Abstract
Location-based services, already popular with end users, are now inevitably becoming part of new wireless infrastructures and emerging business processes. The increasingly popular deep learning (DL) artificial intelligence methods perform very well in wireless fingerprinting localization based on extensive indoor radio measurement data. However, with the increasing complexity, these methods become computationally very intensive and energy hungry, both for their training and subsequent operation. Considering only mobile users, estimated to exceed 7.4 billion by the end of 2025, and assuming that the networks serving these users will need to perform only one localization per user per hour on average, the machine learning models used for the calculation would need to perform 65 × 1012 predictions per year. Add to this equation tens of billions of other connected devices and applications that rely heavily on more frequent location updates, and it becomes apparent that localization will contribute significantly to carbon emissions unless more energy-efficient models are developed and used. In this chapter, we discuss the latest results and trends in wireless localization and look at paths toward achieving more sustainable AI. We then elaborate on a methodology for computing DL model complexity, energy consumption, and carbon footprint and show on a concrete example how to develop a more resource-aware model for fingerprinting. We finally compare relevant works in terms of complexity and training CO2 footprint.
Gregor Cerar and Blaž Bertalanič contributed equally to this work.
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
Arnold M, Dorner S, Cammerer S, et al (2018) On deep learning-based massive mimo indoor user localization. In: 2018 IEEE 19th international workshop on signal processing advances in wireless communications, pp 1–5. https://doi.org/10.1109/SPAWC.2018.8446013
Arnold M, Hoydis J, t. Brink S (2019) Novel massive mimo channel sounding data applied to deep learning-based indoor positioning. In: SCC 2019; 12th international itg conference on systems, communications and coding, pp 1–6. https://doi.org/10.30420/454862021
Bast SD, Guevara AP, Pollin S (2020) Csi-based positioning in massive mimo systems using convolutional neural networks. In: 2020 IEEE 91st vehicular technology conference (VTC2020-Spring), pp 1–5. https://doi.org/10.1109/VTC2020-Spring48590.2020.9129126
Cerar G, Švigelj A, Mohorčič M, et al (2021) Improving csi-based massive mimo indoor positioning using convolutional neural network. In: 2021 joint European conference on networks and communications & 6G summit, pp 276–281. https://doi.org/10.1109/EuCNC/6GSummit51104.2021.9482604
Chin WL, Hsieh CC, Shiung D, et al (2020) Intelligent indoor positioning based on artificial neural networks. IEEE Network 34(6):164–170. https://doi.org/10.1109/MNET.011.2000096
Dario A, Danny H (2019) Ai and compute. https://openai.com/blog/ai-and-compute/
De Bast S, Pollin S (2020) Mamimo csi-based positioning using cnns: Peeking inside the black box. In: 2020 IEEE international conference on communications workshops, pp 1–6. https://doi.org/10.1109/ICCWorkshops49005.2020.9145412
Devarakonda A, Naumov M, Garland M (2017) Adabatch: Adaptive batch sizes for training deep neural networks. Preprint. arXiv:171202029
Dhar P (2020) The carbon impact of artificial intelligence. Nat Mach Intell 2(8):423–425
García-Martín E, Rodrigues CF, Riley G, et al (2019) Estimation of energy consumption in machine learning. J Parallel Distrib. Comput. 134:75–88. https://doi.org/https://doi.org/10.1016/j.jpdc.2019.07.007, https://www.sciencedirect.com/science/article/pii/S0743731518308773
He K, Zhang X, Ren S, et al (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778. https://doi.org/10.1109/CVPR.2016.90
Hsueh G (2020) Carbon footprint of machine learning algorithms. Senior Projects Spring 2020, 296. https://digitalcommons.bard.edu/senproj_s2020/296
Huang G, Liu Z, Maaten LVD, et al (2017) Densely connected convolutional networks. In: 2017 IEEE conference on computer vision and pattern recognition. IEEE Computer Society, Los Alamitos, CA, USA, pp 2261–2269. https://doi.org/10.1109/CVPR.2017.243, https://doi.ieeecomputersociety.org/10.1109/CVPR.2017.243
INÜ (2019) IEEE CTW 2019 challenge. https://data.ieeemlc.org/Ds1Detail
Jurj SL, Opritoiu F, Vladutiu M (2020) Environmentally-friendly metrics for evaluating the performance of deep learning models and systems. In: International conference on neural information processing. Springer, pp 232–244
Kanhere O, Rappaport TS (2021) Position location for futuristic cellular communications: 5g and beyond. IEEE Commun Mag 59(1):70–75. https://doi.org/10.1109/MCOM.001.2000150
LeCun Y, Bengio Y, et al (1995) Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, vol 3361(10), 1995
Nessa A, Adhikari B, Hussain F, et al (2020) A survey of machine learning for indoor positioning. IEEE Access 8:214,945–214,965. https://doi.org/10.1109/ACCESS.2020.3039271
Pirnat A, Bertalanič B, Cerar G, et al (2022) Towards sustainable deep learning for wireless fingerprinting localization. In: ICC 2022 - IEEE International conference on communications, pp 3208–3213. https://doi.org/10.1109/ICC45855.2022.9838464
Savic V, Larsson EG (2015) Fingerprinting-based positioning in distributed massive mimo systems. In: 2015 IEEE 82nd vehicular technology conference (VTC2015-Fall), pp 1–5. https://doi.org/10.1109/VTCFall.2015.7390953
Schwartz R, Dodge J, Smith NA, et al (2020) Green ai. Commun ACM 63(12):54–63
Strubell E, Ganesh A, McCallum A (2019) Energy and policy considerations for deep learning in nlp. In: 57th annual meeting of the association for computational linguistics, pp 3645–3650
Strubell E, Ganesh A, McCallum A (2020) Energy and policy considerations for modern deep learning research. In: Proceedings of the AAAI conference on artificial intelligence, vol 34(09), pp 13,693–13,696. https://doi.org/10.1609/aaai.v34i09.7123, https://ojs.aaai.org/index.php/AAAI/article/view/7123
Verhelst M, Moons B (2017a) Embedded deep neural network processing: Algorithmic and processor techniques bring deep learning to iot and edge devices. IEEE Solid State Circuits Mag 9(4):55–65. https://doi.org/10.1109/MSSC.2017.2745818
Widmaier M, Arnold M, Dorner S, et al (2019) Towards practical indoor positioning based on massive mimo systems. In: 2019 IEEE 90th vehicular technology conference (VTC2019-Fall), pp 1–6. https://doi.org/10.1109/VTCFall.2019.8891273
Yan J, Qi G, Kang B, et al (2021) Extreme learning machine for accurate indoor localization using rssi fingerprints in multi-floor environments. IEEE Internet Things J 8(18):14623–14637
Acknowledgements
The authors would like to acknowledge Anze Pirnat for insightful discussions and the Slovenian Research Agency programme P-0016 for funding this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Cerar, G., Bertalanič, B., Fortuna, C. (2023). Resource-Aware Deep Learning for Wireless Fingerprinting Localization. In: Tiku, S., Pasricha, S. (eds) Machine Learning for Indoor Localization and Navigation. Springer, Cham. https://doi.org/10.1007/978-3-031-26712-3_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-26712-3_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-26711-6
Online ISBN: 978-3-031-26712-3
eBook Packages: EngineeringEngineering (R0)