Workflow-based automatic processing for Internet of Floating Things crowdsourced data

Montella, Raffaele; Luccio, Diana Di; Marcellino, Livia; Galletti, Ardelio; Kosta, Sokol; Giunta, Giulio; Foster, Ian

doi:10.1016/j.future.2018.11.025

Cited by 19 publications

(14 citation statements)

References 72 publications

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“…At the best of our knowledge, there are some business applications leveraging on bathymetry crowdsourced data in order to improve marine digital maps such as, but not limited to, Navionics Sonar Maps. 2 From the open data point of view, the project run in the context of OpenSeaMap 3 has the aim to collect leisure yachts GPS positions and depth data from the on board echo sounder on a volunteer basis. Table 1 compares and contrasts these last two applications and DYNAMO.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we envision the utilization of leisure and working vessels as potential instruments for collecting coastal marine data in a crowdsourcing fashion. Indeed, these vessels are equipped with on-board sensors, which can collect precious data whenever the ships are on the water following the paradigm of Internet of Floating Things (IoFT) defined in [2]. Moreover, in this work we show how the IoFT, and the collection of coastal marine data from on-board vessel instruments and sensors, can be exploited to improve the numerical forecast of pollutants transport and diffusion for marine farms' products quality assessment.…”

Section: Introductionmentioning

confidence: 99%

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Coastal Marine Data Crowdsourcing Using the Internet of Floating Things: Improving the Results of a Water Quality Model

et al. 2020

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While the everything as a sensor is a typical data gathering pattern in the Internet of Things (IoT) applications in contexts such as smart cities, smart factories, and precision agriculture, among others, the use of the same technique in the coastal marine environment is still not explored at full potential. Nevertheless, when it comes to maritime scenarios, the application of IoT and networks of distributed sensors and actuators are still limited, even though the development of marine electronics and extreme network technologies are present for decades also in this area. In this paper, we first introduce the concept of the Internet of Floating Things (IoFT), which extends the IoT to the maritime scenario. Next, we present our latest implementation of the DYNAMO (Distributed leisure Yachts sensor Network for Atmosphere and Marine Observations) system, a framework for coastal data collection from sensors and devices deployed in marine equipment. To demonstrate the importance of IoFT data collection in the real-world environmental science context, we consider a scientific workflow for coastal water quality. The selected application focuses on predicting the spatial and temporal pattern of sea pollutants and their possible presence and time of persistence in the proximity of mussel farm areas in the Bay of Pozzuoli in Italy. The pollutants are simple Lagrangian particles, so the ocean dynamics play an important role in the simulation. Our results show that integrating crowdsourced bathymetry data in the workflow numerical model setup improves the accuracy of the final results, allowing for a more detailed spatial distribution pattern of the sea current driving the Lagrangian tracers. INDEX TERMSThe Internet of Floating Things, marine data crowdsourcing, food quality, mussel farm.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coastal Marine Data Crowdsourcing Using the Internet of Floating Things: Improving the Results of a Water Quality Model

et al. 2020

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“…During the 2008 Beijing Olympics, 11 prefecture-level cities under the jurisdiction of Beijing, Tianjin and Hebei Province established the joint control mechanism for air pollution joint defense for the first time [3], [27]. 13 urban areas implement unified standards, unified deployment, unified management, and unified decision-making.…”

Section: Related Workmentioning

confidence: 99%

“…Its main function is to determine whether the information in the memory cell C t at the current moment can enter the hidden layer state h t . The mathematics of memory cells is formulated as (3).…”

Section: ) Long Short Term Memory Networkmentioning

confidence: 99%

Air Quality Forecasting Based on Gated Recurrent Long Short Term Memory Model in Internet of Things

et al. 2019

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With the continuous development of the Chinese economy and the gradual acceleration of urbanization, it has caused tremendous damage to the environment. The bad air environment seriously damages the physical and mental health of the people. The change in smog concentration will be affected by many realistic factors and exhibit nonlinear characteristics. The method proposed in this paper is to use the Internet of Things (IoT) technology to monitor the acquired data, process the data, and predict the next data using a neural network. The existing prediction models have limitations. They don't accurately capture the law between the concentration of haze and the factors affecting reality. It is difficult to accurately predict the nonlinear smog data. One algorithm proposed in this paper is a two-layer model prediction algorithm based on Long Short Term Memory Neural Network and Gated Recurrent Unit (LSTM&GRU). We set a doublelayer Recurrent Neural Network to predict the PM2.5 value. This model is an improvement and enhancement of the existing prediction method Long Short Term Memory (LSTM). The experiment integrates data monitored by the IoT node and information released by the national environmental protection department. First, the data of 96 consecutive hours in four cities were selected as the experimental samples. The experimental results are close to the true value. Then, we selected daily smog data from 2014/1/1 to 2018/1/1 as a train and test dataset. It contains smog data for 74 city sites. The first 70% of the data was used for training and the rest for testing. The results of this experiment show that our model can play a better prediction.

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“…They detected air exchange rate within and outside of vehicle before and after window is open on an internet of things (Iot) platform [2]. Another IoT application using ANN is proposed by overcoming the limitation of existing approaches which have lengthyshort term memory based artificial intelligence technique using neural network for prediction of contaminants concentration in a polluted environment [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Simulation and optimization of genetic algorithm-artificial neural network based air quality estimator

Pandey

Saeed

Kidwai

2020

IJEECS

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In this work intelligent model for estimation of the concentration of carbon monoxide in a polluted environment is developed on mat Lab platform. The results are validated using data collected from repository linked to University of California. The data records are over the duration of one year using E nose sensor placed in main city of Italy. The records are rectified and segmented at different length to extract the Base and Divergence Values features. An Artificial Neural Network Model (ANN) is developed and the result is validated manually. Another optimized Genetic Algorithm-Artificial Neural Network based air quality estimation model is developed which validate the result using artificial intelligence technique to get a better performance network.

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Workflow-based automatic processing for Internet of Floating Things crowdsourced data

Cited by 19 publications

References 72 publications

Coastal Marine Data Crowdsourcing Using the Internet of Floating Things: Improving the Results of a Water Quality Model

Coastal Marine Data Crowdsourcing Using the Internet of Floating Things: Improving the Results of a Water Quality Model

Air Quality Forecasting Based on Gated Recurrent Long Short Term Memory Model in Internet of Things

Simulation and optimization of genetic algorithm-artificial neural network based air quality estimator

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