RETRACTED CHAPTER: Future of Smart Parking: Automated Valet Parking Using Deep Q-Learning

Shoeibi, Nastaran; Shoeibi, Niloufar

doi:10.1007/978-3-030-23946-6_20

Cited by 19 publications

(9 citation statements)

References 44 publications

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“…In these situations the Q-table can be replaced by a neural network. Thus, we move from a Q-Learning algorithm to a Deep Q-Learning algorithm [80].…”

Section: Thus the Equation (2) Results Inmentioning

confidence: 99%

Deep Reinforcement Learning for the Management of Software-Defined Networks and Network Function Virtualization in an Edge-IoT Architecture

et al. 2020

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The Internet of Things (IoT) paradigm allows the interconnection of millions of sensor devices gathering information and forwarding to the Cloud, where data is stored and processed to infer knowledge and perform analysis and predictions. Cloud service providers charge users based on the computing and storage resources used in the Cloud. In this regard, Edge Computing can be used to reduce these costs. In Edge Computing scenarios, data is pre-processed and filtered in network edge before being sent to the Cloud, resulting in shorter response times and providing a certain service level even if the link between IoT devices and Cloud is interrupted. Moreover, there is a growing trend to share physical network resources and costs through Network Function Virtualization (NFV) architectures. In this sense, and related to NFV, Software-Defined Networks (SDNs) are used to reconfigure the network dynamically according to the necessities during time. For this purpose, Machine Learning mechanisms, such as Deep Reinforcement Learning techniques, can be employed to manage virtual data flows in networks. In this work, we propose the evolution of an existing Edge-IoT architecture to a new improved version in which SDN/NFV are used over the Edge-IoT capabilities. The proposed new architecture contemplates the use of Deep Reinforcement Learning techniques for the implementation of the SDN controller.

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“…In these situations the Q-table can be replaced by a neural network. Thus, we move from a Q-Learning algorithm to a Deep Q-Learning algorithm [80].…”

Section: Thus the Equation (2) Results Inmentioning

confidence: 99%

Deep Reinforcement Learning for the Management of Software-Defined Networks and Network Function Virtualization in an Edge-IoT Architecture

et al. 2020

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“…1. The agent takes action, apply it to the environment, and see how good its action was, by the value of the reward function [7].…”

Section: Proposed Methodsmentioning

confidence: 99%

“…As containers can be utilized denser in comparison with virtual machines, they increase the need for efficient cloud resource utilization. Containerization, as a lightweight remedy, is applied for management and positioning the portable packages and applications on a large number of servers [2][3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED CHAPTER: An Energy-Aware Dynamic Resource Management Technique Using Deep Q-Learning Algorithm and Joint VM and Container Consolidation Approach for Green Computing in Cloud Data Centers

Gholipour

Shoeibi

Arianyan

2021

Advances in Intelligent Systems and Computing

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These days by a high increase in the amount of computation and big data gathering and analysis, everybody needs more resources. Buying more computational and storage resources are so expensive. However, cloud computing solved this problem by providing a "pay as you go" plans therefor, users will only pay for resources that they used. However, using this technology has its challenges. One of them is resource management, which is focusing on the methodologies of dedicating resources to the users with the minimum of waste. In this paper, we propose a novel energy-aware resource management technique, using the concepts of both joint VM and container consolidation approach and deep Q-Learning algorithm for green computing in cloud data centers in order to minimize the waste of resources, migration rate, and energy.

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“…However, the prediction aspects has not been addressed with this technology. Another case study [24] introduced an automated valet for parking based on hybrid robotic arms. The robotic helps to optimize the usage of parking space and make use of Deep Q-Learning as a learning method.…”

Section: Related Workmentioning

confidence: 99%

Forecasting Parking Lots Availability: Analysis from a Real-World Deployment

Barraco

Bicocchi

Mamei

et al. 2021

2021 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Work

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Smart parking technologies are rapidly being deployed in cities and public/private places around the world for the sake of enabling users to know in real time the occupancy of parking lots and offer applications and services on top of that information. In this work, we detail a real-world deployment of a full-stack smart parking system based on industrial-grade components. We also propose innovative forecasting models (based on CNN-LSTM) to analyze and predict parking occupancy ahead of time. Experimental results show that our model can predict the number of available parking lots in a ±3% range with about 80% accuracy over the next 1-8 hours. Finally, we describe novel applications and services that can be developed given such forecasts and associated analysis.

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RETRACTED CHAPTER: Future of Smart Parking: Automated Valet Parking Using Deep Q-Learning

Cited by 19 publications

References 44 publications

Deep Reinforcement Learning for the Management of Software-Defined Networks and Network Function Virtualization in an Edge-IoT Architecture

Deep Reinforcement Learning for the Management of Software-Defined Networks and Network Function Virtualization in an Edge-IoT Architecture

RETRACTED CHAPTER: An Energy-Aware Dynamic Resource Management Technique Using Deep Q-Learning Algorithm and Joint VM and Container Consolidation Approach for Green Computing in Cloud Data Centers

Forecasting Parking Lots Availability: Analysis from a Real-World Deployment

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