Fog node placement using multi-objective genetic algorithm

• Published in: International journal of information technology (Singapore. Online) Vol. 16; no. 2; pp. 713 - 719
• Main Authors: Singh, Satveer, Vidyarthi, Deo Prakash
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.02.2024; Springer Nature B.V
• Subjects: Actuation; Artificial Intelligence; Cloud computing; Computer Imaging; Computer Science; Design; Energy consumption; Genetic algorithms; Heuristic; Image Processing and Computer Vision; Internet of Things; Machine Learning; Mathematical models; Multiple objective analysis; Network latency; Original Research; Pattern Recognition and Graphics; Placement; Real time; Software Engineering; Vision; Waste management; Fog computing; Genetic algorithm (GA); Internet of things; Fog node placement
• ISSN: 2511-2104; 2511-2112
• DOI: 10.1007/s41870-023-01530-1

The emergence of the Internet of Things (IoT) has paved the way for numerous activities leading to smart life, such as health care, surveillance, and smart cities. Since many IoT applications are real-time, they need prompt processing and actuation. To enable this, a network of Fog devices has been developed to provide services close to the data generation points, i.e., the network's edge. Designing a feasible Fog network for managing the explosion of data from the edge to the Cloud requires intelligent monitoring. This work develops a model to address the issue of Fog node placement in a geographical area. The proposed model determines the location of the Fog devices and how to connect them to the Cloud in the best feasible way. Using a multi-objective genetic algorithm (MOGA), the proposed model minimizes the Deployment Cost (DC) and network latency (NL). The simulation setting is designed in Spyder (Anaconda3) using Python programming.