INDOOR POSITIONING SYSTEM

Wireless indoor tracking systems face challenges due to environmental conditions and signal attenuation, affecting location accuracy, crucial in wireless sensor network (WSN) applications. Many tracking techniques rely on specific path loss models proposed by previous researches, but these models are susceptible to changes in environmental conditions, impacting estimation outcomes. In order to solve these problems, this paper propose adaptive tracking system using received signal strength indicator (RSSI) measurement parameter called as RIBATS. Adaptive in this system refers to the reliability of an algorithm for obtaining the accurate location without any path loss modelling at dynamic indoor environments. The enhancement of weighted centroid localization (eWCL) scheme calculates the location estimation only using RSSI data measurement without propagation characterisic determination. However, estimation result from eWCL still have high error at certain area. Hence, by defining a multiplier factor as adaptive scaled to the covariance matrix of EKF can eliminate distortion effects from eWCL called as adaptive scaled extended Kalman filter (ASEKF) algorithm. An effective variance estimation algorithm for adaptive indoor tracking system using eWCL and ASEKF combination achieve 0.82 meters mean square error (MSE) value with 55.67% error reduction. Then, without using multiplier scale factor at EKF algorithm only reduce previous eWCL at 3.78% with 1.78 meters MSE value.

Indoor Positioning Systems (IPSs) are used to estimate the position of mobile devices in indoor environments. Fingerprinting is the most used technique because of its higher accuracy. However, this technique requires a labor-intensive training phase that measures the Received Signal Strength Indicator (RSSI) at all Reference Points (RPs) locations. On the other hand, model-based IPSs use signal propagation models to estimate distances from RSSI. Thus, they do not require expensive training but result in higher positioning errors. In this work, we propose SynTra-IPS (Synthetic Training Indoor Positioning System), a hybrid approach between a fingerprint and a model-based IPS that uses synthetic, simulated datasets combined with data fusion techniques to eliminate the fingerprint collection cost. In our solution, we use the map of the scenario, with known anchor nodes' positions and the log-distance signal propagation model, to generate several synthetic, model-based, fingerprint training datasets. In the online phase of our solution, the positions estimated by the several synthetic datasets using K-Nearest Neighbors (KNN) are combined using data fusion techniques into a single, more accurate position. We evaluated the performance of our SynTra solution in a real-world, large-scale environment using mobile devices with Bluetooth Low Energy (BLE) technology, and we compared our solution to classic approaches from the literature. Our results show that SynTra can locate mobile devices with an average error of only 2.36 m while requiring no real-world environment training.

Indoor Positioning Systems (IPSs) are used to estimate the position of mobile devices in indoor environments. Fingerprinting is the most used technique because of its higher accuracy. However, this technique requires a labor-intensive training phase that measures the Received Signal Strength Indicator (RSSI) at all Reference Points (RPs) locations. On the other hand, model-based IPSs use signal propagation models to estimate distances from RSSI. Thus, they do not require expensive training but result in higher positioning errors. In this work, we propose SynTra-IPS (Synthetic Training Indoor Positioning System), a hybrid approach between a fingerprint and a model-based IPS that uses synthetic, simulated datasets combined with data fusion techniques to eliminate the fingerprint collection cost. In our solution, we use the map of the scenario, with known anchor nodes' positions and the log-distance signal propagation model, to generate several synthetic, model-based, fingerprint training datasets. In the online phase of our solution, the positions estimated by the several synthetic datasets using K-Nearest Neighbors (KNN) are combined using data fusion techniques into a single, more accurate position. We evaluated the performance of our SynTra solution in a real-world, large-scale environment using mobile devices with Bluetooth Low Energy (BLE) technology, and we compared our solution to classic approaches from the literature. Our results show that SynTra can locate mobile devices with an average error of only 2.36 m while requiring no real-world environment training.

The number of studies on the development of indoor positioning systems has increased recently due to the growing demands of the various location-based services. Inertial sensors available in commercial smartphones play an important role in indoor localization and navigation owing to their highly accurate localization performance. In this study, the inertial sensors of a smartphone, which generate distinct patterns for physical activities and action units (AUs), are employed to localize a target in an indoor environment. These AUs, (such as a left turn, right turn, normal step, short step, or long step), help to accurately estimate the indoor location of a target. By taking advantage of sophisticated deep learning algorithms, we propose a novel approach for indoor navigation based on long short-term memory (LSTM). The LSTM accurately recognizes physical activities and related AUs by automatically extracting the efficient features from the distinct patterns of the input data. Experime...

