A Wireless Sensor Network Framework for Real-Time Monitoring of Height and Volume Variations on Sandy Beaches and Dunes

Pozzebon, Alessandro; Andreadis, Alessandro; Bertoni, Duccio; Bove, Carmine

doi:10.3390/ijgi7040141

Cited by 11 publications

(14 citation statements)

References 39 publications

(36 reference statements)

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“…In such environments, not only reflection but also absorption, multi-scattering, and refraction affect the transmitted signal. Similar to the objectives of these studies on sand lands [30,34,35], Pozzebon et al focused on determining the testing and the realization of a WSN in order to remote sense and measure the variations of the sand layer and dunes [36]. They received the data with the ZigBee protocol by measuring the level changes with a precision of 5 cm.…”

Section: Related Workmentioning

confidence: 99%

Empirical path loss models for 5G wireless sensor network in coastal pebble/sand environments

Başyiğit¹

2021

Int. J. Microw. Wireless Technol.

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Propagation modeling of small/big pebbles and air-dry/wet sand environments for wireless sensor networks has not been extensively studied in the 5G frequency band. This study is necessary for the proper coverage planning and efficient operation of wireless sensors in various applications such as monitoring summer sporting activities, and environmental/ground surveillance on coastal pebble/sand environments, or tracking pebble mobility and including the rescue of the flood-type avalanche in Gravel-Bed Rivers. In this study, empirical path loss models are proposed for wireless sensor networks in pebble/sand environments at two discrete frequencies, namely 3.5 and 4.2 GHz. The theoretical models and proposed models are compared to indicate the accuracy of proposed models in predicting the path loss in these environments. Additionally, R-squared and RMSE values of eight different generated models are calculated in the range of 0.931–0.877 and 2.284–2.837, respectively. These comparisons indicate that empirical model parameters have a significant effect on the path loss model.

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Section: Related Workmentioning

confidence: 99%

Empirical path loss models for 5G wireless sensor network in coastal pebble/sand environments

Başyiğit¹

2021

Int. J. Microw. Wireless Technol.

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“…Applying Equation (19) returns an MAE value of 4.6, we can see that the value of MAE is 4.6. Accordingly, our algorithm was rather accurate but can still make reasonably good predictions.…”

Section: An Algorithm For Sand Movement Predictionmentioning

confidence: 99%

“…The article in Pozzebon et al 19 proposes a WSN framework, which is believed to be a novel solution to remotely measure sand level movement for beaches or dunes in real time. The study proposes a low‐cost sensing structure, which can measure level changes and send data via Zigbee communication technology.…”

Section: Related Workmentioning

confidence: 99%

Wireless sensor networks and machine learning meet climate change prediction

Khoa

Minh

Son

et al. 2020

Int J Communication

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Summary Climate change is one of the main challenges faced by the development of every country. For countries producing agricultural commodities, the climate affects the quantity and quality of products. Many methods have been proposed to keep track of climate. One traditional method is the weather station model, which indicates the temperature, wind speed, and direction and extent of cloud cover. However, this method of predicting climate change has low accuracy due to geographical variation, for example, mountainous or forested areas. Recently, a combination of wireless sensor networks (WSN) and machine learning (ML) has been considered for prediction with the Internet of Things (IoT), for instance, through a wireless body area network. For climate change prediction, we design and develop a control system that uses node sensors to collect data in sandhills and beaches, with data management conducted via a web application with three components. The first component is designed to collect data from the node sensors. The second component is mainly used to control the system through a web application. The third component uses linear regression in ML to analyze the data to predict weight and volume. The complete system has been tried and tested in real time on a 10‐m2 area of a beach at Binh Thuan province, Vietnam, where sensor node data were wirelessly collected over a cloud using a web application. This enabled assessment of the current state of the land at a coastal sandy beach, as well as prediction of the risk level of desertification and natural disasters.

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“…Laska et al (2018) presented an architecture for real-time processing of spatiotemporal IoT stream data. Pozzebon et al (2018) proposed a wireless sensor network framework for realtime monitoring of height and volume Variations on sandy beaches and dunes.…”

Section: Geoinformation and Iotmentioning

confidence: 99%

An IOT architecture for facilitating integration of geoinformation

Işıkdağ

2020

International Journal of Engineering and Geosciences

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Background: GeoInformation, is very valuable for a range of fields ranging from location based services and navigation to smart cities and homes. On the other hand today many fields benefit from Internet of Things (IoT) implementations, where the machine-to-machine and machine-to-human transmission of GeoInformation frequently occurs. This transmission usually occurs in multi-source/multi-target and multiplatform IoT environments. Problem Statement: In many cases real-time GeoInformation stays in its own island of automation, and thus its real value cannot be uncovered. This happens mainly due to inefficiencies and problems that occur in the storage, sharing and exchange of real-time GeoInformation as a result of multi-source/multi-target and multi-platform nature of the IoT architectures. Research Approach: Integration appears as a critical paradigm which should be focused in order to store, manage and transfer of GeoInformation efficiently in these complex environments. In this context, the focus of the study was to test the applicability of different technologies and integration methods for acquisition, transmission and visualisation of multi-source GeoInformation through implementing an IoT Integration Testbed Architecture which is utilizing low-cost hardware (to acquire information), graph databases(to store information) and standard IoT protocols (to exchange information). The implementation explained in this paper covers acquisition of real time GeoInformation from a set of real and virtual sensors, storage of this GeoInformation in Graph Databases, exchange of information through two different communication models (request/response and publish/subscribe) based on standard IoT protocols, and visualization of information by web pages, web mapping services and using a GIS software. Results: The implementation results demonstrated a proof-ofconcept on how multi-source GeoInformation acquired from different type of IoT nodes can be integrated, stored and visualised on different platforms by utilising a standard IoT communication paradigms and multiple communication models.

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A Wireless Sensor Network Framework for Real-Time Monitoring of Height and Volume Variations on Sandy Beaches and Dunes

Cited by 11 publications

References 39 publications

Empirical path loss models for 5G wireless sensor network in coastal pebble/sand environments

Empirical path loss models for 5G wireless sensor network in coastal pebble/sand environments

Wireless sensor networks and machine learning meet climate change prediction

An IOT architecture for facilitating integration of geoinformation

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