Open Access

ARTICLE

Design of Latency-Aware IoT Modules in Heterogeneous Fog-Cloud Computing Networks

Syed Rizwan Hassan¹, Ishtiaq Ahmad¹, Jamel Nebhen², Ateeq Ur Rehman³, Muhammad Shafiq⁴, Jin-Ghoo Choi^4,*

1 Department of Electrical Engineering, The University of Lahore, Lahore, 54000, Pakistan
2 College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia
3 Department of Electrical Engineering, Government College University, Lahore, 54000, Pakistan
4 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Korea

* Corresponding Author: Jin-Ghoo Choi. Email:

Computers, Materials & Continua 2022, 70(3), 6057-6072. https://doi.org/10.32604/cmc.2022.020428

Received 24 May 2021; Accepted 10 August 2021; Issue published 11 October 2021

Abstract

The modern paradigm of the Internet of Things (IoT) has led to a significant increase in demand for latency-sensitive applications in Fog-based cloud computing. However, such applications cannot meet strict quality of service (QoS) requirements. The large-scale deployment of IoT requires more effective use of network infrastructure to ensure QoS when processing big data. Generally, cloud-centric IoT application deployment involves different modules running on terminal devices and cloud servers. Fog devices with different computing capabilities must process the data generated by the end device, so deploying latency-sensitive applications in a heterogeneous fog computing environment is a difficult task. In addition, when there is an inconsistent connection delay between the fog and the terminal device, the deployment of such applications becomes more complicated. In this article, we propose an algorithm that can effectively place application modules on network nodes while considering connection delay, processing power, and sensing data volume. Compared with traditional cloud computing deployment, we conducted simulations in iFogSim to confirm the effectiveness of the algorithm. The simulation results verify the effectiveness of the proposed algorithm in terms of end-to-end delay and network consumption. Therein, latency and execution time is insensitive to the number of sensors.

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Keywords

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