A Scalable Deep Neural Network Architecture for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting

One of the key technologies for future large-scale location-aware services covering a complex of multi-story buildings --- e.g., a big shopping mall and a university campus --- is a scalable indoor localization technique. In this paper, we report the current status of our investigation on the use of deep neural networks (DNNs) for scalable building/floor classification and floor-level position estimation based on Wi-Fi fingerprinting. Exploiting the hierarchical nature of the building/floor estimation and floor-level coordinates estimation of a location, we propose a new DNN architecture consisting of a stacked autoencoder for the reduction of feature space dimension and a feed-forward classifier for multi-label classification of building/floor/location, on which the multi-building and multi-floor indoor localization system based on Wi-Fi fingerprinting is built. Experimental results for the performance of building/floor estimation and floor-level coordinates estimation of a given location demonstrate the feasibility of the proposed DNN-based indoor localization system, which can provide near state-of-the-art performance using a single DNN, for the implementation with lower complexity and energy consumption at mobile devices.Comment: 9 pages, 6 figure

Indoor Positioning for Monitoring Older Adults at Home: Wi-Fi and BLE Technologies in Real Scenarios

This paper presents our experience on a real case of applying an indoor localization system formonitoringolderadultsintheirownhomes. Sincethesystemisdesignedtobeusedbyrealusers, therearemanysituationsthatcannotbecontrolledbysystemdevelopersandcanbeasourceoferrors. This paper presents some of the problems that arise when real non-expert users use localization systems and discusses some strategies to deal with such situations. Two technologies were tested to provide indoor localization: Wi-Fi and Bluetooth Low Energy. The results shown in the paper suggest that the Bluetooth Low Energy based one is preferable in the proposed task

STCP: Receiver-agnostic Communication Enabled by Space-Time Cloud Pointers

Department of Electrical and Computer Engineering (Computer Engineering)During the last decade, mobile communication technologies have rapidly evolved and ubiquitous network connectivity is nearly achieved. However, we observe that there are critical situations where none of the existing mobile communication technologies is usable. Such situations are often found when messages need to be delivered to arbitrary persons or devices that are located in a specific space at a specific time. For instance at a disaster scene, current communication methods are incapable of delivering messages of a rescuer to the group of people at a specific area even when their cellular connections are alive because the rescuer cannot specify the receivers of the messages. We name this as receiver-unknown problem and propose a viable solution called SpaceMessaging. SpaceMessaging adopts the idea of Post-it by which we casually deliver our messages to a person who happens to visit a location at a random moment. To enable SpaceMessaging, we realize the concept of posting messages to a space by implementing cloud-pointers at a cloud server to which messages can be posted and from which messages can fetched by arbitrary mobile devices that are located at that space. Our Android-based prototype of SpaceMessaging, which particularly maps a cloud-pointer to a WiFi signal fingerprint captured from mobile devices, demonstrates that it first allows mobile devices to deliver messages to a specific space and to listen to the messages of a specific space in a highly accurate manner (with more than 90% of Recall)

Providing Databases for Different Indoor Positioning Technologies: Pros and Cons of Magnetic Field and Wi-Fi Based Positioning

Localization is one of the main pillars for indoor services. However, it is still very difficult for the mobile sensing community to compare state-of-the-art indoor positioning systems due to the scarcity of publicly available databases. To make fair and meaningful comparisons between indoor positioning systems, they must be evaluated in the same situation, or in the same sets of situations. In this paper, two databases are introduced for studying the performance of magnetic field and Wi-Fi fingerprinting based positioning systems in the same environment (i.e., indoor area). The “magnetic” database contains more than 40,000 discrete captures (270 continuous samples), whereas the “Wi-Fi” one contains 1,140 ones. The environment and both databases are fully detailed in this paper. A set of experiments is also presented where two simple but effective baselines have been developed to test the suitability of the databases. Finally, the pros and cons of both types of positioning techniques are discussed in detail.The authors gratefully acknowledge funding from the European Union through the GEO-C project (H2020-MSCA-ITN- 2014, Grant Agreement no. 642332, http://www.geo-c.eu/). The authors also gratefully acknowledge funding from the Spanish Ministry of Economy and Competitiveness through the “Metodolog´ıas avanzadas para el diseno, desarrollo, eval- ˜ uacion e integraci ´ on de algoritmos de localizaci ´ on en inte- ´ riores” project (Proyectos I+D Excelencia, codigo TIN2015- ´ 70202-P) and the “Red de Posicionamiento y Navegacion en ´ Interiores” network (Redes de Excelencia, codigo TEC2015- ´ 71426-REDT). The authors would like to thank all the current and past members of the Geospatial Technologies Research Group and Ubik Geospatial Solutions S.L. for their valuable help in creating the SmartUJI platform and providing us with the supporting services that allowed integrating the existing GIS services in the applications developed to create both databases

Crowdsourced Reconstruction of Cellular Networks to Serve Outdoor Positioning: Modeling, Validation and Analysis

