Abstract
The number of Smartphone users in the world is expected to pass the five billion in 2019. The major credit for this exponential growth is the competition between Smartphones manufacturing companies and increasing Internet availability in the world. Processing power considered to be one of the most important features in Smartphones and it is evolving year by year. Until now, building a distributed computing system done exclusively using PCs and other server infrastructure. In this paper we will propose a new architecture for a distributed computing system consists of a network of Smartphones and use their computation power to execute machine learning models on each Smartphone. As proof of concept, our solution will provide a stable layer to execute large data-sets using common machine learning algorithms such as Linear Regression.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang, C., Patras, P., Haddadi, H.: Deep learning in mobile and wireless networking: a survey. IEEE Commun. Surv. Tutorials. arXiv:1803.04311, March 2018
Cisco: Cisco visual networking index: forecast and methodology, June (2017). https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-741490.html
Sun, Y., Peng, M., Zhou, Y., Huang, Y., Mao, S.: Application of machine learning in wireless networks: key techniques and open issues. arXiv:1809.08707, September 2018
Han, S., Chih-Lon, I., Li, G., Wang, S., Sun, Q.: Big data enabled mobile network design for 5G and beyond. IEEE Commun. Mag. 55(9), 150–157 (2017)
Dobriban, E., Shengy, Y.: Distributed linear regression by averaging. arXiv:1810.00412, October 2018
Bader, D.A., Pennington, R.: Applications. Int. J. High Perform. Comput. Appl. 15(2), 181–185 (2001)
Bakery, M., Buyya, R.: Cluster computing at a glance, Chapter One, p. 4, September (2000)
Mengy, X.: Machine learning in apache spark. J. Mach. Learn. Res. 17(34), 17 (2016)
Bala, K., Sharma, S., Kaur, G.: A study on smartphone based operating system. Int. J. Comput. Appl. (0975–8887) 121(1) (2015)
Top machine learning mobile apps \(\bullet \) appy pie. https://www.appypie.com/top-machine-learning-mobile-apps. Accessed 16 Apr 2019
Takawale, H., Thakur, A.: Talos App: on-device machine learning using tensor flow to detect android malware. In: MCSMS (2018)
Yang, K., Jiang, T., Shi, Y., Ding, Z.: Federated learning via over-the-air computation. arXiv 1812(11750) (2018)
Galaxy note features; Samsung phones. https://www.samsung.com/ph/ Smartphones/gal-axy-note8/. Accessed 16 Apr 2019
Burd, G.: NoSQL (2011)
What is ionic framework? http://ionicframework.com/. Accessed 16 Apr 2019
Ionic framework angular JS on the rise. https://blog.codecentric.de/en/2014/11/ionic-angularjs-framework-on-the-rise/. Accessed 16 Apr 2019
Jin, J., Li, M., Jin, L.: Data normalization to accelerate training for linear neural net to predict tropical cyclone tracks. Math. Probl. Eng. 2015, 8 (2014). Hindawi Publishing Corporation
Percentage of US population that own an iPhone smartphone. https://www.statista.com/statistics/236550/percentage-of-us-population-that-own-a-iphone-smartphone. Accessed 16 Apr 2019
Sureddy, S., Rashmi, K., Gayathri, R., Nadhan, A.S.: Flexible deep learning in edge computing for IoT. Int. J. Pure Appl. Math. 119(10), 531–543 (2018)
Strugar, D., Hussain, R., Mazzara, M., Rivera, V., Afanasyev, I., Lee, J.Y.: An architecture for distributed ledger-based M2M auditing for electric autonomous vehicles. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds.) WAINA 2019. AISC, vol. 927, pp. 116–128. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-15035-8_11
Burns, B.: Designing Distributed Systems, pp. 80–81. O’Reilly Media Inc., Sebastopol (2018). ISBN: 9781491983638
Zhu, G., Liu, D., Du, Y., You, C., Zhang, J., Huang, K.: Towards an intelligent edge: wireless communication meets machine learning. arXiv preprint 1809.00343 (2018)
Unified analytics engine for big data. https://spark.apache.org/. Accessed 6 June 2019
Guide to Tenserflow. https://www.tensorflow.org/guide/. Accessed 6 June 2019
Richards, M.: Software Architecture Patterns, pp. 54–55. O’Reilly Media Inc., Sebastopol (2015). ISBN: 9781491971437
Lunt, M.: Introduction to statistical modelling: linear regression. Rheumatology 54(7), 1137–1140 (2015)
Js-Regression. https://github.com/chen0040/js-regression. Accessed 6 June 2019
Firebase. https://firebase.google.com. Accessed 6 June 2019
JavaScript Object Notation (JSON). https://json.org. Accessed 6 June 2019
Marinelli, E.: Hyrax: cloud computing on mobile devices using mapreduce. Master’s thesis, CMU, USA (2009)
Remédios, Diogo, Teófilo, António, Paulino, Hervé, Lourenço, João: Mobile Device-to-Device Distributed Computing Using Data Sets. 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Salem, H. (2019). Distributed Computing System on a Smartphones-Based Network. In: Mazzara, M., Bruel, JM., Meyer, B., Petrenko, A. (eds) Software Technology: Methods and Tools. TOOLS 2019. Lecture Notes in Computer Science(), vol 11771. Springer, Cham. https://doi.org/10.1007/978-3-030-29852-4_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-29852-4_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29851-7
Online ISBN: 978-3-030-29852-4
eBook Packages: Computer ScienceComputer Science (R0)