Indoor Positioning Systems lokaliserar människor och objekt inomhus med hjälp av minst tre kända referenspunkter. System för inomhuspositionering som använder kända referenspunkter kallas anchor-based lokalisering medan de som beräknar deras positioner själva kallas anchor-free lokalisering. Syftet med detta arbete är att utveckla en algoritm som är anpassad efter ett anchor-free lokaliseringssystem. Den ska vara oberoende av nätverksuppkopplingen, hårdvaran och hur avstånden mellan mottagare och sensorer beräknats. Utgångspunkten för algoritmen är enbart avstånd mellan en mottagare och tre sensorer vilket kan beskrivas som arbetets huvudsakliga problem. Algoritmen implementerades i Java med en simulering som återspeglar positioneringen i en perfekt miljö och sedan testas på en Android-applikation. Simuleringen tillåter användaren att rita ut flera mätpunkter som skapar en rutt. Dessa mätpunkter utnyttjas för att dynamiskt lokalisera referenspunkterna och mätpunkterna genom att hitta ett minsta avstånd mellan sensorerna. Dessa avstånd kan beskrivas som sidorna för en referenstriangel som möjliggör att ett koordinatsystem kan spännas upp. Resultatet av den empiriska studien visade en felmarginal mellan 0, 3 -6m utan signalstörningar, vilket inte var tillräckligt noggrant. Efter att algoritmen implementerats lades fokus på en korrigering som kan itereras igenom för att uppskatta bättre mätvärden för referenstriangeln. Korrigeringen gav positiva resultat med lägre felmarginal. Arbetet kan vidareutvecklas genom att implementeras i ett verkligt IPS-system och algoritmen kan förbättras genom att skapa utökade funktioner som kan hantera fler än tre beacons.

This paper develops a ZigBee indoor positioning scheme based on the location fingerprinting approach. The proposed scheme includes four workflows: (1) creating the location fingerprint table, (2) training the locating model using neural network (NN), (3) preprocessing data through the Signal-Index-Pair method, and (4) estimating the coordinate of the mobile target instantly. Testing results show that within the error distance of 5 meters, the NN locating model with the Signal-Index-Pair data preprocess method can increase the positioning precision by 17% compared with the original NN, in terms of the cumulative error probability (CEP). It also achieves 5% CEP higher than the k (k=5) nearest neighbor method and the weighted k (k=5) nearest neighbor method. Potential applications include patient tracking in hospitals, object tracking for factory monitoring, self-navigation of autonomous robots, and visitors monitoring in military buildings, and so on.

BACKGROUND: Hearing loss is a common disorder which is usually treated with hearing aids. The context of use, whether being outside, inside of large or small rooms, in a quiet or noisy environment, or talking with someone near or at a distance, often requires the hearing aid user to adjust the settings of the hearing aids manually in order to reach an optimal or even acceptable hearing experience. Knowing the context of a hearing aid user in the home setting, including the position within the home, could likely automate and optimize relevant hearing aid settings as needed. Several modern hearing aids are already equipped with Bluetooth Low Energy (BLE) antennas enabling their connectivity with smartphones and other compatible devices. This allows for the development of positioning and localization services using BLE. OBJECTIVES: The aim of this study was to investigate the potential of using the BLE radio signal of hearing aids to provide indoor localization and positioning support when used in combination with fixed radio access points in a home setting. METHODS: A research platform based on a single Oticon hearing aid and three embedded Raspberry Pi computers placed at three strategic locations in a home setting was developed. The Raspberry Pis used the two statistical learning methods K-Nearest Neighbors and Decision Trees for non-obtrusive detection, classification, and determination of the location of the hearing aid. The efficacy of the research platform was evaluated during two hearing experience relevant efficacy studies: 1) room classification and 2) proximity detection of hearing aid users to their conversation partners. RESULTS: Room classification results provided an accuracy of 88.79% and it was found feasible and reliable to differentiate whether the hearing aid user was within a comfortable conversation distance or not. CONCLUSION: These results open up for a wide range of audiological applications in indoor environments for supporting new context-aware services for improving the hearing experience of the users.

Visible Light Positioning (VLP) is considered to be one of the most promising candidates for future Location Based Service (LBSs). The traditional Received Signal Strength Indication (RSSI) based VLP system is highly sensitized with receiver's orientation. However, the assumption of the receiver's orientation fixed or perfectly known is not realistic in practice. Thus, a random angle between receiver and the horizontal plane inevitably appears among localization, which extremely affects positioning results. This paper proposed an Environment-Aware RSSI based positioning algorithm for VLP system. It enables to mainly eliminate random angle interference without the help of extra equipment. The positioning error caused by random angle was theoretically analyzed and validated by numerical simulation. Moreover, a demonstration on a real 5G New Radio (NR) signal platform was implemented to verify the feasibility of our algorithm. According to the results, the average positioning error declined by 73.28%.

This paper addresses reliable and accurate indoor localization using inertial sensors commonly found on commodity smartphones. We believe indoor positioning is an important primitive that can enable many ubiquitous computing applications. To tackle the challenges of drifting in estimation, sensitivity to phone position, as well as variability in user walking profiles, we have developed algorithms for reliable detection of steps and heading directions, and accurate estimation and personalization of step length. We've built an end-to-end localization system integrating these modules and an indoor floor map, without the need for infrastructure assistance. We demonstrated for the first time a meterlevel indoor positioning system that is infrastructure free, phone position independent, user adaptive, and easy to deploy. We have conducted extensive experiments on users with smartphone devices, with over 50 subjects walking over an aggregate distance of over 40 kilometers. Evaluation results showed our system can achieve a mean accuracy of 1.5m for the in-hand case and 2m for the in-pocket case in a 31m×15m testing area.

