive Recurrent Neural Network

by Jehyeok Rew, Sungwoo Park, Yongjang Cho, Seungwon Jung and Eenjun Hwang

Sensors 2019, 19(20), 4411; https://doi.org/10.3390/s19204411 - 11 Oct 2019

Cited by 20 | Viewed by 5180

Abstract

Observing animal movements enables us to understand animal behavior changes, such as migration, interaction, foraging, and nesting. Based on spatiotemporal changes in weather and season, animals instinctively change their position for foraging, nesting, or breeding. It is known that moving patterns are closely related to their traits. Analyzing and predicting animals’ movement patterns according to spatiotemporal change offers an opportunity to understand their unique traits and acquire ecological insights into animals. Hence, in this paper, we propose an animal movement prediction scheme using a predictive recurrent neural network architecture. To do that, we first collect and investigate geo records of animals and conduct pattern refinement by using random forest interpolation. Then, we generate animal movement patterns using the kernel density estimation and build a predictive recurrent neural network model to consider the spatiotemporal changes. In the experiment, we perform various predictions using 14 K long-billed curlew locations that contain their five-year movements of the breeding, non-breeding, pre-breeding, and post-breeding seasons. The experimental results confirm that our predictive model based on recurrent neural networks can be effectively used to predict animal movement. Full article

(This article belongs to the Special Issue Sensor Fusion and Visualization in IoT Applications for Environmental Monitoring)

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15 pages, 2947 KiB

Open AccessArticle

The Potential of Utilizing Air Temperature Datasets from Non-Professional Meteorological Stations in Brno and Surrounding Area

by Karel Dejmal, Petr Kolar, Josef Novotny and Alena Roubalova

Sensors 2019, 19(19), 4172; https://doi.org/10.3390/s19194172 - 26 Sep 2019

Cited by 3 | Viewed by 1898

Abstract

An increasing number of individuals and institutions own or operate meteorological stations, but the resulting data are not yet commonly used in the Czech Republic. One of the main difficulties is the heterogeneity of measuring systems that puts in question the quality of outcoming data. Only after a thorough quality control of recorded data is it possible to proceed with for example a specific survey of variability of a chosen meteorological parameter in an urban or suburban region. The most commonly researched element in the given environment is air temperature. In the first phase, this paper focuses on the quality of data provided by amateur and institutional stations. The following analyses consequently work with already amended time series. Due to the nature of analyzed data and their potential use in the future it is opportune to assess the appropriateness of chronological and possibly spatial interpolation of missing values. The evaluation of seasonal variability of air temperature in the scale of Brno city and surrounding area in 2015–2017 demonstrates, that the enrichment of network of standard (professional) stations with new stations may significantly refine or even revise the current state of knowledge, for example in the case of urban heat island phenomena. A cluster analysis was applied in order to assess the impact of localization circumstances (station environment, exposition, etc.) as well as typological classification of the set of meteorological stations. Full article

(This article belongs to the Special Issue Sensor Fusion and Visualization in IoT Applications for Environmental Monitoring)

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19 pages, 8689 KiB

Open AccessArticle

Physiological Arousal Quantifying Perception of Safe and Unsafe Virtual Environments by Older and Younger Adults

by Sofia Leite, Miguel S. Dias, Sara Eloy, João Freitas, Sibila Marques, Tiago Pedro and Lázaro Ourique

Sensors 2019, 19(11), 2447; https://doi.org/10.3390/s19112447 - 29 May 2019

Cited by 10 | Viewed by 3782

Abstract

Physiological arousal has been increasingly applied to monitor exploration (or navigation) of a virtual environment (VE), especially when the VE is designed to evoke an anxiety-related response. The present work aims to evaluate human physiological reactions to safe and unsafe VEs. We compared the effect of the presence of handrails in the VE in two different samples, young and older adults, through self-reports and physiological data: Electrodermal activation (EDA) and electrocardiogram (ECG) sensors. After navigation, self-report questionnaires were administered. We found that the VEs evoked a clearly differentiated perception of safety and unsafety demonstrated through self-reports, with older adults being more discriminative in their responses and reporting a higher sense of presence. In terms of physiological data, the effect of handrails did not provoke significant differences in arousal. Safety was better operationalized by discriminating neutral/non-neutral spaces, where the reaction of older adults was more pronounced than young adults. Results serve as a basis for orienting future experiments in the line of VE and applied physiology usage in the architectural spaces design process. This specific work also provided a basis for the development of applications that integrate virtual reality and applied biofeedback, tapping into mobility and ageing. Full article

(This article belongs to the Special Issue Sensor Fusion and Visualization in IoT Applications for Environmental Monitoring)

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Review

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21 pages, 1413 KiB

Open AccessReview

Internet of Things in Marine Environment Monitoring: A Review

by Guobao Xu, Yanjun Shi, Xueyan Sun and Weiming Shen

Sensors 2019, 19(7), 1711; https://doi.org/10.3390/s19071711 - 10 Apr 2019

Cited by 150 | Viewed by 12401

Abstract

Marine environment monitoring has attracted more and more attention due to the growing concern about climate change. During the past couple of decades, advanced information and communication technologies have been applied to the development of various marine environment monitoring systems. Among others, the Internet of Things (IoT) has been playing an important role in this area. This paper presents a review of the application of the Internet of Things in the field of marine environment monitoring. New technologies including advanced Big Data analytics and their applications in this area are briefly reviewed. It also discusses key research challenges and opportunities in this area, including the potential application of IoT and Big Data in marine environment protection. Full article

(This article belongs to the Special Issue Sensor Fusion and Visualization in IoT Applications for Environmental Monitoring)

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