Improving the performance of opportunistic routing protocol using the evidence theory for VANETs in highways

Kashani, Ali Azimi; Ghanbari, M.; Rahmani, Amir Masoud

doi:10.1049/iet-com.2019.0473

Cited by 14 publications

(9 citation statements)

References 31 publications

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“…Here, we concluded that the EEFM with IoT in VANET system could generate accurate communication data with IoT in VANET. Our prosed method is different from the existing method [84] because three parameters, namely the (1) distance of each node, (2) remaining energy, and (3) number of neighbors of every node, were considered as fuzzy criteria. Results of this research compared to various other algorithms in relation to parameters like dead node in every round, the first node expires, half node expire, last node expire, and the network lifetime.…”

Section: Discussionmentioning

confidence: 99%

Energy-Efficient Fuzzy Management System for Internet of Things Connected Vehicular Ad Hoc Networks

et al. 2021

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Many algorithms use clustering to improve vehicular ad hoc network performance. The expected points of many of these approaches support multiple rounds of data to the roadside unit and constantly include clustering in every round of single-hop data transmission towards the road side unit; however, the clustering in every round maximizes the number of control messages and there could be the possibility of collision and decreases in network energy. Multi-hop transmission prolongs the cluster head node’s lifetime and boosts the network’s efficiency. Accordingly, this article proposes a new fuzzy-clustering-based routing algorithm to benefit from multi-hop transmission clustering simultaneously. This research has analyzed the limitation of clustering in each round, different algorithms were used to perform the clustering, and multi-hop routing was used to transfer the data of every cluster to the road side unit. The fuzzy logic was used to choose the head node of each cluster. Three parameters, (1) distance of each node, (2) remaining energy, and (3) number of neighbors of every node, were considered as fuzzy criteria. The results of this research were compared to various other algorithms in relation to parameters like dead node in every round, first node expire, half node expire, last node expire, and the network lifetime. The simulation results show that the proposed approach outperforms other methods. On the other hand, the vehicular ad hoc network (VANET) environment is vulnerable at the time of data transmission. The NS-2 software tool was used to simulate and evaluate the proposed fuzzy logic opportunistic routing’s performance results concerning end-to-end delay, packet delivery, and network throughput. We compare to the existing protocols, such as fuzzy Internet of Things (IoT), two fuzzy, and Fuzzy-Based Driver Monitoring System (FDMS). The performance comparison also emphasizes an effective utilization of the resources. Simulations on the highway environment show that the suggested protocol has an improved Quality of Service (QoS) efficiency compared to the above published methods in the literature.

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Section: Discussionmentioning

confidence: 99%

Energy-Efficient Fuzzy Management System for Internet of Things Connected Vehicular Ad Hoc Networks

et al. 2021

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“…Contributions [33] With the help of traffic signals, the issue of irregular vehicle distribution is addressed along with network congestion at peak traffic hours [42] Dynamic moving zones are formed to facilitate dissemination of information, considering moving object modeling with databases [67] Based on a spider model, the proposed model is built on the basis of parking areas using digital maps and geographic information [68] Addresses the link state routing optimization [54] Selects the path with the least number of in-between intersection nodes [16] States that UAVs can increase line-of-sight probability and more efficient use of store-carry-forward mechanism [69] Considers distances, traffic density, and directions when selecting the next forwarding node and the corresponding route [47] Discusses VANET routing protocols' challenges on the basis of real-world experiments [70] Analyzes the link stability in route selection and formulated it as a least cost enabled flow problem [43] For video transmission, numerous independent routes are found instead of just one between the source and destination [71] Handles highly dynamic node mobility using an optimized mechanism [72] Designs a secure payment framework for Drive-thru Internet [73] Prioritizes node transmission using relay set [74] Parallel route is established to the destination to reduce losses in real time applications [75] The effects of node addition, deletion, and loss of link node due to mobility are considered Dynamic node mobility is a challenging issue in multihop data transmission because the connecting links are not stable and the packets are thus not always delivered to the destination. Due to the absence of global information, conventional broadcasting methods cannot accurately determine link dynamicity in VANETs.…”

Section: Itemmentioning

confidence: 99%

A Survey of VANET/V2X Routing From the Perspective of Non-Learning- and Learning-Based Approaches