Indoor positioning systems (IPS) are crucial for determining the precise position of objects or people within a confined space. In this research work, we present a comprehensive system design that uses the Cartesian plane within an office to predict the twodimensional position within a 16.9 square meter area by leveraging machine learning algorithms. For data collection, we use three wireless routers with open-source distribution software (Open-WRT), one ESP32 microcontroller programmed and a tablet to capture the received signal strength indicator (RSSI) at each position within the office. RSSI measurements and open-source software make the system cost-effective and easy to deploy in various scenarios. For comparison purposes, we use four datasets containing RSSI measurements, i.e. two obtained at the user´s device (i.e. ESP32 and tablet) and the other two measured at the routers for the two types of user´s device. The proposed IPS has the potential to provide more accurate location information, making it a powerful tool for several applications such as indoor navigation, asset tracking and locationbased services. This work focuses on selecting the most appropriate machine learning model to predict the 2D position inside an office taking into the type of datasets. Simulation results show that the dataset with RSSIs measured at the microcontroller ESP32 presents the best performance, i.e. lower RMSE and higher R-squared.

The pandemic period caused by the coronavirus disease causes restrictions on the number of users in a room to minimize the spread, for example, libraries. The library is a room area that can cause virus transmission when crowds of visitors. A system is needed to provide information regarding the number of library visitors. Utilizing a network (wifi transmitter) available in the library can provide information on the number of visitors based on the number of network users in a room. Utilization of the network (wifi transmitter) can be in the form of a positioning system by utilizing the wifi signal beam received by the user in RSSI (Received Signal Strength Indicator). To calculate the estimated user position, a trilateration method is used based on the placement of three wifi transmitters. The library visitor positioning system consists of users as wifi transmitter users and admins. At the X-coordinate, the measuring point with a low deviation of the measurement value is indicated by points C and E with a value of 0.1. The large deviation of the measurement value is indicated by the P point of 4.5. At the Y coordinate, point O has a low deviation value of 1.25, and the largest deviation value is at point M of 5.6. The accuracy of the X coordinate shows a value of 97.647% and at the Y coordinate of 96.57%. Both coordinates have good accuracy for the points used as position measurements.

In this paper, we study issues associated with the implementation of a real-time WiFi localization system for an indoor environment. This system utilizes smart antennas to receive signal strength from a mobile target (access point) and send the signal strength information to a data processing station. This information is combined to find the direction of arrival of the signal and triangulate the mobile target position. No prior radio-frequency finger-printing is required, thus reducing the computational complexity. We present experimental results to illustrate the performance and accuracy of the localization system for an indoor environment set-up 1 .

Ultra-wideband (UWB) technology has witnessed tremendous development and advancement in the past few years. Currently available UWB transceivers can provide high-precision time-of-flight measurements which corresponds to range measurements with theoretical accuracy of few centimetres. Position estimation using range measurement is determined by measuring the ranges from a rover or a dynamic node, to a set of anchor points with known positions. However, building a flexible and accurate indoor positioning system requires more than just accurate range measurements. The performance of indoor positioning system is affected by the number and the configuration of the anchor points used, along with the accuracy of the anchor positions. This paper introduces LocSpeck, a dynamic ad-hoc positioning system based on the DW1000 UWB transceiver from Decawave. LocSpeck is composed of a set of identical nodes communicating on a common RF channel, forming a fully or partially connected network where the positioning algorithm run on each node. Each LocSpeck node could act as an anchor or a rover, and the role could change dynamically during the same session. The number of nodes in the network could change dynamically, since the firmware of LocSpeck supports adding and removing nodes on-the-fly. The paper compares the performance of the LocSpeck system with commercially available off-the-shelf UWB positioning system. Different operating scenarios are considered when evaluating the performance of the system, including cases where collaboration between the two systems is considered.

Local positioning systems (LPS) are attracting much attention, both commercially and scientifically, as they represent a natural extension of global positioning systems to indoor scenarios. Amongst all the viable wireless technologies used in this context, WiFi and Bluetooth are the most widely extended. These technologies are also chosen for their simplicity, low cost and integration into mobile devices. The appearance of the new Bluetooth specification, named Bluetooth low energy (BLE), and the emergence of new popular devices that incorporate it, opens the door to new LPS wireless solutions based on BLE. Here, we evaluate the viability of BLE for indoor positioning scenarios. In addition, we develop a framework to analyse, understand and help migrate previous LPS systems, based on other technologies, to BLE. We show experimentally that, with proper configuration of the BLE devices, great performance can be obtained in terms of discovery time and energy consumption.

Indoor positioning systems (IPS) are crucial for determining the precise position of objects or people within a confined space. In this research work, we present a comprehensive system design that uses the Cartesian plane within an office to predict the twodimensional position within a 16.9 square meter area by leveraging machine learning algorithms. For data collection, we use three wireless routers with open-source distribution software (Open-WRT), one ESP32 microcontroller programmed and a tablet to capture the received signal strength indicator (RSSI) at each position within the office. RSSI measurements and open-source software make the system cost-effective and easy to deploy in various scenarios. For comparison purposes, we use four datasets containing RSSI measurements, i.e. two obtained at the user´s device (i.e. ESP32 and tablet) and the other two measured at the routers for the two types of user´s device. The proposed IPS has the potential to provide more accurate location information, making it a powerful tool for several applications such as indoor navigation, asset tracking and locationbased services. This work focuses on selecting the most appropriate machine learning model to predict the 2D position inside an office taking into the type of datasets. Simulation results show that the dataset with RSSIs measured at the microcontroller ESP32 presents the best performance, i.e. lower RMSE and higher R-squared.

