ERL: Edge Based Reinforcement Learning for Optimized Urban Traffic Light Control

Zhou, Pengyuan; Braud, Tristan; Alhilal, Ahmad; Hui, Pan; Kangasharju, Jussi

doi:10.1109/percomw.2019.8730706

Cited by 25 publications

(25 citation statements)

References 8 publications

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“…Liu et al, 2018;Zhou et al, 2019;M. Xu et al, 2020;Shi and F. Chen, 2018 Wei et al, 2018;Zhou et al, 2019;Genders and Razavi, 2020;Chu et al, 2019;Wei et al, 2019a;Zheng et al, 2019a;Wei et al, 2019b;Brys et al, 2014;Vinitsky et al, 2018No Simulation W. Xu et al, 2015Reda et al, 2019;Dowling et al, 2004;El-Tantawy and Abdulhai, 2010;Davarynejad et al, 2010;Xinhai and Lunhui, 2009 No Evaluation/Self-comparison W. Xu et al, 2015;Reda et al, 2019;Dowling et al, 2004;El-Tantawy and Abdulhai, 2010;Davarynejad et al, 2010;Xinhai and Lunhui, 2009;Dusparic and Cahill, 2009b;X.-Y. Liu et al, 2018;Prothmann et al, 2009 distributed approach can be used to store the local information around multiple agents, allowing each one to make its own decision while still communicating with its neighbours (Hüttenrauch et al, 2019).…”

Section: Control Methods Schemementioning

confidence: 99%

“…)), and 127 ( (Zhou et al, 2019)), respectively. The paper (Yongheng is the first paper in the literature that tested the proposed method in a network with the highest number of intersections: 225 intersections in 2016.…”

Section: Simulation and Simulated Networkmentioning

confidence: 95%

“…(R. Gao et al, 2019) designed a framework of edge computing under the NTSC scene and proposed a cooperative NTSC algorithm based on MARL to avoid the curse of dimensionality, provide minimal response time, and reduce network load. (Zhou et al, 2019) proposed a large-scale Edge-based RL (ERL) solution to better alleviate congestion in complex traffic scenarios based on Edge Computing nodes for traffic data collection. They concluded that ERL in distributed edge servers has much better scalability and faster Deep NN training than the cloud service.…”

Section: Traffic Data Collection and Traffic Demandmentioning

confidence: 99%

See 2 more Smart Citations

Reinforcement Learning in Urban Network Traffic Signal Control: A Systematic Literature Review

Noaeen¹,

Naik²,

Goodman³

et al. 2021

Preprint

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Improvement of traffic signal control (TSC) efficiency has been found to lead to improved urban transportation and enhanced quality of life. Recently, the use of reinforcement learning (RL) in various areas of TSC has gained significant traction; thus, we conducted a systematic literature review as a systematic, comprehensive, and reproducible review to dissect all the existing research that applied RL in the network-level TSC (NTSC) domain. The review only targeted the network-level articles that tested the proposed methods in networks with two or more intersections. We used natural language processing to define the search strings and searched Google Scholar, Web of Science, IEEE Xplore, ACM Digital Library, Springer Link, and Science Direct databases. This review covers 160 peer-reviewed articles from 30 countries published from 1994 to March 2020. The goal of this study is to provide the research community with statistical and conceptual knowledge, summarize existence evidence, characterize RL applications in NTSC domains, explore all applied methods and major first events in the defined scope, and identify areas for further research based on the explored research problems in current research.

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Section: Control Methods Schemementioning

confidence: 99%

Section: Simulation and Simulated Networkmentioning

confidence: 95%

Section: Traffic Data Collection and Traffic Demandmentioning

confidence: 99%

See 1 more Smart Citation

Reinforcement Learning in Urban Network Traffic Signal Control: A Systematic Literature Review

Noaeen¹,

Naik²,

Goodman³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Smart cities are a concrete example of pervasive computing and data sensing. A multitude of sensors collect data for AI models to provide insights on traffic management and road safety [65], infrastructure monitoring, or community service planning. However, such data collection will inherently expose users' private data to a risk of misuse by authorities and companies, such as location profiling.…”

Section: Use-case: Trustworthy Ai In Smart Citiesmentioning

confidence: 99%

Trustworthy AI in the Age of Pervasive Computing and Big Data

Kumar

Braud

Tarkoma

et al. 2020

2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

Self Cite

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“…Smarter healthcare [6,7,16], urban transportation [17,18], connected cars [19], social networks [20], and other scenarios 2…”

Section: Related Workmentioning

confidence: 99%

A Secure and Verifiable Outsourcing Scheme for Assisting Mobile Device Training Machine Learning Model

Yang

2020

Wireless Communications and Mobile Computing

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In smart applications such as smart medical equipment, more data needs to be processed and trained locally and near the local end to prevent privacy leaks. However, the storage and computing capabilities of smart devices are limited, so some computing tasks need to be outsourced; concurrently, the prevention of malicious nodes from accessing user data during outsourcing computing is required. Therefore, this paper proposes EVPP (efficient, verifiable, and privacy-preserving), which is a computing outsourcing scheme used in the training process of machine learning models. The edge nodes outsource the complex computing process to the edge service node. First, we conducted a certain amount of testing to confirm the parts that need to be outsourced. In this solution, the computationally intensive part of the model training process is outsourced. Meanwhile, a random encryption perturbation is performed on the outsourced training matrix, and verification factors are introduced to ensure the verifiability of the results. In addition, the system can generate verifiable evidence that can be generated to build a trust mechanism when a malicious service node is found. At the same time, this paper also discusses the application of the scheme in other algorithms in order to be better applied. Through the analysis of theoretical and experimental data, it can be shown that the scheme proposed in this paper can effectively use the computing power of the equipment.

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ERL: Edge Based Reinforcement Learning for Optimized Urban Traffic Light Control

Cited by 25 publications

References 8 publications

Reinforcement Learning in Urban Network Traffic Signal Control: A Systematic Literature Review

Reinforcement Learning in Urban Network Traffic Signal Control: A Systematic Literature Review

Trustworthy AI in the Age of Pervasive Computing and Big Data

A Secure and Verifiable Outsourcing Scheme for Assisting Mobile Device Training Machine Learning Model

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