The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
TOWARD RELIABLE DATA ANALYSIS FOR INTERNET OF THINGS BY BAYESIAN DYNAMIC MODELING AND COMPUTATION
In this paper, a Bayesian dynamic model is proposed to evaluate the sensor nodes' credibilities online, in a paradigm of agricultural Internet of things (IoT). The purpose is to discriminate reliable and unreliable data items before further data analysis, and thus to implement reliable data analysis. The credibility of the sensor node of interest is treated as the state variable of the model. The proposed model is composed of a state transition function, which characterizes the time-varying property of trustworthiness, and a likelihood function, which connects the state variable with the sensor measurements. A voting mechanism employing measurements of neighbor nodes is used to construct the likelihood function. Based on the model, the Bayesian rule is performed for statistical inference on the sensor's credibility, the whole information of which is encoded in the posterior density function. Due to a nonlinear form of the model, there is no closed form solutions to calculate the posterior. So a particle filtering method is chosen to approximate the posterior online. The efficiency of the proposed model is verified by numerical simulations.
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