Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles

Internet of Vehicles (IoV) is a hot research niche exploiting the synergy between Cooperative Intelligent Transportation Systems (C-ITS) and the Internet of Things (IoT), which can greatly benefit of the upcoming development of 5G technologies. The variety of end-devices, applications, and Radio Access Technologies (RATs) in IoV calls for new networking schemes that assure the Quality of Service (QoS) demanded by the users. To this end, network slicing techniques enable traffic differentiation with the aim of ensuring flow isolation, resource assignment, and network scalability. This work fills the gap of 5G network slicing for IoV and validates it in a realistic vehicular scenario. It offers an accurate bandwidth control with a full flow-isolation, which is essential for vehicular critical systems. The development is based on a distributed Multi-Access Edge Computing (MEC) architecture, which provides flexibility for the dynamic placement of the Virtualized Network Functions (VNFs) in charge of managing network traffic. The solution is able to integrate heterogeneous radio technologies such as cellular networks and specific IoT communications with potential in the vehicular sector, creating isolated network slices without risking the Core Network (CN) scalability. The validation results demonstrate the framework capabilities of short and predictable slice-creation time, performance/QoS assurance and service scalability of up to one million connected devices.

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Research on a Task Offloading Strategy for the Internet of Vehicles Based on Reinforcement Learning

Sensors ◽

10.3390/s21186058 ◽

2021 ◽

Vol 21 (18) ◽

pp. 6058

Author(s):

Shuo Xiao ◽

Shengzhi Wang ◽

Jiayu Zhuang ◽

Tianyu Wang ◽

Jiajia Liu

Keyword(s):

Reinforcement Learning ◽

The Internet ◽

Internet Of Vehicles ◽

Vehicle Delay ◽

Multi Agent ◽

Network Status ◽

Local Edge ◽

Communication Methods ◽

The Internet Of Things ◽

Task Offloading

Today, vehicles are increasingly being connected to the Internet of Things, which enables them to obtain high-quality services. However, the numerous vehicular applications and time-varying network status make it challenging for onboard terminals to achieve efficient computing. Therefore, based on a three-stage model of local-edge clouds and reinforcement learning, we propose a task offloading algorithm for the Internet of Vehicles (IoV). First, we establish communication methods between vehicles and their cost functions. In addition, according to the real-time state of vehicles, we analyze their computing requirements and the price function. Finally, we propose an experience-driven offloading strategy based on multi-agent reinforcement learning. The simulation results show that the algorithm increases the probability of success for the task and achieves a balance between the task vehicle delay, expenditure, task vehicle utility and service vehicle utility under various constraints.

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Reinforcement learning-based clustering scheme for the Internet of Vehicles

annals of telecommunications - annales des télécommunications ◽

10.1007/s12243-021-00879-3 ◽

2021 ◽

Author(s):

Hayet Zerrouki ◽

Samira Moussaoui ◽

Abdessamed Derder ◽

Zouina Doukha

Keyword(s):

Reinforcement Learning ◽

The Internet ◽

Internet Of Vehicles

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Deep Reinforcement Learning for Content Caching Optimization in the Internet of Vehicles

10.1109/iscc53001.2021.9631484 ◽

2021 ◽

Author(s):

Anas Knari ◽

Mohammed-Amine Koulali ◽

Ahmed Khoumsi

Keyword(s):

Reinforcement Learning ◽

The Internet ◽

Internet Of Vehicles ◽

Content Caching

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Personalized project recommendations: using reinforcement learning

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-019-1619-6 ◽

2019 ◽

Vol 2019 (1) ◽

Cited By ~ 1

Author(s):

Faxin Qi ◽

Xiangrong Tong ◽

Lei Yu ◽

Yingjie Wang

Keyword(s):

Reinforcement Learning ◽

User Behavior ◽

Collaborative Work ◽

Recursive Least Squares ◽

The Internet ◽

Dynamic Impact ◽

Rls Algorithm ◽

Trust Value ◽

Q Learning ◽

Actual Evaluation

AbstractWith the development of the Internet and the progress of human-centered computing (HCC), the mode of man-machine collaborative work has become more and more popular. Valuable information in the Internet, such as user behavior and social labels, is often provided by users. A recommendation based on trust is an important human-computer interaction recommendation application in a social network. However, previous studies generally assume that the trust value between users is static, unable to respond to the dynamic changes of user trust and preferences in a timely manner. In fact, after receiving the recommendation, there is a difference between actual evaluation and expected evaluation which is correlated with trust value. Based on the dynamics of trust and the changing process of trust between users, this paper proposes a trust boost method through reinforcement learning. Recursive least squares (RLS) algorithm is used to learn the dynamic impact of evaluation difference on user’s trust. In addition, a reinforcement learning method Deep Q-Learning (DQN) is studied to simulate the process of learning user’s preferences and boosting trust value. Experiments indicate that our method applied to recommendation systems could respond to the changes quickly on user’s preferences. Compared with other methods, our method has better accuracy on recommendation.

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RTED-SD: A Real-Time Edge Detection Scheme for Sybil DDoS in the Internet of Vehicles

IEEE Access ◽

10.1109/access.2021.3049830 ◽

2021 ◽

pp. 1-1

Author(s):

Jiabin Li ◽

Zhi Xue ◽

Changlian Li ◽

Ming Liu

Keyword(s):

Edge Detection ◽

Real Time ◽

The Internet ◽

Detection Scheme ◽

Internet Of Vehicles

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Blockchain-Based Solutions for UAV-Assisted Connected Vehicle Networks in Smart Cities: A Review, Open Issues, and Future Perspectives

Telecom ◽

10.3390/telecom2010008 ◽

2021 ◽

Vol 2 (1) ◽

pp. 108-140

Author(s):

Paulo Álvares ◽

Lion Silva ◽

Naercio Magaia

Keyword(s):

Traffic Congestion ◽

Resource Constraints ◽

Smart Cities ◽

The Internet ◽

Connected Vehicle ◽

Sensitive Data ◽

Internet Of Vehicles ◽

Smart Vehicles ◽

Security Properties ◽

Vehicle Networks

It had been predicted that by 2020, nearly 26 billion devices would be connected to the Internet, with a big percentage being vehicles. The Internet of Vehicles (IoVa) is a concept that refers to the connection and cooperation of smart vehicles and devices in a network through the generation, transmission, and processing of data that aims at improving traffic congestion, travel time, and comfort, all the while reducing pollution and accidents. However, this transmission of sensitive data (e.g., location) needs to occur with defined security properties to safeguard vehicles and their drivers since attackers could use this data. Blockchain is a fairly recent technology that guarantees trust between nodes through cryptography mechanisms and consensus protocols in distributed, untrustful environments, like IoV networks. Much research has been done in implementing the former in the latter to impressive results, as Blockchain can cover and offer solutions to many IoV problems. However, these implementations have to deal with the challenge of IoV node’s resource constraints since they do not suffice for the computational and energy requirements of traditional Blockchain systems, which is one of the biggest limitations of Blockchain implementations in IoV. Finally, these two technologies can be used to build the foundations for smart cities, enabling new application models and better results for end-users.

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