Fuzzy Reinforcement Learning for energy efficient task offloading in Vehicular Fog Computing

Vemireddy, Satish; Rout, Rashmi Ranjan

doi:10.1016/j.comnet.2021.108463

Cited by 60 publications

(23 citation statements)

References 24 publications

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“…Once these nodes finish the task they send the results back to the requesting node. Cloud computing [14] solves the problem in a centralized approach while Fog computing [15] takes on a decentralized approach. Mobile Edge Computing is similar to Fog computing but differs-in that Fog sends data to a Fog layer to process before sending on instead of a direct rout to a MEC.…”

Section: Resource Allocation Categorizations 41 Offloadingmentioning

confidence: 99%

“…Not all tasks are suited for offloading such as those needing access to cameras and sensors on vehicles [61]. If a task is chosen for offloading, it does introduce concerns around latency since the task will have to travel to be processed [15]. Some tasks in VANETs will have a low threshold for computational time and will need quick processing with low latency.…”

Section: Vehicular Cloud Computingmentioning

confidence: 99%

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Resource Allocation Methods in Vanets:a Systematic Literature Review

Trombly

Alsmadi

2023

Preprint

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Autonomous vehicles take on a more prominent part of our every day life activities. As the number of vehicles grows so does the need for resources to ensure safe and consistent operations of these vehicles. Today's networks and power grid are already used heavily just supplying resources for our day to day lives and leisure. The continuous increase in demand for such resources drives the need for more advanced resource management tools and techniques and more precise resource allocation schemes. This paper is a systematic literature review on the current methods and research trends for resource allocation in vehicular networks. The purpose of this review is to understand current research trends and motivations. Our hope is to provide a comparative analysis of resource allocation solutions, research trends and also challenges in vehicular networks.

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Section: Resource Allocation Categorizations 41 Offloadingmentioning

confidence: 99%

Section: Vehicular Cloud Computingmentioning

confidence: 99%

Resource Allocation Methods in Vanets:a Systematic Literature Review

Trombly

Alsmadi

2023

Preprint

View full text Add to dashboard Cite

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“…A study [97] presented a ranking-based task scheduling technique that combines user preferences and fog node attributes by rating fog nodes from the most to the least pleasant one using language and fuzzy quantified propositions. The fuzzy reinforcement learning (FRL) approach has been proposed [92] for task allocation of fog vehicles. The FRL merges fuzzy logic and greedy heuristic with on-policy reinforcement learning to reduce the learning process and optimize the selection of optimal fog vehicles to reduce energy and response time.…”

Section: Figure 7 Fuzzy Scheduling Classificationmentioning

confidence: 99%

“…These measurements were most commonly used in previous studies to evaluate fuzzy fog systems and models. Using fuzzy theory improved these measurements; however, the response time and delay still need to be improved by applying more fuzzy rules [92]; [121]. Cost and energy usage continue to register high numbers for a variety of reasons.…”

Section: A Task and Resource Managementmentioning

confidence: 99%

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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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Research on image processing of electric power system terminals based on reinforcement learning and mobile edge computing optimization

Zhou,

Yu,

Luo

et al. 2024

Adv Control Appl

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This research is dedicated to the optimization of power system terminal image processing based on RL and MEC. With the continuous development of power system, the demand for image processing of terminal equipment is increasing day by day. However, traditional image processing methods have the problems of high computing complexity and real‐time and energy consumption. To solve this problem, this study introduces the idea of RL and MEC to improve the efficiency and performance of image processing of power system terminals. By modeling and optimizing the image processing task of the power system terminal equipment, the intelligent adjustment of the processing parameters is realized to adapt to the needs of different scenarios. MEC technology is introduced to move image processing tasks from the central server to the edge device, reducing data transmission delay and network burden, thus improving real‐time performance and reducing energy consumption. The experimental results show that the proposed optimization method based on RL and MEC has a significant performance improvement compared with the traditional method in the power system terminal image processing. The framework our proposed has achieved significant improvement in task completion latency, achieving higher system energy efficiency compared to traditional methods.

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Fuzzy Reinforcement Learning for energy efficient task offloading in Vehicular Fog Computing

Cited by 60 publications

References 24 publications

Resource Allocation Methods in Vanets:a Systematic Literature Review

Resource Allocation Methods in Vanets:a Systematic Literature Review

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

Research on image processing of electric power system terminals based on reinforcement learning and mobile edge computing optimization

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