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
Advances in micro-electro-mechanical systems (MEMS), computing hardware, and software algorithms for wireless sensor networks (WSNs) have boosted the adoption of WSNs, also known as machine-to-machine (M2M) communications systems, in many fields, including vehicle tracking, supply chain management, security, and healthcare. Due to the large scale of the deployments of many commercial applications, and the diversity of the hardware and software, the management of these M2M communications systems is becoming more and more cumbersome. Motivated by the challenges posed by a real-life commercial M2M communications system featuring millions of heterogeneous devices connected to hundreds of applications using a GSM cellular network, we developed a cloud-based solution for cellular traffic analysis aimed at M2M communications systems. The proposed system captures and stores all traffic generated by the M2M communications system 24/7, and can process and analyze one day worth of traffic in 2.5 hours for $2-3 using cloud computing. We also report on case studies, where the proposed solution was employed to detect misbehaving devices and test different configuration for select devices.
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