Performance analysis of Geocast based location aided routing using Cache agent in VANET

Gurumoorthi, E.; Ayyasamy, A.

doi:10.1007/s41870-019-00410-x

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…According to the simulation results, RBVT-R achieves a 40% increase in average delivery rate and RBVP-P achieves an 85% decrease in average delay compared to existing protocols. The protocols proposed in [62] have been implemented both in city and highway environments. The protocol perfomance analysis was performed considering different performance metrics.…”

Section: Non-learning-based V2x Routing Protocolsmentioning

confidence: 99%

“…(1) Details on the overhead of the SDN framework are not highlighted, (2) Implementation complexity Raja et al [94] (1) High packet delivery ratio, (2) High throughput, (3) Low routing overhead, (4) Low energy consumption, (5) Low end-to-to delay (6) High network lifetime Uncertain agreement between energy consumption and transmission delay Gurumoorthi et al [123] (1) Advantages of both geocast and position based routing, (2) Improvement of average end-to-end delay, hop count, packet delivery ratio, and retransmission ratio (1) Lack of details on security and authentication issues, (2) Impact of high node mobility in different network conditions Gurumoorthi et al [62] (1) City and highway environments have been used to deploy and implement the mechanisms (2) Low average hop count, (3) High Throughput…”

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: Non-learning-based V2x Routing Protocolsmentioning

confidence: 99%