Location Prediction for Grid-Based Geographical Routing in Vehicular Ad-Hoc Networks

Cha, Si-Ho; Lee, Keun-Wang

doi:10.1007/978-3-642-27180-9_5

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…It uses machine learning methods to build the model, and then predicts future trajectories by training the existing driving data, as shown in 2.1 [8][9][10]. The second method is to predict the future trajectory of the vehicle by its previous position, direction, velocity and acceleration [11][12][13][14]. This method measures the change in vehicle state through physical motion equations, as detailed in 2.2.…”

Section: Related Workmentioning

confidence: 99%

“…At present, many researchers related to mobility prediction have been proposed. Some researchers presented predictive models, and others developed applications that predict vehicle mobility, which makes significant contributions to driving, safety, and vehicle communications [6][7][8][9][10][11][12][13][14]. In [6], the authors proposed an intuitive and effective regional transformation model to describe vehicle mobility between regions divided by urban intersections based on two urban vehicle trajectories.…”

Section: Introductionmentioning

confidence: 99%

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Trajectory-prediction based relay scheme for time-sensitive data communication in VANETs

2020

KSII TIIS

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In the Vehicular Ad-hoc Network (VANET), the data transmission of time-sensitive applications requires low latency, such as accident warnings, driving guidance, etc. However, frequent changes of topology in VANET will result in data transmission failures. In order to improve the efficiency of VANETs data transmission and increase the timeliness of data, this paper proposes a relay scheme based on Recurrent Neural Network (RNN) trajectory prediction, which can be used to select the optimal relay vehicle to transmit data. The proposed scheme learns vehicle trajectory in a distributed manner and calculates the predicted trajectory, and then the optimal vehicle can be selected to complete the data transmission, which ensures the timeliness of the data. Finally, we carry out a set of simulations to demonstrate the performance of the algorithm. Simulation results show that the proposed scheme enhances the timeliness of the data and the accuracy of the predicted driving trajectory.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trajectory-prediction based relay scheme for time-sensitive data communication in VANETs

2020

KSII TIIS

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show abstract

“…The concept behind full road awareness is to determine the road characteristics for optimal routing in a network. The first protocol in this category is grid-based predictive geographical routing (GPGR) [21] which is based on the prediction mechanism of moving vehicles' positions and mapping the data to generate the road grids. The design assumes that vehicles move only along the road grids and are equipped with location services (i.e., GPS) and a preloaded digital map for road information.…”

Section: Routing Process Of Fullmentioning

confidence: 99%

Road-Aware Routing Strategies for Vehicular Ad Hoc Networks: Characteristics and Comparisons

Qureshi

Abdullah

Lloret

et al. 2016

International Journal of Distributed Sensor Networks

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The tremendous advancement in new wireless technologies has led to a renewed interest in vehicular ad hoc networks. However, due to the high mobility and dynamic nature of vehicular networks, great challenges exist for data delivery. In order to design efficient and smart routing strategies for stable communication, several different types of routing protocols have been integrated. One efficient type is the road-aware routing protocol which is based on road statistics (e.g., traffic density, intersections, and road segments). The main objective of the survey conducted in this paper is to discuss the recent road-aware routing protocols and categorize them according to different aspects. In addition, this review investigates the protocols in terms of their forwarding processes, routing metrics, recovery mechanisms, and performance. Moreover, routing challenges and comparisons are discussed.

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“…The main goal of clustering is to organize the network into clusters that communicate with a local cluster-head (CH), and the CH transmits data to the base station to minimize sending signals from each node directly to the base station. Moreover, data gathering permits conserving energy by removing the redundant message by including some aggregation methods, which allows reducing the traffic data in the network and directly extending the network lifetime [11,12]. Clustering data gathering or other methods that help to reduce the traffic in the network reflects directly how efficient the routing protocol used for communication.…”

Section: Introductionmentioning

confidence: 99%

New efficient GAF routing protocol using an optimized weighted sum model in WSN

Aznaoui¹,

Ullah²,

Raghay³

et al. 2021

IJEECS

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<span>A wireless sensor network (WSN) composed by a large number of sensor nodes that are insufficient in terms of processing power, storage and energy. The principal tasks of nodes is gathering and transmitting data collected to the base station (BS). Consequently the major essential criteria for designing a WSN are the network lifetime. In this paper an efficient GAF routing protocol for gathered data is introduced. It proposes an energy-efficient routing in WSN based on the basic version. In this system sensor nodes are distributed using Gaussian law and an active leader is elected for each virtual grid to reduce the energy dissipated using an optimized weighted sum model where maximum remaining energy and minimum distance criteria are considered. Moreover routing data is based on transmission range for enhancing the energy efficiency during data routing. The experimental results shows that the proposed EE-GAF produces better performance than the existing GAF basic and optimized-GAF routing protocol in terms of number of dead node and energy consumption. It is obviously proves that the proposed EE-GAF can improve the network lifetime</span>

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Location Prediction for Grid-Based Geographical Routing in Vehicular Ad-Hoc Networks

Cited by 6 publications

References 4 publications

Trajectory-prediction based relay scheme for time-sensitive data communication in VANETs

Trajectory-prediction based relay scheme for time-sensitive data communication in VANETs

Road-Aware Routing Strategies for Vehicular Ad Hoc Networks: Characteristics and Comparisons

New efficient GAF routing protocol using an optimized weighted sum model in WSN

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