A greedy traffic light and queue aware routing protocol for urban VANETs

Xia, Yangyang; Qin, Xiaoqi; Liu, Baoling; Zhang, Ping

doi:10.1109/cc.2018.8424605

Cited by 37 publications

(27 citation statements)

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“…Greedy Traffic Light and Queue aware Routing (GTLQR) [24] proposed based on street connectivity, channel quality, queuing delay and distance metrics. This protocol alleviates the packet loss by adopting clustering strategy.…”

Section: Related Workmentioning

confidence: 99%

“…Cluster-based routing utilizes less bandwidth and more scalable to improve network data routing. However, if we compared the cluster-based routing protocols so still some have more overhead and complex processes such as CBRSDN [8], PassCAR [20] and GTLQR [24]. On the other hand, some are designed for highway scenarios and not feasible for urban areas such as SBCA [19].…”

Section: Related Workmentioning

confidence: 99%

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Self-Assessment Based Clustering Data Dissemination for Sparse and Dense Traffic Conditions for Internet of Vehicles

et al. 2020

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Internet of Vehicle (IoV) is a sub class of vehicular ad hoc networks with more advanced cloud and Internet-enabled services. These networks offer various types of safety and infotainment services and provide comfortability and safety to passengers as well as to the drivers. Due to the high mobility of nodes, the nodes are out from its communication range and the information becomes outdated and causes link disconnections and packet dropping. Most feasible routing protocols are needed to provide in-time data communication, handle high mobility of nodes, dynamic topologies and unpredictable environments of these networks. In this paper, we proposed SACBR (Self-Assessment Cluster-based Routing) protocol in which the Cluster Heads (CHs) can communicate with other CHs. Every vehicle node initiates a self-assessment approach based on more appropriate routing metrics and elects the CH for every cluster and then collects the data from member nodes and further forward the data to other CHs. The CH is responsible to manage its own and member nodes' data forwarding process. The proposed protocol provides more stability and less overhead compared to the aggregation method where every node exchanges its data with a one-hop neighbor. Proposed protocol suites sparse and dense traffic scenarios where most of the time vehicle nodes are moving in platoons or snaking structures. The experimental results show the better performance of SACBR compared to state-of-the-art protocols. INDEX TERMS Cluster head, Internet of Vehicles, forwarding, routing, protocol.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Self-Assessment Based Clustering Data Dissemination for Sparse and Dense Traffic Conditions for Internet of Vehicles

et al. 2020

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“…These values are computed for each vehicle at the time of the route discovery phase to transmit data from source to destination, and hence it takes a longer time to discover the route and hence leads to higher communication delay and data loss. Another congestion-aware routing method is designed for urban areas called GTLQR (greedy traffic light & queueaware routing) in [23]. In GTLQR, they used the parameters such as channel quality, queuing delay, street connectivity, and relative distance to reduce the congestion and hence data loss in VANET.…”

Section: A Congestion Detection and Controlmentioning

confidence: 99%

“…The properties like high message rates, unreliable channel quality, and severe mobility lead to a significant problem for VANETs routing. The congestion control protocols [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] were designed to make sure the reliable transmission of security messages in V2V communications. However, adaptive congestion prediction and its utilization during the route discovery are missing in all works.…”

Section: B Research Motivation and Contributionsmentioning

confidence: 99%

Adaptive Congestion Prediction in Vehicular Ad-hoc Networks (VANET) using Type-2 Fuzzy Model to Establish Reliable Routes

Giripunje

Vidyarthi

Shandilya³

2021

Preprint

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In urban areas, a significant increase in vehicles day-by-day leads to challenges like accidents and pollution such as air and noise. The emission of Carbon dioxide (CO2) from vehicles leads to air pollution. The major cause of increased emission is traffic conditions in urban areas. Thus, Quality of Service (QoS) becomes a very challenging research problem by considering eco-friendly and reliable transportation. The appropriate congestion or traffic situation management techniques reduce the possibilities of accidents and pollution. The congestion control methods should take into account the properties such as fairness, decentralization, network characteristics, and application demands in VANET. The current methods failed to address all such properties and trade-offs for VANET communications. This paper proposed Adaptive Congestion Aware Routing Protocol (ACARP) for VANET using the dynamical artificial intelligence (AI) technique. In ACARP, the adaptive congestion detection algorithm is designed using the type-2 fuzzy logic AI technique. The fuzzy model builds to detect the congestion around each vehicle using three fuzzy-inputs such as bandwidth occupation, link quality, and moving speed. Using three parameters, the fuzzy rules are designed in the first phase. In the second phase, the inferences model introduced where the fuzzy decision has been made. In the last phase, defuzzifuzification is applied and the congestion probability estimated in the range of 0-1 for each vehicle. Then the status of congestion detection updated using the pre-defined threshold value for each vehicle. The status of congestion and its probability values have been utilized to establish safe and reliable routes for data transmission. It saves significant communication overhead and hence CO2 emissions in the network. The simulation results prove that the planned protocol improved the QoS presentation and significant CO2 emission compared to underlying fuzzy-based methods.

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“…We also define problems in the following works. A greedy traffic light and queue-aware routing protocol were introduced in urban VANETs [41]. Packet drop is high in transmission since all vehicle selects high connectivity street to discover the path, which increases queue size of the vehicles.…”

mentioning

confidence: 99%

Cellular V2X With D2D Communications for Emergency Message Dissemination and QoS Assured Routing in 5G Environment

Alghamdi

2021

IEEE Access

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Cellular Vehicle to Everything (C-V2X) in 5G communications has attracted researchers' attention due to advantages such as higher throughput, data rate and lower delay, etc. However, achieving Quality of Service (QoS) in disseminating emergency messages is a challenging task due to frequent topology changes and concurrent link losses between vehicles. To address this issue, we propose the QoS guaranteed Routing and Emergency message Dissemination in Device-to-Device(D2D) communication (Q-REDD) assisted C-V2X. Primarily, we perform Stable Matching based Routing that assures low end-to-end delay via selecting the best forwarder. The Q-REDD method adopts the Enhanced Sphere Decoder Like (ESDL) algorithm to select the best device for establishing effective D2D communication in the C-V2X environment. If the requested content is not present in the nearby device, the discoverer device requests a 5G base station through a nearby pedestrian. To reduce broadcast storms, Q-REDD performs Chaotic Crow Search Algorithm (CCSA). To validate the performance of Q-REDD, we perform simulations on Omnet++ 4.6 and SUMO 0.21.0 simulator in terms of Packet Delivery Ratio (PDR), End to End delay, Throughput, and Emergency Information Coverage. The obtained results demonstrate that our method enhances the Throughput and PDR up to 30%, reduces End to End delay by 30%, and enhances Emergency Information Coverage by 23%, compared to the existing methods; GTLQR, GPSR, MIR, 2 , ALQ and TBED.

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A greedy traffic light and queue aware routing protocol for urban VANETs

Cited by 37 publications

References 1 publication

Self-Assessment Based Clustering Data Dissemination for Sparse and Dense Traffic Conditions for Internet of Vehicles

Self-Assessment Based Clustering Data Dissemination for Sparse and Dense Traffic Conditions for Internet of Vehicles

Adaptive Congestion Prediction in Vehicular Ad-hoc Networks (VANET) using Type-2 Fuzzy Model to Establish Reliable Routes

Cellular V2X With D2D Communications for Emergency Message Dissemination and QoS Assured Routing in 5G Environment

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