Aernouts, Michiel

Berkvens, Rafael

Joseph, Wout

Martens, Luc

Plets, David

Podevijn, Nico

Weyn, Maarten

Institutional Repository Universiteit Antwerpen

Experimental TDoA localisation in real public LoRa networks

Ghent University Academic Bibliography

Experimental TDoA Localisationin Real Public LoRa NetworksNico Podevijn1, David Plets1, Michiel Aernouts2, Rafael Berkvens2, LucMartens1, Maarten Weyn2, and Wout Joseph11 University of Ghent, IMEC-WAVES, Ghent, Belgium2 University of Antwerp, IMEC-IDLab, Antwerp, Belgiumnico.podevijn@ugent.beAbstract. The performance of Time Difference of Arrival (TDoA) local-isation for outdoor tracking purposes has been experimentally evaluatedin 3 different public LoRaWAN networks in two countries. The local-isation performance is compared for different networks, environments,devices, gateway density and algorithms. We demonstrate that the mostdense network provided the most accurate time difference stamps with astandard deviation of 1.2 to 1.5 µs. Therefore, localisation results weremuch better on this network when compared to the other networks. Fordifferent scenarios (walking, driving) we obtained a median localisationerror of 200m and in 90% of the cases this error was less than 450mIndex Terms—LoRaWAN, TDoA, Localisation.1 IntroductionNationwide public LoRa networks are currently available in different countries.Some of the deployments also keep track of the arrival times of a packet ondifferent gateways. Using a Time Difference of Arrival (TDoA) algorithm enablesus to localise sensor nodes in the network. A popular use case or LoRaWANTDoA is tracking non-powered assets. The advantages of localizing sensors usingthis technique is the fact that all complexity is shifted to the network side.This keeps the sensors simple, low cost and low power when compared withusing GPS. The downside is that the mobile node itself has no knowledge on itslocation. In [1] a comparison of the performance of different TDoA algorithmson a single public LoRa network (KPN) was performed. A comparable approachwas done in the work of [2], but the measurement campaign was performed ona private LoRa network. Unfortunately a maximum likelihood (ML) algorithmwas not taken into consideration in these works, although it is shown that MLalgorithms outperform many other TDoA algorithms such as the Taylor series,analytical and least squares methods [3][4][5]. Therefore this paper will extendthe work of [1] by using a ML algorithm on experimental data gathered from 3different public LoRa networks in 2 countries. In the work of [6] and [7] the TDoAperformance is assessed when considering a single network and a commercial2 N. Podevijn et al.solver. This paper will extend these works by comparing the ML algorithm from[8] with the commercial solver implemented by KPN [11]. To the best of ourknowledge the timestamp accuracy on public LoRa networks is not yet quantifiedin any work. This paper will therefore quantify also the timestamp differenceaccuracy. In this paper we consider LoRaWAN TDoA localisation on 3 differentnetworks originating from 3 different telecom providers: KPN (Netherlands),Proximus (Belgium) and Wireless Belgium (Belgium). The novelties and goal ofthis paper are investigating the localisation perfomance for: (i) Different TDoAalgorithms: a maximum likelihood TDoA vs a commercial solver. (ii) Differenttype of networks and environments. Each having a different gateway densitydeployment and gateway timestamping accuracy. (iii) Different devices withinthe same network. The paper is organized as follows. In Section II, the TDoAinputs, algorithm and performance quantification are described. Time differencedistribution and localisation performance results are discussed in Section III. Wesummarize our observations and future work in Section IV.2 Materials and Methods2.1 TDoA AlgorithmThe basic TDoA setup is as follows: A mobile node at location (X,Y ) transmitsa packet. This packet is received by N ≥ 4 gateways which accurately record thetime of the incoming packets: t1, t2, ..., ti, ..., tN are the timestamps for gateways1 to N with locations (Xk, Yk) {k = 1...N.}. The next step is to calculate thetime differences dti,j = ti − tj . with j as reference and i = 1..N, i 6= j. The Ngateway locations together with the (N − 1) time differences are used as inputfor the ML TDoA algorithm. Each of the N nodes is used once as a referencenode, for which the ML TDoA algorithm calculates the resulting ML locationLj :Lj = ML(dt1,j , ..., dti,j , ..., dtN,j) i 6= j, j = 1..N (1)The ML algorithm operates by minimizing a cost function. For more details werefer to [8]. To minimize outliers caused by selecting a less well synchronizedreference gateway, the final ML location is estimated as the median (in X andY ) from all N previous estimated locations:L = median(L1, ..., LN ) (2)For each of the scenarios we calculate the error between the estimated lo-cation and the GPS location, which is considered as ground truth. Next, wedetermine the error cumulative distribution function (CDF). From these CDFsthe median and 90th percentile error metrics can be obtained. For the KPNscenarios we will also compare this ML TDoA with their integrated commercialsolver[11].Experimental TDoA Localisation in Real Public LoRa Networks 32.2 Gateway Locations, timestamps and scenariosFig. 