For location in indoor environments, the fingerprinting technique seems the most attractive one. It gives higher localization accuracy than the parametric technique because of the existence of multipath propagation and fast fading phenomena that are difficult to model. This paper introduces a novel positioning system based on wireless the IEEE802.15.4/ZigBee standard and employs Support Vector Machines (SVMs). The system is cost-effective since it works with real deployed IEEE 802.15.4/ZigBee TM sensors nodes. The whole system requires minimal setup time, which makes it readily available for realworld applications. The resulting algorithm demonstrates a superior performance compared to the conventional algorithms.

Nodes localization in Wireless Sensor Networks (WSN) has arisen as a very challenging problem in the research community. Most of the applications for WSN are not useful without a priori known nodes positions. One solution to the problem is by adding GPS receivers to each node. Since this is an expensive approach and inapplicable for indoor environments, we need to find an alternative intelligent mechanism for determining nodes location. In this paper, we propose our cluster-based approach of multidimensional scaling (MDS) technique. Our initial experiments show that our algorithm outperforms MDS-MAP[8], particularly for irregular topologies in terms of accuracy.

Wireless positioning determination has received increased attention during the past few years. Several wireless applications have been envisaged when mobile terminal location can be determined with sufficient accuracy at any time. In this chapter, we attempt to identify the various indoor and outdoor mobile and wireless positioning techniques that can be used for the provision of location based services. In order to maximize the benefits of this research in the area of positioning technologies, we propose a classification with detailed analysis and evaluation of these techniques based on the accuracy that is needed for various mobile location-based services.

The continuous improvement in the development in the field of communication networks and mobile computing, location based service (LBS) has become very popular in recent years. For outdoor positioning service the Global Positioning Systems (GPS) are the earliest widely used modern systems. In GPS technology Satellite signals cannot penetrate in indoor environment since they are blocked by building obstructions thus satellite signal cannot provide good accuracy in indoor environments due to lack of LoS(Line Of Sight). On doing research it is being observed that in the near future, indoor positioning will gain more demand as people spend more than 70% of their lives in indoor environment. A large variety of technologies have been designed for dealing with the problem since the indoor environments are very difficult to track .The paper also provide brief description on various indoor wireless tracking measurements, methodologies and technologies. The paper illustrates the theoretical points, the main tools, and the most promising technologies for indoor tracking infrastructure.

Visible Light Communication (VLC) is a novel optical wireless communication technology which uses Light Emitting Diodes (LEDs) and Photodiodes for coherent detection and very-high-data rate data communication system. The stringent Line of Sight (LoS) requirement in VLC makes it very suitable for Indoor Positioning System (IPS), to be used for autonomous and smart city infrastructure. The current work aims to implement a real time IPS system using VLC link in Network Simulator (NS-3). The VLC module is implemented by modelling real-time attributes of LEDs, optical channel, and the photodiodes. The localization is carried out using trilateration schemes which measures the Received Signal Strength Indication (RSSI) and the Time Difference of Arrival (TDoA) for position estimation of the target. The project is further extended to obtain a comparative analysis between VLC link and other existing technology, Wi-Fi, as far as positioning accuracy and other important performance metrics are concerned. The simulation results show significant improvement for the VLC link over the Wi-Fi link specially for the TDoA scheme accompanied by increasing number of beacon nodes.

Visible light communication (VLC), have gain much interest in past decade in indoor communication. In particular, it is use for illumination and communication simultaneously. White light emitting diodes which are used as a transmitter for VLC are potential candidates for localization. Conventionally global positioning system (GPS) has very poor performance in indoor environments. Assisted global positioning system (AGPS) has positioning accuracy in few

To support performance requirements for smart services in 6G, user positioning is a crucial component. Indoor user position and orientation estimation based on Light Fidelity (LiFi) system is considered as a promising technology, due to its high precision, along with its ease of installation. The main bottleneck of user position and orientation estimation in LiFi is a non-linearity between the metrics, such as the received signal strength (RSS), position and orientation. A deep learning-based estimation methodology holds promise for addressing this issue, because it can learn complex propagation features dependent on user position and orientation. To fully capitalize on this advantage in the time-domain, we propose utilizing both time-series RSS and its received time, i.e. time-of-arrival (ToA) fingerprints, along with a novel neural network architecture named Deep RSS-ToA Fusion Network (DRTFNet). Simulation results demonstrate that the proposed DRTFNet achieves positioning accuracy of less than 3 cm and orientation accuracy of less than 3 degrees, outperforming both the basic Convolutional Neural Network (CNN) architecture using only RSS data and other baseline systems with more light sources.

