Analysis of Enhanced Hybrid Approach Using Greedy Perimeter Stateless Routing in VANET

Luhach, Ashish Kr.; Isha,; Neha, .; Gao, Xiao‐Zhi

doi:10.1007/978-981-13-3804-5_34

Cited by 2 publications

(5 citation statements)

References 10 publications

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“…Based on these parameters, values for the E2E delay, communication throughput, energy consumption, and PDR were evaluated. These parameters are compared with References 3,7, and 32 due to their similarity with this work, and their high‐performance behavior. Due to the use of blockchain, it is observed that the proposed model has high security, and can identify Sybil, Masquerading, DDoS, and Smurf attacks with 100% efficiency.…”

Section: Results Evaluation and Analysismentioning

confidence: 98%

“…From the evaluations done in Table 4, it is observed that the average value of E2E delay at Reference 3 is 1.28 ms, Reference 7 is 1.24 ms, Reference 32 is 1.05 ms and at QHS‐SDN‐VANET‐MLB is 0.89 ms. The E2E delay has been reduced by 28% when compared with Reference 3, 28% with Reference 7, and 15% with Reference 32, which have similar implementation architectures as the proposed model. This can also be observed from the visual representation of E2E delay as observed from Figure 9 as follows.…”

Section: Results Evaluation and Analysismentioning

confidence: 99%

“…These methods allow SDN architectures to have better security, and QoS/E performance under a wide variety of network conditions. An extension to these approaches can be observed from Reference 32, wherein multichannel blockchains are described.…”

Section: Literature Reviewmentioning

confidence: 99%

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A quality of service‐aware high‐security architecture design for software‐defined network powered vehicular ad‐hoc networks using machine learning‐based blockchain routing

Choudhary

Dorle

2022

Concurrency and Computation

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Software‐defined networks (SDNs) have proven to be highly flexible for vehicular ad‐hoc network (VANET) routing. This is due to packet‐level and bandwidth‐level controls available with SDNs. But SDNs have inherent security issues which include forwarding device attacks, control plane threats, communication channel vulnerability, etc. To counter these attacks, this paper initially proposes a proof‐of‐work‐based blockchain framework that can reduce attack probability by 99%. But, the overall network quality of service (QoS) reduces in terms of end‐to‐end delay, energy consumption, and throughput. To improve these parameters and maintain a high level of security, this work proposes a machine‐learning algorithm that optimizes blockchain parameters in a QoS‐aware manner. These parameters include selected chain length, encryption block length, etc. The machine learning algorithm is inspired by Q‐Learning and aims at reducing the performance effect of blockchain operations on the overall QoS of VANETs. Thus, the underlying network uses sidechain‐based blockchain implementation, which makes it faster, more secure, and less complex when compared with single‐chain implementations. Upon observing the results, it is seen that the proposed architecture can reduce the end‐to‐end delay by 15%, energy consumption by 18%, and increase overall throughput by 38% when compared with existing blockchain‐based SDN VANET architectures.

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Section: Results Evaluation and Analysismentioning

confidence: 98%

Section: Results Evaluation and Analysismentioning

confidence: 99%

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A quality of service‐aware high‐security architecture design for software‐defined network powered vehicular ad‐hoc networks using machine learning‐based blockchain routing

Choudhary

Dorle

2022

Concurrency and Computation

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“…Contributions [14] Fog-based VANET supporting PBR is presented to use parked vehicles for utilizing road intersections to select a path [82] Proposes connectivity aware routing, which helps in internet services, mobile data offloading, and multihop vehicle applications [83] Attempts to find the links with the highest packet reception possibility to be used as relay links [84] Proposes a location prediction technique to improve the routing performance by reducing the location errors [85] Designs vehicles' movements prediction-based routing [86] Presents a comparison and analysis to identify the most acceptable topology-based routing protocols in VANETs [87] Uses the ant colony algorithm such that a road is found with an optimal connection…”

Section: Itemmentioning

confidence: 99%

“…Thus, the work in [84] proposes a location prediction technique that uses a Kalman filter to improve routing performance by reducing location errors. According to [85], routing protocols based on position are considered to be more reasonable while comparing with other VANET routing protocols. That work proposes a movement-prediction-based joint routing and hierarchical location based service.…”

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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Analysis of Enhanced Hybrid Approach Using Greedy Perimeter Stateless Routing in VANET

Cited by 2 publications

References 10 publications

A quality of service‐aware high‐security architecture design for software‐defined network powered vehicular ad‐hoc networks using machine learning‐based blockchain routing

A quality of service‐aware high‐security architecture design for software‐defined network powered vehicular ad‐hoc networks using machine learning‐based blockchain routing

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

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