Opportunities of IoT in Fog Computing for High Fault Tolerance and Sustainable Energy Optimization

A. Reyana, Sandeep Kautish, Khalid Abdulaziz Alnowibet, Hossam M. Zawbaa and Ali Wagdy Mohamed ()
Additional contact information
A. Reyana: Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, Tamilnadu, India
Sandeep Kautish: Department of Computer Science and Engineering, Lord Buddha Education Foundation, Kathmandu 44600, Nepal
Khalid Abdulaziz Alnowibet: Statistics and Operations Research Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
Hossam M. Zawbaa: CeADAR Ireland’s Center for Applied AI, Technological University Dublin, D7 EWV4 Dublin, Ireland
Ali Wagdy Mohamed: Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt

Sustainability, 2023, vol. 15, issue 11, 1-14

Abstract: Today, the importance of enhanced quality of service and energy optimization has promoted research into sensor applications such as pervasive health monitoring, distributed computing, etc. In general, the resulting sensor data are stored on the cloud server for future processing. For this purpose, recently, the use of fog computing from a real-world perspective has emerged, utilizing end-user nodes and neighboring edge devices to perform computation and communication. This paper aims to develop a quality-of-service-based energy optimization (QoS-EO) scheme for the wireless sensor environments deployed in fog computing. The fog nodes deployed in specific geographical areas cover the sensor activity performed in those areas. The logical situation of the entire system is informed by the fog nodes, as portrayed. The implemented techniques enable services in a fog-collaborated WSN environment. Thus, the proposed scheme performs quality-of-service placement and optimizes the network energy. The results show a maximum turnaround time of 8 ms, a minimum turnaround time of 1 ms, and an average turnaround time of 3 ms. The costs that were calculated indicate that as the number of iterations increases, the path cost value decreases, demonstrating the efficacy of the proposed technique. The CPU execution delay was reduced to a minimum of 0.06 s. In comparison, the proposed QoS-EO scheme has a lower network usage of 611,643.3 and a lower execution cost of 83,142.2. Thus, the results show the best cost estimation, reliability, and performance of data transfer in a short time, showing a high level of network availability, throughput, and performance guarantee.

Keywords: sustainable energy optimization; environment; wireless sensor networks; data processing; Internet of Things; fog computing; ant bee colony; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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