Positioning for the Internet of Things: A 3GPP Perspective

Many use cases in the Internet of Things (IoT) will require or benefit from location information, making positioning a vital dimension of the IoT. The 3rd Generation Partnership Project (3GPP) has dedicated a significant effort during its Release 14 to enhance positioning support for its IoT technologies to further improve the 3GPP-based IoT eco-system. In this article, we identify the design challenges of positioning support in Long-Term Evolution Machine Type Communication (LTE-M) and Narrowband IoT (NB-IoT), and overview the 3GPP's work in enhancing the positioning support for LTE-M and NB-IoT. We focus on Observed Time Difference of Arrival (OTDOA), which is a downlink based positioning method. We provide an overview of the OTDOA architecture and protocols, summarize the designs of OTDOA positioning reference signals, and present simulation results to illustrate the positioning performance.Comment: 8 pages; 7 figures; 1 table; submitted for publicatio

Machine learning for localization in narrowband IoT networks

Low power wide area networks (LPWANs) are designed for Internet of Things (IoT) appli- cations because of their long-range coverage, low bit rate, and low battery consumption. In the LPWAN networks, Narrow-band IoT (NB-IoT) is a type of network that uses the licensed cellular spectrum, working over the deployed LTE infrastructure. It is rising as a promising technology because of its characteristics and deployment advantages against other LPWAN networks. In NB-IoT networks, localization is an essential service for applications such as smart cities, traffic control, logistics tracking, and others. The outdoor localization is often performed using a Global Navigation Satellite System (GNSS) like Global Positioning System (GPS) to send the current device position with some meters accuracy. However, due to GPS¿s power and size drawbacks, recent reports focus on alternatives to replace GPS-based localization systems with cost and power efficient solutions. This work analyses a database collected over an NB-IoT deployed network in the city of Antwerp in Belgium and implements a solution for outdoor localization based on Machine Learning (ML) methods for distance estimation. The data analysis starts in the pre-processing step, where the databases are cleaned and prepared for the ML analysis. The following process merges and debugs the data to obtain an integrated database with classification for urban and rural areas. The localization solution performs a support vector regression, random forest regression, and a multi-layer perceptron regression using as input parameters the received signal strength indicator (RSSI) and the base station (BS) position details in order to predict the distance to the IoT nodes and estimate the current position (latitude and longitude) of them. This implementation includes hyper-parameter tuning, the train and test process, and mathematical calculations to obtain the estimated position with mean and median location estimation errors expressed in meters. The implementation of the methodology processes results in 280 and 220 meters corre- sponding to the mean and median location errors for the urban area and 920 and 570 meters for the rural area. The accuracy levels obtained in the results turn this solution suitable for the most common uses of localization in IoT instead of using a GPS device. As a result, this study proposes a new approach for localization in IoT networks. In addition to the implemented solution defines valuable research lines to improve the accuracy levels and generate more contributions to optimize the equipment resources and reduce the IoT device¿s final cost.OutgoingObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenible

Positioning by multicell fingerprinting in urban NB-IoT networks

Narrowband Internet of Things (NB-IoT) has quickly become a leading technology in the deployment of IoT systems and services, owing to its appealing features in terms of coverage and energy efficiency, as well as compatibility with existing mobile networks. Increasingly, IoT services and applications require location information to be paired with data collected by devices; NB-IoT still lacks, however, reliable positioning methods. Time-based techniques inherited from long-term evolution (LTE) are not yet widely available in existing networks and are expected to perform poorly on NB-IoT signals due to their narrow bandwidth. This investigation proposes a set of strategies for NB-IoT positioning based on fingerprinting that use coverage and radio information from multiple cells. The proposed strategies were evaluated on two large-scale datasets made available under an open-source license that include experimental data from multiple NB-IoT operators in two large cities: Oslo, Norway, and Rome, Italy. Results showed that the proposed strategies, using a combination of coverage and radio information from multiple cells, outperform current state-of-the-art approaches based on single cell fingerprinting, with a minimum average positioning error of about 20 m when using data for a single operator that was consistent across the two datasets vs. about 70 m for the current state-of-the-art approaches. The combination of data from multiple operators and data smoothing further improved positioning accuracy, leading to a minimum average positioning error below 15 m in both urban environments

Observed time difference of arrival based position estimation for LTE systems: simulation framework and performance evaluation

