Ye Kong, Dai Penglin, Wu Xiao, Ding Yan, Xing Huanlai, Yu Zhaofei
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China.
College of Computer Science, Chongqing University, Chongqing 400040, China.
Sensors (Basel). 2019 Aug 14;19(16):3547. doi: 10.3390/s19163547.
Traffic sensing is one of the promising applications to guarantee safe and efficient traffic systems in vehicular networks. However, due to the unique characteristics of vehicular networks, such as limited wireless bandwidth and dynamic mobility of vehicles, traffic sensing always faces high estimation error based on collected traffic data with missing elements and over-high communication cost between terminal users and central server. Hence, this paper investigates the traffic sensing system in vehicular networks with mobile edge computing (MEC), where each MEC server enables traffic data collection and recovery in its local server. On this basis, we formulate the bandwidth-constrained traffic sensing (BCTS) problem, aiming at minimizing the estimation error based on the collected traffic data. To tackle the BCTS problem, we first propose the bandwidth-aware data collection (BDC) algorithm to select the optimal uploaded traffic data by evaluating the priority of each road segment covered by the MEC server. Then, we propose the convex-based data recovery (CDR) algorithm to minimize estimation error by transforming the BCTS into an l 2 -norm minimization problem. Last but not the least, we implement the simulation model and conduct performance evaluation. The comprehensive simulation results verify the superiority of the proposed algorithm.
流量感知是保障车载网络中安全高效交通系统的一项很有前景的应用。然而，由于车载网络的独特特性，如无线带宽有限和车辆的动态移动性，基于收集到的存在缺失元素的交通数据进行流量感知总是面临较高的估计误差，并且终端用户与中央服务器之间的通信成本过高。因此，本文研究了具有移动边缘计算（MEC）的车载网络中的流量感知系统，其中每个MEC服务器在其本地服务器中实现交通数据的收集和恢复。在此基础上，我们提出了带宽受限流量感知（BCTS）问题，旨在基于收集到的交通数据最小化估计误差。为了解决BCTS问题，我们首先提出了带宽感知数据收集（BDC）算法，通过评估MEC服务器覆盖的每个路段的优先级来选择最优上传交通数据。然后，我们提出了基于凸优化的数据恢复（CDR）算法，通过将BCTS转化为l2范数最小化问题来最小化估计误差。最后但同样重要的是，我们实现了仿真模型并进行了性能评估。综合仿真结果验证了所提算法的优越性。