1 shows the network deployment of the LoRaWAN gateways for the 3 tele-com operators. We note that deployment of the gateways is denser in the largercities. Table 1 lists the available timestamp resolution TR and number of gate-ways Nx that have been deployed for each operator. Wireless Belgium and Prox-imus cover the Northern part of Belgium, while KPN is covering whole of theNetherlands. The area covered (in km2) by these gateways is also noted in thetable. The gateway density D is defined as the average number of gateways per10km by 10km area (100km2). Table 1 shows that KPN has a denser network(2.2/100 km2) than Proximus (1.8/100 km2) which in turn is more dense thanWireless Belgium (1.2/100 km2).Fig. 1. Gateway deployment of Wireless Belgium (Green), Proximus (Red) and KPN(Blue). The black pointers are the stationary beacons in Bruges and Brussels, Belgium.The black squares denote the measurement area for Proximus in Antwerp and KPN inEindhoven, the NetherlandsTable 1. Network Deployment Properties for the 3 Networks. TR is the provided times-tamp resolution. Nx is the number of gateways deployed for an area S. The gatewaydensity D is defined as the average number of gateways for an area of 10km x 10km.Operator TR Nx S [km2] D [#/100km2]Wireless Belgium 1 µs 166 14000 1.2Proximus 1 ns 248 14000 1.8KPN 1 ns 951 42500 2.2Accuracy of the provided timestamps is important to obtain an accurate loca-tion estimate. The gateways deployed by KPN and Proximus provide timestampswith nanosecond resolution, while only microsecond resolution timestamps areprovided by the Wireless Belgium network. This is due to the fact that thegateways deployed by Proximus and KPN are based on the Semtech version 2reference design [9] vs. version 1 for the Wireless Belgium network. The error4 N. Podevijn et al.(∆dt) on time difference stamps (dt) is quantified as follows. For each uplinkwith known transmit location (GPS as ground truth) we estimate all theoreticaltime differences as follows:dtT,i,j = (di − dj)/c (3)Here di and dj denote the distance between the transmitting device and gatewayi, j respectively. c is the speed of the propagating wave and is equal to 3x108m/s. The measured time differences are calculated asdtM,i,j = ti − tj (4)with ti and tj the timestamps of the received packets on gateway i and j. Thesetime differences are then compared to (subtracted from) the theoretical ones:∆dti,j = dtT,i,j − dtM,i,j (5)In the ideal case, the difference between the measured and calculated time dif-ferences is always 0 ns. Due to multipath and synchronization errors we expectthis error to be normally distributed. A histogram can be obtained by recording∆dti,j for each uplink for a particular gateway combination i and j. The totalobtained (∆dt) histogram can be obtained by considering all combinations of iand j. For the different networks and scenarios this histogram is given in SectionIII. For each of the 3 networks, two different (A and B) measurement campaignswere performed leading to a total of 6 datasets. Table 2 lists a description of thescenarios.– In the network of Wireless Belgium, 2 stationary TX beacons A and B areconsidered. The first one (A) is located in Bruges, while the second one (B) islocated in Brussels. The 2 beacons are also displayed as black pointers in Fig.1. A TX beacon is a gateway which is configured to transmit packets. Sincethese gateways are placed on rooftops, we expect often Line of Sight (LOS)conditions between the beacons and RX gateways. Therefore a high numberof gateways (50+) received the TX packets from the stationary beacon(s).The beacons were configured to transmit on spreading factor 12 with a newtransmission every 10 minutes. Data was collected over a meaurement periodof 18 hours.– In the Proximus scenarios, two LoRaWAN devices were provisioned in theProximus LoRa network, which were attached to Bpost (Belgium Post) carsdriving around in the city of Antwerp. These devices were configured totransmit a packet with their GPS location every 30 seconds during a datacollection time of 4 days. The used spreading factor was not fixed but wascontrolled by the network with the use of ADR (Adaptive Data Rate). Werefer to [10] which describes in detail how the Proximus data was obtained.In the Proximus network we measured and obtained data during 4 full days.