Kalman filter-based localization for Internet of Things LoRaWAN™ end points

Bakkali, Wafae; Kieffer, Michel; Lalam, Massinissa; Lestable, Thierry

doi:10.1109/pimrc.2017.8292242

Cited by 25 publications

(18 citation statements)

References 15 publications

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“…The Internet of Things (IoT) will consist of billions of connected devices that have a large number of applications, such as smart metering, logistics [1], and localization and tracking [2]. Other potential uses of IoT devices include health monitoring [3] and massive sensor networks for smart farming and environmental monitoring [4].…”

Section: Introductionmentioning

confidence: 99%

On the Error Rate of the LoRa Modulation With Interference

Afisiadis

Cotting

Burg

et al. 2020

IEEE Trans. Wireless Commun.

101

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LoRa is a chirp spread-spectrum modulation developed for the Internet of Things. In this work, we examine the performance of LoRa in the presence of both additive white Gaussian noise and interference from another LoRa user. To this end, we extend an existing interference model, which assumes perfect alignment of the signal of interest and the interference, to the more realistic case where the interfering user is neither chip-nor phase-aligned with the signal of interest and we derive an expression for the error rate. We show that the existing aligned interference model overestimates the effect of interference on the error rate. Moreover, we prove two symmetries in the interfering signal and we derive low-complexity approximate formulas that can significantly reduce the complexity of computing the symbol and frame error rates compared to the complete expression. Finally, we provide numerical simulations to corroborate the theoretical analysis and to verify the accuracy of our proposed approximations. ). layer, which uses an ALOHA-based channel access scheme in which collisions are not explicitly avoided. These collisions lead to same-technology inter-user interference which may ultimately become the capacity-limiting factor in massive IoT scenarios [13]. For this reason, it is of great interest and importance to study the performance of LoRa under sametechnology interference.The authors of [14] present a mathematical network model for LoRa that includes the capture effect, i.e., the fact that a LoRa packet can be correctly decoded even under interference from another LoRa packet. A stochastic geometry framework for modeling the performance of a single gateway LoRa network is used in [15]. An investigation of the latency, collision rate, and throughput for LoRaWAN under duty-cycle restrictions is performed in [16]. Several real-world deployments of LoRa have been tested, but in order to assess the network scalability of LoRaWAN to future network densities that are expected to be orders of magnitude larger, evaluations through network simulators need to be performed. For this reason, the works of [17], [18] added LoRa functionality to the well-known ns-3 network simulator. A simpler Python-based network simulator for the LoRa uplink was first described in [19], and later extended for the LoRa downlink in [20]. The impact of the downlink feedback on LoRa capacity was also studied in [21]. A general overview and performance evaluations of LoRaWAN can be found in [13], [22].The impact of interference coming from different technologies on the performance of the LoRa modulation has received some attention in the literature. Specifically, [23] studies the co-existence of LoRa with IEEE 802.15.4g, while [24] studies the co-existence of LoRa with ultra-narrowband technologies, such as Sigfox. The impact of interference coming from other LoRa nodes has also received some attention. Specifically, the work of [25] extended the simulator of [19] in order to study the impact of imperfect orthogonality between different LoRa spr...

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Section: Introductionmentioning

confidence: 99%

On the Error Rate of the LoRa Modulation With Interference

Afisiadis

Cotting

Burg

et al. 2020

IEEE Trans. Wireless Commun.

101

View full text Add to dashboard Cite

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“…LoRaWAN is used for different application types ranging from health and wellbeing monitoring [ 12 , 13 ], agriculture monitoring [ 14 , 15 , 16 , 17 ], wireless sensor networks [ 18 , 19 ], traffic monitoring [ 20 ], localization [ 21 , 22 , 23 ], smart city applications [ 24 ] up to smart grids and tele-measurements [ 25 , 26 , 27 , 28 ]. It is used mainly for non-latency-sensitive applications and for applications where large-scale deployments are needed.…”

Section: Applications and Deploymentsmentioning

confidence: 99%

“…Using fingerprinting algorithms, LoRaWAN based localization exhibited a mean error of 398.4 m and median error of 273.03 m, when 11 nearest neighbors were used to estimate the location. In [ 22 ], TDoA algorithm was used to estimate the location of the end node in LoRaWAN. The location of gateways was assumed to be known.…”

Section: Applications and Deploymentsmentioning

confidence: 99%

A Survey of LoRaWAN for IoT: From Technology to Application

Haxhibeqiri

Poorter

Moerman

et al. 2018

Sensors

466

213

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LoRaWAN is one of the low power wide area network (LPWAN) technologies that have received significant attention by the research community in the recent years. It offers low-power, low-data rate communication over a wide range of covered area. In the past years, the number of publications regarding LoRa and LoRaWAN has grown tremendously. This paper provides an overview of research work that has been published from 2015 to September 2018 and that is accessible via Google Scholar and IEEE Explore databases. First, a detailed description of the technology is given, including existing security and reliability mechanisms. This literature overview is structured by categorizing papers according to the following topics: (i) physical layer aspects; (ii) network layer aspects; (iii) possible improvements; and (iv) extensions to the standard. Finally, a strengths, weaknesses, opportunities and threats (SWOT) analysis is presented along with the challenges that LoRa and LoRaWAN still face.

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“…Further improvements have been suggested in [ 10 ]. In [ 11 ] the authors improved the TDoA geolocation results using the Kalman filter, but only on stationary placed devices and without further details. Another study combined TDoA and RSSI data and used the k nearest neighbors (kNN) algorithm, but mostly with LoRaWAN (V2) gateways that offer a better performance with result of 332.6 m of median [ 12 ].…”

Section: Current State Of Geolocation In Lorawan Technologymentioning

confidence: 99%

Investigation of the Performance of TDoA-Based Localization Over LoRaWAN in Theory and Practice

Pospisil

Fujdiak

Mikhaylov

2020

Sensors

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The paper deals with the localization in a low-power wide-area-network (LPWAN) operating long-range wide-area-network (LoRaWAN) technology. The LoRaWAN is, today, one of the most widely used connectivity-enabling technologies for the battery-powered smart devices employed in a broad range of applications. Many of these applications either require or can benefit from the availability of geolocation information. The use of global positioning system (GPS) technology is restrained by the bad propagation of the signal when the device is hidden indoors, and by energy consumption such a receiver would require. Therefore, this paper focuses on an alternative solution implying the use of the information readily available in the LoRaWAN network and application of the time difference of arrival (TDoA) method for the passive geolocation of end-devices in the network. First, the limits of geolocation services in networks that use narrow-band communication channels are discussed, as well as the relevant challenges faced by the TDoA approach. Then, we select five classic TDoA algorithms and evaluate their performance using simulation. Based on these results, we select the two providing the best accuracy (i.e., Chan’s and Foy’s). These algorithms were tested by the field measurements, using the specially designed low-cost gateways and test devices to estimate their real-life performance.

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Kalman filter-based localization for Internet of Things LoRaWAN™ end points

Cited by 25 publications

References 15 publications

On the Error Rate of the LoRa Modulation With Interference

On the Error Rate of the LoRa Modulation With Interference

A Survey of LoRaWAN for IoT: From Technology to Application

Investigation of the Performance of TDoA-Based Localization Over LoRaWAN in Theory and Practice

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