FIWARE Open Source Standard Platform in Smart Farming - A Review

[EN] FIWARE is an open source platform for the deployment of Internet of Things (IoT) applications, driven by European Union and managed by FIWARE Foundation. Recently, FIWARE Foundation has launched his new product Agricolus, which focus on Smart Farming and it uses FIWARE infrastructure. Agricolus manages to bring Hardware and Software together in a decision-making process that support farming activities and offers a "plug and play" interface for precision agriculture. This is encompassed by the phenomenon of Smart Farming, which is a development that take advantage of the use of Information Communication Technologies (ICT) in the daily farm management. This review aims to gain insight into the state-of-the-art of FIWARE in Smart Farming and identify the components of Agricolus in comparison with essential FIWARE architecture.This research has been carried out in the framework of 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.Rodríguez-Sánchez, MDLÁ.; Cuenca, L.; Ortiz Bas, Á. (2018). FIWARE Open Source Standard Platform in Smart Farming - A Review. IFIP Advances in Information and Communication Technology. 534:581-589. https://doi.org/10.1007/978-3-319-99127-6_50S581589534Robert, 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)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 Freire L.: Internet of food and farm 2020. In: Vermesan, O., Friess, P. (eds.) Digitising the Industry - Internet of Things Connecting Physical, Digital and Virtual Worlds, pp. 129–151. River Publishers, Gistrup/Delft (2016)Lin, J., Liu, C.: Monitoring system based on wireless sensor network and a SocC platform in precision agriculture. In: Proceedings of the International Conference on Communication Technology (ICCT), Hangzhou, pp. 101–104 (2008)Kaewmard, N., Saiyod, S.: Sensor data collection and irrigation control on vegetable crop using smart phone and wireless sensor networks for smart farm. In: Proceedings of the International Conference on Wireless Sensors (ICWiSE), pp. 106–112 (2014)FIWARE. https://www.fiware.org/Future Internet Private Public Partnership (FI-PPP). https://www.fi-ppp.eu/Agricolus. https://www.agricolus.comFIWARE Generic Enablers. http://edu.fiware.org/FIWARE Catalogue. https://catalogue.fiware.org/enablersKamilaris, A., Gao, F., Prenafeta-Boldu, F.X., Ali, M.I.: Agri-IoT: a semantic framework for Internet of Things-enabled smart farming applications. In: IEEE 3rd World Forum on Internet of Things, WF-IoT 2016, pp. 442–447 (2017)López-Riquelme, J.A., Pavón-Pulido, N., Navarro-Hellín, H., Soto-Valles, F., Torres-Sánchez, R.: A software architecture based on FIWARE cloud for precision agriculture. Agric. Water Manag. 183, 123–135 (2017)Martínez, R., Pastor, J.Á., Álvarez, B., Iborra, A.: A testbed to evaluate the FIWARE-based IoT platform in the domain of precision agriculture. Sensors (Switzerland), 16(11) (2016)Pesonen, L.A., et al.: Cropinfra - an internet-based service infrastructure to support crop production in future farms. Biosys. Eng. 120, 92–101 (2014)Barmpounakis, S., et al.: Management and control applications in agriculture domain via a future internet business-to-business platform. Inf. Process. Agric. 2(1), 51–63 (2015)Kaloxylos, A., et al.: Farm management systems and the future internet era. Comput. Electron. Agric. 89, 130–144 (2012)Kaloxylos, A., et al.: A cloud-based farm management system: architecture and implementation. Comput. Electron. Agric. 100, 168–179 (2014)Ryu, M., Yun, J., Miao, T., Ahn, I.Y., Choi, S.C., Kim, J.: Design and implementation of a connected farm for smart farming system. In: 2015 IEEE SENSORS Proceedings, pp. 1–4 (2015)Layton, A.W., Balmos, A.D., Sabpisal, S., Ault, A., Krogmeier, J.V., Buckmaster, D.: ISOBlue: an open source project to bring agricultural machinery data into the cloud, Montreal, 13 July–16 July 2014. American Society of Agricultural and Biological Engineers (2014)SmartAgriFood. http://smartagrifood.com/FIWARE MarketPlace. https://marketplace.fiware.orgFIWARE iHubs. https://www.fiware.org/community/fiware-ihubs/Agricolus in FIWARE MarketPlace. https://marketplace.fiware.org/pages/solutions/2ec3c741ef4dd8f83bab4e83Implementation example of Agricolus. http://www.libelium.com/increasing-tobacco-crops-quality-by-climatic-conditions-control/FIspace. https://www.fispace.eu/whatisfispace.htm

