Context Aware Handover Algorithms For Mobile Positioning Systems

This work proposes context aware handover algorithms for mobile positioning systems. The algorithms perform handover among positioning systems based on important contextual factors related to position determination with efficient use of battery. The proposed solution is implemented in the form of an Android application named Locate@nav6. The performance of the proposed solution was tested in selected experimental areas. The handover performance was compared with other existing location applications. The proposed solution performed correct handover among positioning systems in 95 percent of cases studied while two other applications performed correct handover in only 50 percent of cases studied. Battery usage of the proposed solution is less than one third of the battery usage of two other applications. The analysis of the positioning error of the applications demonstrated that, the proposed solution is able to reduce positioning error indirectly by handing over the task of positioning to an appropriate positioning system. This kept the average error of positioning below 42.1 meters for Locate@nav6 while the average error for two other applications namely Google Latitude and Malaysia maps was between 92.7 and 171.13 meters

WiFiPoz -- an accurate indoor positioning system

Location based services are becoming an important part of life. Wide adoption of GPS in mobile devices combined with cellular networks has practically solved the problem of outdoor localization needs. The problem of locating an indoor user has being studied only recently. Much research contributed to the innovative concept of an indoor positioning system. By analyzing different technologies and algorithms, this thesis concluded that, considering a trade-off between accuracy and cost, a Wi-Fi based Fingerprint method is proved to be the most promising approach to determine the location of a mobile device. However, the Fingerprint method works in two phases-an offline training phase (collection of Received Signal Strength signatures) and an online phase in which data from the first phase is used to determine the current position of a mobile user. The number of training points in a certain area has a direct impact on the accuracy of the system. As a result, the offline phase is a tedious and cumbersome process and the positioning systems are only as accurate as the offline training phase has been detailed. Moreover, the offline phase must be repeated every time a change in the environment occurs. To avoid these limitations, we focus on improving the accuracy of the indoor positioning system, without increasing the number of training points. This thesis presents a Wi-Fi based system for locating a user inside a building. The system is named WiFiPoz, which means Wi-Fi positioning system based on the zoning method. WiFiPoz has a novel approach to Fingerprint method that incorporates Propagation and zoning methods. Experimental results show that WiFiPoz is highly efficient both in accuracy and costs. Compared to traditional Fingerprint methods, with the optimization of the accuracy of the location estimation, WiFiPoz reduces the number of training points. This feature makes it possible to quickly adapt to changes in the environment. In order to explore another possible solution, this thesis also developed, implemented and tested an indoor positioning system named GIS (Geometric Information based positioning System), which is based on a model proposed by another researcher. Several experiments were run in the offline phase and results were compared between the traditional Fingerprint method, GIS and proposed WiFiPoz. We concluded that WiFiPoz is a more efficient and simple way to increase the accuracy of the location determination with fewer training points --Document

Context Aware Handover Algorithms for Mobile Positioning Systems

Abstract: This work proposes context aware handover algorithms for mobile positioning systems. The algorithms perform handover among positioning systems based on important contextual factors related to position determination with efficient use of battery. The proposed solution which consists of the algorithms is implemented in the form of an Android application named Locate@nav6. The performance of the proposed solution was tested in selected experimental areas. The handover performance was compared with other existing location applications. The proposed solution performed correct handover among positioning systems in 95% of cases studied while two other applications performed correct handover in only 50% of cases studied. Battery usage of the proposed solution is less than one third of the battery usage of two other applications. The analysis of the positioning error of the applications demonstrated that, the proposed solution is able to reduce positioning error indirectly by handing over the task of positioning to an appropriate positioning system. This kept the average error of positioning below 42.1 meters for Locate@nav6 while the average error for two other applications namely Google Latitude and Malaysia maps was between 92.7 and 171.13 meters

A survey of deep learning approaches for WiFi-based indoor positioning

One of the most popular approaches for indoor positioning is WiFi fingerprinting, which has been intrinsically tackled as a traditional machine learning problem since the beginning, to achieve a few metres of accuracy on average. In recent years, deep learning has emerged as an alternative approach, with a large number of publications reporting sub-metre positioning accuracy. Therefore, this survey presents a timely, comprehensive review of the most interesting deep learning methods being used for WiFi fingerprinting. In doing so, we aim to identify the most efficient neural networks, under a variety of positioning evaluation metrics for different readers. We will demonstrate that despite the new emerging WiFi signal measures (i.e. CSI and RTT), RSS produces competitive performances under deep learning. We will also show that simple neural networks outperform more complex ones in certain environments

Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap

Indoor positioning is a core enabler for various 5G identity and context-aware applications requiring precise and real-time simultaneous localisation and mapping (SLAM). In this work, we propose a K-nearest neighbours and deep neural network (K-DNN) algorithm to improve 3D indoor positioning. Our implementation uses a novel data-augmentation concept for the received signal strength (RSS)-based fingerprint technique to produce a 3D fused hybrid. In the offline phase, a machine learning (ML) approach is used to train a model on a radiomap dataset that is collected during the offline phase. The proposed algorithm is implemented on the constructed hybrid multi-layered radiomap to improve the 3D localisation accuracy. In our implementation, the proposed approach is based on the fusion of the prominent 5G IoT signals of Bluetooth Low Energy (BLE) and the ubiquitous WLAN. As a result, we achieved a 91% classification accuracy in 1D and a submeter accuracy in 2D

3D Indoor Positioning in 5G networks

Over the past two decades, the challenge of accurately positioning objects or users indoors, especially in areas where Global Navigation Satellite Systems (GNSS) are not available, has been a significant focus for the research community. With the rise of 5G IoT networks, the quest for precise 3D positioning in various industries has driven researchers to explore various machine learning-based positioning techniques. Within this context, researchers are leveraging a mix of existing and emerging wireless communication technologies such as cellular, Wi-Fi, Bluetooth, Zigbee, Visible Light Communication (VLC), etc., as well as integrating any available useful data to enhance the speed and accuracy of indoor positioning. Methods for indoor positioning involve combining various parameters such as received signal strength (RSS), time of flight (TOF), time of arrival (TOA), time difference of arrival (TDOA), direction of arrival (DOA) and more. Among these, fingerprint-based positioning stands out as a popular technique in Real Time Localisation Systems (RTLS) due to its simplicity and cost-effectiveness. Positioning systems based on fingerprint maps or other relevant methods find applications in diverse scenarios, including malls for indoor navigation and geo-marketing, hospitals for monitoring patients, doctors, and critical equipment, logistics for asset tracking and optimising storage spaces, and homes for providing Ambient Assisted Living (AAL) services. A significant challenge facing all indoor positioning systems is the objective evaluation of their performance. This challenge is compounded by the coexistence of heterogeneous technologies and the rapid advancement of computation. There is a vast potential for information fusion to be explored. These observations have led to the motivation behind our work. As a result, two novel algorithms and a framework are introduced in this thesis

Localisation en intérieur et gestion de la mobilité dans les réseaux sans fils hétérogènes émergents

