The DTN Routing Algorithm Based on Markov Meeting Time Span Prediction Model

Wang, En; Yang, Yintang; Jia, Bing; Guo, Tingting

doi:10.1155/2013/736796

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…QGrid_G uses the greedy method of packet transmission, which preferentially selects the neighbor node closest to the destination. QGrid_M uses the two-hop Markov prediction [ 26 , 27 , 28 ] method to predict the grid that the packet will pass through in the future, and preferentially selects the neighbor node with a higher probability of entering the next-hop grid as the next-hop node. The above two methods are microscopic transfer considerations.…”

Section: Related Workmentioning

confidence: 99%

A Software-Defined Directional Q-Learning Grid-Based Routing Platform and Its Two-Hop Trajectory-Based Routing Algorithm for Vehicular Ad Hoc Networks

Yang

Yen

Chang

2022

Sensors

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Dealing with the packet-routing problem is challenging in the V2X (Vehicle-to-Everything) network environment, where it suffers from the high mobility of vehicles and varied vehicle density at different times. Many related studies have been proposed to apply artificial intelligence models, such as Q-learning, which is a well-known reinforcement learning model, to analyze the historical trajectory data of vehicles and to further design an efficient packet-routing algorithm for V2X. In order to reduce the number of Q-tables generated by Q-learning, grid-based routing algorithms such as the QGrid have been proposed accordingly to divide the entire network environment into equal grids. This paper focuses on improving the defects of these grid-based routing algorithms, which only consider the vehicle density of each grid in Q-learning. Hence, we propose a Software-Defined Directional QGrid (SD-QGrid) routing platform in this paper. By deploying an SDN Control Node (CN) to perform centralized control for V2X, the SD-QGrid considers the directionality from the source to the destination, real-time positions and historical trajectory records between the adjacent grids of all vehicles. The SD-QGrid further proposes the flows of the offline Q-learning training process and the online routing decision process. The two-hop trajectory-based routing (THTR) algorithm, which depends on the source–destination directionality and the movement direction of the vehicle for the next two grids, is proposed as a vehicle node to forward its packets to the best next-hop neighbor node in real time. Finally, we use the real vehicle trajectory data of Taipei City to conduct extensive simulation experiments with respect to four transmission parameters. The simulation results prove that the SD-QGrid achieved an over 10% improvement in the average packet delivery ratio and an over 25% reduction in the average end-to-end delay at the cost of less than 2% in average overhead, compared with two well-known Q-learning grid-based routing algorithms.

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Section: Related Workmentioning

confidence: 99%

A Software-Defined Directional Q-Learning Grid-Based Routing Platform and Its Two-Hop Trajectory-Based Routing Algorithm for Vehicular Ad Hoc Networks

Yang

Yen

Chang

2022

Sensors

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“…假设无人机所处位置位于图中的中心位置, 即无人机当前在位置 5, 并设定无人机受到的威胁预测范围为周围的 8 个位置. 由于无人机飞行过程中状态转移具有马尔可夫性质, 即当前转移概率仅与上一状态有关, 用公式可表示为 [22] :…”

Section: 基于马尔可夫模型的无人机威胁预测方法unclassified

Determining safe flight area of UAVs based on variable weight threat assessment

闫东¹,

陈谋²,

吴庆宪³

et al. 2021

Sci. Sin.-Inf.

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“…DPR updates delivery probability vector of nodes which can be used to decide message copies assignation and tactics of message transmission. In [26], the Predicted And Forward (PAF) based on Markov meeting time span prediction model is provided. The main idea of PAF is as follows.…”

Section: Probability Forwarding Protocolmentioning

confidence: 99%