Gateway selection techniques in heterogeneous vehicular network: Review and challenges

Alawi, Mahmoud; Sundararajan, Elankovan; Alsaqour, Raed; Ismail, Mahamod

doi:10.1109/iceei.2017.8312425

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…PPO uses the trust-region (TRPO) method to ensure that the new updated policy never goes far away from the current policy, making it more stable and reliable. The primary objective function of PPO is denoted as L CLIP(θ) : (6) where A t is an abbreviation of A(s t , a t ), ϵ denotes a small positive constant, and the policy ratio (R t ) measures the similarity between the updated policy and old policy as shown in Equation ( 7): (7) while clipping function, clip (R t , 1-ϵ, 1+ϵ) ensures the R t moving inside the interval [1ϵ, 1+ ϵ]. For these reasons, the PPO algorithm is adopted in the proposed gateway selection system, namely PPO-GS.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Reinforcement Learning based Gateway Selection in VANETs

Alabbas

Huszák

2022

Int. j. electr. comput. eng. syst. (Online)

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In vehicular ad hoc networks (VANETs), providing the Internet has become an urgent necessity, where mobile gateways are used to ensure network connection to all customer vehicles in the network. However, the highly dynamic topology and bandwidth limitations of the network represent a significant issue in the gateway selection process. Two objectives are defined to overcome these challenges. The first objective aims to maximize the number of vehicles connected to the Internet by finding a suitable gateway for them depending on the connection lifetime. The second objective seeks to minimize the number of connected vehicles to the same gateway to overcome the limitation of gateways' bandwidth and distribute the load in the network. For this purpose, A gateway discovery system assisted by the vehicular cloud is implemented to find a fair trade-off between the two conflicting objectives. Proximal Policy Optimization, a well-known reinforcement learning strategy, is used to define and train the agent. The trained agent was evaluated and compared with other multi-objective optimization methods under different conditions. The obtained results show that the proposed algorithm has better performance in terms of the number of connected vehicles, load distribution over the mobile gateways, link connectivity duration, and execution time.

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Section: Reinforcement Learningmentioning

confidence: 99%

Reinforcement Learning based Gateway Selection in VANETs

Alabbas

Huszák

2022

Int. j. electr. comput. eng. syst. (Online)

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“…However, the reliability performance of HVNs, such as outage probability, has not been considered in Huang et al [Huang, Ding, Meng et al (2018)] so that it cannot guarantee reliable transmission. Cheng et al [Cheng, Mi, Huang et al (2018)] proposed a theoretical method to evaluate the connectivity of HVNs, while protocol strategy such as gateway selection and routing are discussed in Chen et al [Chen, Liu, Qiu et al (2018); Alawi, Sundararajan, Alsaqour et al (2017)]. IoV varies in multiple network architecture forms.…”

Section: Introductionmentioning

confidence: 99%

A Capacity Improving Scheme in Multi-RSUs Deployed V2I

Chen¹,

Zeng²,

Chen³

et al. 2019

Computers, Materials &Amp; Continua

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The communication reliability and system capacity are two of the key performance indicators for Internet of Vehicles (IoV). Existing studies have proposed a variety of technologies to improve reliability and other performance, such as channel selection and power allocation in Vehicle-to-Infrastructure (V2I). However, these researches are mostly applied in a single roadside unit (RSU) scenario without considering inter-cell interference (ICI) of multi-RSUs. In this paper, considering the distribution characteristics of multi-RSUs deployment and corresponding ICI, we propose a reliable uplink transmission scheme to maximize the total capacity and decrease the interference of multi-RSUs (mRSU-DI) in condition of the uplink interruption performance. In the proposed mRSU-DI scheme, ICI is depressed by dynamic channel and power allocation algorithm. A heuristic algorithm based on penalty function is proposed to obtain the optimal power allocation solution of the model. In addition, we realize the scheme in both given conditions of channel state information (CSI) and channel state distribution, respectively. The results show that the proposed scheme can both improve the system capacity and guarantee the reliable transmission in both premises.

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A Comprehensive Review of Recent Developments in VANET for Traffic, Safety & Remote Monitoring Applications

Dutta,

Samaniego Campoverde,

Tropea

et al. 2024

J Netw Syst Manage

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Strategic integration of Wireless Sensor Networks (WSNs) and IoT (Internet of Things) into VANET infrastructure is crucial for ensuring vehicular safety, mobility management, and vehicular applications. The integration collects information on traffic and road conditions without relying on traditional internet connectivity. It also addresses applications such as early warnings in areas with limited coverage, safety and health emergency messages in highly congested zones, and air monitoring without depending on traditional TCP/IP internet connectivity. This article provides a comprehensive view of network technologies, data acquisition devices, clustering techniques, and energy-efficient routing protocols to optimize Vehicle-to-Everything (V2X) communications in VANETs. This study also addresses how to leverage the frequency channels of the 802.11p protocol and expands the possibilities for developing numerous applications dedicated to remote ambient, traffic and safety monitoring without compromising network performance. Moreover, the recent developments of clustering algorithms and energy-efficient schemes for these VANET applications are analyzed from a novel perspective.

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Gateway selection techniques in heterogeneous vehicular network: Review and challenges

Cited by 6 publications

References 15 publications

Reinforcement Learning based Gateway Selection in VANETs

Reinforcement Learning based Gateway Selection in VANETs

A Capacity Improving Scheme in Multi-RSUs Deployed V2I

A Comprehensive Review of Recent Developments in VANET for Traffic, Safety & Remote Monitoring Applications

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