RTK-LoRa: High-Precision, Long-Range, and Energy-Efficient Localization for Mobile IoT Devices

Mayer, Philipp; Magno, Michele; Berger, Armin; Benini, Luca

doi:10.1109/tim.2020.3042296

Cited by 22 publications

(6 citation statements)

References 22 publications

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“…Prototype basic validation in a relevant environment [47], [54], [67], [70] 6-7 Prototype demonstration in a relevant and operational environment [28], [29], [49], [53], [55]- [60], [68], [72], [73], [76], [77], [79] 8…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review of Real-Time Deployments of UAV-Based LoRa Communication Network

2021

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The term Internet of Things (IoT) has emerged in recent decades because this network revolutionizes almost every aspect of our daily life, including products such as smartphones and intelligent vehicles, and crucial tasks such as precision agriculture and environmental monitoring. Myriads of communication technologies have been developed to fulfill the two main features of the IoT: long-range transmissions and low power consumption. Long-range (LoRa) has become one of the vital parts of IoT communication. In this study, the real-time deployments of an unmanned aerial vehicle (UAV)-based LoRa communication network are systematically reviewed, with a focus on the communication setup and its reported performance. Importantly, the UAV-based LoRa communication network has a low bit rate connectivity to ensure the high reliability of connections, especially in applications that require long transmission ranges. This study provides recommendations for researchers on what research perspectives need to be explored when implementing UAVs for IoT-based LoRa communication. This study also describes publication trends related to UAV-based LoRa communication networks. A supplementary Excel file that contains the reported publications on UAV-based LoRa communication networks is included to show this publication trend.

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Section: Discussionmentioning

confidence: 99%

A Systematic Review of Real-Time Deployments of UAV-Based LoRa Communication Network

2021

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“…Similarly, the authors of [19] present a self-optimizing wireless water level monitoring system that can improve battery life based on operating conditions. Energy consumption is also a major constraint for the authors of [20], where a LoRa-based localization system is addressed. In [21], a battery lifetime analysis is proposed.…”

Section: Related Work and Contributionmentioning

confidence: 99%

learning model for battery lifetime prediction of LoRa sensors in additive manufacturing

et al. 2023

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Today, an innovative leap for wireless sensor networks, leading to the realization of novel and intelligent industrial measurement systems, is represented by the requirements arising from the Industry 4.0 and Industrial Internet of Things (IIoT) paradigms. In fact, unprecedented challenges to measurement capabilities are being faced, with the ever-increasing need to collect reliable yet accurate data from mobile, battery-powered nodes over potentially large areas. Therefore, optimizing energy consumption and predicting battery life are key issues that need to be accurately addressed in such IoT-based measurement systems. This is the case for the additive manufacturing application considered in this work, where smart battery-powered sensors embedded in manufactured artifacts need to reliably transmit their measured data to better control production and final use, despite being physically inaccessible. A Low Power Wide Area Network (LPWAN), and in particular LoRaWAN (Long Range WAN), represents a promising solution to ensure sensor connectivity in the aforementioned scenario, being optimized to minimize energy consumption while guaranteeing long-range operation and low- cost deployment. In the presented application, LoRa equipped sensors are embedded in artifacts to monitor a set of meaningful parameters throughout their lifetime. In this context, once the sensors are embedded, they are inaccessible, and their only power source is the originally installed battery. Therefore, in this paper, the battery lifetime prediction and estimation problems are thoroughly investigated. For this purpose, an innovative model based on an Artificial Neural Network (ANN) is proposed, developed starting from the discharge curve of lithium-thionyl chloride batteries used in the additive manufacturing application. The results of experimental campaigns carried out on real sensors were compared with those of the model and used to tune it appropriately. The results obtained are encouraging and pave the way for interesting future developments.

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“…In DGNSS, the position information received by the base station receiver is corrected with the precise position of the fixed base station, and then the correction values are broadcast to nearby users for improving the positioning accuracy. Then centimeter-level accuracy or decimeter-level accuracy is achieved by the real-time kinematic (RTK) in an outdoor environment [12]. Precision point positioning (PPP) can reach a level of positioning accuracy at the decimetre level; the performance level of PPP is usually close to the level that can be achieved by the differential method [13,14].…”

Section: Introductionmentioning

confidence: 99%

An Application-Oriented Method Based on Cooperative Map Matching for Improving Vehicular Positioning Accuracy

Zhou

Chen

et al. 2022

Electronics

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Accurate vehicular positioning is important for intelligent and connected vehicles (ICVs). However, in urban canyons, vehicles that rely solely on global navigation satellite system (GNSS) are susceptible to factors such as signal blocking and multi-path, reducing the positioning performance. In this paper, an application-oriented cooperative map matching (CMM) method is proposed, and a low-cost Global Positioning System (GPS)/BeiDou navigation satellite system (BDS) integrated positioning system is designed. The road constraints of a real traffic environment, which simplifies the computational complexity and facilitates practical applications, are modeled. The positioning system is designed to collect and store the positioning data for experimental analysis. Static and dynamic experiments are conducted to verify the effectiveness of the CMM method. From the experimental results, the mean absolute error (MAE) and root mean square error (RMSE) of the positioning with CMM correction in the static experiment are reduced by 9.0% and 4.9%, respectively. In the dynamic experiment, compared with the original positioning error, the MAE is reduced by 44.2% while the RMSE is reduced by 24.3%. The results show that the proposed method can improve vehicular positioning accuracy effectively in both static and dynamic environments.

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RTK-LoRa: High-Precision, Long-Range, and Energy-Efficient Localization for Mobile IoT Devices

Cited by 22 publications

References 22 publications

A Systematic Review of Real-Time Deployments of UAV-Based LoRa Communication Network

A Systematic Review of Real-Time Deployments of UAV-Based LoRa Communication Network

learning model for battery lifetime prediction of LoRa sensors in additive manufacturing

An Application-Oriented Method Based on Cooperative Map Matching for Improving Vehicular Positioning Accuracy

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