Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Distributed Computing and Artificial Intelligence

DCAI 2018: Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference pp 431–436Cite as

A Data Mining Approach Applied to Wireless Sensor Neworks in Greenhouses

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 801))

Abstract

This research presents an innovative multi-agent system based on virtual organizations. It has been designed to manage the information collected by wireless sensor networks for knowledge discovery and decision making in greenhouses. The developed multi-agent system allowed us to take decisions on the basis of the analysis of the historical data obtained from sensors. The proposed approach improves the efficiency of greenhouses by optimizing the use of resources.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Shrouf, F., Miragliotta, G.: Energy management based on Internet of Things: practices and framework for adoption in production management. J. Clean. Prod. 100, 235–246 (2015)

    Article  Google Scholar 

  2. Fang, S., Da Xu, L., Zhu, Y., Ahati, J., Pei, H., Yan, J., Liu, Z.: An integrated system for regional environmental monitoring and management based on Internet of Things. IEEE Trans. Industr. Inf. 10(2), 1596–1605 (2014)

    Article  Google Scholar 

  3. Sicari, S., Rizzardi, A., Grieco, L.A., Coen-Porisini, A.: Security, privacy and trust in Internet of Things: the road ahead. Comput. Netw. 76, 146–164 (2015)

    Article  Google Scholar 

  4. Mumtaz, S., Alsohaily, A., Pang, Z., Rayes, A., Tsang, K.F., Rodriguez, J.: Massive Internet of Things for industrial applications: addressing wireless IIoT connectivity challenges and ecosystem fragmentation. IEEE Ind. Electron. Mag. 11(1), 28–33 (2017)

    Article  Google Scholar 

  5. Li, T., Sun, S., Bolić, M., Corchado, J.M.: Algorithm design for parallel implementation of the SMC-PHD filter. Sig. Process. 119, 115–127 (2016). https://doi.org/10.1016/j.sigpro.2015.07.013

    Article  Google Scholar 

  6. Lima, A.C.E.S., De Castro, L.N., Corchado, J.M.: A polarity analysis framework for Twitter messages. Appl. Math. Comput. 270, 756–767 (2015). https://doi.org/10.1016/j.amc.2015.08.059

    Article  MATH  Google Scholar 

  7. Redondo-Gonzalez, E., De Castro, L.N., Moreno-Sierra, J., Maestro De Las Casas, M.L., Vera-Gonzalez, V., Ferrari, D.G., Corchado, J.M.: Bladder carcinoma data with clinical risk factors and molecular markers: a cluster analysis. BioMed Res. Int. (2015). https://doi.org/10.1155/2015/168682

    Article  Google Scholar 

  8. Li, T., Sun, S., Corchado, J.M., Siyau, M.F.: Random finite set-based Bayesian filters using magnitude-adaptive target birth intensity. In: FUSION 2014 - 17th International Conference on Information Fusion (2014). https://www.scopus.com/inward/record.uri?eid=2-s2.0-84910637788&partnerID=40&md5=bd8602d6146b014266cf07dc35a681e0

  9. Choon, Y.W., Mohamad, M.S., Deris, S., Illias, R.M., Chong, C.K., Chai, L.E., Omatu, S., Corchado, J.M.: Differential bees flux balance analysis with OptKnock for in silico microbial strains optimization. PLoS ONE 9(7) (2014). https://doi.org/10.1371/journal.pone.0102744

    Article  Google Scholar 

  10. Li, T., Sun, S., Corchado, J.M., Siyau, M.F.: A particle dyeing approach for track continuity for the SMC-PHD filter. In: FUSION 2014 - 17th International Conference on Information Fusion (2014). https://www.scopus.com/inward/record.uri?eid=2-s2.0-84910637583&partnerID=40&md5=709eb4815eaf544ce01a2c21aa749d8f

  11. Coria, J.A.G., Castellanos-Garzón, J.A., Corchado, J.M.: Intelligent business processes composition based on multi-agent systems. Expert Syst. Appl. 41(4 PART 1), 1189–1205 (2014). https://doi.org/10.1016/j.eswa.2013.08.003

    Article  Google Scholar 

  12. Tapia, D.I., Fraile, J.A., Rodríguez, S., Alonso, R.S., Corchado, J.M.: Integrating hardware agents into an enhanced multi-agent architecture for ambient intelligence systems. Inf. Sci. 222, 47–65 (2013). https://doi.org/10.1016/j.ins.2011.05.002

    Article  Google Scholar 

  13. Costa, Â., Novais, P., Corchado, J.M., Neves, J.: Increased performance and better patient attendance in a hospital with the use of smart agendas. Logic J. IGPL 20(4), 689–698 (2012). https://doi.org/10.1093/jigpal/jzr021

