Intelligent decision making for energy efficient fog nodes selection and smart switching in the IOT: a machine learning approach

Rahat Ullah; Muhammad Yahya; Leonardo Mostarda; Abdullah Alshammari; Ahmed I Alutaibi; Nadeem Sarwar; Farhan Ullah; Sibghat Ullah

doi:10.7717/peerj-cs.1833

Intelligent decision making for energy efficient fog nodes selection and smart switching in the IOT: a machine learning approach

PeerJ Comput Sci. 2024 Mar 20:10:e1833. doi: 10.7717/peerj-cs.1833. eCollection 2024.

Authors

Rahat Ullah¹, Muhammad Yahya², Leonardo Mostarda³, Abdullah Alshammari⁴, Ahmed I Alutaibi⁵, Nadeem Sarwar⁶, Farhan Ullah⁷, Sibghat Ullah⁸

Affiliations

¹ Institute of Optics and Electronics, Nanjing University of Information Science and Technology, Nanjing, China.
² Qurtaba University, Peshawar, Pakistan.
³ Computer Science School of science and technology, University of Camerino, Camerino, Italy.
⁴ College of Computer Science and Engineering, University of Hafr Albatin, Hafr Albatin, Saudi Arabia.
⁵ Department of Computer Engineering, Majmaah University, Majmaah, Saudi Arabia.
⁶ Department of Computer Science, Bahria University Lahore Campus, Lahore, Pakistan.
⁷ School of Software, Northwestern Polytechnical University, Xian, China.
⁸ National Research Center for Optical Sensors/Communications Integrated Networks, School of Electronic Science and Engineering, Southeast University, Nanjing, China.

Abstract

With the emergence of Internet of Things (IoT) technology, a huge amount of data is generated, which is costly to transfer to the cloud data centers in terms of security, bandwidth, and latency. Fog computing is an efficient paradigm for locally processing and manipulating IoT-generated data. It is difficult to configure the fog nodes to provide all of the services required by the end devices because of the static configuration, poor processing, and storage capacities. To enhance fog nodes' capabilities, it is essential to reconfigure them to accommodate a broader range and variety of hosted services. In this study, we focus on the placement of fog services and their dynamic reconfiguration in response to the end-device requests. Due to its growing successes and popularity in the IoT era, the Decision Tree (DT) machine learning model is implemented to predict the occurrence of requests and events in advance. The DT model enables the fog nodes to predict requests for a specific service in advance and reconfigure the fog node accordingly. The performance of the proposed model is evaluated in terms of high throughput, minimized energy consumption, and dynamic fog node smart switching. The simulation results demonstrate a notable increase in the fog node hit ratios, scaling up to 99% for the majority of services concurrently with a substantial reduction in miss ratios. Furthermore, the energy consumption is greatly reduced by over 50% as compared to a static node.

Keywords: Cloud computing; Fog computing; Internet of things; Machine learning; Networks.

Grants and funding

The work was supported by the financial supports from National Key Research and Development Program of China (2020YFB1805801), the National Natural Science Foundation of China (62171227, 62225503, 61835005, 62205151, 62275127, U2001601, 61935005, U22B2010); Jiangsu Provincial Key Research and Development Program (BE2022079, BE2022055-2), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (22KJB510031), and the Startup Foundation for Introducing Talent of NUIST. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.