A novel software-defined networking controlled vehicular named-data networking for trustworthy emergency data dissemination and content retrieval assisted by evolved interest packet

Alowish, Mazen; Shiraishi, Yoshiaki; Takano, Yasuhiro; Mohri, Masami; Morii, Masakatu

doi:10.1177/1550147720909280

Cited by 12 publications

(6 citation statements)

References 42 publications

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“…An SDN controller operates a direct path between the content requester and the provider, similar to the dynamic unicast method. Alowish et al proposed content delivery architecture for vehicular networks called Cuckoo, in which the RSU delivers the user content via controller nodes [7]. In other words, RSU selects the optimal routing path to the provider location with the help of controller nodes.…”

Section: Related Workmentioning

confidence: 99%

“…After the training, the network is used to predict the optimal action to take in a given situation in order to maximize the content deliverability reward r . In every intersection, a provider is aiming to take the optimal action in current state s as shown in (6), where Q(s, a) is the list Q-values yielded from the neural network output; Therefore, VeSoNet chooses the action that yielded the maximum rewards, as shown in (7) where s, a are the current action and state.…”

Section: Content Provider Distributionmentioning

confidence: 99%

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VeSoNet: Traffic-Aware Content Caching for Vehicular Social Networks Using Deep Reinforcement Learning

Aung

Dhelim

Chen

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Vehicular social networking is an emerging application of the Internet of Vehicles (IoV) which aims to achieve seamless integration of vehicular networks and social networks. However, the unique characteristics of vehicular networks, such as high mobility and frequent communication interruptions, make content delivery to end-users under strict delay constraints extremely challenging. In this paper, we propose a social-aware vehicular edge computing architecture that solves the content delivery problem by using some vehicles in the network as edge servers that can store and stream popular content to close-by end-users. The proposed architecture includes three main components: 1) the proposed social-aware graph pruning search algorithm computes and assigns the vehicles to the shortest path with the most relevant vehicular content providers.2) the proposed traffic-aware content recommendation scheme recommends relevant content according to its social context. This scheme uses graph embeddings in which the vehicles are represented by a set of low-dimension vectors (vehicle2vec) to store information about previously consumed content. Finally, we propose a deep reinforcement learning (DRL) method to optimise the content provider vehicle distribution across the network. The results obtained from a real-world traffic simulation show the effectiveness and robustness of the proposed system when compared to the state-of-the-art baselines.

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Section: Related Workmentioning

confidence: 99%

Section: Content Provider Distributionmentioning

confidence: 99%

VeSoNet: Traffic-Aware Content Caching for Vehicular Social Networks Using Deep Reinforcement Learning

Aung

Dhelim

Chen

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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“…The farthest node will then forward the packet. SDN controlled VNDN in Reference [17] is an efficient forwarding mechanism that uses SDN controller for greater view of network for user. Packet classification triggers the Emergency packet dissemination.…”

Section: Related Workmentioning

confidence: 99%

“…They did not handle the priority and emergency packets efficiently in WSNs and IoTs. In References [15][16][17], proposed forwarding mechanisms to mitigate the broadcast storm issue but lack of work to find efficient forwarding path and more overhead on the controller. In References [18][19][20], authors proposed energy efficient forwarding schemes in IoT.…”

Section: Introductionmentioning

confidence: 99%

EPF—An Efficient Forwarding Mechanism in SDN Controller Enabled Named Data IoTs

2020

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The vision of the Internet of Things (IoT) is that it connects all kinds of things by leveraging the creation of increasingly affordable and small devices that can be embedded for sensing, processing, wireless communication, and actuation. Named data networking (NDN) is a newly emerging Internet paradigm that may replace the current Internet architecture and that fulfills most of the expectations of the IoT. Software-defined networking (SDN) is an emerging paradigm of technology that is highly capable of managing overall networks efficiently and transforming complex network architectures into manageable, simple ones. The combination of the SDN controller, NDN, and IoT can be lethal in the overall performance of the network. Broadcast storms, due to the flooding nature of NDN’s wireless channel, are a serious issue when it comes to forwarding interest and data packets. Energy consumption of sensor nodes in dense IoT scenarios causes problems in forwarding as well as unnecessary delays, decreases network performance, and increases the cost and packet delay for important packets. We took these problems as our baseline and proposed an energy-efficient, priority-based forwarding (EPF) in SDN-enabled NDN–IoT. Our scheme EPF used the efficient flow management of the SDN controller to control the broadcast storm and efficiently forward the priority-based packets. A defer timer mechanism was used to prioritized the packet upon its arrival to the node. An energy threshold mechanism was used to control energy consumption and improve overall energy efficiency. We compared our scheme with the traditional flooding mechanism and geographic interest forwarding; EPF outclassed the other schemes and produced the best results in terms of total number of interests and retransmissions, content retrieval time, total number of priority interests, energy consumption, and network lifetime.

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“…Vehicle ad hoc network (VANET) [1,2] is an emerging paradigm that enables communication between vehicles in an ad hoc manner [3,4]. The communication between vehicles makes it possible to exchange more information between vehicles.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Coordination-Based Reinforcement Learning for Driving Policy

Tan

Peng

et al. 2022

Wireless Communications and Mobile Computing

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With the development of communication technology and artificial intelligence technology, intelligent vehicle has become a very important part of Internet of Things technology. At present, the single-vehicle intelligence is gradually improved, and more and more unmanned vehicles appear on the road. In the future, these individual intelligence applications need to be transformed into collective intelligence to give full play to the greater advantages of unmanned driving. For example, individual intelligence is self-interest. If there is no collective cooperation, it may affect the whole traffic flow for its own speed. Although the vehicle ad hoc network technology provides a guarantee for the communication between vehicles and makes cooperation between vehicles possible, there are still challenges in how to adapt to coordination learning. Coordination reinforcement learning is one of the most promising methods to solve the multiagent coordination optimization problems. However, existing coordinative learning approaches that usually rely on static topologies cannot be easily adopted to solve the vehicle coordination problems in the dynamic environment. We propose a dynamic coordination reinforcement learning to help vehicles make their driving decisions. First, we apply driving safety field theory to construct the dynamic coordination graph (DCG), representing the dynamic coordination behaviors among vehicles. Second, we design reinforcement learning techniques on our DCG model to implement the joint optimal action reasoning for the multivehicle system and eventually derive the optimal driving policy for each vehicle. Finally, compared with other multiagent learning methods, our method has a significant improvement in security and speed, which is about 1% higher than other multiagent learning methods, but its training speed is also significantly improved about 8%.

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A novel software-defined networking controlled vehicular named-data networking for trustworthy emergency data dissemination and content retrieval assisted by evolved interest packet

Cited by 12 publications

References 42 publications

VeSoNet: Traffic-Aware Content Caching for Vehicular Social Networks Using Deep Reinforcement Learning

VeSoNet: Traffic-Aware Content Caching for Vehicular Social Networks Using Deep Reinforcement Learning

EPF—An Efficient Forwarding Mechanism in SDN Controller Enabled Named Data IoTs

Dynamic Coordination-Based Reinforcement Learning for Driving Policy

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