Abstract
The energy management in urban and interurban lighting is currently, mainly, based on a centralised or clustered model. The control is mainly based on the level of brightness needed to circulate, without taking into account the presence or not of pedestrians or vehicles. This thesis proposes to review the solutions implemented and to use the Industry 4.0 paradigm as a basis for the design of a highly distributed architecture that efficiently controls the lighting of the roads of urban environments, and is extensible to interurban environments. As results it is expected to be able to verify the hypothesis of, how the distribution of the intelligence at the level of control node, together with the communication between nearby control nodes, allows to optimise the consumption in front of the current solutions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bloder, E., Jäger, G.: Is the green wave really green? the risks of rebound effects when implementing “green’’ policies. Sustainability 13(10), 5411 (2021)
Cao, J., Wang, D., Zhaoyang, Q., Sun, H., Li, B., Chen, C.-L.: An improved network traffic classification model based on a support vector machine. Symmetry 12(2), 301 (2020)
Chiradeja, P., Yoomak, S., Ngaopitakkul, A.: Economic analysis of improving the energy efficiency of nanogrid solar road lighting using adaptive lighting control. IEEE Access 8, 202623–202638 (2020)
Dangi, K., Kushwaha, M.S., Bakthula, R.: An intelligent traffic light control system based on density of traffic. In: Mandal, J., Bhattacharya, D. (eds.) Emerging Technology in Modelling and Graphics, vol. 937, pp. 741–752. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-7403-6_65
Haans, A., De Kort, Y.A.W.: Light distribution in dynamic street lighting: two experimental studies on its effects on perceived safety, prospect, concealment, and escape. J. Environ. Psychol. 32(4), 342–352 (2012)
Hamdi, M.M., Audah, L., Rashid, S.A., Al Shareeda, M.: Techniques of early incident detection and traffic monitoring centre in VANETs: a review. J. Commun. 15(12), 896–904 (2020)
Lee, S., et al.: Intelligent traffic control for autonomous vehicle systems based on machine learning. Expert Syst. Appl. 144, 113074 (2020)
Lom, M., Pribyl, O., Svitek, M.: Industry 4.0 as a part of smart cities. In: 2016 Smart Cities Symposium Prague (SCSP), pp. 1–6. IEEE (2016)
Louati, A., Louati, H., Nusir, M., Hardjono, B.: Multi-agent deep neural networks coupled with LQF-MWM algorithm for traffic control and emergency vehicles guidance. J. Ambient Intell. Humaniz. Comput. 11, 5611–5627 (2020)
Lu, Y.: Industry 4.0: a survey on technologies, applications and open research issues. J. Ind. Inf. Integr. 6, 1–10 (2017)
Sáenz-Peñafiel, J.-J., Poza-Lujan, J.-L., Posadas-Yagüe, J.-L.: Smart cities: a taxonomy for the efficient management of lighting in unpredicted environments. In: Herrera, F., Matsui, K., Rodríguez-González, S. (eds.) Distributed Computing and Artificial Intelligence, vol. 1003, pp. 63–70. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-030-23887-2_8
Acknowledgements
Work supported by the Spanish Science and Innovation Ministry MICINN: CICYT project PRECON-I4: “Predictable and dependable computer systems for Industry 4.0” TIN2017-86520-C3-1-R.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sáenz-Peñafiel, JJ., Poza-Lujan, JL., Posadas-Yagüe, JL. (2022). Distributed Architecture Proposal for Efficient Energy Management of Road Lighting in Urban Environments. In: González, S.R., et al. Distributed Computing and Artificial Intelligence, Volume 2: Special Sessions 18th International Conference. DCAI 2021. Lecture Notes in Networks and Systems, vol 332. Springer, Cham. https://doi.org/10.1007/978-3-030-86887-1_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-86887-1_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86886-4
Online ISBN: 978-3-030-86887-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)