Dissemination Material



  • Gajinov, S., Popovic, T.,  Drajic, D., Gligoric, N. and Krco, S. (2022) ” Qualitative parameter analysis for Botrytis cinerea forecast modelling using IoT sensor networks’, Journal of Networking and Network Applications, Vol.2, Issue 3, pp,120-135 DOI:10.33969/J-NaNA.2022.020305
  • Reis-Pereira, M.,Tosin, R., Martins, R., Neves dos Santos, F., Tavares, F and Cunha, M. (2022) “Kiwi Plant Canker Diagnosis Using Hyperspectral Signal Processing and Machine Learning: Detecting Symptoms Caused by Pseudomonas syringae pv. actinidiae”, Plants 202211, 2154. https://doi.org/10.3390/plants11162154
  • Gonzalez-Vidal, A., Ramallo-Gonzalez, A.P and Skarmeta, A.F. (2022) ‘Intrinsic and extrinsic quality of data for open data repositories’, ICT Express, https://doi.org/10.1016/j.icte.2022.06.001
  • Roussaki, I., Doolin, K., Skarmeta, A., Routis, G., Lopez-Morales, J., Claffey, E., Mora, M., Martinez, J.A (2022) “Building an interoperable space for smart agriculture”, Digital Communication and Networks, https://doi.org/10.1016/j.dcan.2022.02.004
  • Campos, E.M., Saura, P.F., Gonzales-Vidal, A., Herandez-Ramos, J., Bernabe, J.B., Baldini, G., Skarmeta, A. (2021), “Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges”, https://arxiv.org/abs/2108.00974
  • Charvat K., Bergheim R., Bērziņš R., Zadražil F., Langovskis D., Vrobel J., Horakova S. (2021), “Map Whiteboard Cloud Solution for Collaborative Editing of Geographic Information”. Cloud Computing and Data Science 2(2):36-5. Available from: https://ojs.wiserpub.com/index.php/CCDS/article/view/897 
  • da Silva, D.Q., Aguiar, A.S., dos Santos, F.N., Sousa, A.J., Rabino, D.,  Biddoccu, M., Bagagiolo, G., and Delmastro, M. (2021), ” Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards”. Agriculture 202111, 208. https://doi.org/10.3390/agriculture11030208
  • Azpiroz, I., Oses, N,  Quartulli, M., Olaizola, I.G., Guidotti, D., and Marchi, S (2021) “Comparison of Climate Reanalysis and Remote-Sensing Data for Predicting Olive Phenology through Machine-Learning Methods”. Remote Sensors13, 1224. https://doi.org/10.3390/rs1306122
  • López-Morales, J.A., Martínez J.A. and Skarmeta A.F. (2021), “ Improving Energy Efficiency of Irrigation Wells by Using an IoT-Based Platform”. Electronics, 10(3), 250. https://doi.org/10.3390/electronics10030250 
  • Alcaniz, T., Gonzalez-Vidal, A., Ramallo, A. and Skarmeta, A. (2021), “Quality of Information within Internet of Things Data” Available from DOI: 10.5772/intechopen.95844 
  • Bordel, B.  Alcarria R. and Robles T. “Controlling Supervised Industry 4.0 Processes through Logic Rules and Tensor Deformation Functions,” (2021) Informatica, 1-29. doi:10.15388/20-INFOR441
  • González-Vidal, A.,  Rathore, P., Rao, A.S., Mendoza-Bernal, J., Palaniswami, M. and Skarmeta-Gómez., A.F. (2021), “Missing Data Imputation with Bayesian Maximum Entropy for Internet of Things Applications,” in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2020.2987979
  • Oses,N.,  Azpiroz, I., Marchi, S., Guidotti, D., Quartulli, M. and Olaizol, I.G. (2020), “Analysis of Copernicus’ ERA5 Climate Reanalysis Data as a Replacement for Weather Station Temperature Measurements in Machine Learning Models for Olive Phenology Phase Prediction, “ Sensors 2020, 20, 6381. https://doi.org/10.3390/s20216381
  • López-Morales, J.A., Martínez, J.A., and Skarmeta, A.F. (2020), “Digital Transformation of Agriculture through the Use of an Interoperable Platform” Sensors 202020, 1153. https://doi.org/10.3390/s20041153
  • Jallal, M., Gonzalez-Vidal, A., Skarmeta, A.F, Chabaa, S. and Zeroual, A. (2020) , “A hybrid neuro-fuzzy inference system-based algorithm for time series forecasting applied to energy consumption prediction” Applied Energy, 268, 10.1016/j.apenergy.2020.114977
  • González-Vidal, A., Alcaniz, T., Iggena, T., Bin Ilyas, E. and Skarmeta, A.F. “Domain Agnostic Quality of Information Metrics in IoT-Based Smart Environments”, Intelligent Environments 2020, doi:10.3233/AISE200059 pdf
  • Bordel, B., Alcarria, R and Robles T. “Supervising Industrial Distributed Processes Through Soft Models, Deformation Metrics and Temporal Logic Rules”. In: Rocha Á., Adeli H., Reis L., Costanzo S., Orovic I., Moreira F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1160. Springer, Cham. https://doi.org/10.1007/978-3-030-45691-7_12


