Multiobjective Differential Evolution with Discrete Elite Guide in Internet of Vehicles Roadside Unit Deployment

Wang, Feng; Wang, Chenle; Wang, Kan; Jiang, Qiaoyong; Wang, Bin; He, Weiji

doi:10.1155/2021/4207130

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…Wang et al [79] proposed a multi-objective differential evolution with a discrete elitist guide (MODE-deg) to deploy the RSUs at traffic light intersections. This process is started by the establishment of a static model in order to conquer the complexity of urban RSU deployment, and the sigmoid function is applied to discrete individual values in the population.…”

Section: Rsu Deployment Based On Maximum Coverage Modelmentioning

confidence: 99%

Roadside Unit Deployment in Internet of Vehicles Systems: A Survey

Guerna

Bitam

Calafate

2022

Sensors

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In recent years, the network technology known as Internet of Vehicles (IoV) has been developed to improve road safety and vehicle security, with the goal of servicing the digital demands of car drivers and passengers. However, the highly dynamical network topology that characterizes these networks, and which often leads to discontinuous transmissions, is one of the most significant challenges of IoV. To address this issue, IoV infrastructure-based components known as roadside units (RSU) are designed to play a critical role by providing continuous transmission coverage and permanent connectivity. However, the main challenges that arise when deploying RSUs are balancing IoVs’ performances and total cost so that optimal vehicle service coverage is provided with respect to some target Quality of Service (QoS) such as: service coverage, throughput, low latency, or energy consumption. This paper provides an in-depth survey of RSU deployment in IoV networks, discussing recent research trends in this field, and summarizing of a number of previous papers on the subject. Furthermore, we highlight that two classes of RSU deployment can be found in the literature—static and dynamic—the latter being based on vehicle mobility. A comparison between the existing RSU deployment schemes proposed in existing literature, as well as the various networking metrics, are presented and discussed. Our comparative study confirms that the performance of the different RSU placement solutions heavily depends on several factors such as road shape, particularity of road segments (like accident-prone ones), wireless access methods, mobility model, and vehicles’ distribution over time and space. Besides that, we review the most important RSU placement approaches, highlighting their strengths and limitations. Finally, this survey concludes by presenting some future research directions in this domain.

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Section: Rsu Deployment Based On Maximum Coverage Modelmentioning

confidence: 99%

Roadside Unit Deployment in Internet of Vehicles Systems: A Survey

Guerna

Bitam

Calafate

2022

Sensors

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“…Calculate the probability by model ( 3) and use the roulette method for selection As it suppresses the output signal to a permissible range, it is also known as the suppression function. We refer to [24][25][26] for some application examples of sigmoid function. In general, the normal output range of a neuron can be expressed as a unit closed interval ½0, ; ;1.…”

Section: Initialization Related Parametersmentioning

confidence: 99%

Improved Glowworm Swarm Optimization Algorithm Based on a Sigmoid Function for the Absolute Value Equation

Wang

Chen

et al. 2022

Wireless Communications and Mobile Computing

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Solving the absolute value equation (AVE) is a nondifferentiable NP-hard and continuous optimization problem with a wide range of applications. Because its solutions have different forms, it is challenging to design the most efficient algorithm that can solve different AVEs without using overcomplicated technical improvement and problem-dependent objectives. Hence, this paper proposed an improved glowworm swarm optimization (GSO) algorithm with an adaptive step size strategy based on the sigmoid function (SIGGSO) that solves the AVEs. Seven test AVEs, including multisolution and high-dimensional AVEs, are selected for testing and compared with seven metaheuristic algorithms. The experimental results show that the proposed SIGGSO algorithm has higher solution accuracy and stability when seeking multiple solution of AVEs compared to the basic GSO. Moreover, it obtains competitive advantages on multisolution and high-dimensional AVEs compared with other metaheuristic algorithms and provides an effective method for engineering and scientific calculations.

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“…Magsino E. R. et al [20] proposed the Enhanced Information SHAring scheme using RSU allocation (EISHA-RSU) to address the coverage and connectivity between vehicles and infrastructure. Wang F. et al [21] proposed a multi-objective differential evolution method with discrete elitist guidance (MODE-deg) based on crowding distance ranking and non-dominated ranking for RSU selection.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Roadside Unit Deployment on Highways under the Evolution of Intelligent Connected-Vehicle Permeability

Zhang

Lu²,

Chen

et al. 2023

Sustainability

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With the increasing number of Connected and Autonomous Vehicles (CAVs), the heterogeneous traffic flow on highways now consists of a mix of CAVs and Non-networked Autonomous Vehicles (NAVs). The current deployment of Roadside Units (RSUs) on highways is mostly based on uniform or hotspot locations. However, when the permeability of CAVs on the road varies, the communication network may face challenges such as excessive energy consumption due to closely spaced RSU deployments at high CAV permeability or communication interruptions due to widely spaced RSU deployments at low CAV permeability. To address this issue, this paper proposes an improved D-LEACH clustering algorithm based on vehicle clustering; analyzes the impact of RSU and vehicle communication radius, mixed traffic density, and different CAV permeabilities in the heterogeneous traffic flow on the RSU deployment interval; and calculates the rational and effective RSU deployment interval schemes under different CAV permeabilities on highways in the heterogeneous traffic flow. When the heterogeneous traffic flow density is stable and CAV continues to penetrate, the RSU communication radius and deployment interval can be adjusted to ensure that the network connectivity is maintained at a high level. When the RSU and vehicle communication radius are stable, the mixed traffic density is 0.05, and the CAV permeability is 0.2, the RSU deployment interval can be set to 1235 m; when the mixed traffic density is 0.08 and the CAV penetration rate is 0.7, the RSU deployment interval can be set to 1669 m to ensure that the network connectivity is maintained at a high level.

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Multiobjective Differential Evolution with Discrete Elite Guide in Internet of Vehicles Roadside Unit Deployment

Cited by 7 publications

References 33 publications

Roadside Unit Deployment in Internet of Vehicles Systems: A Survey

Roadside Unit Deployment in Internet of Vehicles Systems: A Survey

Improved Glowworm Swarm Optimization Algorithm Based on a Sigmoid Function for the Absolute Value Equation

Optimization of Roadside Unit Deployment on Highways under the Evolution of Intelligent Connected-Vehicle Permeability

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