– In the KPN network the transmitting device was first placed inside a car,driving in and around Eindhoven in the Netherlands (KPN scenario A) .The GPS ground truth was logged with a smartphone. Next, we carried theExperimental TDoA Localisation in Real Public LoRa Networks 5device during a walk to obtain data for the last scenario (KPN scenario B).The device was configured to transmit packets every 15 seconds. In order tocomply with the ETSI duty cycle regulations (1%) on the 868 MHz band wefixed the spreading factor to SF9 for this device. For the KPN scenarios thedata was logged in less than one hour.The datasets from the 6 scenarios were compressed to a useful format only con-taining uplinks received by 4 or more gateways. The number of uplinks (samples)which were received by 4 or more gateways NRX4 can be found in Table 2. Sincethe data collection time of the Proximus scenarios was much longer than theother scenarios, more useful samples were obtained. For each of these uplinksthe arrival time of the packet is known at the gateways. The location of thosegateways is also available with known latitude and longitude coordinates.Table 2. Measured scenarios with description, used Spreading factor SF and configuredinterval between transmissions. NRX4 is the number of obtained uplinks which werereceived by 4 or more gateways.Measurement campaign Interval SF NRX4WB A(Stationary Beacon) 600s 12 106WB B(Stationary Beacon) 600s 12 106Proximus A(Car trajectory) 30s ADR 1140Proximus B(Car trajectory) 30s ADR 663KPN A(Car trajectory) 15s 9 54KPN B(Walk trajectory) 15s 9 423 Results3.1 Time-difference errorsFig. 2 shows the error (∆dt) on time differences (dt) in histograms for the sce-narios considered in Table 2. The histograms ranges were set from -20 to +20 µswith time difference bins of 1µs. From these plots we see that the timing erroris normally distributed with a median of 0 µs. The standard deviations (spread-ing of the obtained histogram) are 3.9/3.9µs, 1.8/3.1µs and 1.2/1.5µs for theWireless Belgium, Proximus and KPN scenarios A/B, respectively. Therefore,the most accurate timestamps were obtained from the KPN network, hence weexpect the localisation performance on this network to be better than the othernetworks. The least accurate timestamps are obtained from Wireless Belgium.This is due to the fact that the different networks use different types of gate-ways. We note that Proximus timestamp accuracy is less than that of the KPNnetwork altough V2 Gateways were used like in the KPN network. This is partof further investigation but might be caused by the environment (multipath).6 N. Podevijn et al.Fig. 2. Histogram of errors on time differences for Wireless Belgium (left) Proximus(Middle) and KPN (right). The top and bottom figures are set A and B respectively3.2 Error CDF when using ML TDoA algorithmFig. 3 (left) shows the localisation error CDF for all 6 scenarios. As predictedfrom the timestamp errors and gateway density (Table 1), localisation on theKPN network is far more accurate when compared with the other 2 networks.We can rank the networks according to their (median) localisation accuracy asfollows. KPN (200m), Proximus (340m), Wireless Belgium (>400m). The ob-tained accuracy is directly related to the provided timestamp accuracy as givenin the previous subsection. The (90th percentile) errors on these networks areas follows: KPN (400m), Proximus (800m), Wireless Belgium (1000m). Again,these differences between networks can be explained by the provided timestampaccuracy. We do note a special case where the Wireless Belgium B (WB B)scenario is almost as good as Proximus (A and B). This can be explained bythe fact that WB B uplink data was received (on average) by far more (50+)gateways from its (high) stationary Line of Sight (LOS) transmit location. Thisis in contrast where the number of gateways that received a packet was alwaysless than 10 for Proximus and KPN.From the CDF it is further shown that the KPN A and KPN B with differentmobility (driving and walking) have similar localisation performance. Thereforethe mobility of the scenario does not have much impact on localisation per-formance. The median (p50) and 90th percentile (p90) errors for the differentscenarios are summarized in Table 3.Experimental TDoA Localisation in Real Public LoRa Networks 7Fig. 3. Left:Error CDF for the different scenarios when using a ML TDoA algorithm.Right:Comparison of ML versus commercial solver on the KPN networkTable 3. Localisation Error quantification for the different scenarios (A/B)Metric/Network ML WB ML Proxi B ML KPN Commercial KPNp50 [m] 610/390 340/330 200/220 130/180p90 [m] 1100/820 840/750 