A Machine Learning based Smart Grid for Home Power Management using Cloud-Edge Computing System

Kavya, B M; Sharmila, N; Naveen, K B; Mallikarjunaswamy, S; Manu, K S; Manjunatha, S

doi:10.1109/icraset59632.2023.10419952

Cited by 13 publications

(1 citation statement)

References 26 publications

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“…So, one of the problems in the fog environment is how to best assign jobs to FNs. For a scheduling algorithm to work well, it should lower costs and energy use while also taking into account how different FNs are and how committed people are to getting things done on time [82][83][84][85][86][87][88][89][90][91][92]. This study gives a thorough taxonomy to help us understand the research problems better and points out important problems with previous work.…”

Section: Fc Task Schedulingmentioning

confidence: 99%

A Comparative Review of Internet of Things Model Workload Distribution Techniques in Fog Computing Networks

Puttaswamy,

Murthy,

Degha

2024

IDA

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In the realm of fog computing (FC), a vast array of intelligent devices collaborates within an intricate network, a synergy that, while promising, has not been without its challenges. These challenges, including data loss, difficulties in workload distribution, a lack of parallel processing capabilities, and security vulnerabilities, have necessitated the exploration and deployment of a variety of solutions. Among these, software-defined networks (SDN), double-Q learning algorithms, service function chains (SFC), virtual network functions (VNF) stand out as significant. An exhaustive survey has been conducted to explore workload distribution methodologies within Internet of Things (IoT) architectures in FC networks. This investigation is anchored in a parameter-centric analysis, aiming to enhance the efficiency of data transmission across such networks. It delves into the architectural framework, pivotal pathways, and applications, aiming to identify bottlenecks and forge the most effective communication channels for IoT devices under substantial workload conditions. The findings of this research are anticipated to guide the selection of superior simulation tools, validate datasets, and refine strategies for data propagation. This, in turn, is expected to facilitate optimal power consumption and enhance outcomes in data transmission and propagation across multiple dimensions. The rigorous exploration detailed herein not only illuminates the complexities of workload distribution in FC networks but also charts a course towards more resilient and efficient IoT ecosystems.

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