Abstract
Environmental sustainability issues have received global attention in recent decades, both at scientific and administrative levels. Despite the scrupulous studies and initiatives around such issues, they remain largely unresolved, and sometimes even unknown. A complete understanding of the quality of our living environment that surrounds us, especially urban places, where we spend most of our lives would help improve living conditions for both humans and other species present. The concept of Intelligent Beehives for urban biodiversity encapsulates and leverages biotic elements such as bio-indicators (e.g. bees), and pollination, with technologies like AI and IoT instrumentation. Together they comprise a smart service that shapes the backbone of a real-time, AI-enabled environmental dashboard. In this vision paper, we outline and discuss our solution architecture and prototypization for such servified intelligent beehives. We focus our discussion on the hives’ predictive modelling abilities that enable Machine-Learning service operations – or MLOps – for increasing the sustainability of urban biodiversity.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
- 2.
This includes Machine Learning (ML) and Deep Learning (DL).
- 3.
References
Celli, G., Maccagnani, B.: Honey bees as bioindicators of environmental pollution. Bull. Insectology 56(1), 137–139 (2003)
De Palma, A., et al.: Ecological traits affect the sensitivity of bees to land-use pressures in european agricultural landscapes. J. Appl. Ecol. 52(6), 1567–1577 (2015)
Ebert, C., Gallardo, G., Hernantes, J., Serrano, N.: Devops. IEEE Softw. 33(3), 94–100 (2016)
Guetté, A., Gaüzère, P., Devictor, V., Jiguet, F., Godet, L.: Measuring the synanthropy of species and communities to monitor the effects of urbanization on biodiversity. Ecol. Ind. 79, 139–154 (2017)
Hallmann, C.A., et al.: More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PloS One 12(10), e0185809 (2017)
van den Heuvel, W.-J., Tamburri, D.A.: Model-driven ML-Ops for intelligent enterprise applications: vision, approaches and challenges. In: Shishkov, B. (ed.) BMSD 2020. LNBIP, vol. 391, pp. 169–181. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52306-0_11
Klein, A.M., et al.: Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B: Biol. Sci. 274(1608), 303–313 (2007)
Klein, D.J., McKown, M.W., Tershy, B.R.: Deep learning for large scale biodiversity monitoring. In: Bloomberg Data for Good Exchange Conference (2015)
Bublitz, F.M., et al.: Disruptive technologies for environment and health research: an overview of artificial intelligence, blockchain, and internet of things. Int. J. Environ. Res. Public Health 16(20), 3847 (2019)
Maksimović, Č., Kurian, M., Ardakanian, R.: Rethinking infrastructure design for multi-use water services. Springer, Cham (2015)
Nilon, C.H., et al.: Planning for the future of urban biodiversity: a global review of city-scale initiatives. BioScience 67(4), 332–342 (2017)
Nogueira, A.F., Ribeiro, J.C., Zenha-Rela, M., Craske, A.: Improving la redoute’s CI/CD pipeline and DevOps processes by applying machine learning techniques. In: 2018 11th International Conference on the Quality of Information and Communications Technology (QUATIC), pp. 282–286. IEEE (2018)
Nürnberger, F., Keller, A., Härtel, S., Steffan-Dewenter, I.: Honey bee waggle dance communication increases diversity of pollen diets in intensively managed agricultural landscapes. Mol. Ecol. 28(15), 3602–3611 (2019)
Olson, Z.H., Briggler, J.T., Williams, R.N.: An edna approach to detect eastern hellbenders (cryptobranchus a. alleganiensis) using samples of water. Wildl. Res. 39(7), 629–636 (2012)
Porrini, C., et al.: Honey bees and bee products as monitors of the environmental contamination. Apiacta 38(1), 63–70 (2003)
Prajogo, D.I., Sohal, A.S.: The integration of TQM and technology R&D management in determining quality and innovation performance. Omega 34(3), 296–312 (2006)
Reeves, J.P., Knight, A.T., Strong, E.A., Heng, V., Cromie, R.L., Vercammen, A.: The application of wearable technology to quantify health and wellbeing co-benefits from urban wetlands. Front. Psychol. 10, 1840 (2019)
Sebba, R.: The landscapes of childhood: the reflection of childhood’s environment in adult memories and in children’s attitudes. Environ. Behav. 23(4), 395–422 (1991)
Sedjo, R.A.: Perspectives on biodiversity: valuing its role in an everchanging world. J. For. 98(2), 45 (2000)
Sinha, A., Sengupta, T., Alvarado, R.: Interplay between technological innovation and environmental quality: formulating the SDG policies for next 11 economies. J. Cleaner Prod. 242, 118549 (2020)
Wario, F., Wild, B., Couvillon, M.J., Rojas, R., Landgraf, T.: Automatic methods for long-term tracking and the detection and decoding of communication dances in honeybees. Front. Ecol. Evol. 3, 103 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Sangiovanni, M., Schouten, G., van den Heuvel, WJ. (2020). An IoT Beehive Network for Monitoring Urban Biodiversity: Vision, Method, and Architecture. In: Dustdar, S. (eds) Service-Oriented Computing. SummerSOC 2020. Communications in Computer and Information Science, vol 1310. Springer, Cham. https://doi.org/10.1007/978-3-030-64846-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-64846-6_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64845-9
Online ISBN: 978-3-030-64846-6
eBook Packages: Computer ScienceComputer Science (R0)