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Performance Evaluation of Lorawan Geolocation in Smart Regions

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Performance Evaluation of Lorawan Geolocation in Smart Regions Performance Evaluation of LoRaWAN Geolocation in Smart Regions Fredrik Lindahl Computer Science and Engineering, bachelor's level 2020 Luleå University of Technology Department of Computer Science, Electrical and Space Engineering Abstract The number of IoT devices is increasing for every year that passes. The possibil- ity to connect more and more devices to the internet creates a lot of possibilities but it also comes with its challenges. Each IoT device has limited resources that make GPS and its high power consummation a bad choice for tracking the device. LoRa and LoRaWAN is a relatively new and interesting technol- ogy developed specifically as a wide area network for IoT devices. With the release of LoRaWAN 2.0 came the possibility to get the position of the sender through triangulation. The purpose of this report is to investigate LoRaWAN Geolocation in Smart Regions in particular, in the city of Skellefte˚a.The result shows that within a specific area you can get an accuracy of below 80 meters but moving outside that area the accuracy starts to drastically decline. The result also shows that adding more gateways doesn't necessarily improve the accuracy but what is more important is the placement of the gateways that makes the difference. Sammanfattning Antal IoT enheter ¨oker f¨orvarje ˚arsom g˚ar.M¨ojlihetenatt kopplat upp mer och mer objekt till internet skaper m˚angam¨ojligheter men det kommer ocks˚a med sina utmaningar. IoT enheter har begr¨anskade resurser vilket g¨oratt GPS med dess h¨ogabatterif¨orbrukninginte ¨aralternativ f¨oratt f¨oljar¨orelsenav en IoT enhet. LoRa och loRaWAN ¨aren relative ny och intressant teknik som ¨arspeciellt utvecklad som ett wide area network till IoT enheter. Med utgivningen av LoRaWAN 2.0 kom m¨ojligheten att med hj¨alpav triangulering att f˚apositionen p˚asendare. syftet med denna rapport ¨aratt unders¨oka hur bra Geolocation fr˚anLoRaWAN ¨arinom smarta regioner s¨arskiltSkellefte˚acity. Resultatet visar att inom ett specifikt omr˚adenkan man f˚aen precision under 80 meter men b¨orjardu r¨oradig utan f¨ordetta omr˚adakommer precisionen drastiskt att f¨orsamras. Resultatet visar ocks˚aatt antal Gateways ¨okar inte n¨odv¨andigtvispersisionen men att plaseringen av varje gateway ¨armer viktig. ii Acknowledgements I would like to thank my supervisors Christer Alund˚ and Karan Mitra for giving me the opportunity to perform my thesis and also for being patient and helping me throughout the duration of the thesis. I would also like to thank Ali Ahmadi and Leif H¨aggmark.Ali for giving me great technical support with the sensor and Leif H¨aggmarkfor helping with the communication with Kerlink. Without you four this would never have been a possibility so you have my deepest thanks. iii List of Figures 1 Map representing Skellefte˚aCity with each gateway location (Red marks) and each test location (Green marks) . 1 2 LoRaWAN . 3 3 Visualising how triangulation is used to find the distance of the object, d. With a baseline L and the angles α and β . 4 4 Illustrating LoPy[10] development board with all its sensors and specifications . 5 5 The project follow the flow illustrated in the figure, as the new solutions developed over the course of the project there were changes done even after testing had begun . 7 6 System architecture . 9 7 The flow each frame . 10 8 Location heatmap simulated by Kerlink . 12 9 Location heatmap simulated by Kerlink when gateway 6 was added 13 10 Boxplot illustrate the variance of values . 15 11 Box chart from test one for location A,B, and C . 17 12 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC algorithm relative to the mobile phone GPS location (Yellow marker) at location A . 18 13 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC OS algorithm relative to the mobile phone GPS location (Yellow marker) at location A . 18 14 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC algorithm relative to the mobile phone GPS location (Yellow marker) at location B . 19 15 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC OS algorithm relative to the mobile phone GPS location (Yellow marker) at location B . 19 16 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC algorithm relative to the mobile phone GPS location (Yellow marker) at location C . 20 17 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC OS algorithm relative to the mobile phone GPS location (Yellow marker) at location C . 20 18 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC algorithm relative to the mobile phone GPS location (Yellow marker) at location during driving test . 22 19 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC OS algorithm relative to the mobile phone GPS location (Yellow marker) during driving test . 22 20 Box chart for test two from location A,B, and C . 24 iv 21 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC algorithm relative to the mobile phone GPS location (Yellow marker) at location A . 24 22 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC OS algorithm relative to the mobile phone GPS location (Yellow marker) at location A . 25 23 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC algorithm relative to the mobile phone GPS location (Yellow marker) at location B . 25 24 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC OS algorithm relative to the mobile phone GPS location (Yellow marker) at location B . 26 25 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC algorithm relative to the mobile phone GPS location (Yellow marker) at location C . 26 26 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC OS algorithm relative to the mobile phone GPS location (Yellow marker) at location C . 27 27 Box chart from test four on location F and G . 29 28 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC algorithm relative to the mobile phone GPS location (Yellow marker) at location F . 30 29 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC OS algorithm relative to the mobile phone GPS location (Yellow marker) at location F . 30 30 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC algorithm relative to the mobile phone GPS location (Yellow marker) at location G . 31 31 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC OS algorithm relative to the mobile phone GPS location (Yellow marker) at location G . 31 32 Box chart from test four on location H and B . 33 33 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC algorithm relative to the mobile phone GPS location (Yellow marker) at location H . 33 34 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC OS algorithm relative to the mobile phone GPS location (Yellow marker) at location H . 34 35 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC algorithm relative to the mobile phone GPS location (Yellow marker) at location B . 34 36 Map illustrating each frame's location (Blue dots) calculated by Kerlink using WANESY GELOC OS algorithm relative to the mobile phone GPS location (Yellow marker) at location B . 35 37 Triangular area estimated to get the best possible accuracy when using LoRa triangulation . 37 v List of Tables 1 The table shows the distance between the estimated location given by Kerlink and the location given by the mobile phone GPS at location A, B and, C using WANESY GEOLOC and WANESY GEOLOC OS. It shows the frame with the smallest and largest distance, an average and standard deviation of all the frames. 16 2 The table shows the calculated precision for estimation of the po- sition given by Kerlink at locations A, C and, C using WANESY GEOLOC and WANESY GEOLOC OS. It shows the frame with the small- est and largest distance, an average and standard deviation of all the frames. 17 3 The table shows the distance between the estimated location given by Kerlink and the location given by the mobile phone GPS during driving test using WANESY GEOLOC and WANESY GEOLOC OS. It shows the frame with the smallest and largest distance, an av- erage and standard deviation of all the frames. 21 4 The table shows the calculated precision for estimation of the po- sition given by Kerlink during driving test using WANESY GEOLOC and WANESY GEOLOC OS. It shows the frame with the small- est and largest distance, an average and standard deviation of all the frames. 21 5 The table shows the distance between the estimated location given by Kerlink and the location given by the mobile phone GPS at location A, B and, C using WANESY GEOLOC and WANESY GEOLOC OS. It shows the frame with the smallest and largest distance, an average and standard deviation of all the frames. 23 6 The table shows the calculated precision for estimation of the po- sition given by Kerlink at location A, B and, C using WANESY GEOLOC and WANESY GEOLOC OS. It shows the frame with the small- est and largest distance, an average and standard deviation of all the frames.
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