Abstract

The emergence and development of the Internet of Things (IoT) has attracted growing attention to low-cost location systems when facing the dramatically increased number of public infrastructure assets in smart cities. Various radio frequency identification (RFID)-based locating systems have been developed. However, most of them are impractical for infrastructure asset inspection and management on a large scale due to their high cost, inefficient deployment, and complex environments such as emergencies or high-rise buildings. In this paper, we proposed a novel locating system by combing the Global Navigation Satellite System (GNSS) with RFID, in which a target tag was located with one RFID reader and one GNSS receiver with sufficient accuracy for infrastructure asset management. To overcome the cost challenge, one mobile RFID reader-mounted GNSS receiver is used to simulate multiple location known reference tags. A vast number of reference tags are necessary for current RFID-based locating systems, which means higher cost. To achieve fine-grained location accuracy, we utilize a distance-based power law weight algorithm to estimate the exact coordinates. Our experiment demonstrates the effectiveness and advantages of the proposed scheme with sufficient accuracy, low cost and easy deployment on a large scale. The proposed scheme has potential applications for location-based services in smart cities.

Highlights

  • The Internet of Things (IoT) equipped with pervasive computing and ubiquitous intelligence is emerging as a novel computing paradigm [1]

  • The current trend is to configure these services as location-aware services, called location-based services (LBS), i.e., applications driven by location information, for example, public infrastructure inspection by location in smart cities initiatives

  • Existing public infrastructure inspection and inventory methods in smart cities include image recognition [3], field survey [4,5] and light detection and ranging (LiDAR) information extraction [6], which has been widely used in traffic-sign detection [3], street-light pole location [6], tree extraction [7], and so on

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Summary

Introduction

The Internet of Things (IoT) equipped with pervasive computing and ubiquitous intelligence is emerging as a novel computing paradigm [1]. Existing public infrastructure inspection and inventory methods in smart cities include image recognition [3], field survey [4,5] and light detection and ranging (LiDAR) information extraction [6], which has been widely used in traffic-sign detection [3], street-light pole location [6], tree extraction [7], and so on. For these methods, complex urban environments limit the practice owing to the heavy occupation of the target object and huge workload. It is essential to deploy devices and collect the location information of objects rapidly and economically

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