IoT monitoring of water consumption for irrigation systems using SEMMA methodology

The efficient use of water is an issue that has captured the attention of scientists, technicians, and the community at large. The sustainability of water resources has been threatened by the current imbalance between water supply and demand. Intelligent consumption of water would contribute to the balance and reduce the waste in applications such as the agriculture. This paper shows the design of a water consumption monitoring system based on the Internet of Things (IoT). With the implementation of this system could be known in real time the consumption of water in a crop. In addition, the user of the system may take corrective actions that optimize their water consumption; this is achieved by applying the SEMMA methodology to evaluate the data obtained by the system using two cluster algorithms, Simple K-means and GenClus++. With the application of SEMMA it was possible to determine periods of water consumption that were considered as waste in the irrigation of crops, applying data analysis with both algorithms

Isotemporal substitution of inactive time with physical activity and time in bed: Cross-sectional associations with cardiometabolic health in the PREDIMED-Plus study

© 2019 The Author(s). Background: This study explored the association between inactive time and measures of adiposity, clinical parameters, obesity, type 2 diabetes and metabolic syndrome components. It further examined the impact of reallocating inactive time to time in bed, light physical activity (LPA) or moderate-To-vigorous physical activity (MVPA) on cardio-metabolic risk factors, including measures of adiposity and body composition, biochemical parameters and blood pressure in older adults. Methods: This is a cross-sectional analysis of baseline data from 2189 Caucasian men and women (age 55-75 years, BMI 27-40 Kg/m2) from the PREDIMED-Plus study (http://www.predimedplus.com/). All participants had ≥3 components of the metabolic syndrome. Inactive time, physical activity and time in bed were objectively determined using triaxial accelerometers GENEActiv during 7 days (ActivInsights Ltd., Kimbolton, United Kingdom). Multiple adjusted linear and logistic regression models were used. Isotemporal substitution regression modelling was performed to assess the relationship of replacing the amount of time spent in one activity for another, on each outcome, including measures of adiposity and body composition, biochemical parameters and blood pressure in older adults. Results: Inactive time was associated with indicators of obesity and the metabolic syndrome. Reallocating 30 min per day of inactive time to 30 min per day of time in bed was associated with lower BMI, waist circumference and glycated hemoglobin (HbA1c) (all p-values < 0.05). Reallocating 30 min per day of inactive time with 30 min per day of LPA or MVPA was associated with lower BMI, waist circumference, total fat, visceral adipose tissue, HbA1c, glucose, triglycerides, and higher body muscle mass and HDL cholesterol (all p-values < 0.05). Conclusions: Inactive time was associated with a poor cardio-metabolic profile. Isotemporal substitution of inactive time with MVPA and LPA or time in bed could have beneficial impact on cardio-metabolic health. Trial registration: The trial was registered at the International Standard Randomized Controlled Trial (ISRCTN: http://www.isrctn.com/ISRCTN89898870) with number 89898870 and registration date of 24 July 2014, retrospectively registered

Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is a genetically complex autoimmune disease characterized by loss of immune tolerance to nuclear and cell surface antigens. Previous genome-wide association studies (GWAS) had modest sample sizes, reducing their scope and reliability. Our study comprised 7,219 cases and 15,991 controls of European ancestry, constituting a new GWAS, a meta-analysis with a published GWAS and a replication study. We have mapped 43 susceptibility loci, including ten new associations. Assisted by dense genome coverage, imputation provided evidence for missense variants underpinning associations in eight genes. Other likely causal genes were established by examining associated alleles for cis-acting eQTL effects in a range of ex vivo immune cells. We found an over-representation (n = 16) of transcription factors among SLE susceptibility genes. This finding supports the view that aberrantly regulated gene expression networks in multiple cell types in both the innate and adaptive immune response contribute to the risk of developing SLE

Polymorphisms within the ARNT2 and CX3CR1 Genes Are Associated with the Risk of Developing Invasive Aspergillosis

