Region-Based Location-Service-Management Protocol for VANETs

Saleet, Hanan; Basir, Otman; Langar, Rami; Boutaba, Raouf

doi:10.1109/tvt.2009.2033079

Cited by 135 publications

(66 citation statements)

References 26 publications

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“…The Q-learning technique offered to vehicles the opportunity to optimize their interactions with the very dynamic environment through their experience in the network. Compared to other techniques present in literature, such as (Saleet et al, 2010;Ibrahim and Weigle, 2008), our scheme gives vehicles the possibility to auto-adapt their gathering process based on their experience in the network and hence adds the dynamicity aspect to the data collection parameters rather than fixing all of them since the beginning. Moreover, adding the instability of links in the learning process variables represents another important feature of our method that distinguishes it from previous works (Yu et al, 2010).…”

Section: Resultsmentioning

confidence: 99%

“…Saleet et al (2010), authors propose a location service management protocol that solves the location querying and updating problems by aggregating the location information data. In this scheme, the vehicle's mobility space is viewed as a grid network which is partitioned into several segments and each segment is divided into a number of cells.…”

Section: Related Workmentioning

confidence: 99%

“…Some works, such as (Saleet et al, 2010;Ibrahim and Weigle, 2008), do not consider at all the effect of the mobility issue on their proposed scheme. In the other cited works, authors investigate the performance of their proposals with a dynamic network configuration.…”

Section: Science Publicationsmentioning

confidence: 99%

See 2 more Smart Citations

Adopel: Adaptive Data Collection Protocol Using Reinforcement Learning for Vanets

Soua¹,

Afifi²

2014

Journal of Computer Science

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Efficient propagation of information over a vehicular wireless network has usually remained the focus of the research community. Although, scanty contributions have been made in the field of vehicular data collection and more especially in applying learning techniques to such a very changing networking scheme. These smart learning approaches excel in making the collecting operation more reactive to nodes mobility and topology changes compared to traditional techniques where a simple adaptation of MANETs propositions was carried out. To grasp the efficiency opportunities offered by these learning techniques, an Adaptive Data collection Protocol using reinforcement Learning (ADOPEL) is proposed for VANETs. The proposal is based on a distributed learning algorithm on which a reward function is defined. This latter takes into account the delay and the number of aggregatable packets. The Q-learning technique offers to vehicles the opportunity to optimize their interactions with the very dynamic environment through their experience in the network. Compared to non-learning schemes, our proposal confirms its efficiency and achieves a good tradeoff between delay and collection ratio.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Adopel: Adaptive Data Collection Protocol Using Reinforcement Learning for Vanets

Soua¹,

Afifi²

2014

Journal of Computer Science

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“…Distinct groups of nodes called clusters are formed based on theirgeographical locations [1].Cluster-based solutions provide less propagation delay and high delivery ratio .Clustering can simplify essential functions as routing, bandwidth allocation, and channel access.…”

Section: Iiiproposed Workmentioning

confidence: 99%

Node Clustering and Efficient Transmission in VANET Environment

Jeevitha¹,

Singh²

2014

IJAREEIE

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ABSTRACT:Recently, the study of VANETs has become a popular research topic in the area of wireless networking. VANET facilitates communication between vehicles to vehicles. Driver assistance and car safety is very important due to increased number of vehicles. In this paper Manet routing protocols are proposed in the Vanet Applications for efficient communication between the Vehicles. Clustering concept is used for the nodes to provide efficient transmission. QOS metrics such as Throughput and End-to-End Delay are evaluated to ensure that Manet routing protocols can communicate effectively without higher rate of packet loss.

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“…It, however, can also be transferred to remote vehicles for special usage, e.g., traffic management of specific roads by urban traffic management system. The urban traffic management system can use the CDNP to manage the traffic flow of the urban area; in this case, the CDNP packets will be transferred to specified vehicles on specified road segment using VANET routing protocols, and the location service [30] is used to determine the destination. The traffic management is not discussed in this proposal.…”

Section: Introductionmentioning

confidence: 99%

Common Driving Notification Protocol Based on Classified Driving Behavior for Cooperation Intelligent Autonomous Vehicle Using Vehicular Ad-Hoc Network Technology

Lin

Dong

Hirota

2015

Journal of Artificial Intelligence and Soft Computing Research

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A protocol, called common driving notification protocol (CDNP), is proposed based on the classified driving behavior for intelligent autonomous vehicles, and it defines a standard with common messages and format for vehicles. The common standard format and definitions of CDNP packet make the autonomous vehicles have a common language to exchange more detail driving decision information of various driving situations, decrease the identification time for one vehicle to identify the driving decisions of other vehicles before or after those driving decisions are performed. The simulation tools, including NS-3 and SUMO, are used to simulate the wireless data packet transmission and the vehicle mobility; the experiment results present that the proposed protocol, CDNP, can increase the reaction preparing time with maximum value 250 seconds, decrease the identification time and the average travel time. Prospectively, it is decided to implement the CDNP as a protocol stack in the Linux kernel to provide the basic protocol capability for real world transmission testing.

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Region-Based Location-Service-Management Protocol for VANETs

Cited by 135 publications

References 26 publications

Adopel: Adaptive Data Collection Protocol Using Reinforcement Learning for Vanets

Adopel: Adaptive Data Collection Protocol Using Reinforcement Learning for Vanets

Node Clustering and Efficient Transmission in VANET Environment

Common Driving Notification Protocol Based on Classified Driving Behavior for Cooperation Intelligent Autonomous Vehicle Using Vehicular Ad-Hoc Network Technology

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