A REAL-TIME DATA STREAM PROCESSING MODEL FOR A SMART TRAFFIC APPLICATION, LEVERAGING INTELLIGENT INTERNET OF THINGS (IOT) CONCEPTS

M. I. Yakubu; E. Okorafor; K. O. Momoh

Authors

M. I. Yakubu
muleekat246@gmail.com
E. Okorafor
K. O. Momoh

Keywords:

Smart traffic, IoT, Real-time, Data stream

Abstract

Smart City of Smart systems is becoming ubiquitous. Improvements in miniaturization and networking capabilities of sensors have contributed to the proliferation of the Internet of Things (IoT) and continuous sensing environments. Data streams generated in such settings must keep pace with generation rates and be processed in real-time to gain insight quickly and make decisions that are in most cases critical and time-sensitive. The challenge lies in not only being able to process vast amounts of data in a given time but also being able to make data-driven decisions quickly or in many cases proactively. Handling the amounts of data generated could be very difficult especially when making data-driven decisions. The difficulty is being diminished using some big data methods to perform real-time stream processing. Among the different dimensions that improve the quality of life of people in a smart city environment, one of the important ones is transportation. In this work, a real-time data stream processing model for a smart traffic application was proposed and data streaming trends were used to monitor traffic which enables people to know if the roads in an IoT environment are congested

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REFERENCES

Amini, S., Gerostathopoulos, I., & Prehofer, C. (2017). Big data analytics architecture for real-time traffic control. 5th IEEE International Conference on Models and Technologies for Intelligent Transportation System (MT – ITS). http://doi.org/10.1109/mtits.2017.8005605.

Campus, K., Campus, K., Mani, V., Campus, K., Sankaranarayanan, S., & Campus, K. (2017). IoT Based Traffic Signalling System, 12(19), 8264–8269.

Conese, A. (2015). Inferring Latent User Attributes in Streams of Multimodal Social Data using Apache Spark.

Gehlot, R. (2016). Storage and Retrieval of Data for Smart City using Hadoop, 3(5), 85–89.

Hahanov, V. (2015). Smart traffic light in terms of the Cognitive road traffic management system (CTMS) based on the Internet of Things. Volodymyr Miz PhD student at Kharkov National University of Radio.

Kleppmann, M., & Kreps, J. (2016). Kafka , Samza and the Unix Philosophy of Distributed Data, 1–11.

Lakshminarasimhan, M. (2016). IoT Based Traffic Management System Advanced Traffic Management System Using Internet of Things, (March), 0-9.

Nagdive, A. S. (2018). A Review of Hadoop Ecosystem for BigData, 180(14), 35–40.

Nuaimi, E. Al, Neyadi, H. Al, Mohamed, N., & Al-jaroodi, J. (2015). Applications of big data to smart cities. Journal of Internet Services and Applications. https://doi.org/10.1186/s13174-015-0041-5.

Prathilothamai, M., Lakshmi, A. M. S., & Viswanthan, D. (2016). Cost Effective Road Traffic Prediction Model using Apache Spark, 9(May). https://doi.org/10.17485/ijst/2016/v9i17/87334

Radek Kuchta, R. N., Kuchta, R., & Kadlec, J. (2014). Smart City Concept, Applications and Services. Journal of Telecommunications System & Management, 03(02), 1–8. https://doi.org/10.4172/2167-0919.1000117.

Santana, E. F. Z., Chaves, A. P., Gerosa, M. A., Kon, F., & Milojicic, D. (2016). Software platforms for smart cities: Concepts, requirements, challenges, and a unified reference architecture. ArXiv Preprint ArXiv:1609.08089, (October). Retrieved from http://arxiv.org/abs/1609.08089.

A REAL-TIME DATA STREAM PROCESSING MODEL FOR A SMART TRAFFIC APPLICATION, LEVERAGING INTELLIGENT INTERNET OF THINGS (IOT) CONCEPTS

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