Au cours des dernières décennies, nous avons été témoins d'une évolution considérable dans l'informatique mobile, réseau sans fil et des appareils portatifs. Dans les réseaux de communication à venir, les utilisateurs devraient être encore plus mobiles exigeant une connectivité omniprésente à différentes applications qui seront de préférence au courant de leur contexte. Certes, les informations de localisation dans le cadre de leur contexte est d'une importance primordiale à la fois la demande et les perspectives du réseau. Depuis l'application ou de point de vue utilisateur, la fourniture de services peut mettre à jour si l'adaptation au contexte de l'utilisateur est activée. Du point de vue du réseau, des fonctionnalités telles que le routage, la gestion de transfert, l'allocation des ressources et d'autres peuvent également bénéficier si l'emplacement de l'utilisateur peuvent être suivis ou même prédit. Dans ce contexte, nous nous concentrons notre attention sur la localisation à l'intérieur et de la prévision transfert qui sont des composants indispensables à la réussite ultime de l'ère de la communication omniprésente envisagé. Alors que les systèmes de positionnement en plein air ont déjà prouvé leur potentiel dans un large éventail d'applications commerciales, le chemin vers un système de localisation à l'intérieur de succès est reconnu pour être beaucoup plus difficile, principalement en raison des caractéristiques difficiles à l'intérieur et l'exigence d'une plus grande précision. De même, la gestion de transfert dans le futur des réseaux hétérogènes sans fil est beaucoup plus difficile que dans les réseaux traditionnels homogènes. Régimes de procédure de transfert doit être sans faille pour la réunion strictes de qualité de service (QoS) des applications futures et fonctionnel malgré la diversité des caractéristiques de fonctionnement des différentes technologies. En outre, les décisions transfert devraient être suffisamment souples pour tenir compte des préférences utilisateur d'un large éventail de critères proposés par toutes les technologies. L'objectif principal de cette thèse est de mettre au point précis, l'heure et l'emplacement de puissance et de systèmes efficaces de gestion de transfert afin de mieux satisfaire applications sensibles au contexte et mobiles. Pour obtenir une localisation à l'intérieur, le potentiel de réseau local sans fil (WLAN) et Radio Frequency Identification (RFID) que l'emplacement autonome technologies de détection sont d'abord étudiés par des essais plusieurs algorithmes et paramètres dans un banc d'essai expérimental réel ou par de nombreuses simulations, alors que leurs lacunes sont également été identifiés. Leur intégration dans une architecture commune est alors proposée afin de combiner leurs principaux avantages et surmonter leurs limitations. La supériorité des performances du système de synergie sur le stand alone homologues est validée par une analyse approfondie. En ce qui concerne la tâche de gestion transfert, nous repérer que la sensibilité au contexte peut aussi améliorer la fonctionnalité du réseau. En conséquence, deux de tels systèmes qui utilisent l'information obtenue à partir des systèmes de localisation sont proposées. Le premier schéma repose sur un déploiement tag RFID, comme notre architecture de positionnement RFID, et en suivant la scène WLAN analyse du concept de positionnement, prédit l'emplacement réseau de la prochaine couche, c'est à dire le prochain point de fixation sur le réseau. Le second régime repose sur une approche intégrée RFID et sans fil de capteur / actionneur Network (WSAN) de déploiement pour la localisation des utilisateurs physiques et par la suite pour prédire la prochaine leur point de transfert à deux couches de liaison et le réseau. Etre indépendant de la technologie d'accès sans fil principe sous-jacent, les deux régimes peuvent être facilement mises en œuvre dans des réseaux hétérogènes [...]Over the last few decades, we have been witnessing a tremendous evolution in mobile computing, wireless networking and hand-held devices. In the future communication networks, users are anticipated to become even more mobile demanding for ubiquitous connectivity to different applications which will be preferably aware of their context. Admittedly, location information as part of their context is of paramount importance from both application and network perspectives. From application or user point of view, service provision can upgrade if adaptation to the user's context is enabled. From network point of view, functionalities such as routing, handoff management, resource allocation and others can also benefit if user's location can be tracked or even predicted. Within this context, we focus our attention on indoor localization and handoff prediction which are indispensable components towards the ultimate success of the envisioned pervasive communication era. While outdoor positioning systems have already proven their potential in a wide range of commercial applications, the path towards a successful indoor location system is recognized to be much more difficult, mainly due to the harsh indoor characteristics and requirement for higher accuracy. Similarly, handoff management in the future heterogeneous wireless networks is much more challenging than in traditional homogeneous networks. Handoff schemes must be seamless for meeting strict Quality of Service (QoS) requirements of the future applications and functional despite the diversity of operation features of the different technologies. In addition, handoff decisions should be flexible enough to accommodate user preferences from a wide range of criteria offered by all technologies. The main objective of this thesis is to devise accurate, time and power efficient location and handoff management systems in order to satisfy better context-aware and mobile applications. For indoor localization, the potential of Wireless Local Area Network (WLAN) and Radio Frequency Identification (RFID) technologies as standalone location sensing technologies are first studied by testing several algorithms and metrics in a real experimental testbed or by extensive simulations, while their shortcomings are also identified. Their integration in a common architecture is then proposed in order to combine their key benefits and overcome their limitations. The performance superiority of the synergetic system over the stand alone counterparts is validated via extensive analysis. Regarding the handoff management task, we pinpoint that context awareness can also enhance the network functionality. Consequently, two such schemes which utilize information obtained from localization systems are proposed. The first scheme relies on a RFID tag deployment, alike our RFID positioning architecture, and by following the WLAN scene analysis positioning concept, predicts the next network layer location, i.e. the next point of attachment to the network. The second scheme relies on an integrated RFID and Wireless Sensor/Actuator Network (WSAN) deployment for tracking the users' physical location and subsequently for predicting next their handoff point at both link and network layers. Being independent of the underlying principle wireless access technology, both schemes can be easily implemented in heterogeneous networks. Performance evaluation results demonstrate the advantages of the proposed schemes over the standard protocols regarding prediction accuracy, time latency and energy savingsEVRY-INT (912282302) / SudocSudocFranceF