With the development of Information and Communication Technology (ICT), the construction of the smart city came into being. Compared with the traditional city, a smart city can reduce resource consumption, improve energy efficiency, reduce environmental pollution, reduce traffic congestion, reduce potential safety hazards, improve the quality of life of citizens, etc. In order to collect a large amount of data to provide accurate decision-making recommendations for the management of smart cities, a large-scale Internet of Things (IoT) system needs to be built as the basis. For most applications in smart cities, it is very important to obtain the physical location information of the data during the data collection. However, it is a challenging issue for most sensor devices in the IoT system, because sensor devices are hard to equip positioning equipment as limited by cost. To tackle this, a Low-Cost Physical Locations Discovery (LCPLD) Scheme is proposed in this paper. In LCPLD scheme, mobile vehicles and unmanned aerial vehicles (UAVs) are used for physical location discovery on the wireless sensor networks which are the important component of the IoT system in a smart city. In order to further reduce cost, we propose a task application mechanism to reduce the cost of vehicle broadcasting and the Adaptive UAV Flight Path Planning (AUPPP) algorithm to reduce UAV flight cost. In order to reduce localization error, the Large Error Rejection (LER) algorithm and the UAV Same Position Broadcast Repeat (USPBR) algorithm are proposed in this paper. After simulation experiments based on real vehicle driving data, the experimental results prove the effectiveness of the proposed scheme: Compared with the comparison scheme, the LCPLD scheme proposed has a cost reduction of 16.58% $\sim$19.88%, an average reduction of 78.80% in positioning error, and an average reduction of 99.88% in the variance of positioning error.

随着信息通信技术（ICT）的发展，智慧城市的建设应运而生。与传统城市相比，智慧城市可以减少资源消耗，提高能源效率，减少环境污染，减少交通拥堵，减少潜在的安全隐患，提高市民的生活质量等。为了收集大量数据为了为智能城市的管理提供准确的决策建议，需要构建大规模的物联网（IoT）系统。对于智慧城市中的大多数应用程序，在数据收集过程中获取数据的物理位置信息非常重要。但是，对于物联网系统中的大多数传感器设备而言，这是一个具有挑战性的问题，因为受成本的限制，传感器设备很难配备定位设备。为了解决这个问题，本文提出了一种低成本的物理位置发现（LCPLD）方案。在LCPLD方案中，移动车辆和无人飞行器（UAV）用于无线传感器网络上的物理位置发现，而无线传感器网络是智慧城市中IoT系统的重要组成部分。为了进一步降低成本，我们提出了一种任务应用机制以降低车辆广播成本，并提出一种自适应无人机飞行路径规划（AUPPP）算法来降低无人机飞行成本。为了减少定位误差，本文提出了大误差抑制（LER）算法和无人机同位置广播重复（USPBR）算法。经过基于真实车辆行驶数据的模拟实验，$〜$19.88％，定位误差平均减少78.80％，定位误差方差平均减少99.88％。