Digital condition monitoring for smart transformers

A digital transformer is the centrepiece of a smart grid that gives agility to the business model of the power sector. It enables self-measurement, monitoring, analysis, and two-way communication of its condition using various electronic devices in real time. However, big data issues, high cost of sensors, rapidly changing digital technologies, and a lack of standardisation protocol restricts the emergence of a truly digital transformer. This paper describes that a multidimensional approach towards storage, analysis, and safety of condition monitoring data is the key to an integrated platform for complete automation of such purposes

Big Data Transformation in Agriculture: From Precision Agriculture Towards Smart Farming

[EN] Big data is a concept that has changed the way to analyse data and information in different environments such as industry and recently, in agriculture. It is used to describe a large volume of data (structured or unstructured data), which are difficult to obtain, process or parse using conventional technologies and tools like relational databases or conventional statistics, in a reasonable time for their insight. However, Big Data is applied differently in each area to take advantage of its potential and capabilities. Specially in agriculture that presents more demanding conditions due to its inherent uncertainty, so Big Data methods and models from other environments cannot be used straight away in this area. In this paper, we present a review/update of term Big Data and analyse the evolution and the role of Big Data in agriculture outlined the element of collaboration.All authors acknowledge the partial support of Project 691249, RUC-APS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems, funded by the EU under its funding scheme H2020-MSCA-RISE-2015; and the project "Development of an integrated maturity model for agility, resilience and gender perspective in supply chains (MoMARGE). Application to the agricultural sector." Ref. GV/2017/025 funded by the Generalitat Valenciana. This first author was supported by the Aid Programme of Research and Development of Universitat Politecnica de Valencia [PAID-01-18].Rodríguez-Sánchez, MDLÁ.; Cuenca, L.; Ortiz Bas, Á. (2019). Big Data Transformation in Agriculture: From Precision Agriculture Towards Smart Farming. IFIP Advances in Information and Communication Technology. 568:467-474. https://doi.org/10.1007/978-3-030-28464-0_40S467474568Cox, M., Ellsworth, D.: Application-controlled demand paging for out-of-core visualization. In: Proceedings of the 8th Conference on Visualization 1997, p. 235. IEEE Computer Society Press (1997)Laney, D.: 3D data management: controlling data volume, velocity and variety. META Group Res. Note 6, 1 (2001)Beyer, M.A., Laney, D.: The Importance of “Big Data”: A Definition. Gartner, Stamford (2012)Kamilaris, A., et al.: A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture 143(C), 23–37 (2017)Marr, B.: How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read (2019). https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#5671a61d60baNIST. The definition of Big Data. https://bigdatawg.nist.gov/home.phpIBM. The definition of Big Data. https://www.ibm.com/analytics/hadoop/big-data-analyticsOracle. The definition of Big Data. https://www.oracle.com/big-data/guide/what-is-big-data.htmlShahbaz, M., Gao, Ch., Zhai, L., Shahzad, F., Hu, Y.: Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change. J. Big Data 6 (2019). https://doi.org/10.1186/s40537-019-0170-yTrom, L., Cronje, J.: Analysis of data governance implications on big data. In: Arai, K., Bhatia, R. (eds.) FICC 2019. LNNS, vol. 69, pp. 645–654. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-12388-8_45Tao, F., et al.: A field programmable gate array implemented fibre channel switch for big data communication towards smart manufacturing. Robotics and Computer Integrated Manufacturing 57, 166–181 (2019)Lu, Y., Li, X., Zhong, J., Xiong, Y.: Research on the innovation of strategic business model in green agricultural products based on Internet of Things (IOT) - May 2010 (2010)Zhao, L., Yin, S., Liu, L., Zhang, Z., Wei, S.: A crop monitoring system based on wireless sensor network - December 2011 (2011)Chi, M., Plaza, A., Benediktsson, J.A., Sun, Z., Shen, J., Zhu, Y.: Big data for remote sensing: challenges and opportunities. Proc. IEEE 104(11), 2207–2219 (2016) https://doi.org/10.1109/jproc.2016.2598228Rodriguez, M.A., Cuenca, L., Bas, A.: FIWARE open source standard platform in smart farming - a review. In: Proceedings of the 19th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2018, Cardiff, UK, 17–19 September 2018 (2018). https://doi.org/10.1007/978-3-319-99127-6_50Stafford, J., LeBars, J.: A GPS backpack system for mapping soil and crop parameters in agricultural fields. J. Navig. 49(1), 9–21 (1996)Robert, P.C.: Precision agriculture: research needs and status in the USA. In: Stafford, J.V. (ed.) Proceedings of the 2nd European Conference on Precision Agriculture, Part 1, pp. 19–33. Academic Press, SCI/Sheffield (1999)Long, D.S., Nielsen, G.A., Henry, M.P., Westcott, M.P.: Remote sensing for northern plains precision agriculture. In: Paper Presented at the Space 2000, pp. 208–214 (2000)Ge, Y., Thomasson, J.A., Sui, R.: Remote sensing of soil properties in precision agriculture: a review. Front. Earth Sci. 5(3), 229–238 (2011)Sundmaeker, H., Verdouw, C., Wolfert, S., Pérez L.: Internet of food and farm 2020. In: Paper presented at Digitising the Industry - Internet of Things Connecting Physical, Digital and Virtual Worlds, River Publishers, Gistrup/Delft, pp. 129–151 (2016)Barmpounakis, S., et al.: Management and control applications in agriculture domain via a FI Business-to-Business platform. Inf. Process. Agric. 2(1), 51–63 (2015)Musat, G., et al.: Advanced services for efficient management of smart farms. J. Parallel Distrib. Comput. 116, 3–17 (2018)FIspace. https://www.fispace.eu/whatisfispace.htmlAgricolus (2019). https://www.agricolus.com/Paton, N.W.: Automating data preparation: can we? Should we? Must we? In: CEUR Workshop Proceedings, p. 2324 (2019)Kim, K.S., Yoo, B.H., Shelia, V., Porter, C.H., Hoogenboom, G.: START: a data preparation tool for crop simulation models using web-based soil databases. Comput. Electron. Agric. 154, 256–264 (2018). https://doi.org/10.1016/j.compag.2018.08.023IoF2020 (2019). https://www.iof2020.eu