With the advent and advancements in the wireless technologies, Wi-Fi ngerprinting-based Indoor Positioning System (IPS) has become one of the most promising solutions for localization in indoor environments. Unlike the outdoor environment, the lack of line-of-sight propagation in an indoor environment keeps the interest of the researchers to develop ef cient and precise positioning systems that can later be incorporated in numerous applications involving Internet of Things (IoTs) and green computing. In this paper, we have proposed a technique that combines the capabilities of multiple algorithms to overcome the complexities experienced indoors. Initially, in the database development phase, Motley Kennan propagation model is used with Hough transformation to classify, detect, and assign different attenuation factors related to the types of walls. Furthermore, important parameters for system accuracy, such as, placement and geometry of Access Points (APs) in the coverage area are also considered. New algorithm for deployment of an additional AP to an already existing infrastructure is proposed by using Genetic Algorithm (GA) coupled with Enhanced Dilution of Precision (EDOP). Moreover, classi cation algorithm based on k-Nearest Neighbors (k-NN) is used to nd the position of a stationary or mobile user inside the given coverage area. For k-NN to provide low localization error and reduced space dimensionality, three APs are required to be selected optimally. In this paper, we have suggested an idea to select APs based on Position Vectors (PV) as an input to the localization algorithm. Deducing from our comprehensive investigations, it is revealed that the accuracy of indoor positioning system using the proposed technique unblemished the existing solutions with signi cant improvements.

The latest advancements in visible light communication (VLC) technology have greatly facilitated the evolution of wireless sensing based on visible light, commonly referred to as visible light sensing (VLS) systems. Similar to other wireless sensing technologies, these systems can be categorized into two primary types: device-based (or active) and devicefree (or passive), depending on whether targets, such as people or objects of interest, are equipped with an optical receiver (e.g., a photodiode or camera). By reusing the existing Lightemitting diode (LED) lighting infrastructure as transmitters in indoor environments, device-free VLS (DF-VLS) systems can help address the growing energy demands of ubiquitous sensing and communication in Sixth-Generation Wireless Networks (6G). This survey meticulously explores the landscape of DF-VLS technology, proposing a redefinition of the classification of VLS systems and delving into the existing literature in this domain. We conduct a comprehensive analysis of various DF-VLS sensing techniques, focusing on their diverse applications in the Internet of things (IoT). By closely examining the integration and utility of DF-VLS in IoT environments, we highlight the potential of this technology. Furthermore, we underscore critical open challenges that demand attention for the effective development of efficient DF-VLS systems. Finally, the paper outlines a future roadmap for DF-VLS systems, shedding light on potential directions this field may embark upon.

Avaliação da composição iónica do meio como barreira química contra a invasora Artemia franciscana 5 A única forma de esta dissertação ficar completa, só poderia ser agradecendo a quem, de alguma forma, me ajudou ou influenciou no percurso percorrido até hoje. À Professora Doutora Natividade Vieira, expresso o meu mais sincero agradecimento pela oportunidade que me concedeu de, já desde a minha licenciatura, a ter como orientadora. São já alguns anos de amizade, partilha, cooperação e mútua compreensão. Obrigado professora. Aos Professores Doutores Nuno Formigo e António Paulo Carvalho, agradeço as longas conversas, o apoio prestado e toda a boa disposição com que sempre me receberam. Agradeço aos percalços que foram aparecendo pelo caminho. Com eles aprendi a lidar melhor com as contrariedades, ultrapassando-os sem lhe dar qualquer valor. Por fim, agradeço aos que estão sempre em primeiro. Agradeço a toda a minha família, que, de uma maneira ou de outra, sempre me ajudou na minha vida académica. Agradeço-te pai, por todo o apoio que sempre me deste. Estiveste sempre ao meu lado quando eu mais precisei, e, tenho a certeza, uma das minhas qualidades aprendia contigo: obrigado por me ensinares a ser responsável e organizado. Obrigado mãe. O teu amor e amizade sempre me ajudaram a ter força para continuar este longo caminho. A minha perspicácia devo-a a ti, mas isso já tu sabes, não é? Obrigado pais pelo orgulho que sempre mostraram ter. Também eu me sinto orgulhoso…de vos ter como pais. A ti, irmã, agradeço-te do fundo do coração. Sempre me deste força e conselhos quando mais precisei, mostrando-me sempre o orgulho que tens por mim. Obrigado "sensata". Ao meu afilhado. Mesmo sendo o membro mais novo da família, consegues sempre arrancar-me um sorriso quando eu mais preciso, e espero, um dia, poder retribuir-te da mesma maneira. Obrigado miúdo. À Filipa. Porque estás sempre ao meu lado. Acompanhaste-me ao longo de todo este percurso, ajudando-me sempre com o teu amor e carinho, dando-me força para ultrapassar todos os obstáculos. Tu sabes bem o quanto significas para mim. Obrigado por seres que és. A todos vós, Amigos, dedico este trabalho. FCUP Avaliação da composição iónica do meio como barreira química contra a invasora Artemia franciscana 7 Resumo A Artemia é um pequeno crustáceo que pertence à família Artemiidae, à ordem Anostraca e à subclasse Branchiopoda. Neste momento, estão descritas 7 espécies de Artemia, 6 bissexuais e uma partenogenética. A sua presença em praticamente todo o mundo é bem representativa da sua alta adaptabilidade e tolerância. A sua distribuição global deve-se também ao facto de conseguir habitar ambientes hipersalinos, pois nestes locais a ocorrência de predadores é reduzida, senão nula. Atualmente, na Península Ibérica é possível encontrar tanto espécie bissexual Artemia franciscana, como a espécie partenogenética. Em Portugal, apenas existe Artemia parthenogenetica como espécie autóctone, no entanto, neste momento está ameaçada de extinção, devido à introdução da espécie invasora, Artemia franciscana, que existe em quase todos os biótopos, principalmente no Sul do país. É então necessário preservar a biodiversidade nos locais onde ainda é possível encontrar a espécie autóctone, e para isso é indispensável conhecer a composição do meio ambiente onde Artemia habita. Assim, este trabalho incide sobre o estudo de uma possível barreira química provocada pela composição iónica da água na salina de Rio Maior, como impeditiva da invasão pela espécie franciscana. O principal objetivo do trabalho foi determinar quais os efeitos causados pela composição iónica da água na população de Artemia autóctone e na população invasora. Os resultados obtidos demonstram que a composição iónica da água da salina de Rio Maior poderá mesmo atuar como barreira química contra a entrada da espécie invasora, abrindo novas perspetivas de investigação, de forma a tentar perceber melhor o mecanismo do impedimento à invasão. Palavras-chave: Composição iónica; Barreira química; Artemia; Cálcio; Magnésio. FCUP Avaliação da composição iónica do meio como barreira química contra a invasora Artemia franciscana