420/450 400/4103.3 ML versus commercial TDoA solverApart from the timestamp data obtained from KPN, we also extracted theirTDoA localisation estimates. This allows us to compare the ML algorithm withtheir (black box) solver. In Fig. 3 (right) both approaches are compared for bothA and B scenarios. Table 3 summarizes the localisation results. The commercialsolver from [11] performs better than the ML algorithm: the median error forthe ML algorithm was 220m and 200 for the KPN A and B scenarios whilethese errors (130m and 180m) were less for the commercial solver. We believethis is a combination of a few causes such as timestamp outlier removal and adifferent approach for selecting the best reference gateway. We also did not yetcombine TDoA with RSSI data and postprocessed the localisation results. Fromthe CDFs we noted that the ML approach was as good as the commercial solverin case the 90th percentile errors are considered: all errors were around 400mirrespective of solver used and mobility scenario.4 ConclusionThe accuracy of obtained time-difference stamps was investigated for 3 differentpublic LoRa networks: Wireless Belgium, Proximus and KPN. Accuracies ofthese time differences were quantified by the standard deviation which is rangingbetween 1.2 and 3.9 µs. The measured scenarios on the KPN network had themost accurate timestamping. This immediately impacted the TDoA localisationresults: the KPN localisation results were significantly better than on the other8 N. Podevijn et al.networks. A median accuracy of around 200m was obtained on this network withthe maximum likelihood algorithm. In 90 percent of the cases the error was lessthan 400m. Our investigation also showed that the mobility of the node did notsignificantly impact the raw TDoA localisation accuracy. Future work includesselection of the best reference gateway and perform only localisation with asubset of timestamps by removing outliers[12][13][14]. Combinations of TDoAwith signal strength and Angle of Arrival data are also part of future research.We will also investigate map-matching with sensor fusion (such as embedding acompass) techniques to further improve the localisation results.References1. David Bissett, ”Analysing TDoA Localisation in LoRa Networks”, 2018 Delft Uni-versity of Technology2. B. C. Fargas and M. N. Petersen, ”GPS-free geolocation using LoRa in low-powerWANs,” 2017 Global Internet of Things Summit (GIoTS), Geneva, 2017, pp. 1-6.3. Guowei Shen, R. Zetik and R. S. Thoma, ”Performance comparison of TOA andTDOA based location estimation algorithms in LOS environment,” 2008 5th Work-shop on Positioning, Navigation and Communication, Hannover, 2008, pp. 71-78.4. O. Cakir, A. Yazgan and I. Kaya, ”Accuracy comparison of time difference of ar-rival based source localization methods,” 2015 38th International Conference onTelecommunications and Signal Processing (TSP), Prague, 2015, pp. 1-4.5. Yiu-Tong Chan, H. Yau Chin Hang and Pak-chung Ching, ”Exact and approximatemaximum likelihood localization algorithms,” in IEEE Transactions on VehicularTechnology, vol. 55, no. 1, pp. 10-16, Jan. 2006.6. Nico Podevijn, David Plets, Jens Trogh, et al., “TDoA-Based Outdoor Positioningwith Tracking Algorithm in a Public LoRa Network,” Wireless Communicationsand Mobile Computing, vol. 2018.7. D. Plets, N. Podevijn, J. Trogh, L. Martens and W. Joseph, ”Experimental Perfor-mance Evaluation of Outdoor TDoA and RSS Positioning in a Public LoRa Net-work,” 2018 International Conference on Indoor Positioning and Indoor Navigation(IPIN), Nantes, 2018, pp. 1-8.8. Y. T. Chan and K. C. Ho, ”A simple and efficient estimator for hyperbolic location,”IEEE Trans. Signal Process., vol. 42, pp. 1905-1915, Aug. 1994.9. https://info.semtech.com/lora-cloud-geolocation-service10. Aernouts, M.; Berkvens, R.; Van Vlaenderen, K.; Weyn, M. Sigfox and LoRaWANDatasets for Fingerprint Localization in Large Urban and Rural Areas. Data 2018,3, 13.11. https://lora-alliance.org/sites/default/files/2018-04/geolocation_whitepaper.pdf12. Y. Zhao, Z. Li, B. Hao, P. Wan and L. Wang, ”How to select the best sensors forTDOA and TDOA/AOA localization?,” in China Communications, vol. 16, no. 2,pp. 134-145, Feb. 2019.13. B. Hao, Y. Zhao, Z. Li and P. Wan, ”A sensor selection method for TDOA andAOA localization in the presence of sensor errors,” 2017 IEEE/CIC InternationalConference on Communications in China (ICCC), Qingdao, 2017, pp. 1-6.14. M. Compagnoni et al., ”A Geometrical–Statistical Approach to Outlier Removalfor TDOA Measurements,” in IEEE Transactions on Signal Processing, vol. 65, no.15, pp. 3960-3975, 1 Aug.1, 2017.

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Experimental TDoA localisation in real public LoRa networks

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