Technologies and solutions for location-based services in smart cities: past, present, and future

Location-based services (LBS) in smart cities have drastically altered the way cities operate, giving a new dimension to the life of citizens. LBS rely on location of a device, where proximity estimation remains at its core. The applications of LBS range from social networking and marketing to vehicle-toeverything communications. In many of these applications, there is an increasing need and trend to learn the physical distance between nearby devices. This paper elaborates upon the current needs of proximity estimation in LBS and compares them against the available Localization and Proximity (LP) finding technologies (LP technologies in short). These technologies are compared for their accuracies and performance based on various different parameters, including latency, energy consumption, security, complexity, and throughput. Hereafter, a classification of these technologies, based on various different smart city applications, is presented. Finally, we discuss some emerging LP technologies that enable proximity estimation in LBS and present some future research areas

Belief Condensation Filtering For Rssi-Based State Estimation In Indoor Localization

Recent advancements in signal processing and communication systems have resulted in evolution of an intriguing concept referred to as Internet of Things (IoT). By embracing the IoT evolution, there has been a surge of recent interest in localization/tracking within indoor environments based on Bluetooth Low Energy (BLE) technology. The basic motive behind BLE-enabled IoT applications is to provide advanced residential and enterprise solutions in an energy efficient and reliable fashion. Although recently different state estimation (SE) methodologies, ranging from Kalman filters, Particle filters, to multiple-modal solutions, have been utilized for BLEbased indoor localization, there is a need for ever more accurate and real-time algorithms. The main challenge here is that multipath fading and drastic fluctuations in the indoor environment result in complex non-linear, non-Gaussian estimation problems. The paper focuses on an alternative solution to the existing filtering techniques and introduce/discuss incorporation of the Belief Condensation Filter (BCF) for localization via BLE-enabled beacons. The BCF is a member of the universal approximation family of densities with performance bound achieving accuracy and efficiency in sequential SE and Bayesian tracking. It is a resilient filter in harsh environments where nonlinearities and non-Gaussian noise profiles persist, as seen in such applications as Indoor Localization

Multiple Model Bayesian Estimation for BLE-based Localization and RL-based Decision Support of Autonomous Agents

With the rapid emergence of Internet of Things (IoT), we are more and more surrounded by smart connected devices (agents) with integrated sensing, processing, and communication capabilities. In particular, IoT-based positioning has become of primary importance for providing advanced Location-based Services (LBSs) in indoor environments. Several LBSs have been developed recently such as navigation assistance in hospitals, localization/tracking in smart buildings, and providing assistive services via autonomous agents collectively act as an Internet of Robotic Things (IoRT). The focus of the thesis is on the following two research topics when it comes to autonomous agents providing LBSs in indoor environments: (i) Self-Localization, which is the autonomous agent’s ability to obtain knowledge of its own location, and; (ii) Localized Decision Support System, which refers to an autonomous agent’s ability to perform optimal actions towards achieving pre-defined objectives. With regards to Item (i), the thesis develops innovative localization solutions based on Bluetooth Low Energy (BLE), referred to Bluetooth Smart. Given unavailability of Global Positioning System (GPS) in indoor environments, BLE has attracted considerable attention due to its low cost, low energy consumption, and widespread availability in smart hand-held devices. Because of multipath fading and fluctuations in the indoor environment, however, BLE-based localization approaches fail to achieve high accuracies. To address these challenges, different linear and non-linear Bayesian-based estimation frameworks are proposed in this thesis. Among which, the thesis proposes a novel Multiple-Model and BLE-based tracking framework, referred to as the STUPEFY. The proposed STUPEFY framework uses set-valued information and is designed by coupling a non-linear Bayesian-based estimation model (Box Particle Filter) with fingerprinting-based methodologies to improve the overall localization accuracy. With regards to the Item (ii), there has been an increasing surge of interest on development of advanced Reinforcement Learning (RL) systems. The objective is development of intelligent approaches to learn optimal control policies directly from smart agents’ interactions with the environment. In this regard, Deep Neural Networks (DNNs) provide an attractive modeling mechanism to approximate the value function using sample transitions. DNN-based solutions, however, suffer from high sensitivity to parameter selection, are prone to overfitting, and are not very sample efficient. As a remedy to the aforementioned problems, the thesis proposes an innovative Multiple-Model Kalman Temporal Difference (MM-KTD) framework, which adapts the parameters of the filter using the observed states and rewards. Moreover, an active learning method is proposed to enhance the sampling efficiency of the overall system. The proposed MM-KTD framework can learn the optimal policy with significantly reduced number of samples as compared to its DNN-based counterparts

RSSI-Based direction-of-departure estimation in bluetooth low energy using an array of frequency-steered leaky-wave antennas

