Deep Reinforcement Learning-Based Traffic Light Scheduling Framework for SDN-Enabled Smart Transportation System

Kumar, Neetesh; Mittal, Sarthak; Garg, Vaibhav; Kumar, Neeraj

doi:10.1109/tits.2021.3095161

Cited by 36 publications

(5 citation statements)

References 21 publications

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“…For experimental purposes, a cellular V2X network environment based on the SUMO platform is established, which consists of a real road network, an MBS, and several VUEs and RSUs. Specifically, to fit the reality, we import the road network around the Beijing University of Posts and Telecommunication from OpenStreetMap to SUMO at first [40]. Then, the whole road network is divided into 9 blocks, which is consistent with the road partitioning strategy of the Manhattan case [51].…”

Section: Simulation Environmentmentioning

confidence: 99%

Slice Sandwich: Jagged Slicing Multi-Tier Dynamic Resources for Diversified V2X Services

Zhuang

et al. 2024

IEEE Trans. on Mobile Comput.

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With the advancement of intelligent transportation systems, a series of diversified V2X applications come into being, which have different key performance indicators (KPIs) and transmission features. Moreover, multi-tier computing as a new system-level architecture distributes computing and communication capabilities anywhere between the cloud and the end-user. Unfortunately, the existing network paradigm for V2X services adopts a one-shot allocation of resources ignoring the inherent differences of V2X service. To cope with these problems, three types of refined network slices for V2X services are first proposed to simultaneously support heterogeneous service characteristics without excessively splitting resources. Considering the spatiotemporal correlation between service traffic and physical resources, a jagged slicing in multi-tier dynamic resources, which forms a "slice sandwich" brightly, is realized by a dual timescale intelligent resource management scheme. The inter-slice resource configuration is based on neural bandits with upper confidence bounds at each large-time period, while the exclusive resources are managed elastically by deep Q-learning in terms of the real-time changing network state in the small slot. We developed a simulation environment by Simulation of Urban Mobility (SUMO) including real-world road conditions and traffic models. The experiment results demonstrate that the proposed scheme can effectively guarantee KPIs of V2X services and improve the system revenue compared with benchmark algorithms.

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Section: Simulation Environmentmentioning

confidence: 99%

Slice Sandwich: Jagged Slicing Multi-Tier Dynamic Resources for Diversified V2X Services

Zhuang

et al. 2024

IEEE Trans. on Mobile Comput.

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“…For example, deep learning technology can be used to optimize supply chain logistics and inventory management, reduce costs and increase efficiency. For example, in the transportation field, machine learning can be used to optimize the timing of traffic lights and reduce traffic congestion [65].…”

Section: Deep Learning and Machine Learning In Mathematical Modelling...mentioning

confidence: 99%

Artificial Intelligence in Mathematical Modeling of Complex Systems

Zhao

2024

EAI Endorsed Trans e-Learn

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This article introduces artificial intelligence techniques in mathematical modelling of complex systems and their applications. Mathematical modelling of complex systems is a method of studying the structure and behaviour of complex systems, aiming to understand interactions and nonlinear effects in the system. Commonly used modelling methods include system dynamics, network theory, and algebraic methods. Artificial intelligence technologies include machine learning and deep learning, which can be used for tasks such as prediction and classification, anomaly detection, optimization and decision-making. In mathematical modelling of complex systems, artificial intelligence technology can learn system patterns and laws from large amounts of data, and can be applied to image and speech recognition, time series analysis and other fields. Deep learning and machine learning are important branches of artificial intelligence. They realize the modelling and analysis of complex systems by building neural network models. Data-driven modelling is a modelling method based on actual data that, combined with traditional theoretical modelling, can better describe and predict the behaviour of complex systems. Self-control of complex systems means that the system realizes its own optimization and adjustment through adaptive control algorithms and feedback mechanisms. In summary, artificial intelligence technology has broad application prospects in mathematical modelling of complex systems and will provide new tools and methods for in-depth understanding and solving problems in complex systems.

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“…• Smart parking systems: By using sensors to detect the presence of cars in parking spots and providing this information to a centralized system, drivers can locate and reserve available spots in real-time [112]. • Traveller information: Each travelling user is provided with real-time information such as travel time, travel speed, delay, accidents on roads, changes in route, diversions, work zone conditions, and so on [118]. • Public transportation systems: Connected bus or train systems allow riders to track the location of their vehicle in real-time, providing more accurate information about arrival times and helping to reduce wait times [138].…”

Section: Smart Transportationmentioning

confidence: 99%

Integrating Edge Computing and Software Defined Networking in Internet of Things: A Systematic Review

Kamarudin,

Ameedeen,

Ab Razak

et al. 2023

ijcsm

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The Internet of Things (IoT) has transformed our interaction with the world by connecting devices, sensors, and systems to the Internet, enabling real-time monitoring, control, and automation in various applications such as smart cities, healthcare, transportation, homes, and grids. However, challenges related to latency, privacy, and bandwidth have arisen due to the massive influx of data generated by IoT devices and the limitations of traditional cloud-based architectures. Moreover, network management, interoperability, security, and scalability issues have emerged due to the rapid growth and heterogeneous nature of IoT devices. To overcome such problems, researchers proposed a new architecture called Software Defined Networking for Edge Computing in the Internet of Things (SDN-EC-IoT), which combines Edge Computing for the Internet of Things (EC-IoT) and Software Defined Internet of Things (SDIoT). Although researchers have studied EC-IoT and SDIoT as individual architectures, they have not yet addressed the combination of both, creating a significant gap in our understanding of SDN-EC-IoT. This paper aims to fill this gap by presenting a comprehensive review of how the SDN-EC-IoT paradigm can solve IoT challenges. To achieve this goal, this study conducted a literature review covering 74 articles published between 2019 and 2023. Finally, this paper identifies future research directions for SDN-EC-IoT, including the development of interoperability platforms, scalable architectures, low latency and Quality of Service (QoS) guarantees, efficient handling of big data, enhanced security and privacy, optimized energy consumption, resource-aware task offloading, and incorporation of machine learning.

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Deep Reinforcement Learning-Based Traffic Light Scheduling Framework for SDN-Enabled Smart Transportation System

Cited by 36 publications

References 21 publications

Slice Sandwich: Jagged Slicing Multi-Tier Dynamic Resources for Diversified V2X Services

Slice Sandwich: Jagged Slicing Multi-Tier Dynamic Resources for Diversified V2X Services

Artificial Intelligence in Mathematical Modeling of Complex Systems

Integrating Edge Computing and Software Defined Networking in Internet of Things: A Systematic Review

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