Stochastic-Network-Calculus-Theory-Based Road Side Unit Location Optimization at an Intersection

Yinsong Wang, Yunyi Liang, Zhizhou Wu, Yuyin Guan
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Abstract

This paper deals with the problem of stochastic network calculus-based road side unit (RSU) location optimization at an intersection. Considering the stochasticity of data arrival rate at RSUs and RSU service capacity, the upper bound of vehicle-to-RSU (V2R) communication delay is derived using stochastic network calculus. Further, to hedge against the impact of the randomness of the traffic density on RSU location optimization, the problem is formulated as a two-stage nonlinear mixed-integer stochastic program. The objective function of this program is to minimize the investment cost of RSU location and the expectation of the penalty cost of the V2R communication delay upper bound. In the first stage of the program, the number and location of RSUs are optimized when the traffic density at the intersection is uncertain. In the second stage, when the traffic density is realized, the subareas of the intersection are assigned to the located RSUs to minimize the penalty cost. To find a global optimal solution to the problem, a Benders decomposition algorithm is proposed. The experiment results show that the proposed model is able to achieve 19.4 ms/per cost lower V2R communication delay, compared with the average-communication-delay-based model.
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基于随机网络-微积分理论的交叉口路边单元选址优化
本文研究了基于随机网络算法的交叉口路侧单元(RSU)位置优化问题。考虑到RSU数据到达率和RSU服务容量的随机性,利用随机网络演算方法推导了车对RSU (V2R)通信延迟上界。进一步,为了规避交通密度随机性对RSU选址优化的影响,将问题表述为两阶段非线性混合整数随机规划。本方案的目标函数是使RSU选址的投资成本和V2R通信延迟上界的惩罚成本期望最小。在方案的第一阶段,在交叉口交通密度不确定的情况下,优化rsu的数量和位置。第二阶段,在实现交通密度的情况下,将交叉口的子区域分配给已定位的rsu,使处罚成本最小。为了寻找问题的全局最优解,提出了一种Benders分解算法。实验结果表明,与基于平均通信延迟的模型相比,该模型能够实现19.4 ms/per cost的V2R通信延迟。
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