    Article  MathSciNet  Google Scholar 

  14. García, E., Rodríguez, S., Martín, B., Zato, C., Pérez, B.: MISIA: middleware infrastructure to simulate intelligent agents. Advances in Intelligent and Soft Computing, vol. 91 (2011). https://doi.org/10.1007/978-3-642-19934-9_14

    Google Scholar 

  15. Rodríguez, S., De La Prieta, F., Tapia, D.I., Corchado, J.M.: Agents and computer vision for processing stereoscopic images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNAI, vol. 6077 (2010). https://doi.org/10.1007/978-3-642-13803-4_12

    Google Scholar 

  16. Rodríguez, S., Gil, O., De La Prieta, F., Zato, C., Corchado, J.M., Vega, P., Francisco, M.: People detection and stereoscopic analysis using MAS. In: INES 2010 - 14th International Conference on Intelligent Engineering Systems, Proceedings (2010). https://doi.org/10.1109/INES.2010.5483855

  17. Baruque, B., Corchado, E., Mata, A., Corchado, J.M.: A forecasting solution to the oil spill problem based on a hybrid intelligent system. Inf. Sci. 180(10), 2029–2043 (2010). https://doi.org/10.1016/j.ins.2009.12.032

    Article  Google Scholar 

  18. Tapia, D.I., Corchado, J.M.: An ambient intelligence based multi-agent system for alzheimer health care. International J. Ambient Comput. Intell. 1(1), 15–26 (2009). https://doi.org/10.4018/jaci.2009010102

    Article  Google Scholar 

  19. Mata, A., Corchado, J.M.: Forecasting the probability of finding oil slicks using a CBR system. Expert Syst. Appl. 36(4), 8239–8246 (2009). https://doi.org/10.1016/j.eswa.2008.10.003

    Article  Google Scholar 

  20. Glez-Peña, D., Díaz, F., Hernández, J.M., Corchado, J.M., Fdez-Riverola, F.: geneCBR: a translational tool for multiple-microarray analysis and integrative information retrieval for aiding diagnosis in cancer research. BMC Bioinform. 10 (2009). https://doi.org/10.1186/1471-2105-10-187

    Article  Google Scholar 

  21. Fernández-Riverola, F., Díaz, F., Corchado, J.M.: Reducing the memory size of a Fuzzy case-based reasoning system applying rough set techniques. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 37(1), 138–146 (2007). https://doi.org/10.1109/TSMCC.2006.876058

    Article  Google Scholar 

  22. Méndez, J.R., Fdez-Riverola, F., Díaz, F., Iglesias, E.L., Corchado, J.M.: A comparative performance study of feature selection methods for the anti-spam filtering domain. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNAI, vol. 4065, 106–120 (2006). https://www.scopus.com/inward/record.uri?eid=2-s2.0-33746435792&partnerID=40&md5=25345ac884f61c182680241828d448c5

    Chapter  Google Scholar 

  23. Méndez, J.R., Fdez-Riverola, F., Iglesias, E.L., Díaz, F., Corchado, J.M.: Tracking concept drift at feature selection stage in SpamHunting: an anti-spam instance-based reasoning system. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNAI, vol. 4106, pp. 504–518 (2006). https://www.scopus.com/inward/record.uri?eid=2-s2.0-33750974465&partnerID=40&md5=f468552f565ecc3af2d3ca6336e09cc2

    Google Scholar 

  24. Fdez-Rtverola, F., Corchado, J.M.: FSfRT: forecasting system for red tides. Appl. Intell. 21(3), 251–264 (2004). https://doi.org/10.1023/B:APIN.0000043558.52701.b1

    Article  Google Scholar 

  25. Corchado, J.M., Pavón, J., Corchado, E.S., Castillo, L.F.: Development of CBR-BDI agents: a tourist guide application. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3155, pp. 547–559 (2004). https://doi.org/10.1007/978-3-540-28631-8

  26. Laza, R., Pavn, R., Corchado, J.M.: A reasoning model for CBR_BDI agents using an adaptable fuzzy inference system. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3040, pp. 96–106. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  27. Corchado, J.A., Aiken, J., Corchado, E.S., Lefevre, N., Smyth, T.: Quantifying the Ocean’s CO2 budget with a CoHeL-IBR system. In: Advances in Case-Based Reasoning, Proceedings, vol. 3155, pp. 533–546 (2004)

    Google Scholar 

  28. Corchado, J.M., Borrajo, M.L., Pellicer, M.A., Yáñez, J.C.: Neuro-symbolic system for business internal control. In: Industrial Conference on Data Mining, pp. 1–10 (2004). https://doi.org/10.1007/978-3-540-30185-1_1

    Google Scholar 

  29. Corchado, J.M., Corchado, E.S., Aiken, J., Fyfe, C., Fernandez, F., Gonzalez, M.: Maximum likelihood hebbian learning based retrieval method for CBR systems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2689, pp. 107–121 (2003). https://doi.org/10.1007/3-540-45006-8_11

