Fuzzy-based beaconless probabilistic broadcasting for information dissemination in urban VANET

Srivastava, Ankita; Prakash, Arun; Tripathi, Rajeev

doi:10.1016/j.adhoc.2020.102285

Cited by 23 publications

(18 citation statements)

References 30 publications

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“…e network is trained using various types of signal and noise data. Data can be handled by using the latest technology called big data handling [30,31], though this decision is made on the confidence of the noise class. Since the method includes a recurrent neural network, it automatically learns the energy features and adapts to any untrained noise or signal in a real and dynamic environment.…”

Section: Trust (T)mentioning

confidence: 99%

Deep Learning-Based Dynamic Stable Cluster Head Selection in VANET

Saleem

Zhou

Sarwar

et al. 2021

Journal of Advanced Transportation

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VANET is the spontaneous evolving creation of a wireless network, and clustering in these networks is a challenging task due to rapidly changing topology and frequent disconnection in networks. The cluster head (CH) stability plays a prominent role in robustness and scalability in the network. The stable CH ensures minimum intra- and intercluster communication, thereby reducing the overhead. These challenges lead the authors to search for a CH selection method based on a weighted amalgamation of four metrics: befit factor, community neighborhood, eccentricity, and trust. The stability of CH depends on the vehicle’s speed, distance, velocity, and change in acceleration. These all are included in the befit factor. Also, the accurate location of the vehicle in changing the model is very vital. Thus, the predicted location with the Kalman filter’s help is used to evaluate CH stability. The results have shown better performance than the existing state of the art for the befit factor. The change in dynamics and frequent disconnection in communication links due to the vehicle’s high speed are inevitable. To comprehend this problem, a graphing approach is used to evaluate the eccentricity and the community neighborhood. The link reliability is calculated using the eigengap heuristic. The last metric is trust; this is one of the concepts that has not been included in the weighted approach to date as per the literature. An adaptive spectrum sensing is designed for evaluating the trust values specifically for the primary users. A deep recurrent learning network, commonly known as long short-term memory (LSTM), is trained for the probability of detection with various signals and noise conditions. The false rate has drastically reduced with the usage of LSTM. The proposed scheme is tested on the real map of Chengdu, southwestern China’s Sichuan province, with different vehicular mobilities. The comparative study with the individual and weighted metric has shown significant improvement in the cluster head stability during high vehicular density. Also, there is a considerable increase in network performance in energy, packet delay, packet delay ratio, and throughput.

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Section: Trust (T)mentioning

confidence: 99%

Deep Learning-Based Dynamic Stable Cluster Head Selection in VANET

Saleem

Zhou

Sarwar

et al. 2021

Journal of Advanced Transportation

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“…erefore, in order to solve these two problems, the typical way is to select some vehicles for forwarding, instead of repeating the broadcasting of all vehicles. Broadcast schemes are divided into five main categories according to the criteria for selecting forwarding nodes: geographic location-based [4][5][6][7][8][9][10][11][12][13], probability-based [14], [15], [16], [17], [18], [19], and [20], delay-based [21][22][23][24][25], clustering-based [26][27][28], and encoding based [29,30]. In Dynamic Hybrid Broadcasting Protocol (DHBP) [9], to solve the broadcast storm problem, the relay node stops rebroadcasting depending on the value of decision-making function (DMF).…”

Section: Related Workmentioning

confidence: 99%

“…In the density-aware probabilistic interest forwarding (DAPIF) method [17], the forwarding probability depends on the neighbor density and the time distance from the sender. A fuzzy-based beaconless probabilistic broadcasting algorithm (FBBPA) is proposed in [19] to decide the packets forwarding probability of the relay node on the basis of their distance, angular orientation, movement direction, and buffer load delay. Volunteer's dilemma game for VANET broadcast (VDGVB) [25], the timer value of relay node, depends on previous transmission, forwarding probability and nearby density.…”

Section: Related Workmentioning

confidence: 99%

Design and Analysis of Cooperative Broadcast Scheme Based on Reliability in Mesh Network

Dou

Tian

Yang

et al. 2021

Mobile Information Systems

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In order to ensure reliable data transmission of sender, the tree-based network is extended to Mesh network by collaborative technology, and the SW-HBH-CARQ broadcast scheme is introduced for the first time. Then, in order to solve the problems of broadcast storm and energy saving, SW-HBH-oiCARQ, SW-HBH-ieCARQ, and SW-HBH-oieCARQ are further proposed. Then, the four broadcasting schemes are analyzed quantitatively, and the performance indicators such as system energy consumption and time delay are obtained. Finally, the relationships between the performance indexes and the parameters are discussed by numerical simulation. At the same time, the effectiveness of the proposed schemes in energy saving is verified by comparing with the corresponding noncollaborative broadcasting scheme. However, the latency of proposes scheme is greater than uncooperative scheme, which is the cost of saving energy and resolving broadcast storms.

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“…Jan (in IEEE) [32] Using Certain recent research that does not depend on beacon information transmits a large number of data packets, hence increasing bandwidth wastage. A method is known as the Fuzzy-based Beaconless Probability Broadcasting Algorithm (FBBPA) [36] was created to overcome these issues and alert automobiles of an occurrence with the minimal broadcast. For broadcasting suppressing, this is a receiver-oriented strategy that determines the forwarding probabilities of packets in the vehicles' buffers based on their distances, angular orientation, and movements path.…”

Section: Ali (In Ieeementioning

confidence: 99%

Comparison of Different Clustering Algorithms to Secured VANETs Communication

Shrivastava¹,

Vishwamitra

2021

IJSRCSEIT

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ITS (Intelligent Transportation Systems) are growing increasingly popular because of the necessity for superior cyber-physical systems and comfort applications and services required for usage in autonomous vehicles. There are two types of Vehicular Ad-Hoc Networks (VANETs) that are vital to ITS: V2I (Vehicle-to-Infrastructure) and V2V (Vehicle-to-Vehicle). VANETs are a new technology with several potential uses in the ITS. It comprises smart vehicles and roadside equipment that connect over open-access wireless networks. An attacker may disrupt vehicular communication which can lead to potentially life-threatening scenarios because of the significant expansion in the number of vehicles in use today. VANETs must use robust security and authentication procedures to provide safe vehicular communication. This paper provides a comprehensive analysis ofthe VANET system including its characteristics and challenges. There is a concept of data dissemination that has been provided in brief. Clustering is the most important topic in VANET that is used to cluster the vehicles to secure and safely message transmission over the network. There is a taxonomy of clustering techniques has provided in a detailed manner. Besides, it has also shown the comparison of different clustering parameters-based mechanisms and MAC protocols in VANET.

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Fuzzy-based beaconless probabilistic broadcasting for information dissemination in urban VANET

Cited by 23 publications

References 30 publications

Deep Learning-Based Dynamic Stable Cluster Head Selection in VANET

Deep Learning-Based Dynamic Stable Cluster Head Selection in VANET

Design and Analysis of Cooperative Broadcast Scheme Based on Reliability in Mesh Network

Comparison of Different Clustering Algorithms to Secured VANETs Communication

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