Legislative and market initiatives are requiring that gestating sows move from individual housing to group settings. Little information is known about coping styles of individual sows in these more socially complex environments and thus the impact of different behavioral strategies on sow reproductive success and efficiency was investigated. The movements of 70 sows during periods of reintroduction into large pen gestational housing following insemination was measured with a commercially available system that tracked animal location and accelerations. Principle component analysis (PCA) was used to establish composite variables characterizing each animal's behavioral response to social reintroduction and revealed the presence of two new variables accounting for over 60% of the variance in behaviors: one pertaining to total movement and the other pertaining to social dominance/rapid movements. Component scores of total movement predicted measures of reproductive successes whereas social dominance/rapid movements predicted piglet birthweight. These findings suggest that different coping styles as measured by an automated, non-invasive, real time tracking system are correlated with the productivity of sows housing in socially complex settings.

Legislative and market initiatives are requiring that gestating sows move from individual housing to group settings. Little information is known about coping styles of individual sows in these more socially complex environments and thus the impact of different behavioral strategies on sow reproductive success and efficiency was investigated. The movements of 70 sows during periods of reintroduction into large pen gestational housing following insemination was measured with a commercially available system that tracked animal location and accelerations. Principle component analysis (PCA) was used to establish composite variables characterizing each animal's behavioral response to social reintroduction and revealed the presence of two new variables accounting for over 60% of the variance in behaviors: one pertaining to total movement and the other pertaining to social dominance/rapid movements. Component scores of total movement predicted measures of reproductive successes whereas social dominance/rapid movements predicted piglet birthweight. These findings suggest that different coping styles as measured by an automated, non-invasive, real time tracking system are correlated with the productivity of sows housing in socially complex settings.

Locating objects or people in an indoor environment quickly, accurately and at low cost is a great need nowadays and in different scenarios. Some examples are the location of products in a warehouse or the quick location of patients, medical personnel or equipment in a hospital. A location system is necessary for health care, home care, stock control or inventory. In this context, this paper presents the design and implementation of a low-cost centralized indoor location system that uses BLE technology (Bluetooth Low Energy) together with the particle filter algorithm. Experimental results show that the system can achieve an accuracy of 1.8 m, at best, with an accuracy of 74% within 3 m. The cost of the infrastructure goes hand in hand with the number of objects or people to be located.

Locating objects or people in an indoor environment quickly, accurately and at low cost is a great need nowadays and in different scenarios. Some examples are the location of products in a warehouse or the quick location of patients, medical personnel or equipment in a hospital. A location system is necessary for health care, home care, stock control or inventory. In this context, this paper presents the design and implementation of a low-cost centralized indoor location system that uses BLE technology (Bluetooth Low Energy) together with the particle filter algorithm. Experimental results show that the system can achieve an accuracy of 1.8 m, at best, with an accuracy of 74% within 3 m. The cost of the infrastructure goes hand in hand with the number of objects or people to be located.

In this contribution a DC magnetic signal based positioning system is presented which shows no special multipath effects and has excellent characteristics for penetrating various obstacles. The proposed system allows high ranging resolution in indoor environments by using adaptive signal processing algorithms.

