IoT sensor network data processing using the TWLGA Scheduling Algorithm and the Hadoop Cloud Platform

Authors

  • Mohanad Hameed Rashid Computer Science Quality Department,Anbar Education Directorate, Ramadi, Iraq
  • Wisam Mohammed Abed Computer Science Preparation and Training Department, Anbar Education Directorate, Ramadi, Iraq

DOI:

https://doi.org/10.31185/wjcm.122

Keywords:

IOT, Cloud computing , Hadoop, Sensor network

Abstract

Monitoring environmental conditions can be done effectively with the help of the Internet of Things (IOT) sensor network. Massive data generated by IOT sensor networks presents technological hurdles in terms of storage, processing, and querying. A Hadoop cloud platform is suggested as a fix for the issue. The data processing platform makes it possible for one node's work to be shared with others employing the time and workload genetic algorithm (TWLGA), which lowers the risk of software and hardware compatibility while simultaneously increasing the efficiency of a single node. For the experiment, a Hadoop cluster platform employing the TWLGA scheduling algorithm is built, and its performance is assessed. The outcomes demonstrate that processing huge volumes of data from the IOT sensor network is acceptable for the Hadoop cloud platform .

References

P. Liu, “Cloud Computing, Electronic Industry Press Beijing,” 2010.

H. Yedidsion, S. Ashur, and A. Banik, “Sensor network topology design and analysis for efficient data gathering by a mobile mule,” Algorithmica, vol. 82, no. 10, pp. 2784–2808, 2020.

E. Suganya and C. Rajan, “An adaboost-modified classifier using particle swarm optimization and stochastic diffusion search in wireless IoT networks,” Wireless Networks, vol. 4, pp. 1–13, 2020.

K. Preethi and R. Tamilarasan, “Monitoring of air pollution to establish optimal less polluted path by utilizing wireless sensor network,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 6375–6386, 2020.

P. Yadav, “Workload Analysis in a Grid Computing Environment: A Genetic Approach,” International Journal of Computer Applications, vol. 93, no. 16, pp. 26–29, 2014.

H. Aziza and S. Krichen, “A hybrid genetic algorithm for scientific workflow scheduling in cloud environment,” Neural Computing and

Applications, vol. 32, no. 12, pp. 1–16, 2020.

A. Tuncer and M. Yildirim, “Chromosome Coding Methods in Genetic Algorithm for Path Planning of Mobile Robots,” Computer and information Sciences II, pp. 377–383, 2011.

L. Li, M. Guo, and L. Ma, “Online Workload Allocation via Fog-Fog-Cloud Cooperation to Reduce IoT Task Service Delay,” Sensors, vol. 19, no. 18, pp. 3830–3830, 2019.

S. Shah, M. Rashid, and M. Arif, “Estimating WCET using prediction models to compute fitness function of a genetic algorithm,” Real-Time Syst, vol. 56, pp. 28–63, 2020.

Z. Zhou, F. Li, and H. Zhu, “An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments,” Neural Comput & Applic, vol. 32, pp. 1531–1541, 2020.

D. Bandyopadhyay and J. Sen, “Internet of Things: Applications and Challenges in Technology and Standardization,” Wireless Pers Commun, vol. 58, no. 1, pp. 49–69, 2011.

R. Roman, “Key Management Systems for Sensor Networks in the Context of the Internet of Things,” Computers & Electrical Eng, vol. 37, no. 2, pp. 147–159, 2011

font vol 2 number 1

Downloads

Published

2023-03-30

Issue

Vol. 2 No. 1 (2023)

Section

Computer

License

Copyright (c) 2023 Mohanad Hameed Rashid, Wisam Mohammed Abed

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Mohanad Hameed Rashid and Wisam Mohammed Abed, “IoT sensor network data processing using the TWLGA Scheduling Algorithm and the Hadoop Cloud Platform”, WJCMS, vol. 2, no. 1, pp. 90–96, Mar. 2023, doi: 10.31185/wjcm.122.