  30. Fdez-Riverola, F., Corchado, J.M.: CBR based system for forecasting red tides. Knowl. Based Syst. 16(5–6), 321–328 (2003). https://doi.org/10.1016/S0950-7051(03)00034-0

    Article  Google Scholar 

  31. Glez-Bedia, M., Corchado, J.M., Corchado, E.S., Fyfe, C.: Analytical model for constructing deliberative agents. Int. J. Eng. Intell. Syst. Electr. Eng. Commun. 10(3), 173 (2002)

    Google Scholar 

  32. Corchado, J.M., Aiken, J.: Hybrid artificial intelligence methods in oceanographic forecast models. IEEE Trans. Syst. Man Cybern. Part C-Appl. Rev. 32(4), 307–313 (2002). https://doi.org/10.1109/tsmcc.2002.806072

    Article  Google Scholar 

  33. Fyfe, C., Corchado, J.: A comparison of Kernel methods for instantiating case based reasoning systems. Adv. Eng. Inform. 16(3), 165–178 (2002). https://doi.org/10.1016/S1474-0346(02)00008-3

    Article  Google Scholar 

  34. Fyfe, C., Corchado, J.M.: Automating the construction of CBR systems using kernel methods. Int. J. Intell. Syst. 16(4), 571–586 (2001). https://doi.org/10.1002/int.1024

    Article  MATH  Google Scholar 

  35. Li, T.-C., Su, J.-Y., Liu, W., Corchado, J.M.: Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond. Front. Inf. Technol. Electron. Eng. 18(12), 1913–1939 (2017)

    Article  Google Scholar 

  36. Wang, X., Li, T., Sun, S., Corchado, J.M.: A survey of recent advances in particle filters and remaining challenges for multitarget tracking. Sensors (Switzerland), 17(12), Article no. 2707 (2017)

    Article  Google Scholar 

  37. Morente-Molinera, J.A., Kou, G., González-Crespo, R., Corchado, J.M., Herrera-Viedma, E.: Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods. Knowl. Based Syst. 137, 54–64 (2017)

    Article  Google Scholar 

  38. Pinto, T., Gazafroudi, A.S., Prieto-Castrillo, F., Santos, G., Silva, F., Corchado, J.M., Vale, Z.: Reserve costs allocation model for energy and reserve market simulation. In: 2017 19th International Conference on Intelligent System Application to Power Systems, ISAP 2017, art. no. 8071410 (2017)

    Google Scholar 

  39. Chamoso, P., Rivas, A., Martín-Limorti, J.J., Rodríguez, S.: A hash based image matching algorithm for social networks. Advances in Intelligent Systems and Computing, vol. 619, pp. 183–190 (2018). https://doi.org/10.1007/978-3-319-61578-3_18

    Google Scholar 

  40. Sittón, I., Rodríguez, S.: Pattern extraction for the design of predictive models in Industry 4.0. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 258–261 (2017)

    Google Scholar 

  41. García, O., Chamoso, P., Prieto, J., Rodríguez, S., De La Prieta, F.: A serious game to reduce consumption in smart buildings. Communications in Computer and Information Science, vol. 722, pp. 481–493 (2017). https://doi.org/10.1007/978-3-319-60285-1_41

    Google Scholar 

  42. Palomino, C.G., Nunes, C.S., Silveira, R.A., González, S.R., Nakayama, M.K.: Adaptive agent-based environment model to enable the teacher to create an adaptive class. Advances in Intelligent Systems and Computing, vol. 617 (2017). https://doi.org/10.1007/978-3-319-60819-8_3

    Google Scholar 

  43. Canizes, B., Pinto, T., Soares, J., Vale, Z., Chamoso, P., Santos, D.: Smart city: A GECAD-BISITE energy management case study. In: 15th International Conference on Practical Applications of Agents and Multi-Agent Systems PAAMS 2017, Trends in Cyber-Physical Multi-Agent Systems, vol. 2, pp. 92–100 (2017). https://doi.org/10.1007/978-3-319-61578-3_9

    Google Scholar 

  44. Chamoso, P., de La Prieta, F., Eibenstein, A., Santos-Santos, D., Tizio, A., Vittorini, P.: A device supporting the self management of tinnitus. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNCS, vol. 10209, pp. 399–410 (2017). https://doi.org/10.1007/978-3-319-56154-7_36

    Chapter  Google Scholar 

  45. Román, J.A., Rodríguez, S., de da Prieta, F.: Improving the distribution of services in MAS. Communications in Computer and Information Science, vol. 616 (2016). https://doi.org/10.1007/978-3-319-39387-2_4

