Improving TDoA Based Positioning Accuracy Using Machine Learning in a LoRaWan Environment

Cho, JaeSik; Hwang, DongYeop; Kim, Ki-Hyung

doi:10.1109/icoin.2019.8718160

Cited by 12 publications

(11 citation statements)

References 1 publication

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“…Ninety-five percent percentile values improved from 2200 m to 840 m in a simulated environment. Another approach, correcting the received timestamps of a mobile node by the use of machine learning in combination with stationary reference nodes, reported an accuracy around 61 m [ 22 ]. However, it is unclear how many reference nodes are needed, making it potentially unsuitable for large deployments from an economic point-of-view.…”

Section: Related Workmentioning

confidence: 99%

LoRaWAN Geo-Tracking Using Map Matching and Compass Sensor Fusion

Podevijn

Trogh

Aernouts

et al. 2020

Sensors

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In contrast to accurate GPS-based localization, approaches to localize within LoRaWAN networks offer the advantages of being low power and low cost. This targets a very different set of use cases and applications on the market where accuracy is not the main considered metric. The localization is performed by the Time Difference of Arrival (TDoA) method and provides discrete position estimates on a map. An accurate “tracking-on-demand” mode for retrieving lost and stolen assets is important. To enable this mode, we propose deploying an e-compass in the mobile LoRa node, which frequently communicates directional information via the payload of the LoRaWAN uplink messages. Fusing this additional information with raw TDoA estimates in a map matching algorithm enables us to estimate the node location with a much increased accuracy. It is shown that this sensor fusion technique outperforms raw TDoA at the cost of only embedding a low-cost e-compass. For driving, cycling, and walking trajectories, we obtained minimal improvements of 65, 76, and 82% on the median errors which were reduced from 206 to 68 m, 197 to 47 m, and 175 to 31 m, respectively. The energy impact of adding an e-compass is limited: energy consumption increases by only 10% compared to traditional LoRa localization, resulting in a solution that is still 14 times more energy-efficient than a GPS-over-LoRa solution.

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Section: Related Workmentioning

confidence: 99%

LoRaWAN Geo-Tracking Using Map Matching and Compass Sensor Fusion

Podevijn

Trogh

Aernouts

et al. 2020

Sensors

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“…Other than the two main machine learning-based tasks in this paper (profiling and prediction), machine learning has been applied in LoraWAN networks for several applications. For example, in [25], the authors used Q-Learning for resource allocation; in [26] a deep learning-based approach has been used to exploit time difference of arrival for correct mobile station positioning; in [27] an ARIMA model has been used to predict future trends starting from air pollution sensors; in [28], the authors exploit LoRaWAN sensors and supervised learning for activity recognition; in [29,30], machine learning has been used for localization purposes and, finally, in [31] a mixture of unsupervised, supervised and decision model techniques has been used for load balancing in LoRaWAN networks.…”

Section: Related Workmentioning

confidence: 99%

Predicting LoRaWAN Behavior: How Machine Learning Can Help

et al. 2020

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Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation. In this work, we discuss how machine learning approaches can be used to improve network performance (and if and how they can help). To this aim, we describe a methodology to process LoRaWAN packets and apply a machine learning pipeline to: (i) perform device profiling, and (ii) predict the inter-arrival of IoT packets. This latter analysis is very related to the channel and network usage and can be leveraged in the future for system performance enhancements. Our analysis mainly focuses on the use of k-means, Long Short-Term Memory Neural Networks and Decision Trees. We test these approaches on a real large-scale LoRaWAN network where the overall captured traffic is stored in a proprietary database. Our study shows how profiling techniques enable a machine learning prediction algorithm even when training is not possible because of high error rates perceived by some devices. In this challenging case, the prediction of the inter-arrival time of packets has an error of about 3.5% for 77% of real sequence cases.

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“…It was shown that the 95th percentile was improved from 2200 m to 840 m in a simulated environment. Correcting the received timestamps of a mobile node by the use of machine learning in combination with stationary reference nodes is reported in [7]. Using this method, the best reported accuracy was around 61 m. It remains unclear how many reference nodes are needed and this approach might not be economically feasible for large deployments.…”

Section: Related Workmentioning

confidence: 99%

Compass Aided TDoA Tracking in LoRaWAN networks

Podevijn

Trogh

Aernouts

et al. 2020

2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)

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LoRWAN nodes can be localized using the TDoA (Time Difference of Arrival) approach. A big advantage is that the geo-location does not consume a lot of energy when compared to GPS approaches. Depending on the use case, the accuracy of the geo-localization (200m) might be a disadvantage. Therefore we propose activating an E-compass in the LoRa node and frequently communicating this directional information over the LoRaWAN network. This extra information can then be fused with TDoA in a map matching algorithm to improve the estimations. We show that our sensor fusion technique is 8 times more accurate than raw TDoA at the cost of only embedding a low-cost e-compass.

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Improving TDoA Based Positioning Accuracy Using Machine Learning in a LoRaWan Environment

Cited by 12 publications

References 1 publication

LoRaWAN Geo-Tracking Using Map Matching and Compass Sensor Fusion

LoRaWAN Geo-Tracking Using Map Matching and Compass Sensor Fusion

Predicting LoRaWAN Behavior: How Machine Learning Can Help

Compass Aided TDoA Tracking in LoRaWAN networks

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