Precise user equipment (UE) location is paramount for the reliable operation of location-based services provided by mobile network operators and other emerging applications. In this paper, the Long Term Evolution (LTE) network positioning performance based on mobile assist Observed Time Difference of Arrival (OTDoA) method is considered. The received signal time difference (RSTD) measurements are estimated by the UE using dedicated position reference signal (PRS) transmitted in the downlink frame where the reported time measurements are used by the network for location calculation. A simulation framework for the position estimation in LTE networks is presented where the LTE downlink communication link is implemented. The correlation-based method for the time of arrival measurement is used for the implementation of OTDoA. The simulation framework provides different configurations and adjustments for the system and network parameters for evaluating the performance of LTE positioning using OTDoA over multipath fading channels. Different simulation scenarios are conducted to identify the influence of various parameters of LTE system and positioning procedure setup on the positioning accuracy. Simulation results demonstrated that the positioning accuracy is highly affected by the channel fading condition where the accuracy of time of arrival measurements is deteriorated in severe fading environments; however, the positioning accuracy can be significantly improved by increasing the positioning sequences involved in the estimation process either in the frequency domain or in the time domain

Observed time difference of arrival based position estimation for LTE systems: simulation framework and performance evaluation

Precise user equipment (UE) location is paramount for the reliable operation of location-based services provided by mobile network operators and other emerging applications. In this paper, the Long Term Evolution (LTE) network positioning performance based on mobile assist Observed Time Difference of Arrival (OTDoA) method is considered. The received signal time difference (RSTD) measurements are estimated by the UE using dedicated position reference signal (PRS) transmitted in the downlink frame where the reported time measurements are used by the network for location calculation. A simulation framework for the position estimation in LTE networks is presented where the LTE downlink communication link is implemented. The correlation-based method for the time of arrival measurement is used for the implementation of OTDoA. The simulation framework provides different configurations and adjustments for the system and network parameters for evaluating the performance of LTE positioning using OTDoA over multipath fading channels. Different simulation scenarios are conducted to identify the influence of various parameters of LTE system and positioning procedure setup on the positioning accuracy. Simulation results demonstrated that the positioning accuracy is highly affected by the channel fading condition where the accuracy of time of arrival measurements is deteriorated in severe fading environments; however, the positioning accuracy can be significantly improved by increasing the positioning sequences involved in the estimation process either in the frequency domain or in the time domain

