Abstract
Amid the augmentation of the Internet of Things (IoT), applications have become smarter and coupled devices give escalation to their exploitation in all facets of a modern city. As the capacity of the collected data increases, Machine Learning (ML) methods are applied to auxiliary boosting of intelligence and the abilities of an application. The field of smart transportation has fascinated many researchers and it has been accosted with both ML and IoT techniques. In this evaluation, smart transportation is contemplated to be a canopy term that conceals the route optimization, accident prevention/detection, parking, street lights, road anomalies, and infrastructure applications. The purpose of this document is to make a self-contained evaluation of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and attain a clear view of the developments in the above-mentioned fields and spot possible coverage musts. From the reviewed articles it becomes insightful that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers, henceforth there is a huge possibility in implementing the IoT with real world applications with the support of various Big data concepts and Machine learning algorithms along with Block chain technology.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Balaji, S., Harold Robinson, Y., Golden Julie, E.: GBMS: A new centralized graph based mirror system approach to prevent evaders for data handling with arithmetic coding in wireless sensor networks. Ingénierie des Systèmes d’Information. 24(5), 481–490 (2019)
Harold Robinson, Y., Rajaram, M.: Energy-aware multipath routing scheme based on particle swarm optimization in mobile ad hoc networks. Sci. World J., 1–9 (2015)
https://analyticstraining.com/how-to-apply-machine-learning-algorithms-to-iot-data/
Harold Robinson, Y., Rajaram, M.: A memory aided broadcast mechanism with fuzzy classification on a device-to-device mobile ad hoc network. Wirel. Pers. Commun. 90(2), 769–791 (2016)
Harold Robinson, Y., Golden Julie, E., Balaji, S., Ayyasamy, A.: Energy aware clustering scheme in wireless sensor network using neuro-fuzzy approach. Wirel. Pers. Commun. 95(2), 703–721 (2017)
Corrin, L., Kennedy, G., de Barba, P.: Asking the Right Questions of Big Data in Education (2017). https://pursuit.unimelb.edu.au/articles/asking-the-right-questions-of-big-data-in-education
Marr, B.: How Big Data Is Changing Healthcare. Forbes/Tech (2015). https://www.forbes.com/sites/bernardmarr/2015/04/21/how-big-data-is-changing-healthcare/#6643a6972873
Smyth, D.: Why blockchain? What can it do for big data? (2016). http://bigdata-madesimple.com/why-blockchain-what-can-it-do-for-big-data-2/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Arun Sampaul Thomas, G., Harold Robinson, Y. (2020). IoT, Big Data, Blockchain and Machine Learning Besides Its Transmutation with Modern Technological Applications. In: Balas, V., Solanki, V., Kumar, R. (eds) Internet of Things and Big Data Applications. Intelligent Systems Reference Library, vol 180. Springer, Cham. https://doi.org/10.1007/978-3-030-39119-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-39119-5_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39118-8
Online ISBN: 978-3-030-39119-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)