A Fuzzy Rule-Based Fog-Cloud for Control the Traffic Light Duration Based On-road Density

Septyanto, Arif Wicaksono; Rosyida, Isnaini; Suryono, Suryono

doi:10.1109/icic54025.2021.9632941

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Maximize energy trading profits while reducing electricity consumption Can use more parameters as an input [193] Automatic traffic light control system…”

Section: Matlab Electrical Monitoringmentioning

confidence: 99%

“…Using the RMSE and PSNR parameters, the segmented image was evaluated and calculated. A study [193] proposed an automatic traffic light control system that can be implemented using RFID technology and artificial intelligence to determine the length of intersection traffic lights. The average vehicle speed value and the percentage of road occupancy are inputs into the fuzzy rule-based algorithm.…”

Section: Fuzzy Inference Systemmentioning

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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“…Maximize energy trading profits while reducing electricity consumption Can use more parameters as an input [193] Automatic traffic light control system…”

Section: Matlab Electrical Monitoringmentioning

confidence: 99%

Section: Fuzzy Inference Systemmentioning

confidence: 99%