Artificial Intelligence Empowered Edge Computing and Caching for Internet of Vehicles

Dai, Yueyue; Du, Xuehui; Maharjan, Sabita; Qiao, Guanhua; Zhang, Yan

doi:10.1109/mwc.2019.1800411

Cited by 245 publications

(102 citation statements)

References 11 publications

Supporting

Mentioning

102

Contrasting

Order By: Relevance

“…Reference [18] used game theory to solve the optimization problem and proved the existence of Nash equilibrium. Reference [19] calculated the theoretical upper limit of server-side task processing, and proved that their algorithm can be close to the theoretical value. It transformed nonconvex quadratic functions into separable semi-definite programming problems by relaxation techniques under quadratic constraints.…”

Section: Related Workmentioning

confidence: 84%

A collaborative scheduling strategy for IoV computing resources considering location privacy protection in mobile edge computing environment

2020

View full text Add to dashboard Cite

This paper proposes a collaborative scheduling strategy for computing resources of the Internet of vehicles considering location privacy protection in the mobile edge computing environment. Firstly, a multi area multi-user multi MEC server system is designed, in which a MEC server is deployed in each area, and multiple vehicle user equipment in an area can offload computing tasks to MEC servers in different areas by a wireless channel. Then, considering the mobility of users in Internet of vehicles, a vehicle distance prediction based on Kalman filter is proposed to improve the accuracy of vehicle-to-vehicle distance. However, when the vehicle performs the task, it needs to submit the real location, which causes the problem of the location privacy disclosure of vehicle users. Finally, the total cost of communication delay, location privacy of vehicles and energy consumption of all users is formulated as the optimization goal, which take into account the system state, action strategy, reward and punishment function and other factors. Moreover, Double DQN algorithm is used to solve the optimal scheduling strategy for minimizing the total consumption cost of system. Simulation results show that proposed algorithm has the highest computing task completion rate and converges to about 80% after 8000 iterations, and its performance is more ideal compared with other algorithms in terms of system energy cost and task completion rate, which demonstrates the effectiveness of our proposed scheduling strategy.

show abstract

Section: Related Workmentioning

confidence: 84%

A collaborative scheduling strategy for IoV computing resources considering location privacy protection in mobile edge computing environment

2020

View full text Add to dashboard Cite

show abstract

“…Moreover, blockchain tehcnologies can be used to protect both the energy and information interactions between electric vehicles [94] and hybrid electric vehicles in smart grids [95], [96]. In the future, incorporating artificial intelligence, mobile edge computing and blockchain can further optimize the resource allocation in IoVs [97].…”

Section: F Internet Of Vehicles and Unmanned Aerial Vehiclesmentioning

confidence: 99%

Blockchain for Internet of Things: A Survey

Dai

Zheng

Zhang

2019

IEEE Internet Things J.

Self Cite

1,015

397

View full text Add to dashboard Cite

Internet of Things (IoT) is reshaping the incumbent industry to smart industry featured with data-driven decisionmaking. However, intrinsic features of IoT result in a number of challenges such as decentralization, poor interoperability, privacy and security vulnerabilities. Blockchain technology brings the opportunities in addressing the challenges of IoT. In this paper, we investigate the integration of blockchain technology with IoT. We name such synthesis of blockchain and IoT as Blockchain of Things (BCoT). This paper presents an in-depth survey of BCoT and discusses the insights of this new paradigm. In particular, we first briefly introduce IoT and discuss the challenges of IoT. Then we give an overview of blockchain technology. We next concentrate on introducing the convergence of blockchain and IoT and presenting the proposal of BCoT architecture. We further discuss the issues about using blockchain for 5G beyond in IoT as well as industrial applications of BCoT. Finally, we outline the open research directions in this promising area.

show abstract

“…• ML-Aware Networking: The networking entities are aware of the availability of ML functionalities and optionally might exploit advantages of ML, for example, when the user load escalates. [344] UAV network trajectory control a model-free UAV trajectory control scheme in smart cities relying on DQN [345] ITS train control jointly optimize the communication handoff strategy and control performances [153] energy-aware network energy scheduling associate the stacked auto-encoder and the deep Q-learning model [346] CRAN power allocation decide RRU's sleeping mode and the optimal beamformer's power allocation [347] cognitive IoT traffic scheduling construct the mapping between states and actions relying on stacked auto-encoder [348] content-centric IoT cache allocation jointly design cache allocation and transmission rate for maximizing long-term QoE [350] IA network user scheduling obtain the action-value function relying on DQN for lowering complexity [351] vehicular network resource allocation consider programmable SDN, information-centric caching and mobile edge computing [352] CR network spectrum access distributed spectrum access for maximizing network utility without message exchanges [353] CR network multichannel access adaptive DQN aided multichannel access yielding a near-optimal performance [356] MEC network resource allocation DQN assisted cache, computation and power resources joint association [357] CR network energy scheduling energy efficiency optimization for distributed cooperative spectrum sensing [358] vehicular network resource allocation edge computing and caching resources dynamic association for IoV and resource-allocation is excessive for full search. Depending on the specific application, this may rely either on off-line ML or on on-line ML.…”

Section: A Future Machine Learning Aided Network Applicationsmentioning

confidence: 99%

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Wang

Jiang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

469

200

View full text Add to dashboard Cite

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (Het-Nets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.

show abstract

Artificial Intelligence Empowered Edge Computing and Caching for Internet of Vehicles

Cited by 245 publications

References 11 publications

A collaborative scheduling strategy for IoV computing resources considering location privacy protection in mobile edge computing environment

A collaborative scheduling strategy for IoV computing resources considering location privacy protection in mobile edge computing environment

Blockchain for Internet of Things: A Survey

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Contact Info

Product

Resources

About