VANET Broadcast Protocol Based on Fuzzy Logic and Lightweight Retransmission Mechanism

Wu, Celimuge; Ohzahata, Satoshi; Kato, Toshihiko

doi:10.1587/transcom.e95.b.415

Cited by 72 publications

(68 citation statements)

References 9 publications

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“…For this purpose, we take advantage of the fuzzy logic system (FLS) which received signal strength and mobility speed factors are the fuzzy inputs and quality of link received signal (QLRS) is fuzzy output. More details of FLS are given to papers [17], [18].…”

Section: Design Of Fuzzy Inference Based Mobility and Rssmentioning

confidence: 99%

Improving Routing Performances to Provide Internet Connectivity in VANETs over IEEE 802.11p

Abada¹,

Massaq²,

Boulouz³

2017

ijacsa

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Abstract-In the intelligent transportation systems, many applications and services could be offered in the road via Internet. Providing these applications over vehicular ad hoc network (VANETs) technology may require good performances in routing. The channel fading and quality of received signal are the two main factors which affect the Mobile ad hoc network performances as well as mobility of vehicles, in terms of throughput and delay packets that are relevant to the performance evaluation of the routing protocols. In this paper, we propose an efficient relay selection scheme based on Contention Based Forwarding (CBF) and Fuzzy Logic System (FLS) that considers two important Quality of Service parameters such as link stability, and quality of received signal to select a potential relay vehicle, in order to improve the routing performances in the network. The simulation results show that the proposed relay selection scheme enhances throughput, and decreases packet delay and overhead comparing it with an existing link stability based routing protocol(MBRP) and M-AODV+.

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Section: Design Of Fuzzy Inference Based Mobility and Rssmentioning

confidence: 99%

Improving Routing Performances to Provide Internet Connectivity in VANETs over IEEE 802.11p

Abada¹,

Massaq²,

Boulouz³

2017

ijacsa

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show abstract

“…FUZZBR scheme in [22] models the forwarding ability based on distance, velocity, and communication quality and selects two relays within a particular range by using fuzzy logic. In [23], fuzzy logic is utilized to select next-hop nodes, considering factors such as the distance between vehicles and vehicle velocity and density.…”

Section: Related Workmentioning

confidence: 99%

Data Dissemination Based on Fuzzy Logic and Network Coding in Vehicular Networks

Tang

Geng

Chen

et al. 2017

Wireless Communications and Mobile Computing

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.Vehicular networks, as a significant technology in intelligent transportation systems, improve the convenience, efficiency, and safety of driving in smart cities. However, because of the high velocity, the frequent topology change, and the limited bandwidth, it is difficult to efficiently propagate data in vehicular networks. This paper proposes a data dissemination scheme based on fuzzy logic and network coding for vehicular networks, named SFN. It uses fuzzy logic to compute a transmission ability for each vehicle by comprehensively considering the effects of three factors: the velocity change rate, the velocity optimization degree, and the channel quality. Then, two nodes with high abilities are selected as primary backbone and slave backbone in every road segment, which propagate data to other vehicles in this segment and forward them to the backbones in the next segment. The backbone network helps to increase the delivery ratio and avoid invalid transmissions. Additionally, network coding is utilized to reduce transmission overhead and accelerate data retransmission in interbackbone forwarding and intrasegment broadcasting. Experiments show that, compared with existing schemes, SFN has a high delivery ratio and a short dissemination delay, while the backbone network keeps high reliability.

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“…In this way, the proposed metric can be suited to all kinds of scenarios by adjusting fuzzy membership functions. In here, we use the same fuzzy reasoning Bad VeryBad method used in [20]. We use Fuzz(x, y) to show the evaluation result of the link between the node x and node y. Bandwidth factor and RSSI factor are considered in the calculation.…”

Section: Link Evaluation Based On Fuzzy Logicmentioning

confidence: 99%

“…DDCAR uses the output membership function as defined in Fig. 3, and uses Center of Gravity (COG) [20] method to convert the fuzzy output value to Fuzz(x, y).…”

Section: Link Evaluation Based On Fuzzy Logicmentioning

confidence: 99%

See 1 more Smart Citation

A Distributed Dynamic Channel Assignment and Routing Framework for Cognitive Sensor Systems

Ohzahata

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYWith the increase of the number of wireless sensing or metering devices, the collection of sensing data using wireless communication becomes an important part of a smart grid system. Cognitive radio technology can be used to facilitate the deployment of smart grid systems. In this paper, we propose a data collection and dissemination framework for cognitive radio smart grid systems to fully utilize wireless resources while maintaining a reliably connected and efficient topology for each channel. In the proposed framework, each sensor node selects a channel considering the primary user (PU) channel utilization and network connectivity. In this way, the data collection and dissemination can be performed with a high reliability and short delay while avoiding a harmful effect on primary users. We use computer simulations to evaluate the proposed framework.

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VANET Broadcast Protocol Based on Fuzzy Logic and Lightweight Retransmission Mechanism

Cited by 72 publications

References 9 publications

Improving Routing Performances to Provide Internet Connectivity in VANETs over IEEE 802.11p

Improving Routing Performances to Provide Internet Connectivity in VANETs over IEEE 802.11p

Data Dissemination Based on Fuzzy Logic and Network Coding in Vehicular Networks

A Distributed Dynamic Channel Assignment and Routing Framework for Cognitive Sensor Systems

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