Feedback-based fuzzy resource management in IoT using fog computing

Reddy, D Arunkumar; Krishna, P. Venkata

doi:10.1007/s12065-020-00377-w

Cited by 10 publications

(14 citation statements)

References 18 publications

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“…In our experimentation, we initially derive the total energy consumption and processing time metrics for our proposed MFHS model. Subsequently, we conduct a comparative analysis between the outcomes of MFHS and those of the feedback-based optimized fuzzy scheduling approach (FOFSA) algorithm, along with the adaptive task allocation technique (ATAT) and the osmosis load balancing (OLB) algorithm as documented in [ 15 ].…”

Section: Resultsmentioning

confidence: 99%

“…Figure 14 presents a comparison of the energy costs between our proposed MFHS approach and the FOFSA, OLB, and ATAT algorithms discussed in [ 15 ], with respect to the number of packets. The chart clearly illustrates that our MFHS approach achieves superior energy cost results compared to the existing methods, particularly as the number of packets increases.…”

Section: Resultsmentioning

confidence: 99%

“…In their work, Reddy A et al (2021) [ 15 ] proposed an algorithm called the feedback-based optimized fuzzy scheduling approach (FOFSA) to enhance power efficiency, execution time, and makespan in IoT networks while meeting quality of service (QoS) requirements. The algorithm intelligently allocates appropriate resources from Cloud resources, including virtual machines, applications, and data centres, to compute tasks.…”

Section: Related Workmentioning

confidence: 99%

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Enhancing Energy Efficiency and Fast Decision Making for Medical Sensors in Healthcare Systems: An Overview and Novel Proposal

Almudayni,

Soh,

2023

Sensors

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In the realm of the Internet of Things (IoT), a network of sensors and actuators collaborates to fulfill specific tasks. As the demand for IoT networks continues to rise, it becomes crucial to ensure the stability of this technology and adapt it for further expansion. Through an analysis of related works, including the feedback-based optimized fuzzy scheduling approach (FOFSA) algorithm, the adaptive task allocation technique (ATAT), and the osmosis load balancing algorithm (OLB), we identify their limitations in achieving optimal energy efficiency and fast decision making. To address these limitations, this research introduces a novel approach to enhance the processing time and energy efficiency of IoT networks. The proposed approach achieves this by efficiently allocating IoT data resources in the Mist layer during the early stages. We apply the approach to our proposed system known as the Mist-based fuzzy healthcare system (MFHS) that demonstrates promising potential to overcome the existing challenges and pave the way for the efficient industrial Internet of healthcare things (IIoHT) of the future.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Enhancing Energy Efficiency and Fast Decision Making for Medical Sensors in Healthcare Systems: An Overview and Novel Proposal

Almudayni,

Soh,

2023

Sensors

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“…Other researchers 50 presented a fuzzy algorithm for managing resources and tasks in fog computing. The cloud can also be used to manage resources better.…”

Section: Energy Management In Fog Computingmentioning

confidence: 99%

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

Hashemi,

Sahafi,

Rahmani

et al. 2023

Softw Pract Exp

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The energy demand for Internet of Things (IoT) applications is increasing with a rise in IoT devices. Rising costs and energy demands can cause serious problems. Fog computing (FC) has recently emerged as a model for location‐aware tasks, data processing, fast computing, and energy consumption reduction. The Fog computing model assists cloud computing in fast processing at the network's edge, which also exerts a vital role in cloud computing. Due to the fast computing in fog servers, different quality of service (QoS) approaches have been proposed in various sections of the fog system, and several quality factors have been considered in this regard. Despite the significance of QoS in Fog computing, no extensive study has focused on QoS and energy consumption methods in this area. Therefore, this article investigates previous research on the use and guarantee of Fog computing. This article reviews six general approaches that discuss the published articles between 2015 and late May 2023. The focal point of this paper is evaluating Fog computing and the energy consumption strategy. This article further shows the advantages, disadvantages, tools, types of evaluation, and quality factors according to the selected approaches. Based on the reviewed studies, some open issues and challenges in Fog computing energy consumption management are suggested for further study.

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“…Fuzzy inference systems have also been used in recent years to improve scheduling. A FOFSA model was proposed in [113]. The proposed technique automatically decreases end-to-end latency by drastically reducing the transmission between IoT devices and clouds.…”

Section: Figure 7 Fuzzy Scheduling Classificationmentioning

confidence: 99%

Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open Issues

et al. 2022

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Geographically dispersed Fog Computing architecture ubiquitously connected to a range of heterogeneous nodes at the edge of the network can provide cooperative flexible, and variable computations, communications, and storage services. Several fog computing methods, models, and techniques have been used to solve cloud issues. The fuzzy theory has also been used in many aspects of fog computing. Objectives: This work presents a systematic literature review of the use of fuzzy theory in Fog Computing, highlighting the main practical motivations, classification types in research approaches, fuzzy methods used, popular evaluation tools, open issues, and future trends. Methods: The investigations were systematically performed using fuzzy theory in fog computing, and four databases which are ScienceDirect, Web of Science (WoS), Scopus, and IEEE Xplore Digital Library from 2015 to 2022, were used to analyse their performance evaluation, architecture, and applications. Results: 94 articles were selected based on fuzzy theory in fog computing using different methods, models, and techniques, based on the proposed exclusion and inclusion criteria. The results of the taxonomy were divided into five major classes: task and resource management, intrusion detection systems, trust management, and healthcare services. Discussion: Applications requiring real-time, low latency, and quick responses are well suited for fog computing. These studies show that resource sharing improves the fog computing architecture by delivering reduced latency, distributed processing, improved scalability, better security, fault tolerance, and privacy. Conclusion: The majority of the time, research areas on fuzzy theory in fog computing are crucially significant. We conclude that this review will enhance research capacity, thereby expanding and creating new research domains.

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Feedback-based fuzzy resource management in IoT using fog computing

Cited by 10 publications

References 18 publications

Enhancing Energy Efficiency and Fast Decision Making for Medical Sensors in Healthcare Systems: An Overview and Novel Proposal

Enhancing Energy Efficiency and Fast Decision Making for Medical Sensors in Healthcare Systems: An Overview and Novel Proposal

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open Issues

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