Cloud services integration for farm animals’ behavior studies based on smartphones as activity sensors

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Smartphones, particularly iPhone, can be relevant instruments for researchers in animal behavior because they are readily available on the planet, contain many sensors and require no hardware development. They are equipped with high performance Inertial Measurement Units (IMU) and absolute positioning systems analyzing users’ movements, but they can easily be diverted to analyze likewise the behaviors of domestic animals such as cattle. The study of animal behavior using smartphones requires the storage of many high frequency variables from a large number of individuals and their processing through various relevant variables combinations for modeling and decision-making. Transferring, storing, treating and sharing such an amount of data is a big challenge. In this paper, a lambda cloud architecture innovatively coupled to a scientific sharing platform used to archive, and process high-frequency data are proposed to integrate future developments of the Internet of Things applied to the monitoring of domestic animals. An application to the study of cattle behavior on pasture based on the data recorded with the IMU of iPhone 4s is exemplified. Performances comparison between iPhone 4s and iPhone 5s is also achieved. The package comes also with a web interface to encode the actual behavior observed on videos and to synchronize observations with the sensor signals. Finally, the use of Edge computing on the iPhone reduced by 43.5% on average the size of the raw data by eliminating redundancies. The limitation of the number of digits on individual variable can reduce data redundancy up to 98.5%.

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  1. 1.

    edited by Wavefront Labs and available in the Apple App store:

  2. 2.

  3. 3.

    Apache Samza (2017) Accessed 5 April 2017.

  4. 4.

    Tschetter E (2011) Introducing druid: Real-time analytics at a billion rows per second., Accessed 4 April 2017.

  5. 5.

    LZ4., Accessed 4 April 2017.

  6. 6.

    Liblzf (2013) Accessed 4 April 2017.

  7. 7.

    Apache Zookeeper (2017) http://hadoop.apache.orf/zookeper, Accessed 10 April 2017.

  8. 8.

    Merkel D (2014) Docker: lightweight Linux containers for consistent development and deployment. Linux Journal 239, Article n°2.


  1. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G. Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray DG, Steiner B, Tucker P, Vasudevan V, Warden P, Wicke M, Yu Y, Zheng X, Google Brain (2016) TensorFlow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Sytemps Design and Implementation (OSDI’16). Novembre 2–4, 2016, ISBN: 978-1-931971-33-11

  2. Altintas I, Berkley C, Jaeger E, Jones M, Ludascher B, Mock S (2004) Kepler: an extensible system for design and execution of scientific workflows. In: Scientific and Statistical Database Management. In: Proceedins of the 16th International Conference on, IEEE, pp 423–424.

  3. Andriamandroso ALH, Bindelle J, Mercatoris B, Lebeau F (2016) Review on the use of sensors for jaw movements’ detection. Biotechnol Agron Soc Environ 20(S1):273–286

  4. Andriamandroso ALH, Lebeau F, Beckers Y, Froidmond E, Dufrasne I, Heinesch B, Dumortier P, Blanchy G, Blaise Y, Bindelle J (2017) Development of an open-source algorithm based on inertial measurement units (IMU) of a smartphone to detect cattle grass intake and ruminating behaviors. Comput Electron Agric 139:126–137.

  5. Andriamasinoro ALH, Lebeau F, Bindelle J (2015) Changes in biting characteristics recorded using the inertial measurement unit of a smartphone reflect differences in sward attributes. Precision Livestock Farming 15:283–289

  6. Apache Foundation (2017a) Apache Hama. Accessed 7 June 2017

  7. Apache Foundation (2017b) Aurora is a Mesos framework for long-running services and cron jobs. Accessed 7 June 2017

  8. Chikhaoui B, Ye B, Mihailidis A (2017) Aggressive and agitated behavior recognition from accelerometer data using non-negative matrix factorization. J Ambient Intell Human Comput.

  9. Debauche O, Mahmoudi S, Adriamandroso ALH, Manneback P, Bindelle J, Lebeau F (2017) Web-based cattle behavior service for researches based on the smartphone inertial central. Proc Comput Sci 110:110–116.

  10. Debauche O, El Moulat M, Mahmoudi S, Manneback P, Lebeau F (2018a) Irrigation pivot-center connected at low cost for the reduction of crop water requirements. In: International Conference on Advanced Communication Technologies and Networking (CommNet 2018). IEEE. ISBN: 978-1-5386-4609 (in press)

  11. Debauche O, El Moulat M, Mahmoudi S, Boukraa S, Manneback P, Lebeau F (2018b) Web monitoring of bee health for researchers and beekeepers based on the internet of things. Proc Comput Sci (in press)

  12. Debauche O, Mahmoudi S, Manneback P, Massinon M, Tadrist N, Lebeau F, Mahmoudi SA (2018c) Cloud architecture for digital phenotyping and automation. In: The 3rd International Conference on Cloud Computing Technologies and Applications—CloudTech’17.

  13. Diáz M, Martin C, Rubio B (2016) State-of-the art, challenges, and open issues in the integration of Internet of things and cloud computing. J Netw Comput Appl 67:99–117.

  14. Doel T, Shakir DI, Pratt R, Aertsen M, Moggridge J, Bellon E, David AL, Deprest J, Vercauteren T, Ourselin S (2017) GIF-Cloud: a data sharing and collaboration platform for medical imaging research. Comput Methods Programs Biomed 139:181–190.

  15. Frost AG, Schofield CP, Beaulah SA, Mottram TT, Lines JA, Wathes CM (1997) A review of livestock monitoring and the need for integration systems. Comput Electron Agric 7(2):139–159.

