BRAIN-F: Beacon Rate Adaption Based on Fuzzy Logic in Vehicular Ad Hoc Network

Soleymani, Seyed Ahmad; Abdullah, Abdul Hanan; Anisi, Mohammad Hossein; Altameem, Ayman; Hasan, Wan Haslina; Mandala, Satria; Razak, Zaidi; Noor, Noorzaily Mohamed

doi:10.1007/s40815-016-0171-3

Cited by 21 publications

(17 citation statements)

References 29 publications

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“…The authors in [21] propose to give equal consideration to the congestion control and the awareness control integrating two state of the art protocols namely LIMERIC [22] and PULSAR [23]. The authors in [24] proposed a beacon rate adaptation based on Fuzzy Logic. The work in [25] introduces a distributed rate and power adaptation protocol that relies on context and environment awareness.…”

Section: Related Workmentioning

confidence: 99%

An Altruistic Prediction-Based Congestion Control for Strict Beaconing Requirements in Urban VANETs

Zemouri

Djahel

Murphy

2019

IEEE Trans. Syst. Man Cybern, Syst.

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Periodic Beacon Messages are one of the building blocks that enable the operation of VANET applications. In vehicular networks environments, congestion and awareness control mechanisms are key for a reliable and efficient functioning of vehicular applications. In order to control the channel load, a reliable mechanism allowing real time measurements of parameters like the local density of vehicles is a must. These measurements can then serve as an input to perform a fast adaptation of the transmit parameters. In this context, considerable efforts have been directed in the recent years towards designing flexible yet robust protocols solving this problem; yet, very few have considered a proactive adaptation of the transmit parameters as a preventive measure from channel load peaks. To this end, we take the opportunity to introduce P&A-A, a new congestion control protocol that performs a joint adaptation of the transmit rate and power, relying on an altruistic short-term prediction algorithm that estimates the vehicular density around a given vehicle within the next short while. Additionally, P&A-A adapts the transmit parameters in a way that guarantees the strict beaconing requirements and satisfies the level of awareness required for the operation of most critical VANET applications. The results of the simulations performed in a realistic scenario justify our theoretical considerations and confirm the efficiency and the effectiveness of our protocol by showing significant improvements in terms of network performance (up to 8% and 14% improvement in collision rate; and up to 10% and 20% increase in busy ratio compared to our previous scheme and the ETSI schemes respectively) as well as the achieved level of awareness (higher coverage with higher transmission rate and power in dense scenarios, and up to 8% and 55% improvement in density perception accuracy compared to our previous scheme and the ETSI schemes respectively).

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Section: Related Workmentioning

confidence: 99%

An Altruistic Prediction-Based Congestion Control for Strict Beaconing Requirements in Urban VANETs

Zemouri

Djahel

Murphy

2019

IEEE Trans. Syst. Man Cybern, Syst.

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“…Existing algorithms for the vertical handover decision such as those that include computational intelligence methods were proposed in recent studies [8][9][10][11][12][13]. Wilson et al [14] reported that certain algorithms are based on multiple criteria [15,16] which need assistance from artificial intelligence mechanisms including fuzzy logic [17], neural networks, as well as algorithms that genetically suffered from problems of modularity and scalability. These were not able to easily manage the increasing number of RATs as well as the criteria for heterogeneous wireless networks.…”

Section: Introductionmentioning

confidence: 99%

“…Where stands for the anticipated reward, stands for the set with the potential action (such as the network to utilize), r , stands for the function of reward, and P s | s, a stands for the state transition probability in various access technologies. Moreover, [17] stands for the anticipated reward at 1 :…”

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confidence: 99%

A hybrid intelligent model for network selection in the industrial Internet of Things

Goudarzi

Anisi

Abdullah

et al. 2019

Applied Soft Computing

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This works proposes a hybrid intelligent model for network selection in Industrial Internet of Things.  The proposed model merges the biogeography-based optimization (BBO) with the markov decision process (MDP).  The MDP is utilized to establish the radio access technology (RAT) selection's probability that behaves as the input to the BBO process  The BBO determines the best radio access technology (RAT) using the described multi-point algorithm in the heterogeneous networks.

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“…Therefore, placing RSUs along a long stretch of highway to offer ubiquitous connectivity is not economically viable. Hence, vehicles should be able to use other vehicles to transmit and receive driver critical data feeds with limited support from fixed road side infrastructures [ 1 , 2 ]. In this paper, we developed smart prediction scheme for vehicle-to-vehicle (V2V) communication [ 3 , 4 ], where the vehicles can obtain predicted information using their on-board units (OBUs) which is computed by RSUs.…”

Section: Introductionmentioning

confidence: 99%

“…Accurately anticipating traffic time is an imperative element of IoV and intelligent transportation frameworks [ 5 , 6 ]. There are a wide range of traffic time prediction techniques incorporating time arrangement examination [ 7 , 8 ], Bayesian systems [ 9 ], neural networks (NNs) [ 10 , 11 , 12 , 13 ], fuzzy systems [ 2 ], fuzzy NNs [ 14 , 15 ], nonparametric regression (NP) [ 16 , 17 ], and other computational intelligence approaches [ 18 ]. The availability of travel time data is increasingly being used for modelling traffic behaviour to assist road users and city authorities to make better informed decisions about travel choices, levels of pollution and congestion, the effect on public and private transportation policies, and effective repair and maintenance of the road network.…”

Section: Introductionmentioning

confidence: 99%

Self-Organizing Traffic Flow Prediction with an Optimized Deep Belief Network for Internet of Vehicles

Goudarzi

Kama

Anisi

et al. 2018

Sensors

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To assist in the broadcasting of time-critical traffic information in an Internet of Vehicles (IoV) and vehicular sensor networks (VSN), fast network connectivity is needed. Accurate traffic information prediction can improve traffic congestion and operation efficiency, which helps to reduce commute times, noise and carbon emissions. In this study, we present a novel approach for predicting the traffic flow volume by using traffic data in self-organizing vehicular networks. The proposed method is based on using a probabilistic generative neural network techniques called deep belief network (DBN) that includes multiple layers of restricted Boltzmann machine (RBM) auto-encoders. Time series data generated from the roadside units (RSUs) for five highway links are used by a three layer DBN to extract and learn key input features for constructing a model to predict traffic flow. Back-propagation is utilized as a general learning algorithm for fine-tuning the weight parameters among the visible and hidden layers of RBMs. During the training process the firefly algorithm (FFA) is applied for optimizing the DBN topology and learning rate parameter. Monte Carlo simulations are used to assess the accuracy of the prediction model. The results show that the proposed model achieves superior performance accuracy for predicting traffic flow in comparison with other approaches applied in the literature. The proposed approach can help to solve the problem of traffic congestion, and provide guidance and advice for road users and traffic regulators.

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BRAIN-F: Beacon Rate Adaption Based on Fuzzy Logic in Vehicular Ad Hoc Network

Cited by 21 publications

References 29 publications

An Altruistic Prediction-Based Congestion Control for Strict Beaconing Requirements in Urban VANETs

An Altruistic Prediction-Based Congestion Control for Strict Beaconing Requirements in Urban VANETs

A hybrid intelligent model for network selection in the industrial Internet of Things

Self-Organizing Traffic Flow Prediction with an Optimized Deep Belief Network for Internet of Vehicles

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