    Chapter  Google Scholar 

  46. Durik, B.O.: Organisational metamodel for large-scale multi-agent systems: first steps towards modelling organisation dynamics. Adv. Distrib. Comput. Artif. Intell. J. (ADCAIJ) 6(3), 17 (2017)

    Article  Google Scholar 

  47. Bremer, J., Lehnhoff, S.: Decentralized coalition formation with agent-based combinatorial heuristics. Adv. Distrib. Comput. Artif. Intell. J. (ADCAIJ) 6(3), 29 (2017)

    Article  Google Scholar 

  48. Cardoso, R.C., Bordini, R.H: A multi-agent extension of a hierarchical task network planning formalism. Adv. Distrib. Comput. Artif. Intell. J. (ADCAIJ) 6(2), 5 (2017)

    Article  Google Scholar 

  49. Gonçalves, E., Cortés, M., Oliveira, M.D., Veras, N., Falcão, M., Castro, J.: An analysis of software agents, environments and applications school: retrospective, relevance, and trends. Adv. Distrib. Comput. Artif. Intell. J. (ADCAIJ) 6(2), 19 (2017)

    Article  Google Scholar 

  50. Teixeira, E.P., Goncalves, E.M.N., Adamatti, D.F.: Ulises: a agent-based system for timbre classification. Adv. Distrib. Comput. Artif. Intell. J. (ADCAIJ) 7(1), 29 (2017)

    Article  Google Scholar 

  51. de Castro, L.F.S., Alves, G.V., Borges, A.P.: Using trust degree for agents in order to assign spots in a Smart Parking. Adv. Distrib. Comput. Artif. Intell. J. (ADCAIJ) 6(2) (2017)

    Google Scholar 

  52. Cunha, R., Billa, C., Adamatti, D.: Development of a graphical tool to integrate the Prometheus AEOlus methodology and Jason platform. Adv. Distrib. Comput. Artif. Intell. J. (ADCAIJ) 6(2), 57 (2017)

    Article  Google Scholar 

  53. Ramos, J., Castellanos-Garzón, J.A., González-Briones, A., de Paz, J.F., Corchado, J.M.: An agent-based clustering approach for gene selection in gene expression microarray. Interdiscip. Sci. Comput. Life Sci. 9(1), 1–13 (2017)

    Google Scholar 

  54. Castellanos-Garzón, J.A., Ramos, J.: A gene selection approach based on clustering for classification tasks in colon cancer. Adv. Distrib. Comput. Artif. Intell. Journal. 4(3), 1–10 (2015)

    Google Scholar 

Download references

Acknowledgements

This work has been supported by the project “IOTEC: Development of Technological Capacities around the Industrial Application of Internet of Things (IoT)”. 0123_IOTEC_3_E. Project financed with FEDER funds, Interreg Spain-Portugal (PocTep).

Author information

Authors and Affiliations

  1. BISITE Digital Innovation Hub, University of Salamanca, Edificio Multiusos I+D+i, 37007, Salamanca, Spain

    José A. Castellanos-Garzón & Yeray Mezquita Martín

  2. Instituto Universitario de Estudios de la Ciencia y la Tecnología, University of Salamanca, Salamanca, Spain

    José Luis Jaimes S. & Santiago M. López G.

Authors
  1. José A. Castellanos-Garzón
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yeray Mezquita Martín
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José Luis Jaimes S.
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Santiago M. López G.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to José A. Castellanos-Garzón .

Editor information

Editors and Affiliations

  1. BISITE Digital Innovation Hub, University of Salamanca, Salamanca, Spain

    Sara Rodríguez

  2. BISITE Digital Innovation Hub, University of Salamanca, Salamanca, Spain

    Javier Prieto

  3. GECAD - Instituto Superior de Engenharia, Porto, Portugal

    Pedro Faria

  4. Department of Computer Science and Production Management, University of Zielona Góra, Zielona Góra, Poland

    Sławomir Kłos

  5. Computing Science and Artificial Intelligence, Rey Juan Carlos University, Móstoles, Madrid, Spain

    Alberto Fernández

  6. Basque Center for Applied Mathematics, Bilbao, Spain

    Santiago Mazuelas

  7. Basque Center for Applied Mathematics, Universidad de Alcalá, Alcalá de Henares, Spain

    M. Dolores Jiménez-López

  8. Departamento de Informática y Automática, University of Salamanca, Salamanca, Spain

    María N. Moreno

  9. Departamento de Sistemas Informáticos, University of Castilla-La Mancha, Albacete, Spain

    Elena M. Navarro

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Castellanos-Garzón, J.A., Mezquita Martín, Y., Jaimes S., J.L., López G., S.M. (2019). A Data Mining Approach Applied to Wireless Sensor Neworks in Greenhouses. In: Rodríguez, S., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-319-99608-0_60

Download citation

Publish with us

Policies and ethics

Search

Navigation