A low-cost physical location discovery scheme for large-scale Internet of Things in smart city through joint use of vehicles and UAVs

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% similar to 19.88%, an average reduction of 78.80% in positioning error, and an average reduction of 99.88% in the variance of positioning error. (C) 2021 Elsevier B.V. All rights reserved.