Enhancing localization accuracy with multi-antenna UHF RFID fingerprinting

Aguilar-Garcı́a, Alejandro; Fortes, Sergio; Barco, Raquel; Colin, Elizabeth

doi:10.1109/ipin.2015.7346938

Cited by 10 publications

(7 citation statements)

References 12 publications

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“…Passive RFID, primarily used for tracking objects, can operate only at very close ranges of 1-3 m [5], whereas active RFID used for positioning has a much greater range of 15 m to 1 km (Table 1) [124][125][126][127]. RFID tagging is of increasing interest in precision forest management for tracking forest products through the supply chain [124]. By monitoring individual logs, companies gain a greater understanding of the quality and quantity of wood being processed from diverse sources [128].…”

Section: Rfid and Acoustic Positioningmentioning

confidence: 99%

Positioning Methods and the Use of Location and Activity Data in Forests

Keefe

Wempe

Becker

et al. 2019

Forests

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In this paper, we provide an overview of positioning systems for moving resources in forest and fire management and review the related literature. Emphasis is placed on the accuracy and range of different localization and location-sharing methods, particularly in forested environments and in the absence of conventional cellular or internet connectivity. We then conduct a second review of literature and concepts related to several emerging, broad themes in data science, including the terms location-based services (LBS), geofences, wearable technology, activity recognition, mesh networking, the Internet of Things (IoT), and big data. Our objective in this second review is to inform how these broader concepts, with implications for networking and analytics, may help to advance natural resource management and science in the future. Based on methods, themes, and concepts that arose in our systematic reviews, we then augmented the paper with additional literature from wildlife and fisheries management, as well as concepts from video object detection, relative positioning, and inventory-tracking that are also used as forms of localization. Based on our reviews of positioning technologies and emerging data science themes, we present a hierarchical model for collecting and sharing data in forest and fire management, and more broadly in the field of natural resources. The model reflects tradeoffs in range and bandwidth when recording, processing, and communicating large quantities of data in time and space to support resource management, science, and public safety in remote areas. In the hierarchical approach, wearable devices and other sensors typically transmit data at short distances using Bluetooth, Bluetooth Low Energy (BLE), or ANT wireless, and smartphones and tablets serve as intermediate data collection and processing hubs for information that can be subsequently transmitted using radio networking systems or satellite communication. Data with greater spatial and temporal complexity is typically processed incrementally at lower tiers, then fused and summarized at higher levels of incident command or resource management. Lastly, we outline several priority areas for future research to advance big data analytics in natural resources.

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Section: Rfid and Acoustic Positioningmentioning

confidence: 99%

Positioning Methods and the Use of Location and Activity Data in Forests

Keefe

Wempe

Becker

et al. 2019

Forests

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“…According to the literature [ 47 ], the cost and power consumption of using WiFi technology to realize an indoor positioning system is very high compared to other technologies. The RFID technology is viewed as a potential candidate as it requires relatively low configuration time and battery power as well as benefiting from easy control [ 13 ]. The choice of the robot and the RFID technology significantly affect the granularity, accuracy and cost of the proposed positioning solution.…”

Section: Experimental Setup and Localization Processing Detailsmentioning

confidence: 99%

“…To overcome this issue, specific localization methods are needed. While multiple techniques and technologies have been proposed, the approach based on fingerprinting via the Received Signal Strength (RSS) may be the most common [ 12 ], where the radio technology may vary between Radio Frequency Identification (RFID) [ 13 ], Wireless Fidelity (WiFi) [ 14 ], Bluetooth [ 15 ] and cellular [ 16 ] methods.…”

Section: Introductionmentioning

confidence: 99%

Accurate and Low-Complexity Auto-Fingerprinting for Enhanced Reliability of Indoor Localization Systems

Hatem

Fortes

Colin

et al. 2021

Sensors

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Indoor localization is one of the most important topics in wireless navigation systems. The large number of applications that rely on indoor positioning makes advancements in this field important. Fingerprinting is a popular technique that is widely adopted and induces many important localization approaches. Recently, fingerprinting based on mobile robots has received increasing attention. This work focuses on presenting a simple, cost-effective and accurate auto-fingerprinting method for an indoor localization system based on Radio Frequency Identification (RFID) technology and using a two-wheeled robot. With this objective, an assessment of the robot’s navigation is performed in order to investigate its displacement errors and elaborate the required corrections. The latter are integrated in our proposed localization system, which is divided into two stages. From there, the auto-fingerprinting method is implemented while modeling the tag-reader link by the Dual One Slope with Second Order propagation Model (DOSSOM) for environmental calibration, within the offline stage. During the online stage, the robot’s position is estimated by applying DOSSOM followed by multilateration. Experimental localization results show that the proposed method provides a positioning error of 1.22 m at the cumulative distribution function of 90%, while operating with only four RFID active tags and an architecture with reduced complexity.

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“…Conversely, the improvement of User Equipment (UE) positioning in both outdoors and indoors [ 3 , 4 , 5 , 6 ], allows for the generation and application of position information for operations, administration and maintenance (OAM) automatic mechanisms [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach

Fortes

Baena

Villegas

et al. 2021

Sensors

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Recent years have seen the proliferation of different techniques for outdoor and, especially, indoor positioning. Still being a field in development, localization is expected to be fully pervasive in the next few years. Although the development of such techniques is driven by the commercialization of location-based services (e.g., navigation), its application to support cellular management is considered to be a key approach for improving its resilience and performance. When different approaches have been defined for integrating location information into the failure management activities, they commonly ignore the increase in the dimensionality of the data as well as their integration into the complete flow of networks failure management. Taking this into account, the present work proposes a complete integrated approach for location-aware failure management, covering the gathering of network and positioning data, the generation of metrics, the reduction in the dimensionality of such data, and the application of inference mechanisms. The proposed scheme is then evaluated by system-level simulation in ultra-dense scenarios, showing the capabilities of the approach to increase the reliability of the supported diagnosis process as well as reducing its computational cost.

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Enhancing localization accuracy with multi-antenna UHF RFID fingerprinting

Cited by 10 publications

References 12 publications

Positioning Methods and the Use of Location and Activity Data in Forests

Positioning Methods and the Use of Location and Activity Data in Forests

Accurate and Low-Complexity Auto-Fingerprinting for Enhanced Reliability of Indoor Localization Systems

Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach

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