Software Platforms for Smart Cities: Concepts, Requirements, Challenges, and a Unified Reference Architecture

Making cities smarter help improve city services and increase citizens' quality of life. Information and communication technologies (ICT) are fundamental for progressing towards smarter city environments. Smart City software platforms potentially support the development and integration of Smart City applications. However, the ICT community must overcome current significant technological and scientific challenges before these platforms can be widely used. This paper surveys the state-of-the-art in software platforms for Smart Cities. We analyzed 23 projects with respect to the most used enabling technologies, as well as functional and non-functional requirements, classifying them into four categories: Cyber-Physical Systems, Internet of Things, Big Data, and Cloud Computing. Based on these results, we derived a reference architecture to guide the development of next-generation software platforms for Smart Cities. Finally, we enumerated the most frequently cited open research challenges, and discussed future opportunities. This survey gives important references for helping application developers, city managers, system operators, end-users, and Smart City researchers to make project, investment, and research decisions.Comment: Accepted for publication in ACM Computing Survey

Integrating big data into a sustainable mobility policy 2.0 planning support system

It is estimated that each of us, on a daily basis, produces a bit more than 1 GB of digital content through our mobile phone and social networks activities, bank card payments, location-based positioning information, online activities, etc. However, the implementation of these large data amounts in city assets planning systems still remains a rather abstract idea for several reasons, including the fact that practical examples are still very strongly services-oriented, and are a largely unexplored and interdisciplinary field; hence, missing the cross-cutting dimension. In this paper, we describe the Policy 2.0 concept and integrate user generated content into Policy 2.0 platform for sustainable mobility planning. By means of a real-life example, we demonstrate the applicability of such a big data integration approach to smart cities planning process. Observed benefits range from improved timeliness of the data and reduced duration of the planning cycle to more informed and agile decision making, on both the citizens and the city planners end. The integration of big data into the planning process, at this stage, does not have uniform impact across all levels of decision making and planning process, therefore it should be performed gradually and with full awareness of existing limitations

Special Session on Industry 4.0

No abstract available