Indoor localization has become increasingly important due to its wide-ranging applications in indoor navigation, emergency services, the Internet of Things (IoT), and accessibility for individuals with special needs. Traditional localization systems often require extensive calibration to achieve high accuracy. We introduce UbiLoc, an innovative, calibration-free indoor localization system that leverages Apple AirTags in a novel way to localize users instead of tracking objects. By utilizing the ubiquitous presence of AirTags and their Ultra-Wideband (UWB) technology, UbiLoc achieves centimeterlevel accuracy, surpassing traditional WiFi and Bluetooth Low Energy (BLE) systems. UbiLoc addresses key challenges, including ranging errors caused by multipath and noise, through a novel AirTag selection technique. The system operates without the need for manual calibration, ensuring robustness and self-maintenance. Deployed on various Apple devices and tested in real-world environments, UbiLoc achieved median localization errors as low as 26 𝑐𝑚 in a campus building and 31.5 𝑐𝑚 in an apartment setting. These results demonstrate that UbiLoc is the first system to offer reliable, cm-level accuracy using widely available technology without requiring calibration, making it a promising solution for next-generation indoor localization systems.

In the following we discuss how a high precision, in-door positioning system (UNIZAR positioning system) can be part of a general positioning system to fit into different application areas depending on different needs and requirements for precision. Specific focus is put on applications for learning and training contexts. We argue that many innovative user interfaces for learning are based on digital manipulatives, which could be substantially enhanced by positioning augmentation.

In this study, we present a novel Visible Light Positioning (VLP) method to reduce the localization error in an indoor environment. Machine Learning (ML) methods including Decision Tree (DT), Support Vector Machine (SVM), and Neural Networks (NNs) are used in combination with the LED Received Signal Strength (RSS) and the angle of a steerable laser. Zemax optics studio simulator is used to build a real indoor scene. Orange data mining software is utilized to apply ML techniques. Our numerical findings show that the suggested system outperforms the other RSS Visible Light Communication (VLC)-based models by reducing the localization error by more than 90% in some areas.

Improvement of Visible Light Communication (VLC) technology and the presence of the illumination encouraged the researches to research in VLC-based indoor positioning. Recently, researchers published several papers on VLC based localization systems, however there are not a lot of survey papers in this issue. In this paper, a deep analysis survey of VLC based localization systems is introduced. Researches from pioneering papers to the stateof-the-art in the field of VLC based position are analyzed and classified based on the localization techniques, types of transmitters and receivers, and multiplexing techniques. Different from other surveys, we analyze the accuracy of VLC based localization system in the practical and simulation environments.

Widespread adoption of indoor positioning systems based on WiFi fingerprinting is at present hindered by the large efforts required for measurements collection during the offline phase. Two approaches were recently proposed to address such issue: crowdsourcing and RSS radiomap prediction, based on either interpolation or propagation channel model fitting from a small set of measurements. RSS prediction promises better positioning accuracy when compared to crowdsourcing, but no systematic analysis of the impact of system parameters on positioning accuracy is available. This paper fills this gap by introducing ViFi, an indoor positioning system that relies on RSS prediction based on Multi-Wall Multi-Floor (MWMF) propagation model to generate a discrete RSS radiomap (virtual fingerprints). Extensive experimental results, obtained in multiple independent testbeds, show that ViFi outperforms virtual fingerprinting systems adopting simpler propagation models in terms of accuracy, and allows a sevenfold reduction in the number of measurements to be collected, while achieving the same accuracy of a traditional fingerprinting system deployed in the same environment. Finally, a set of guidelines for the implementation of ViFi in a generic environment, that saves the effort of collecting additional measurements for system testing and fine tuning, is proposed.

Visible light positioning (VLP) is a cost-effective solution to the increasing demand for real-time indoor positioning. However, owing to high computational costs and complicated image processing procedures, most of the existing VLP systems fail to deliver real-time positioning ability and better accuracy for image sensor-based large-area indoor environments. In this study, an effective method is proposed to receive coordinate information from multiple light-emitting diode (LED) lights simultaneously. It provides better accuracy in large experimental areas with many LEDs by using a smartphone-embedded image sensor as a terminal device and the existing LED lighting infrastructure. A flicker-free frequency shift on–off keying line coding modulation scheme was designed for the positioning system to ensure a constant modulated frequency. We tested the performance of the decoding accuracy with respect to vertical and horizontal distance, which utilizes a rolling shutter mechanism of a co...

The global population of older adults has increased, leading to a rising number of older adults in nursing homes without
adequate care. This study proposes a smart wearable device for detecting and classifying abnormal behaviour in older adults
in nursing homes. The device utilizes artificial intelligence technology to detect abnormal movements through behavioural
data collection and target positioning. The intelligent recognition system and hardware sensors were tested using cloud
computing and wireless sensor networks (WSNs), comparing their performance with other technologies through
simulations. A triple-axis acceleration sensor collected motion behaviour data, and Zigbee enabled the wireless transfer of
the sensor data. The Backpropagation (BP) neural network detected and classified abnormal behaviour based on simulated
sensor data. The proposed smart wearable device offers indoor positioning, detection, and classification of abnormal
behaviour. The embedded intelligent system detects routine motions like walking and abnormal behaviours such as falls. In
emergencies, the system alerts healthcare workers for immediate safety measures. This study lays the groundwork for
future AI-based technology implementation in nursing homes, advancing care for older adults.