Invasive aspergillosis (IA) is a life-threatening infection that affects an increasing number of patients undergoing chemotherapy or allo-transplantation, and recent studies have shown that genetic factors contribute to disease susceptibility. In this two-stage, population-based, case-control study, we evaluated whether 7 potentially functional single nucleotide polymorphisms (SNPs) within the ARNT2 and CX3CR1 genes influence the risk of IA in high-risk hematological patients. We genotyped selected SNPs in a cohort of 500 hematological patients (103 of those had been diagnosed with proven or probable IA), and we evaluated their association with the risk of developing IA. The association of the most interesting markers of IA risk was then validated in a replication population, including 474 subjects (94 IA and 380 non-IA patients). Functional experiments were also performed to confirm the biological relevance of the most interesting markers. The meta-analysis of both populations showed that carriers of the ARNT2rs1374213G, CX3CR1rs7631529A, and CX3CR1rs9823718G alleles (where the RefSeq identifier appears as a subscript) had a significantly increased risk of developing IA according to a log-additive model (P value from the meta-analysis [PMeta]\u2009=\u20099.8 \ub7 10-5, PMeta\u2009=\u20091.5 \ub7 10-4, and PMeta\u2009=7.9 \ub7 10-5, respectively). Haplotype analysis also confirmed the association of the CX3CR1 haplotype with AG CGG with an increased risk of IA (P\u2009=\u20094.0 \ub7 10-4). Mechanistically, we observed that monocyte-derived macrophages (MDM) from subjects carrying the ARNTR2rs1374213G allele or the GG genotype showed a significantly impaired fungicidal activity but that MDM from carriers of the ARNT2rs1374213G and CX3CR1rs9823718G or CX3CR1rs7631529A alleles had deregulated immune responses to Aspergillus conidia. These results, together with those from expression quantitative trait locus (eQTL) data browsers showing a strong correlation of the CX3CR1rs9823718G allele with lower levels of CX3CR1 mRNA in whole peripheral blood (P\u2009=\u20092.46 \ub7 10-7) and primary monocytes (P\u2009=\u20094.31 \ub7 10-7), highlight the role of the ARNT2 and CX3CR1 loci in modulating and predicting IA risk and provide new insights into the host immune mechanisms involved in IA development

Polymorphisms within the ARNT2 and CX3CR1 Genes Are Associated with the Risk of Developing Invasive Aspergillosis.

Invasive aspergillosis (IA) is a life-threatening infection that affects an increasing number of patients undergoing chemotherapy or allo-transplantation, and recent studies have shown that genetic factors contribute to disease susceptibility. In this two-stage, population-based, case-control study, we evaluated whether 7 potentially functional single nucleotide polymorphisms (SNPs) within the ARNT2 and CX3CR1 genes influence the risk of IA in high-risk hematological patients. We genotyped selected SNPs in a cohort of 500 hematological patients (103 of those had been diagnosed with proven or probable IA), and we evaluated their association with the risk of developing IA. The association of the most interesting markers of IA risk was then validated in a replication population, including 474 subjects (94 IA and 380 non-IA patients). Functional experiments were also performed to confirm the biological relevance of the most interesting markers. The meta-analysis of both populations showed that carriers of the ARNT2rs1374213G, CX3CR1rs7631529A, and CX3CR1rs9823718G alleles (where the RefSeq identifier appears as a subscript) had a significantly increased risk of developing IA according to a log-additive model (P value from the meta-analysis [PMeta] = 9.8 · 10-5, PMeta = 1.5 · 10-4, and PMeta =7.9 · 10-5, respectively). Haplotype analysis also confirmed the association of the CX3CR1 haplotype with AG CGG with an increased risk of IA (P = 4.0 · 10-4). Mechanistically, we observed that monocyte-derived macrophages (MDM) from subjects carrying the ARNTR2rs1374213G allele or the GG genotype showed a significantly impaired fungicidal activity but that MDM from carriers of the ARNT2rs1374213G and CX3CR1rs9823718G or CX3CR1rs7631529A alleles had deregulated immune responses to Aspergillus conidia. These results, together with those from expression quantitative trait locus (eQTL) data browsers showing a strong correlation of the CX3CR1rs9823718G allele with lower levels of CX3CR1 mRNA in whole peripheral blood (P = 2.46 · 10-7) and primary monocytes (P = 4.31 · 10-7), highlight the role of the ARNT2 and CX3CR1 loci in modulating and predicting IA risk and provide new insights into the host immune mechanisms involved in IA development.status: Published onlin