A receiver-forwarding decision scheme based on Bayesian for NDN-VANET

Xian, Guo; Chen, Yuxi; Cao, Liqin; Zhang, Di; Jiang, Yongbo

doi:10.23919/jcc.2020.08.009

Cited by 24 publications

(16 citation statements)

References 20 publications

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“…For example, when laws and regulations are automatically integrated, they will be affected by the environment and the rate of use of regulations. It is difficult to cooperate with each other, relatively independent, and the security is not too high, and the system maintenance ability is weak [9]. At this time, for these shortcomings of distributed cloud computing, edge computing can be used to improve.…”

Section: Related Workmentioning

confidence: 99%

The Legal Regulation of Artificial Intelligence and Edge Computing Automation Decision-Making Risk in Wireless Network Communication

Sun

2022

Wireless Communications and Mobile Computing

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This article is aimed at studying the legal regulation of artificial intelligence and edge computing automated decision-making risks in wireless network communications. The data under artificial intelligence is full of flexibility and vitality, which has changed the way of data existence in the whole society. Its core is various algorithm programs, which determine the existence of artificial intelligence. In this environment, society develops rapidly with unstoppable momentum. However, from a legal perspective, artificial intelligence has algorithmic discrimination, such as gender discrimination, clothing discrimination, and racial discrimination. It does not possess openness, objectivity, and accountability. The consequences are sometimes serious enough to endanger the public interest of the entire society, leading to market disorder, etc. Therefore, the problem of artificial intelligence algorithm discrimination remains to be solved. This article uses algorithms to adjust algorithm discrimination to reduce the harm caused by artificial intelligence algorithm discrimination to a certain extent. First of all, this article introduces a regulatory-based edge cloud computing architecture model. It is mentioned that distributed cloud computing can use subsystems to calculate various resources and storage resources and can make automated decisions when calculating certain data. In order to reduce the impact of algorithm discrimination and trigger data diversification to reduce the probability of discrimination, an edge computing network data capture system is designed. And this article mentions the BP neural network model. The BP neural network model is divided into input layer, output layer, and hidden layer. The training samples are passed from the input layer to the output layer through the hidden layer. If the output information does not meet expectations, the error will be back-propagated, and the connection weight will be adjusted continuously. This paper proposes a deep learning system model in real-time artificial intelligence driven by edge computing. When this model is applied to legal regulations, it can cooperate with edge computing and artificial intelligence algorithms to provide high-precision automated decision-making. Finally, this paper designs an artificial intelligence-assisted automated decision-making experiment based on the theory of legal computing. This paper proposes a Bayesian algorithm that uses edge algorithms to merge into artificial intelligence and verifies the feasibility of this hypothesis through experiments. The experimental results show that it has a certain ability to regulate algorithmic discrimination caused by artificial intelligence in legal regulations. It can improve the regulatory effects of laws and regulations to a certain extent, and the improved artificial intelligence Bayesian algorithm clustering effect of edge computing is increased by about 7.2%.

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Section: Related Workmentioning

confidence: 99%

The Legal Regulation of Artificial Intelligence and Edge Computing Automation Decision-Making Risk in Wireless Network Communication

Sun

2022

Wireless Communications and Mobile Computing

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“…In VANETs, named data networks (NDNs) use contentcentered mechanisms to name content instead of the host, which is associated with a broadcast problem. The work in [100] addresses this by proposing a Bayesian receiver forwarding decision to resolve the broadcast problem. Vehicles receiving interest packets make forwarding decisions with the help of Bayesian decision theory pertaining to present network conditions, which are determined by interacting with neighbors.…”

Section: F Named Data Network-based Approachmentioning

confidence: 99%

A Survey of VANET/V2X Routing From the Perspective of Non-Learning- and Learning-Based Approaches

Chatterjee¹,

Karmakar

Kaddoum

et al. 2022

IEEE Access

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Intelligent transportation systems (ITSs) have become increasingly popular because they support effective coordination in connected vehicles. ITSs present an integrated approach for exchanging relevant information in order to improve the safety, efficiency, and reliability of road transportation systems. A variant of mobile ad-hoc networks (MANETs) called vehicular ad-hoc networks (VANETs) are an integral component of ITSs. VANETs consist of interconnected vehicles with sensing abilities that exchange information related to traffic, positioning, weather, and emergency services. In general, vehicle-to-everything (V2X) refers to communications between any entity and a vehicle, where the entity may be a vehicle, a cloud-based network, a pedestrian, or equipment installed along a road. One of the crucial challenges in V2X is the reliable and timely circulation of information among vehicular nodes to allow drivers to make decisions that increase road safety. In this context, efficient V2X routing protocols play a key role in supporting reliability and safety, and enhancing the overall quality of service (QoS) in VANETs. However, VANETs have distinct characteristics, such as high vehicular node mobility, unsteady connectivity, rapid changes in network topology, and unbounded network size, that can significantly affect routing in the network. Various routing protocols for V2X communication exist in the open technical literature. In this survey, we categorize the routing mechanisms as non-learning-and learning-based approaches, and discuss existing V2X routing protocols and their contributions to and impacts on VANET performance. Here, the learning-based approach implies the use of machine learning algorithms. This survey also summarizes open challenges in designing effective V2X routing protocols and future research directions to consider when developing smart routing mechanisms for next-generation intelligent VANET technologies.INDEX TERMS VANET, V2X, routing protocol, non-learning-based routing, learning-based routing.

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“…Currently, all major research efforts have been made to develop and optimize the communication schemes for improving road safety and traffic management. As a consequence, a suite of routing, transmission control and congestion control protocols have been proposed, such as [11], [14], [15], [25]- [35]. Typically, such protocols focus on the NDN strategy layer.…”

Section: Description Of Channel and Propagation Models 31 The Channel...mentioning

confidence: 99%

The impact of channel model on the performance of distance-based schemes in vehicular named data networks

Ahed

Benamar²,

Ouazzani³

2022

IJECE

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<span lang="EN-US">Distance-based schemes present one of the methods to avoid the broadcast problem in vehicular named data networks. However, such schemes overlook the most factor in performance evaluation which is the variation in received signal strength caused by the propagation model choice. Thus, they are evaluated under one propagation model while neglecting the effect of the others. This paper evaluates the impact of the propagation variation model over three distance-based schemes, namely rapid named data networking (RNDN), enhanced vehicle on named data networking (EVNDN) and opportunistic interest forwarding protocol (OIFP). Simulation experiments are performed over three propagation models. Simulation results show that Nakagami significantly degrades network performance. However, it has a noticeable positive effect over higher distance resulting in a higher interest satisfaction ratio as compared to the other models. The RNDN exhibits a higher number of retransmissions across the Nakagami. In contrast, a higher number of retransmissions is exhibited by EVNDN when compared to the other schemes over the Friis and random. The OIFP show a higher interest satisfaction ratio when compared to EVNDN and RNDN under all models. OIFP shows robustness towards the adverse fading effects resulting from the Nakagami and exhibits lower end to end delays.</span>

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A receiver-forwarding decision scheme based on Bayesian for NDN-VANET

Cited by 24 publications

References 20 publications

The Legal Regulation of Artificial Intelligence and Edge Computing Automation Decision-Making Risk in Wireless Network Communication

The Legal Regulation of Artificial Intelligence and Edge Computing Automation Decision-Making Risk in Wireless Network Communication

A Survey of VANET/V2X Routing From the Perspective of Non-Learning- and Learning-Based Approaches

The impact of channel model on the performance of distance-based schemes in vehicular named data networks

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