Chatterjee¹,

Karmakar

Kaddoum

et al. 2022

IEEE Access

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Intelligent transportation systems (ITSs) have become increasingly popular because they support effective coordination in connected vehicles. ITSs present an integrated approach for exchanging relevant information in order to improve the safety, efficiency, and reliability of road transportation systems. A variant of mobile ad-hoc networks (MANETs) called vehicular ad-hoc networks (VANETs) are an integral component of ITSs. VANETs consist of interconnected vehicles with sensing abilities that exchange information related to traffic, positioning, weather, and emergency services. In general, vehicle-to-everything (V2X) refers to communications between any entity and a vehicle, where the entity may be a vehicle, a cloud-based network, a pedestrian, or equipment installed along a road. One of the crucial challenges in V2X is the reliable and timely circulation of information among vehicular nodes to allow drivers to make decisions that increase road safety. In this context, efficient V2X routing protocols play a key role in supporting reliability and safety, and enhancing the overall quality of service (QoS) in VANETs. However, VANETs have distinct characteristics, such as high vehicular node mobility, unsteady connectivity, rapid changes in network topology, and unbounded network size, that can significantly affect routing in the network. Various routing protocols for V2X communication exist in the open technical literature. In this survey, we categorize the routing mechanisms as non-learning-and learning-based approaches, and discuss existing V2X routing protocols and their contributions to and impacts on VANET performance. Here, the learning-based approach implies the use of machine learning algorithms. This survey also summarizes open challenges in designing effective V2X routing protocols and future research directions to consider when developing smart routing mechanisms for next-generation intelligent VANET technologies.INDEX TERMS VANET, V2X, routing protocol, non-learning-based routing, learning-based routing.

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Section: Congestion Controlmentioning

confidence: 99%

“…Traffic congestion is a common problem due to the dynamic environment, rush hours, and deadlocks [61], [75], [76]. VANETs have the potential to solve this problem using a routing technique to direct vehicles in a way that reduces congestion in traffic [61], [63], [71], [75], [76]. Xia et al propose a greedy traffic light and queue aware routing protocol (GTLQR) using network theory to reduce traffic congestion [75].…”

Section: Congestion Controlmentioning

confidence: 99%

“…Perronnet et al also focus on routing at intersections, but also includes road reservation using a graph theory method to prevent deadlocks in traffic [76]. Other papers focus on an opportunistic routing protocol like the Dempster-Shafer evidence theory based method proposed by Kashani et al or propose temporal graph methods to solve the routing problem due to the high mobility of a VANET, such as in Zhao et al [61], [63]. Khan et al propose a different approach using oriented evolving graph and clustering to improve reliable routing requests in the network [71].…”

Section: Congestion Controlmentioning

confidence: 99%

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A Network-Centric Analysis for the Internet of Vehicles and Simulation Tools

et al. 2020

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The Internet of Vehicles (IoV) is an emerging research framework, with network and graph theories as two of the major fields. Researchers in these topics use a variety of tools and approaches to simulate and perform experimentation on their proposed methodologies. A comprehensive study to facilitate the selection of such simulation tools is lacking from the literature. In this work, we provide a systematic review of the different simulation platforms. More precisely, the contributions of this paper are fourfold: firstly, we propose a two-tier hierarchical taxonomy based on the trends in the literature; secondly, we investigate the strengths and limitations of different simulation platforms; and thirdly, we take a network theoretic approach to identify the patterns in IoV research. To this end, we create a network of the publications and populate the edges among them. Community detection is performed using Louvian and Clauset-Newman-Moore algorithms. To the best of our knowledge, this is a novel approach to reviewing the literature which provides a more in-depth analysis of the trends in the literature. Finally, we review the common datasets for IoV experimentation.

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Improving the performance of opportunistic routing protocol using the evidence theory for VANETs in highways

Cited by 14 publications

References 31 publications

Energy-Efficient Fuzzy Management System for Internet of Things Connected Vehicular Ad Hoc Networks

Energy-Efficient Fuzzy Management System for Internet of Things Connected Vehicular Ad Hoc Networks

A Survey of VANET/V2X Routing From the Perspective of Non-Learning- and Learning-Based Approaches

A Network-Centric Analysis for the Internet of Vehicles and Simulation Tools

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