Smart Manufacturing Real-Time Analysis Based on Blockchain and Machine Learning Approaches

Shahbazi, Zeinab; Byun, Yung-Cheol

doi:10.3390/app11083535

Cited by 38 publications

(19 citation statements)

References 51 publications

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“…Fuzzy logic is used to control the learning parameters. Additionally, a higher tracing value is one of its advantages [39,40]. Xu et al [41] used flow prediction machine learning in intelligent support vector regression for short-term traffic flow prediction.…”

Section: Urban Traffic Control Based On Fuzzy Controller and Multi-ag...mentioning

confidence: 99%

Improving the Performance of Single-Intersection Urban Traffic Networks Based on a Model Predictive Controller

2021

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The use of a Model Predictive Controller (MPC) in an urban traffic network allows for controlling the infrastructure of a traffic network and errors in its operations. In this research, a novel, stable predictive controller for urban traffic is proposed and state-space dynamics are used to estimate the number of vehicles at an isolated intersection and the length of its queue. This is a novel control strategy based on the type of traffic light and on the duration of the green-light phase and aims to achieve an optimal balance at intersections. This balance should be adaptable to the unchanging behavior of time and to the randomness of traffic situations. The proposed method reduces traffic volumes and the number of crashes involving cars by controlling traffic on an urban road using model predictive control. A single intersection in Tehran, the capital city of Iran, was considered in our study to control traffic signal timing, and model predictive control was used to reduce traffic. A model of traffic systems was extracted at the intersection, and the state-space parameters of the intersection were designed using the model predictive controller to control traffic signals based on the length of the vehicle queue and on the number of inbound and outbound vehicles, which were used as inputs. This process demonstrates that this method is able to reduce traffic volumes at each leg of an intersection and to optimize flow in a road network compared to the fixed-time method.

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Section: Urban Traffic Control Based On Fuzzy Controller and Multi-ag...mentioning

confidence: 99%

Improving the Performance of Single-Intersection Urban Traffic Networks Based on a Model Predictive Controller

2021

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“…This section presents a detailed explanation of the existing studies in smart home security based on IoT [25][26][27][28][29][30][31], blockchain, and machine learning [10,[32][33][34][35][36][37][38][39][40]. Due to the flexibility of the blockchain framework, a smart home ecosystem can shape easily.…”

Section: Related Workmentioning

confidence: 99%

Smart Home Gateway Based on Integration of Deep Reinforcement Learning and Blockchain Framework

2021

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The development of information and communication technology in terms of sensor technologies cause the Internet of Things (IoT) step toward smart homes for prevalent sensing and management of resources. The gateway connections contain various IoT devices in smart homes representing the security based on the centralized structure. To address the security purposes in this system, the blockchain framework is considered a smart home gateway to overcome the possible attacks and apply Deep Reinforcement Learning (DRL). The proposed blockchain-based smart home approach carefully evaluated the reliability and security in terms of accessibility, privacy, and integrity. To overcome traditional centralized architecture, blockchain is employed in the data store and exchange blocks. The data integrity inside and outside of the smart home cause the ability of network members to authenticate. The presented network implemented in the Ethereum blockchain, and the measurements are in terms of security, response time, and accuracy. The experimental results show that the proposed solution contains a better outperform than recent existing works. DRL is a learning-based algorithm which has the most effective aspects of the proposed approach to improve the performance of system based on the right values and combining with blockchain in terms of security of smart home based on the smart devices to overcome sharing and hacking the privacy. We have compared our proposed system with the other state-of-the-art and test this system in two types of datasets as NSL-KDD and KDD-CUP-99. DRL with an accuracy of 96.9% performs higher and has a stronger output compared with Artificial Neural Networks with an accuracy of 80.05% in the second stage, which contains 16% differences in terms of improving the accuracy of smart homes.

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“…In [42], the smart vehicle fueling mechanisem is presented in a blockchain network. In [43][44][45], the transportation system based on an electric vehicle is presented. This development is based on the smart contract centric inference and combination of blockchain and machine learning.…”

Section: Taxi Demand Based On Blockchain Approachmentioning

confidence: 99%

A Framework of Vehicular Security and Demand Service Prediction Based on Data Analysis Integrated with Blockchain Approach

Shahbazi

Byun

2021

Sensors

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The prediction of taxi demand service has become a recently attractive area of research along with large-scale and potential applications in the intelligent transportation system. The demand process is divided into two main parts: Picking-up and dropping-off demand based on passenger habit. Taxi demand prediction is a great concept for drivers and passengers, and is designed platforms for ride-hailing and municipal managers. The majority of research has focused on forecasting the pick-up part of demand service and specifying the interconnection of spatial and temporal correlations. In this study, the main focus is to overcome the access point of non-registered users for having fake transactions using taxi services and predicting taxi demand pick-up and drop-off information. The integration of machine learning techniques and blockchain framework is considered a possible solution for this problem. The blockchain technique was selected as an effective technique for protecting and controlling the real-time system. Historical data analysis was processed by extracting the three higher related sections for the intervening time, namely closeness and trend. Next, the pick-up and drop-off taxi prediction task was processed based on constructing the components of multi-task learning and spatiotemporal feature extraction. The combination of feature embedding performance and Long Short-Term Memory (LSTM) obtain the pick-up and drop-off correlation by fusing the historical data spatiotemporal features. Finally, the taxi demand pick-up and drop-off prediction were processed based on the combination of the external factors. The experimental result is based on a real dataset in Jeju Island, South Korea, to show the proposed system’s efficacy and performance compared with other state-of-art models.

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Smart Manufacturing Real-Time Analysis Based on Blockchain and Machine Learning Approaches

Cited by 38 publications

References 51 publications

Improving the Performance of Single-Intersection Urban Traffic Networks Based on a Model Predictive Controller

Improving the Performance of Single-Intersection Urban Traffic Networks Based on a Model Predictive Controller

Smart Home Gateway Based on Integration of Deep Reinforcement Learning and Blockchain Framework

A Framework of Vehicular Security and Demand Service Prediction Based on Data Analysis Integrated with Blockchain Approach

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