Positioning via outdoor fingerprinting, which exploits the radio signals emitted by cellular towers, is fundamental in many applications. In most cases, the localization performance is affected by the availability of information about the emitters, such as their coverage. While several projects aim at collecting cellular network data via crowdsourcing observations, none focuses on information about the structure of the networks, which is paramount to correctly model their topology. The difficulty of such a modeling is exacerbated by the inherent differences among cellular technologies, the strong spatio-temporal nature of positioning, and the continuously evolving configuration of the networks. In this paper, we first show how to synthesize a detailed conceptual schema of cellular networks on the basis of the signal fingerprints collected by devices. We turned it into a logical one, and we exploited that to build a relational spatio-temporal database capable of supporting a crowdsourced collection of data. Next, we populated the database with heterogeneous cellular observations originating from multiple sources. In addition, we illustrate how the developed system allows us to properly deal with the evolution of the network configuration, e.g., by detecting cell renaming phenomena and by making it possible to correct inconsistent measurements coming from mobile devices, fostering positioning tasks. Finally, we provide a wide range of basic, spatial, and temporal analyses about the arrangement of the cellular network and its evolution over time, demonstrating how the developed system can be used to reconstruct and maintain a deep knowledge of the cellular network, possibly starting from crowdsourced information only

Smart luminaire positioning and lighting control in collaborative spaces

Abstract. Smart lighting systems have become more common as they provide energy savings with various occupancy detection methods and better lighting control opportunities for users. This thesis explores two aspects of these smart lighting systems, configuration and control, by utilizing an ActiveAhead controlled smart luminaire installation at the University of Oulu. Smart luminaire identification is a common configuration task that needs to performed before being able to control the individual luminaires and can be especially tedious with large installations. However, this task can be partly automated by positioning the smart luminaires based on passive infrared (PIR) sensors or the received signal strength indicators (RSSI) the luminaires broadcast with Bluetooth Low Energy (BLE) advertisements. For PIR sensor-based positioning, a centroid-based method is presented and evaluated with two datasets reflecting a typical and optimal scenarios of triggering the sensors. For RSSI-based positioning, a log-distance path loss distance estimation with mean squared error (MSE) based position optimization is presented and evaluated. Moreover, relevant literature concerning the RSSI-based device positioning is discussed. Second, the design, implementation and evaluation of a lighting control prototype for collaborative spaces are presented. The prototype uses near-field communication (NFC) tags to indicate the user position and to initiate a lighting preference input to an Android application. The user preferences are transmitted to a local server responsible for the control logic and communication with the luminaires. The potential conflicts between users are resolved with distance weighted preference averaging, which makes the prototype especially convenient for cases where the users share the surrounding luminaires with others. Furthermore, related smart lighting control systems are compared.Älyvalaisinten paikantaminen ja valaistuksen säätö yhteistyötiloissa. Tiivistelmä. Älykkäät valaistusjärjestelmät ovat yleistyneet mahdollistaen energiansäästöt useilla läsnäolon tunnistusratkaisuilla ja paremmat valaistuksen säätömahdollisuudet käyttäjille. Tämä työ käsittelee älyvalaistusjärjestelmiä kahdesta näkökulmasta hyödyntäen ActiveAhead älyvalaisinasennusta Oulun yliopistossa. Älyvalaisinten paikan tunnistaminen on yleinen konfigurointivaihe ennen kuin yksittäisiä valaisimia on mahdollista säätää ja se voi osoittautua erityisen työlääksi suurissa asennuksissa. Tämä vaihe on kuitenkin mahdollista automatisoida paikantamalla älyvalot hyödyntäen PIR-liiketunnistimia tai vastaanotetun signaalin voimakkuutta (RSSI), joita valaisimet lähettävät matalanenergian (BLE) Bluetoothin mainosviesteillä. PIR-liiketunnistimiin pohjautuvaan paikantamiseen esitellään painopisteeseen perustuva metodi, joka myös evaluoidaan kahdella datasetillä, jotka kuvaavat yleistä ja optimaalista PIR-liiketunnistimien laukaisua. RSSI pohjaiseen paikantamiseen esitellään ja arvioidaan metodi, joka hyödyntää logaritmisen signaalin vaimenemisen etäisyys-mallia ja keskimääräiseen neliövirheeseen perustuvaa paikan optimointia. Lisäksi esitellään käytettyjä menetelmiä RSSI-pohjaiseen paikantamiseen. Toiseksi esitellään yhteisöllisiin työtiloihin tarkoitetun valaistuksensäätöprotyypin suunnittelu, toteutus ja evaluointi. Prototyyppi hyödyntää NFC (near field communication) tarroja käyttäjän sijainnin ilmaisuun ja valaistuspreferenssien syöttämisen osoittamiseen Android sovellukselle. Käyttäjäpreferenssit välitetään paikalliselle palvelimelle, joka vastaa ohjauslogiikasta ja viestinnästä valaisimien kanssa. Mahdolliset konfliktit käyttäjien välillä ratkaistaan etäisyydellä painotetulla keskiarvolla, mikä tekee prototyypistä kätevän erityisesti tilanteisiin missä käyttäjät jakavat ympäröivät valaisimet toistensa kanssa. Lisäksi vertaillaan muita älykkäitä järjestelmiä valaistuksen säätämiseen