Business Investigation Study For The Nordic Telemedicine Center Using Business Model Canvas and Monte Carlo Simulation

The eHealth industry has caught huge attention during the last decade especially in Nordic countries. The concept of telemedicine is becoming an essential factor in the healthcare sector owing to its advantageous edges that made remote diagnosis and monitoring become more viable. Such role that brought numerous patients’ cases within the reach of healthcare professionals, facilitated the continuous monitoring of their vital signs and kept records of their previous health history for better treatments. However, telemedicine projects –as any other type of projects– should possess a preliminary feasibility plan described in business terms to speculate the likelihood of failure or success based on the resources and the value proposition. Various worldwide approaches have been conducted in many countries to provide the suitable business frames for the telemedicine business model. One of the main objectives of the Nordic telemedicine Center (NTC) project is to establish a running center that operates and sustains itself even after the project period is concluded thus, a feasible business model is required. In this thesis, an approach is designed upon the business model Canvas structure. The proposed canvas is based on the Nordic telemedicine Center project’s resources and objectives. Nevertheless, the output canvas is assigned with a conducted Monte Carlo simulation to obtain some business insights relying on both real and assumed input data.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Survey on Recent Advances in Integrated GNSSs Towards Seamless Navigation Using Multi-Sensor Fusion Technology

During the past few decades, the presence of global navigation satellite systems (GNSSs) such as GPS, GLONASS, Beidou and Galileo has facilitated positioning, navigation and timing (PNT) for various outdoor applications. With the rapid increase in the number of orbiting satellites per GNSS, enhancements in the satellite-based augmentation systems (SBASs) such as EGNOS and WAAS, as well as commissioning new GNSS constellations, the PNT capabilities are maximized to reach new frontiers. Additionally, the recent developments in precise point positioning (PPP) and real time kinematic (RTK) algorithms have provided more feasibility to carrier-phase precision positioning solutions up to the third-dimensional localization. With the rapid growth of internet of things (IoT) applications, seamless navigation becomes very crucial for numerous PNT dependent applications especially in sensitive fields such as safety and industrial applications. Throughout the years, GNSSs have maintained sufficiently acceptable performance in PNT, in RTK and PPP applications however GNSS experienced major challenges in some complicated signal environments. In many scenarios, GNSS signal suffers deterioration due to multipath fading and attenuation in densely obscured environments that comprise stout obstructions. Recently, there has been a growing demand e.g. in the autonomous-things domain in adopting reliable systems that accurately estimate position, velocity and time (PVT) observables. Such demand in many applications also facilitates the retrieval of information about the six degrees of freedom (6-DOF - x, y, z, roll, pitch, and heading) movements of the target anchors. Numerous modern applications are regarded as beneficiaries of precise PNT solutions such as the unmanned aerial vehicles (UAV), the automatic guided vehicles (AGV) and the intelligent transportation system (ITS). Hence, multi-sensor fusion technology has become very vital in seamless navigation systems owing to its complementary capabilities to GNSSs. Fusion-based positioning in multi-sensor technology comprises the use of multiple sensors measurements for further refinement in addition to the primary GNSS, which results in high precision and less erroneous localization. Inertial navigation systems (INSs) and their inertial measurement units (IMUs) are the most commonly used technologies for augmenting GNSS in multi-sensor integrated systems. In this article, we survey the most recent literature on multi-sensor GNSS technology for seamless navigation. We provide an overall perspective for the advantages, the challenges and the recent developments of the fusion-based GNSS navigation realm as well as analyze the gap between scientific advances and commercial offerings. INS/GNSS and IMU/GNSS systems have proven to be very reliable in GNSS-denied environments where satellite signal degradation is at its peak, that is why both integrated systems are very abundant in the relevant literature. In addition, the light detection and ranging (LiDAR) systems are widely adopted in the literature for its capability to provide 6-DOF to mobile vehicles and autonomous robots. LiDARs are very accurate systems however they are not suitable for low-cost positioning due to the expensive initial costs. Moreover, several other techniques from the radio frequency (RF) spectrum are utilized as multi-sensor systems such as cellular networks, WiFi, ultra-wideband (UWB) and Bluetooth. The cellular-based systems are very suitable for outdoor navigation applications while WiFi-based, UWB-based and Bluetooth-based systems are efficient in indoor positioning systems (IPS). However, to achieve reliable PVT estimations in multi-sensor GNSS navigation, optimal algorithms should be developed to mitigate the estimation errors resulting from non-line-of-sight (NLOS) GNSS situations. Examples of the most commonly used algorithms for trilateration-based positioning are Kalman filters, weighted least square (WLS), particle filters (PF) and many other hybrid algorithms by mixing one or more algorithms together. In this paper, the reviewed articles under study and comparison are presented by highlighting their motivation, the methodology of implementation, the modelling utilized and the performed experiments. Then they are assessed with respect to the published results focusing on achieved accuracy, robustness and overall implementation cost-benefits as performance metrics. Our summarizing survey assesses the most promising, highly ranked and recent articles that comprise insights into the future of GNSS technology with multi-sensor fusion technique.©2021 The Authors. Published by ION.fi=vertaisarvioimaton|en=nonPeerReviewed

Application of Machine Learning to GNSS/IMU Integration for High Precision Positioning on Smartphone