  • Azpiroz, I., Quartulli, M. and Olaizola, I.G (2022) ‘Methodology for Online Phenology Prediction Service Creation’ IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium, 17-22 July 2022, Kuala Lumpur, Malaysia  10.1109/IGARSS46834.2022.9883164
  • Juska,V.B and O’Riordan, A (2022) ‘Micro-Surface Engineering of Integrated Silicon Microtechnologies for the Development of Sensing and Biosensing Platforms;, ECS Meeting Abstracts, Volume MA2022-02, 2260 DOI 10.1149/MA2022-02612260mtgabs
  • Mueller, S., Plociennik, M. Zacharczuk, M., Fojud, A., Blaszczak, M., Laskowska, A., Palma, R and Wojtowicz, A. (2022) “Leveraging IoT solutions as a base for development of the agriculture advisory services”, 2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS), 1-3 August, Barcelona, Spain  10.1109/COINS54846.2022.9854950
  • Bilbao-Arechabala, S. and Martinez-Rodriguez, B. (2022) “A practical approach to cross-agri-domain interoperability and integration” 2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS), 1-3 August, Barcelona, Spain 10.1109/COINS54846.2022.9854999
  • Routis, G., Paraskevopoulos, M., Vetsikas, I.A, Roussaki, I., Stavrakoudis, D. and Katsantonis, D. (2022) “Data-Driven and Interoperable Smart Agriculture: An IoT-based Use-Case for Arable Crops”, 2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS), 1-3 August, Barcelona, Spain.10.1109/COINS54846.2022.9855001
  • Gallo, P., Daidone, F., Sgroi, F. and Avantaggiato, M. (2022) “AgriChain: Blockchain Syntactic and Semantic Validation for Reducing Information Asymmetry In Agri-Food’, CEUR Workshop Proceedings June 20-23, Rome, Italy http://ceur-ws.org/Vol-3166/paper08.pdf
  • Bordel, B., Alcarria, R., de la Torre, G., Carretero, I. and Robles, T. (2022) “Increasing the efficiency and workers wellbeing in the European Bakery Industry: An Industry 4.0 Case Study”, ICITS 2022, pp646-658 DOI: 10.1007/978-3-030-96293-7_54
  • Abdipourchenarestansofla, M., Schroth, C. (2022) “The importance of data quality assessment for machinery data in the field of agriculture”, 79th International Conference on Agricultural Engineering, Online, DOI:10.51202/9783181023952-495
  • Bordel, B., Alcarria, R., Robles, T., de la Torre, G. and Carretero, I. (2021) ‘Digital user-industry interactions and Industry 4.0 services to improve customers’ experience and satisfaction in the European bakery sector’, 2021 16th Iberian Conference on Information Systems and Technologies (CISTI), 23 – 26 June 2021, Chaves, Portugal, doi: 10.23919/CISTI52073.2021.9476568
  • Fernandez Pesado, P.J., Rubio Melon, A., Escudero Barbero, R., Sanchez Hernandez B., Caceres Losada, J.L, Calero Gil, R. (2021) ‘The Digitalization of The Field. Use Of Remote Sensing and New Technologies as Sustainable Tools In The Management Of Modern Irrigation. R&D&I Projects: Optireg and DEMETER’, XXXVIII Congreso Nacional de Riegos, Cartagena, 3-5 November 2021,  https://repositorio.upct.es/bitstream/handle/10317/10111/B-03-2021.pdf?sequence=4&isAllowed=y
  • M. Płóciennik et al., “Leveraging Agri-food IoT Solutions to Connect Apiary Owners and Farmers,” 2021 16th International Conference on Telecommunications (ConTEL), 2021, pp. 152-157, doi: 10.23919/ConTEL52528.2021.9495980.
  • Bader, S., Pullmann, J., Mader, C., Tramp, S., Quix, C., Muller, A., Akyurek, H., Bockmann, M., Imbusch, B., Lipp, J.,Geilser, S. and Lange, C. (2020), “The International Data Spaces Information Model – An Ontology for Sovereign Exchange of Digital Content”, International Semantic Web Conference 2020, pp 176-192. DOI:10.1007/978-3-030-62466-8_12
  • Bordel, B., Alcarria, R. and Robles, T. (2020) “Supervising Industrial Distributed Processes Through Soft Models, Deformation Metrics and Temporal Logic Rules”, WorldCIST 2020: Trends and Innovations in Information Systems and Technologies, June 2020, https://doi.org/10.1007/978-3-030-45691-7_12
  • R. Arias Calderon, S. Arias Calderon, P. Martins, A. Cordeiro, J.M Silvestre (2020), “Validation of a fluorometer sensor for characterization of olive varieties and evaluation of fruit ripeness index with non-destructive measurement: preliminary results. A: II Simposio Ibérico de Ingeniería Hortícola: Agricultura 4.0: Ponte de Lima, Portugal: 4- 6 de marzo, 2020, p. 489-493.
  • JA. Lopez-Morales, A. F. Skarmeta and J. A. Martinez, “Agri-food Research Centres as Drivers of Digital Transformation for Smart Agriculture,” 2020 Global Internet of Things Summit (GIoTS), Dublin, Ireland, 2020, pp. 1-5, doi: 10.1109/GIOTS49054.2020.9119646.
  • N. Oses, I. Azpiroz, M. Quartulli, I. Olaizola, S. Marchi and D. Guidotti, “Machine Learning for olive phenology prediction and base temperature optimisation,” 2020 Global Internet of Things Summit (GIoTS), Dublin, Ireland, 2020, pp. 1-6, doi: 10.1109/GIOTS49054.2020.9119611.
  • I. Roussaki, P. Kosmides, G. Routis, K. Doolin, V. Pevtschin and A. Marguglio, “A Multi-Actor Approach to promote the employment of IoT in Agriculture,” 2019 Global IoT Summit (GIoTS), Aarhus, Denmark, 2019, pp. 1-6, doi: 10.1109/GIOTS.2019.8766416.

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Design Principles

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