Integrated Networking, Caching, and Computing for Connected Vehicles: A Deep Reinforcement Learning Approach

He, Ying; Zhao, Nan; Yin, Hongxi

doi:10.1109/tvt.2017.2760281

Cited by 513 publications

(251 citation statements)

References 30 publications

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“…A DRL algorithm based on echo state network (ESN) cells is proposed in [82], in order to provide an interference-aware path planning scheme for a network of cellular-connected unmanned aerial vehicles (UAVs). In [83], the authors develop an integration framework that enables dynamic orchestration of networking, caching, and computing resources to improve the performance of vehicular networks. The resource allocation strategy is formulated as a joint optimization problem, in which the gains of networking, caching and computing are all taken into consideration.…”

Section: B Aiot Perception Layer -Smart Vehiclesmentioning

confidence: 99%

Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

Lei

Tan

Zheng

et al. 2020

IEEE Commun. Surv. Tutorials

225

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The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices around the world, which collect and share information to reflect the status of physical world. The Autonomous Control System (ACS), on the other hand, performs control functions on the physical systems without external intervention over an extended period of time. The integration of IoT and ACS results in a new concept -autonomous IoT (AIoT). The sensors collect information on the system status, based on which intelligent agents in IoT devices as well as Edge/Fog/Cloud servers make control decisions for the actuators to react. In order to achieve autonomy, a promising method is for the intelligent agents to leverage the techniques in the field of artificial intelligence, especially reinforcement learning (RL) and deep reinforcement learning (DRL) for decision making. In this paper, we first provide comprehensive survey of the state-of-art research, and then propose a general model for the applications of RL/DRL in AIoT. Finally, the challenges and open issues for future research are identified.

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Section: B Aiot Perception Layer -Smart Vehiclesmentioning

confidence: 99%

Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

Lei

Tan

Zheng

et al. 2020

IEEE Commun. Surv. Tutorials

225

View full text Add to dashboard Cite

show abstract

“…Moreover, humanlike speed control of autonomous vehicles using deep RL with double Q-learning is presented in [92] that uses scenes generated by naturalistic driving data for learning. In [93], authors presented an integrated framework that uses a deep RL based approach for dynamic orchestration of networking, caching, and computing resources for connected vehicles.…”

Section: Applications Of ML For the Decision Making And Controlmentioning

confidence: 99%

Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and the Way Forward

Qayyum

Usama

Qadir

et al. 2020

IEEE Commun. Surv. Tutorials

201

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Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelligent transportation systems (ITS) providing travel comfort, road safety, along with a number of value-added services. Such a transformation-which will be fuelled by concomitant advances in technologies for machine learning (ML) and wireless communications-will enable a future vehicular ecosystem that is better featured and more efficient. However, there are lurking security problems related to the use of ML in such a critical setting where an incorrect ML decision may not only be a nuisance but can lead to loss of precious lives. In this paper, we present an in-depth overview of the various challenges associated with the application of ML in vehicular networks. In addition, we formulate the ML pipeline of CAVs and present various potential security issues associated with the adoption of ML methods. In particular, we focus on the perspective of adversarial ML attacks on CAVs and outline a solution to defend against adversarial attacks in multiple settings.

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“…An actor-critic method with deep deterministic policy gradient updates was used in [20]. Boosted network performance using DRL was documented in several other applications, such as connected vehicular networks [21], and smart cities [22].…”

Section: A Prior Art On Cachingmentioning

confidence: 99%

Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks

Sadeghi

Wang

Giannakis

2019

IEEE Trans. Cogn. Commun. Netw.

116

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Caching is envisioned to play a critical role in nextgeneration content delivery infrastructure, cellular networks, and Internet architectures. By smartly storing the most popular contents at the storage-enabled network entities during off-peak demand instances, caching can benefit both network infrastructure as well as end users, during on-peak periods. In this context, distributing the limited storage capacity across network entities calls for decentralized caching schemes. Many practical caching systems involve a parent caching node connected to multiple leaf nodes to serve user file requests. To model the two-way interactive influence between caching decisions at the parent and leaf nodes, a reinforcement learning (RL) framework is put forth. To handle the large continuous state space, a scalable deep RL approach is pursued. The novel approach relies on a hyper-deep Q-network to learn the Q-function, and thus the optimal caching policy, in an online fashion. Reinforcing the parent node with ability to learnand-adapt to unknown policies of leaf nodes as well as spatiotemporal dynamic evolution of file requests, results in remarkable caching performance, as corroborated through numerical tests.

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Integrated Networking, Caching, and Computing for Connected Vehicles: A Deep Reinforcement Learning Approach

Cited by 513 publications

References 30 publications

Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and the Way Forward

Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks

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