NB-IoT ja sen soveltuvuus paikannuksessa

Esineiden internet ja siihen liittyvät teknologiat ovat yksi langattomien verkkojen kasvavista sovellusalueista. Esineiden internetillä tarkoitetaan erilaisia laitteita, jotka on liitetty internetiin ja jotka kykenevät esimerkiksi havainnoimaan ympäristöään sensoreiden avulla. Laitteiden havainnoimaa dataa voidaan tallentaa sovelluskohtaisille palvelimille jatkokäsittelyä ja analysointia varten. Käyttökohteen mukaan IoT-laitteille (engl. Internet of Things) ja IoT-verkoille on olemassa erilaisia suunnitteluvaatimuksia ja ominaisuuksia. Yksi IoT:n sovellusalueista on vähäisen virrankulutusten ja kustannuksien, sekä pitkien välimatkojen etäisyyksillä toimivat LPWAN-verkot (engl. Low Power Wide Area Network). Pitkät kantamat mahdollistavat LPWAN-tekniikoille uusia käyttökohteita, sekä niitä voidaan toteuttaa erilaisin tavoin riippuen järjestelmän käyttötarkoituksista. Tämän työn tavoitteena oli selvittää pitkän kantaman IoT-verkoissa toimivien laitteiden, erityisesti NB-IoT-laitteiden (engl. Narrow Band Internet of Things) paikannuskykyä ja paikannuksen tarkkuutta. Tässä työssä käsiteltävä NB-IoT-teknologia hyödyntää LTE-tekniikkaa (engl. Long Term Evolution) tiedonsiirrossa ja kuuluu LPWAN-teknologioihin. Työ on toteutettu suurilta osin teoreettisena kirjallisuuskatsauksena erilaisiin IoT-teknologioihin keskittyen pitkän kantaman verkkoihin. Työssä on lisäksi toteutettu käytännön mittauksia, joissa pyrittiin selvittämään ja arvioimaan NB-IoT-verkon paikannuksen suorituskykyä ja toimivuutta. Työssä suoritetuissa mittauksissa havaittiin NB-IoT-laitteen paikannuksen sopivan hyvin tekniikan nykyisille käyttökohteille ja niiden vaatimuksille. Mittauksista voidaan myös todeta NB-IoT-laitteella tehdyn paikannuksen olevan toimiva niin kaupunkialueella kuin kaupunkialueen ulkopuolella. Pitkät kantamat IoT-laitteiden muodostamissa verkoissa tuottavat järjestelmään erilaisia haasteita. Matalat signaalitehot ja häiriöt tiedonsiirtokanavissa tekevät pitkien kantamien saavuttamisesta vaikeaa samalla, kun laitteiden virrankulutus ja kustannukset pyritään minimoimaan. Standardointi pitkien kantamien IoT-verkkojen ja erityisesti mobiiliverkkopohjaisten ratkaisujen kehityksessä pyrkii nyt ja tulevaisuudessa kehittämään mahdollisimman tehokkaita ratkaisuja ongelmien selvittämiseksi. Pitkien kantamien IoT-verkot voidaan toteuttaa käyttämällä joko olemassa olevaa verkkoinfrastruktuuria tai kaupalliset toimijat voivat toteuttaa omia ratkaisujaan. Markkinoiden painostus ja käyttökohteiden monimuotoistuminen edesauttavat alan jatkuvaa kehitystä ja täten standardointi voi olla paikoin hyvin nopeaa. Pitkän kantaman IoT-verkoille on olemassa erilaisia käyttökohteita ja sovellusalueita. Nykyisissä pitkien kantamien IoT-verkoissa, kuten NB-IoT-verkkojen tiedonsiirrossa, on usein pidempiä viiveitä kuin esimerkiksi matkapuhelinten tiedonsiirrossa. Myös tiedonsiirrossa lähetettävät datamäärät ovat usein pienempiä, joten tällaiset ominaisuudet asettavat reunaehtoja myös tekniikan käyttökohteille. Useimmiten pitkien kantamien IoT-tekniikat soveltuvat parhaiten erilaisiin mittaus- ja havainnointijärjestelmiin. Monissa mittaussovelluksissa pitkien kantamien IoT-tekniikoiden tiedonsiirto on tarpeeksi reaaliaikaista ja datamäärät säilyvät pieninä. Pitkien kantamien IoT-verkot toimivat hyvin myös haastavissa olosuhteissa, joissa WLAN-tekniikoiden (engl. Wireless Local Area Network) tiedonsiirtokyky heikkenee nopeasti. Tämän ominaisuuden vuoksi esimerkiksi NB-IoT-tekniikka soveltuu hyvin käytettäväksi myös kaupunkialueilla ja sisätiloissa. Käyttökohteiden kasvaessa pitkien kantamien IoT-tekniikoihin kehitetään uusia ominaisuuksia, jotka on toteutettu huomioiden laitteiden ja verkkojen rajoitukset. Esimerkiksi paikantaminen NB-IoT-tekniikassa on toteutettu soluverkkopohjaisesti eikä käyttämällä perinteisempiä satelliittiperusteisia järjestelmiä. Yksi suurimmista syistä soluverkkopohjaisen paikannuksen kehittämiselle satelliittiperusteisen järjestelmän sijaan on virrankulutuksen minimointi; satelliittiperusteiset järjestelmät, kuten GNSS (engl. Global Navigation Satellite System) vaativat toimiakseen huomattavasti enemmän virtaa päätelaitteelta suhteessa soluverkkopohjaiseen paikantamiseen. Paikannusominaisuus on tärkeä pitkien kantamien IoT-verkkojen kehityksessä ja se mahdollistaa useita uusia sovelluskohteita tekniikan hyödyntämiselle

Wireless Localization in Narrowband-IoT Networks

Internet of things (IoT) is an emerging technology, which connects devices to the internet and with the upcoming of 5G, even more devices will be connected. Narrowband-IoT (NB-IoT) is a promising cellular technology that supports the connection of IoT devices and their integration with the existing long-term evolution (LTE) networks. The Increase of location-based services that requires localization for IoT devices is growing with the increase in IoT devices and applications. This thesis considers the localization of IoT devices in the NB-IoT wireless network. Localization emulation is produced in which Software Defined Radio (SDR) used to implement Base stations (BS) and user equipment (UE). Channel emulator was used to emulate wireless channel conditions, and a personal computer (PC) to calculate the UE location. The distance from each BS to the UE is calculated using Time of arrival (TOA). Triangulation method used to estimate the UE's position from the different BSs distances to the UE. The accuracy of positioning is analysed with various simulation scenarios and the results compared with third generation partnership project (3GPP) Release 14 standards for NB-IoT. The positioning accuracy requirement of 50 m horizontal accuracy for localization in NB-IoT 3GPP standardized have been achieved, under Line of Sight (LOS) full triangulation scenarios 1 and 2