Quality-aware energy efficient scheduling model for fog computing comprised IoT network. (January 2022)

• Record Type: Journal Article
• Authors: POTU, NARAYANA; BHUKYA, SREEDHAR; JATOTH, CHANDRASHEKAR; PARVATANENI, PREMCHAND
• Is Part Of: Computers & electrical engineering. Volume 97(2022)
• Publication Date: 2022-01
• Subjects: IoT networks -- Fog computing -- Moving averages -- Edge node computing -- Resource scheduling -- Heiken-Ashi patterns; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107603
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The present phase of the IoT (Internet of Things) networks has no limits to perform loaded data transmissions and computational requirements, which is made possible by Fog Computing technology. However, excessive energy resources and considerable loss of quality in loaded data transmission remains a significant challenge to the Fog Computing comprised IoT networks. This manuscript endeavors to portray a novel scheduling algorithm that optimizes edge node and fog node scheduling processes. The proposed approach is a supervised learning approach, titled "Quality-aware Energy Efficient Scheduling Model for Fog computing comprised IoT networks. The statistical measures moving averages and Heiken-Ashi patterns (Heiken-Ashi has been used in conjunction with candlestick charts to predict the present scheduling opportunities easily) have been used as a base for the proposed scheduling model. The experimental study signifies the performance of the proposed model and is scaled against the contemporary models.