This paper presents a study on identifying different individuals using accelerometer data from a smartphone presented on their walking patterns. The identifier algorithm was trained and evaluated in an experiment with twenty human subjects including 12 males and 8 females in real-world conditions. Various classifiers were tested using descriptive statistical features. Our model recognizes patterns and regularities in human walking movement using limited accelerometer data captured from a mobile device. The accelerometer data are decomposed into gravitational acceleration and body motion acceleration using a low pass filter, and then extracted features of those acceleration components is fed to multi-class classifiers. The proposed model is developed based on an informative and stable body acceleration feature set that gives rise to a high performance multi-class identification model. The results show that using the Decision tables as our classification method enables the identificat...

Indoor positioning systems (IPS) use sensors and communication technologies to locate objects in indoor environments. IPS are attracting scientific and enterprise interest because there is a big market opportunity for applying these technologies. There are many previous surveys on indoor positioning systems; however, most of them lack a solid classification scheme that would structurally map a wide field such as IPS, or omit several key technologies or have a limited perspective; finally, surveys rapidly become obsolete in an area as dynamic as IPS. The goal of this paper is to provide a technological perspective of indoor positioning systems, comprising a wide range of technologies and approaches. Further, we classify the existing approaches in a structure in order to guide the review and discussion of the different approaches. Finally, we present a comparison of indoor positioning approaches and present the evolution and trends that we foresee.

Positioning systems are used in a wide range of applications which require determining the position of an object in space, such as locating and tracking assets, people and goods; assisting navigation systems; and mapping. Indoor Positioning Systems (IPSs) are used where satellite and other outdoor positioning technologies lack precision or fail. Ultra-WideBand (UWB) technology is especially suitable for an IPS, as it operates under high data transfer rates over short distances and at low power densities, although signals tend to be disrupted by various objects. This paper presents a comprehensive study of the precision, failure, and accuracy of 2D IPSs based on UWB technology and a pseudo-range multilateration algorithm using Time Difference of Arrival (TDoA) signals. As a case study, the positioning of a 4 × 4 m 2 area, four anchors (transceivers), and one tag (receiver) are considered using bitcraze's Loco Positioning System. A Cramér-Rao Lower Bound analysis identifies the convex hull of the anchors as the region with highest precision, taking into account the anisotropic radiation pattern of the anchors' antennas as opposed to ideal signal distributions, while bifurcation envelopes containing the anchors are defined to bound the regions in which the IPS is predicted to fail. This allows the formulation of a so-called flyable area, defined as the intersection between the convex hull and the region outside the bifurcation envelopes. Finally, the static bias is measured after applying a built-in Extended Kalman Filter (EKF) and mapped using a Radial Basis Function Network (RBFN). A debiasing filter is then developed to improve the accuracy. Findings and developments are experimentally validated, with the IPS observed to fail near the anchors, precision around ±3 cm, and accuracy improved by about 15 cm for static and 5 cm for dynamic measurements, on average.

Indoor location systems can identify and locate persons in real time. The use of these systems requires the management of wireless sensor networks that can position users. WIFI networks can serve as an alternative to technologies such as ZigBee since their infrastructure can be more easily deployed in most cases. This study presents an indoor location system that uses WIFI networks and various sensors such as accelerometers and compasses to locate individuals.

Geographical location solutions have a wide diversity of applications, ranging from emergency services to access to tourist and entertainment services. GPS (Global Positioning System) is the most widely used system for outdoor areas. Since it requires a direct line of sight with the satellites, it cannot be used indoors. For indoor positioning, the most commonly used technology to calculate the position of mobile devices is Wi-Fi. In this article, we present a positioning tool called WiFiPos based on Wi-Fi signal processing. This tool uses different variations of the Fingerprint technique to analyze the performance and accuracy of the system both indoors and outdoors.

In this study, we present a novel Visible Light Positioning (VLP) method to reduce the localization error in an indoor environment. Machine Learning (ML) methods including Decision Tree (DT), Support Vector Machine (SVM), and Neural Networks (NNs) are used in combination with the LED Received Signal Strength (RSS) and the angle of a steerable laser. Zemax optics studio simulator is used to build a real indoor scene. Orange data mining software is utilized to apply ML techniques. Our numerical findings show that the suggested system outperforms the other RSS Visible Light Communication (VLC)-based models by reducing the localization error by more than 90% in some areas.

Indoor positioning systems has become popular in this era where it is a network of devices used to locate people or object especially in indoor environment instead of satellite-based positioning. The satellite-based positioning global positioning system (GPS) signal is affected and loss incurred by the wall of the building causes the GPS lack of precision which leads to large positioning error. As a solution to the indoor area coverage problem, an indoor positioning based on bluetooth low energy (BLE) and long range (LoRa) system utilising the receive signal strength indicator (RSSI) is proposed, designed and tested. In this project, the prototype of indoor positioning system is built using node MCU ESP 32, LoRa nodes and BLE beacons. The node MCU ESP 32 will collect RSSI data from each BLE beacons that deployed at decided position around the area. Then, linear regression algorithm will be used in distance estimation. Next, particle filteris implemented to overcome the multipath fad...