This paper describes our solution for the Google smartphone decimeter challenge (GSDC), which was held from May to August 2022. The GSDC is a competition for improving positioning accuracy of smartphones. The global navigation satellite system (GNSS) data from smartphones have lower signal levels and higher noise in GNSS observations compared to commercial GNSS receivers. Therefore, it is difficult to directly apply the existing GNSS high-precision positioning methods like precise point positioning (PPP) and real-time kinematic (RTK). The smartphones used to collect the raw GNSS data have multi-constellation, dual-frequency GNSS receivers, and Inertial Measurement Unit (IMU) sensors. Multi-sensor fusion technology has become very prominent for seamless navigation systems due to its complementary capabilities to GNSS positioning. In this work, we developed a machine learning (ML) based adaptive positioning approach to estimate the positions of the smartphone by utilizing post-processed kinematic (PPK) precise positioning techniques to process the GNSS datasets. The ML model is used to predict the driving paths (highways, tree-lined streets, or downtown areas). Depending on the predicted driving path, PPK technique uses the carrier phase to compute the user position using differential corrections from known GNSS base stations. We then use of the Rauch–Tung–Striebel (RTS) smoother, which consists of a forward pass Kalman Filter (KF) and a backward recursion smoother to achieve a loosely coupled integration of GNSS and IMU measurements for positioning estimation of the smartphone. We refer to this method as LC-GNSS/IMU/ML using ML based adaptive positioning (MAP) real-time kinematic (RTK) post-processing algorithm (MAP RTK). This method is validated using reference data from GNSS survey-grade receivers provided with the training datasets. The final validation of this proposed method is done on Kaggle.com, the host of the GSDC competition. Using the proposed method, we estimated the location of the smartphone and tackled the competition. The final public score was 2.61 m, while the final private score was 2.29 m.© 2022 The Authors. Published by the Institute of Navigation.fi=vertaisarvioitu|en=peerReviewed

A Novel Beam-Based Positioning Paradigm Via Opportunistic Signal of Future Massive MIMO LEO Satellite Constellations

LEO satellites are to be equipped with mMIMO in the near future. Introducing the beam-based positioning paradigm marks the advent of a new research topic that is concerned with exploiting 5G beamforming features of LEO satellites’ communications to infer UT position in Earth’s coordinates. Considering the passive SoO nature of the proposed method, the simulation results (MAE = 0.22 km with 100 satellites) are considered very promising, as positioning is possible even in poor GDOP situations. Furthermore, the proposed method does not require high-accuracy measurements at the receiver side, thus reducing costs and sensitivity to interference. As future work, more sophisticated simulation environment will be built to model mMIMO beamforming components, the communication channel, and various sources of noise and interference. In addition, LEO satellite orbits and realistic mega constellations will be modelled. Additional positioning algorithms will be developed and analyzed for enhanced accuracy and reliability. The proposed ideas within this article are currently under the process of IPR patenting (Finnish patent application number: 20235545).</p

Precision Positioning for Smart Logistics Using Ultra-Wideband Technology-Based Indoor Navigation: A Review

Logistics is an important driver for the competitiveness of industries and material supply. The development of smart logistics, powered by precise positioning and communication technologies can significantly improve the efficiency of logistics. The emerging technology of ultra-wideband (UWB) precision positioning has attracted significant attention throughout the previous decade owing to its promising capabilities over other radio frequency-based indoor localisation systems. In addition, UWB is characterised by large bandwidth and data rate, short message length, low transmission power and high penetration capability, which are all favourable for indoor positioning applications. However, UWB localisation technology faces several challenges that are somewhat similar to other technologies, such as mitigating errors that originate from non-line-of-sight (NLOS) situations and tackling signal interference in dense environments, and when required to operate in extreme conditions. This paper reviews the most recent advances made in UWB positioning systems over the last five years, with a focus on high-ranking articles. In addition to going through more conventional solutions to UWB challenges, modern solutions, which involve the use of machine learning and sensor data fusion, are discussed. We highlight the most promising findings of the recently implemented and foreseen UWB positioning systems by providing a summary of each reviewed article. Additionally, we address a major challenge that faces the UWB positioning technology: NLOS situations, focusing on some proposed remedies such as multi-sensor fusion and machine learning. As an application, this study introduces how UWB technology promotes smart logistics by offering indoor positioning to improve efficiencies in the delivery of goods from the source to the customer. Furthermore, it demonstrates the benefits of UWB technology for accurate positioning and tracking of both stationary and moving items, and machinery in an indoor logistics environment

Is LEO-Based Positioning with Mega-Constellations the Answer for Future Equal Access Localization?

Capital expenditures and indoor challenges are two of the main obstacles toward equal access positioning services worldwide. Global Navigation Satellite Systems (GNSS) do not function well indoors and in some challenging outdoor scenarios, such as dense forest canopies or hilly terrains rich in vegetation due, for example, to multipaths and low carrier-To-noise ratios. Terrestrial solutions nowadays can be used to complement GNSS, but they are typically costly to deploy with high coverage and do not offer equal access, for example in some low-revenue countries, in regions forbidding wireless 5G access due to health concerns, or in areas hard to reach with terrestrial infrastructure, such as deep jungle, desert areas with sandy dunes, and deep valleys/deep canyons. As many low Earth orbit (LEO) mega-constellations are emerging and their satellites are significantly closer to Earth than GNSS satellites, solutions based on LEO could complement GNSS. LEO-based communications are expected to be widespread in the next decade, and they will offer a global and easy-To-Access infrastructure, with the main costs to the end user coming from the receiver equipment. It is our assumption that future wireless receivers will support the integration of terrestrial and satellite infrastructure, and thus, the LEO-based positioning tasks could be mainly implemented as software add-ons on existing future receivers. Nevertheless, closer proximity to Earth does not automatically mean stronger received signals or acceptable positioning accuracy, especially when the carrier frequencies of the new LEO signals are higher than those in GNSS. Here, we present a feasibility study of LEO-based equal access localization by looking at the current opportunities, benefits, and challenges of LEO megaconstellations used as signals of opportunities. We show that there is an unharnessed potential of future LEO megaconstellations for equal access localization, although several challenges are still to be overcome.acceptedVersionPeer reviewe