  16. González LA, Bishop-Hurley GJ, Handcock RN, Crossman C (2015) Behavioral classification of data from collars containing motion sensors in grazing cattle. Comput Electron Agric 110:91–102.

  17. Gropp W, Lusk E, Doss N, Skjellum A (1996) A high-performance, portable implementation of the MPI message passing interface standard. Parellel Comput 22(6):789–828.

  18. Hindman B, Konwinski A, Zaharia M, Ghodsi A, Joseph AD, Katz R, Shenker S, Stoica I (2011) Mesos: a platform for fine-grained resource sharing in the data center. NSDI’11, pp 1–14.

  19. Hull D, Wolstencroft K, Stevens R, Goble C, Pocock MR, Li P, Oinn T (2006) Taverna: a tool for building and runing workflows of services. Nucleic Acids Res 34(2):W729-W732.

  20. Kipf A, Brunette W, Kellerstrass J, Podolsky M, Rosa J, Sundt M, Wilson D, Borriello G, Brewer E, Thomas E (2016) A proposed integrated data collection, analysis and sharing platform for impact evalution. Dev Eng 1:36–44.

  21. Kozhirbayev Z, Sinnott RO (2017) A performance comparison of container-based technologies for the Cloud. Future Gen Comput Syst 68:175–182.

  22. Lee D, Choi J, Kim J-H, Noh SH, Min SL, Cho Y, Kim CS (2001) LRFU: a spectrum of policies that subsumes the least recently used and least frequently used policies. IEEE Trans Comput 50(12):1352–1361

  23. Linke B, Giegerich R, Goesmann A (2011) Conveyor: a workflow engine for bioinformatic analyses. Bioinformatics 27(1):903–911.

  24. Manya A, Braa J, Øverland L, Titlestad O, Mumo J, Nzioka C (2012) National roll out of district health information software (DHIS2) in kenya, 2011—central server and cloud based infrastructure. In: IST-Africa 2012 Conference Proceedings. ISBN: 978-1-905824-34-2

  25. Mariotti M, Gervasi O, Vella F, Cuzzocrea A, Costantini (2017) Strategies and systems towards grid and clouds integration: a DBMS-based solution. Future Gen Comput Syst.

  26. McNab T, James DA, Rowlands D (2011) Iphone sensor plaforms: applications to sports monitoring. 5th Asia-Pacific Congress on Sport Technology (APCST). Proc Eng 13:507–512.

  27. Miceli G, Pekkarinen A, Leppanen M (2011) Open foris initiative—tools for forest monitoring and reporting. Concept Note

  28. Micucci D, Mobilio M, Napoletano P, Tisato F (2017) Falls as anomalies? An experimental evaluation using smartphone accelerometer data. J Ambient Intell Human Comput 8:87–99.

  29. Milani P, Coccetta CA, Rabini A, Sciarra T, Massazza G, Ferriero G (2014) Mobile smartphone application for body position measurement in reabilitation: a review of goniometric tools. PM R 2014 6:1038–1043.

  30. Néron B, Ménager H, Maufrais C, Joly N, Maupetit J, Letort S, Carrere S, Tuffery P, Letondal C (2009) Mobyle: a new full web bioinformatics framework. Bioinformatics 25(22):3005–3011.

  31. Rowlands D, James D (2011) Real time data streaming from smart phones. Procedia. 5th Asia–Pacific Congress on Sports Technology. (APCST) Eng 13:464–469.

  32. Shvachko K, Kuang H, Radia S, Chansler R (2010) The hadoop distributed file system. In Mass Storage Systems and Tdchnologies. IEEE 26th Symposium on pp 1–10

  33. Veith AS, Anjos JCS, de Freitas EP, Lampoltshammer TJ, Geyer CF (2016) Strategies for big data analytics through lambda architectures in volatile environments. IFAC-PapersOnLine 49–50:114–119.

  34. Yang F, Tschetter E, Léauté X, Ray N, Merlino G, Ganguli D (2014) A real-time analytical data store. SIGMOD’14, June 22–27, 2014, Snowbird, UT, USA. ACM 978-1-4503-2376-5$414/06.

  35. Yang F, Merlino G, Ray N, Léauté X, Gupta H, Tschetter E (2017a) The RADStack: open source lambda architecture for interactive analytics. In: Proceedings of the 50th Hawaii International Conference on System Sciences, pp 1703–1712

  36. Yang L, Grooten WJA, Forsman M (2017b) An iPhone application for upper arm posture and movement measurements. Appl Ergon 65:492–500.

  37. Zawodny J (2009) Redis: lightweight key/value store that goes the extra mile. Linux Magazin 31 August, 2009

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We would like to thank our colleagues from the CARE AgricultureIsLife (TERRA Teaching and Research Unit, Gembloux Agro-Bio Tech) and the Precision Livestock and Nutrition Axis, without whom this work would not have been possible. We would also like to thank Prof. Ghalem Belalem for the architecture brainstorming.

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Correspondence to Olivier Debauche.

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Data from this paper were partially presented and published in the proceedings of the 14th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2017) (Debauche et al. 2017).

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Debauche, O., Mahmoudi, S., Andriamandroso, A.L.H. et al. Cloud services integration for farm animals’ behavior studies based on smartphones as activity sensors. J Ambient Intell Human Comput 10, 4651–4662 (2019) doi:10.1007/s12652-018-0845-9

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  • Animals’ behavior
  • Smart agriculture
  • IMU
  • iPhone
  • Lambda architecture
  • Precision livestock farming