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Roadside Units Non-full Coverage Optimization Deployment Based on Simulated Annealing Particle Swarm Optimization Algorithm

机译:基于模拟退火粒子群算法的路边单元非全覆盖优化部署

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This paper studies a scheme for optimal deployment of roadside units (RSU) with non-full coverage in the Vehicular Ad-hoc Network (VANET). The goal of RSU Deployment Optimization (RDO) is to achieve the best economic benefit between the deployment cost and the vehicle's need for positioning accuracy. That is, when the positioning accuracy of the vehicle keeps at an acceptable threshold, the number of deployed RSU is minimized. So an efficient RSU deployment pattern is one of the core aspects that must be considered in the designing of VANET. The paper first analyzes the cumulative error estimation based on the Inertial Navigation System (INS) when the vehicle travels in the Non-covered Area (NCA) of the road, and formulates the deployment model with reference to the coverage radius of RSU. Since the optimal solution for RDO is a NP-hard problem, another contribution of this paper is to use Geometric Dilution of Precision (GDOP) to evaluate the positioning error in NCA, and then to propose a heuristic algorithm to solve the problem. The simulation results show that the proposed scheme can effectively solve the medium-sized optimization problem of road incomplete coverage.
机译:本文研究了在车辆自组织网络(VANET)中具有非完全覆盖的路边单元(RSU)的最佳部署方案。 RSU部署优化(RDO)的目标是在部署成本和车辆对定位精度的需求之间获得最佳的经济利益。即,当车辆的定位精度保持在可接受的阈值时,所部署的RSU的数量被最小化。因此,有效的RSU部署模式是VANET设计中必须考虑的核心方面之一。本文首先分析了基于惯性导航系统(INS)的车辆在道路非覆盖区域(NCA)行驶时的累积误差估计,并参考RSU的覆盖半径制定了部署模型。由于RDO的最优解是一个NP难题,因此本文的另一贡献是使用精度几何稀释(GDOP)评估NCA中的定位误差,然后提出一种启发式算法来解决该问题。仿真结果表明,该方案可以有效解决道路不完全覆盖的中型优化问题。

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