This paper presents a novel advanced Bluetooth Low Energy (BLE) beacon, which is based on an array of frequency-steered leaky-wave antennas (LWAs), as a transmitter for a Direction-of-Departure (DoD) estimation system. The LWA array is completely passive, fabricated in a low-cost FR4 printed-circuit board and designed to multiplex to different angular directions in space each one of the three associated BLE advertising channels that are used for periodically transmitting the ID of the beacon. This way, the use of more expensive hardware associated to electronic phased-array steering/beam-switching is avoided. Four commercial BLE modules are connected to the four ports of the array, producing an advanced BLE beacon that synthesizes twelve directive beams (one per each port and advertising channel) distributed over a wide Field of View (FoV) of 120 degrees in the azimuthal plane. Then, any BLE enabled IoT device located within this FoV can scan the messages from the beacon and obtain the corresponding Received Signal Strength Indicator (RSSI) of these twelve beams to estimate the relative DoD by using amplitude-monopulse signal processing, thus dispensing from complex In-phase/Quadrature (IQ) data acquisition or high computational load.We propose an angular windowing technique to eliminate angular ambiguities and increase the angular resolution, reporting a root mean squared angular error of 3.7º in a wide FoV of 120º.This work was supported in part by the Spanish National projects TEC2016-75934-C4-4-R and TEC2016-76465-C2-1-R, and in part by the 2018 UPCT Santander Research Grant

Sisäpaikannus: Teknologiat ja käyttötapaukset vähittäiskaupan alalla

Indoor positioning systems (IPS) are required in buildings to offer the possibility to position people and assets indoors, as the widely utilized GPS signal cannot penetrate through walls. IPSs are already implemented in many indoor environments. Several indoor positioning technologies exist, but none of them is clearly a dominant technology over the others. Consequently, this study identifies the different kinds of indoor positioning technologies and methods as well as the use cases they are used in. For this purpose, six companies using or developing indoor positioning systems were interviewed. The interviews were held in person, and they were 60-minute long semi-structured interviews with a set of questions in Appendix 1. In addition, two companies interested in indoor positioning, and that are working with retail were interviewed in 30-minute semi-structured interviews with questions in Appendix 2. Indoor positioning is employed in the interviewed companies to help users to navigate in public spaces; raise employee satisfaction in an office; improve customer service and satisfaction in malls, stores, and restaurants and develop processes and safety in warehouses. These different use cases have distinctive specifications and needs for indoor positioning, and thus, there is not a simple solution as to which technology is the right choice for a particular use case. Nevertheless, three points affecting the choice of indoor positioning technology were concluded from the interviews: 1) the accuracy of a technology, 2) whether the positioning happens through a tag or a mobile device, and 3) if positioning infrastructure, such as anchor nodes, can be installed in the building. Finally, based on the interviews, a suggested model for an indoor positioning system for a retail company is presented in a form of a Value Network Configuration.Sisäpaikannusjärjestelmiä tarvitaan rakennuksissa, jotta ihmisiä ja tavaroita voidaan paikantaa sisätiloissa, sillä ulkona yleisesti käytetty GPS signaali ei pysty läpäisemään rakennusten seiniä. Vaikka sisäpaikannusta käytetäänkin jo useissa eri sisätiloissa ja useita eri sisäpaikannusteknologioita on olemassa, mikään niistä ei ole selvästi hallitseva teknologia. Tässä tutkimuksessa tunnistetaan eri sisäpaikannusteknologiat ja –tekniikat kuten myös niitä hyödyntävät käyttötapaukset. Tätä varten haastateltiin kuutta eri yritystä, jotka käyttävät tai tarjoavat sisäpaikannusjärjestelmiä. Haastattelut olivat puolistrukturoituja, kestivät 60 minuuttia ja ne pidettiin kasvotusten. Lisäksi haastateltiin 30 minuutin puolistrukturoiduissa haastatteluissa kahta kaupan alaan liittyvää yritystä, jotka ovat kiinnostuneita sisäpaikannuksesta. Haastattelukysymykset ovat liitteissä 1 ja 2. Sisäpaikannusta käytetään haastatelluissa yrityksissä käyttäjien navigoinnin helpottamiseksi julkisissa tiloissa, työntekijöiden tyytyväisyyden kasvattamiseen toimistossa, asiakaspalvelun ja asiakkaiden tyytyväisyyden parantamiseen ostoskeskuksissa, kaupoissa ja ravintoloissa sekä prosessien ja turvallisuuden kehittämiseen varastoissa. Näillä eri käyttötapauksilla on hyvin erilaiset vaatimukset ja tarpeet sisäpaikannukselle, joten ei ole olemassa vain yhtä hyvää teknologista ratkaisua tietylle käyttötapaukselle. Haastatteluista oli kuitenkin mahdollista muodostaa kolme sisäpaikannusteknologian valintaan vaikuttavaa asiaa: 1) sisäpaikannusteknologian tarkkuus, 2) tapahtuuko paikannus mobiililaitteen vai käyttäjän kantaman tunnisteen kautta ja 3) voiko paikannusjärjestelmän tukiasemia asentaa rakennukseen. Lopuksi esitellään ehdotelma sisäpaikannusmallista arvoverkkokonfiguraatiolla (Value Network Configuration) vähittäiskaupan alan yritykselle haastatteluiden perusteella