A Probability-Based Multimetric Routing Protocol for Vehicular Ad Hoc Networks in Urban Scenarios

Cárdenas, Leticia Lemus; Mezher, Ahmad Mohamad; Bautista, Pablo Barbecho; Igartua, Mónica Aguilar

doi:10.1109/access.2019.2958743

Cited by 12 publications

(7 citation statements)

References 24 publications

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“…An efficient VANET routing protocol shows good performance in terms of packet loss, end-to-end delays, etc. In the interest of achieving these promising results, the work in [37] proposes a probabilistic multimetric routing protocol (ProMRP) that is especially designed for VANETs. It uses probability to estimate the percentage of packets successfully delivered to the destination on the basis of four metrics, namely, distance to the destination, position of a node, available bandwidth, and density of the nodes.…”

Section: Probabilistic Routingmentioning

confidence: 99%

“…Contributions [37] Uses the probability to estimate the percentage of successful packet delivery to the destination and estimates the current node positions before sending the data [129] Intersecting nodes help maintain and update records by taking data from every passing vehicle, and the delivery delay and vehicles' current positions are recorded to compute the connectivity probability…”

Section: Itemmentioning

confidence: 99%

“…(1) Handling variable network conditions is not highlighted, (2) Impact of irregular topology Sudheera et al [70] (1) Flow instantiation (FI) based source routing for software-defined vehicular network (SDVN) analyzes link stability in route selection, (2) Incremental packet allocation plan decreases time complexity, (3) Multiple shortest paths Impact of variable node mobility Zhu et al [35] (1) A good delivery ratio, (2) The proposed scheme reduces average hop count Lack of analysis of complex node distribution and transference conditions in multilevel VANET environments Salkuyeh et al [43] (1) Packet loss ratio is decreased, (2) Freezing delay is improved, (3) Estimation of connectivity possibility to select the best-connected route Routing overhead with variable node mobil-Sun et al [36] (1) Good communication quality, (2) High success rate, (3) Low delay Overhead of routing and rerouting Cárdenas et al [37] (1) Low packet loss rate, (2) Low end-to-end delay (1) Impact of short communication link lifetime, (2) Lack of details of variable topology under different network conditions [131] (1) High probability of connectivity, (2) Low end-to-end delay, (3) High packet delivery ratio, (4) Low control overhead Impact of high node mobility and irregular traffic are not highlighted Gao et al [38] Advantageous in terms of throughput, average delay, average hop count and delivery ratio…”

Section: Itemmentioning

confidence: 99%

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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: Probabilistic Routingmentioning

confidence: 99%

Section: Itemmentioning

confidence: 99%

Section: Itemmentioning

confidence: 99%

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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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“…Additionally, the dimensions of the rectangle that were used in the proposed RALAR algorithm can be enhanced to more accurately estimate nodes location. Several other novel solutions can also be explored that were recently proposed, such as an enhanced probabilistic multimeric routing protocol (EProMRP) [47] and Multimedia Multimetric Map-aware Routing Protocol (3MRP) [48]. This approach not only can improve the end-to-end packet delay and pack loss performance but also can help to identify the current node location while transmitting the data.…”

Section: Limitation and Future Workmentioning

confidence: 99%

Genetic Optimized Location Aided Routing Protocol for VANET Based on Rectangular Estimation of Position

2020

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Vehicle Ad-Hoc Network (VANET) is a dynamic decentralized network that consists of various wireless mobile vehicles with no individual user management. Several routing protocols can be used for VANETs, for example, the Location-Aided Routing (LAR) protocol that utilizes location information provided by the Global Positioning System (GPS) sensors. It can help to reduce the search space for the desired route—limiting the search space results in fewer route discovery messages. However, two essential aspects are ignored while applying the LAR protocol in the VANET-based environment. Firstly, the LAR does not exploit the fact that nodes in VANET do not have pure random movement. In other words, nodes in LAR predict the position of destination node by ignoring the fact that the pre-defined constraint on the destination node navigation is met. Secondly, the nodes in the conventional LAR (or simply stated as LAR) protocol use the location information of the destination node before selecting the route location, which is most likely to expire because of the fast movement of the nodes in the VANET environment. This study presents an estimation based on a heuristic approach that was developed to reject weak GPS location data and accept accurate ones. The proposed routing protocol stated as Rectangle-Aided LAR (RALAR) is based on a moving rectangular zone according to the node′s mobility model. Additionally, the proposed RALAR protocol was optimized by using the Genetic Algorithm (GA) by selecting the most suitable time-out variable. The results were compared with LAR and Kalman-Filter Aided-LAR (KALAR), the most commonly utilized protocols in VANET for performance metrics using Packet Delivery Ratio (PDR), average End-to-End Delay (E2E Delay), routing overhead and average energy consumption. The results showed that the proposed RALAR protocol achieved an improvement over the KALAR in terms of PDR of 4.7%, average E2E delay of 60%, routing overhead of 15.5%, and 10.7% of energy consumption. The results proved that the performance of the RALAR protocol had outperformed the KALAR and LAR protocol in terms of regular network performance measures in the VANET environment.

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“…6 IoV-based services must be compatible with the urban characteristics and road environments, such as infrastructure barriers, traffic lights, buildings, or road connections, to maintain the desired quality of service. 7 The demand for reliable and efficient communications between vehicles (V2V) and between vehicles and infrastructure (V2I) is increasing significantly. Sending data in IoV requires a reliable and efficient routing protocol.…”

mentioning

confidence: 99%

Reliable and infrastructure‐independent routing for the internet of vehicles

Taj

Bakhshi

Zandhessami

2023

Int J Communication

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The Internet of Vehicles (IoV) is a new framework for intelligent transportation systems. One of its goals is to improve safety and increase the quality of service for passengers. Topology changes in IoV present significant challenges to the function of safety applications. Most routing algorithms offer their solution, independent of infrastructure or based exclusively on infrastructure. Although the lack of access to roadside units disrupts infrastructure-based algorithms, methods that do not use infrastructure will also not enjoy the benefits of high-speed data transmission. This research proposes the reliable and infrastructure-independent routing (RIIR) routing algorithm to increase the reliability of real-time routing in the IoV. The RIIR, in addition to simultaneous covering capability to use intelligent infrastructure for accelerating data transmission and flexibility in conditions of lack of access to infrastructure, by introducing three new criteria entitled "history of vehicle movement," "conformity of vehicle speed with the harmonic speed average of nearby vehicles," and "number of reliable neighbors," scores vehicles and then selects the most suitable vehicles as route members. The RIIR measures the conformity of the previous presence of vehicles on the current route and the stability of their link with adjacent vehicles. Furthermore, it prevents congestion by halting the spread of route request messages at intersections not leading to the destination. The efficiency of the RIIR has been theoretically proven, and extensive simulations with multiple scenarios in SUMO and NS3 show the superiority of the RIIR in increasing the packet delivery rate and reducing route failure, average delay, and control overhead.

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A Probability-Based Multimetric Routing Protocol for Vehicular Ad Hoc Networks in Urban Scenarios

Cited by 12 publications

References 24 publications

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

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

Genetic Optimized Location Aided Routing Protocol for VANET Based on Rectangular Estimation of Position

Reliable and infrastructure‐independent routing for the internet of vehicles

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