Distributed localisation algorithm for wireless ad hoc networks of moving nodes

Existing ad hoc network localisation solutions rely either on external location references or network-wide exchange of information and centralised processing and computation of location estimates. Without these, nodes are not able to estimate the relative locations of other nodes within their communication range. This thesis defines a new distributed localisation algorithm for ad hoc networks of moving nodes. The Relative Neighbour Localisation (RNL) algorithm works without any external localisation signal or systems and does not assume centralised information processing. The idea behind the location estimates produced by the RNL algorithm is the relationship between the relative locations of two nodes, their mobility parameters and the signal strengths measured between them. The proposed algorithm makes use of the data available to each node to produce a location estimate. The signal strength each node is capable of measuring is used as one algorithm input. The other input is the velocity vector of the neighbouring node, composed of its speed and direction of movement, which each node is assumed to periodically broadcast. The relationship between the signal strength and the mobility parameters on one, and the relative location on the other side can be analytically formulated in an ideal case. The limitations of a realistic scenario complicate this relationship, making it very difficult to formulate analytically. An empirical approach is thus used. The angle and the distance estimates are individually computed, together forming a two-dimensional location estimate. The performance of the algorithm was analysed in detail using simulation, showing a median estimate error of under 10m, and its application was tested through design and evaluation of a distributed sensing coverage algorithm, showing RNL location estimates can provide 90% of the coverage achievable with true locations being known

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

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

Cell identification based on received signal strength fingerprints: concept and application towards energy saving in cellular networks

The increasing deployment of small cells aimed at off-loading data traffic from macrocells in heterogeneous networks has resulted in a drastic increase in energy consumption in cellular networks. Energy consumption can be optimized in a selforganized way by adapting the number of active cells in response to the current traffic demand. In this paper we concentrate on the complex problem of how to identify small cells to be reactivated in situations where multiple cells are concurrently inactive. Solely based on the received signal strength, we present cell-specific patterns for the generation of unique cell fingerprints. The cell fingerprints of the deactivated cells are matched with measurements from a high data rate demanding mobile device to identify the most appropriate candidate. Our scheme results in a matching success rate of up to 100% to identify the best cell depending on the number of cells to be activated

CSI-based fingerprinting for indoor localization using LTE Signals

Abstract This paper addresses the use of channel state information (CSI) for Long Term Evolution (LTE) signal fingerprinting localization. In particular, the paper proposes a novel CSI-based signal fingerprinting approach, where fingerprints are descriptors of the "shape" of the channel frequency response (CFR) calculated on CSI vectors, rather than direct CSI vectors. Experiments have been carried out to prove the feasibility and the effectiveness of the proposed method and to study the impact on the localization performance of (i) the bandwidth of the available LTE signal and (ii) the availability of more LTE signals transmitted by different eNodeB (cell diversity). Comparisons with other signal fingerprinting approaches, such as the ones based on received signal strength indicator or reference signal received power, clearly show that using LTE CSI, and in particular, descriptors as fingerprints, can bring relevant performance improvement

Véhicules connectés contributions à la communication véhicule-réseau mobile et la localisation coopérative

RÉSUMÉ Véhicules connectés, ou « connected vehicles », est un nouveau paradigme des systèmes de transport intelligents (STI) qui vise à améliorer la sécurité et l’efficacité du trafic routier en utilisant les communications sans fil. Les communications des véhicules connectés (ou, V2X) englobent les communications sans fil entre véhicules et infrastructures (V2I), entre véhicule et véhicule (V2V), et entre les véhicules et les dispositifs sans fil (V2D). Considéré comme la norme de facto pour les communications V2X, le DSRC/WAVE est le principal standard de communication sans fil spécifiquement conçu pour les communications véhiculaires. L’efficacité du DSRC/WAVE pour les communications V2V et V2I a été prouvée par de nombreuses études et bancs d'essai dans le monde réel. En ce qui concerne la communication V2V, le passage au stade de déploiement à grande échelle est prévu à l’horizon 2020. En ce qui a trait à la communication V2I, bien que le déploiement d’une infrastructure DSRC (RSU) soit critique pour plusieurs applications STI, il n'y a toujours pas de plan pour son déploiement à grande échelle, essentiellement en raison de la nécessité d'investissements publics considérables. Avec les progrès réalisés au niveau des dernières versions du réseau mobile 4G LTE-A, les réseaux mobiles émergent comme l'une des principales technologies pour les communications V2I. En effet, le réseau mobile LTE-A permet aujourd’hui un plus grand débit (100Mb/s - 1Gb/s) avec relativement un faible délai (10ms) grâce à une évolution au niveau de l’architecture du réseau et l’introduction de nouvelles technologies telles que la densification du réseau à l'aide de petites cellules, relais (fixes et mobiles), la connectivité double (Dual Connectivity, DC), l’agrégation de porteuses (Carrier Aggregation, CA), etc.; des évolutions qui ouvrent la voie vers la 5G à l’horizon 2020 avec la promesse d’un débit encore plus élevé (10Gb/s) et d’un plus faible délai (1ms), ce qui renforce ainsi la tendance pour une future intégration véhicule et réseau mobile. Afin d’atteindre une plus grande efficacité spectrale, les petites cellules sont largement adoptées par les opérateurs de réseaux mobiles, dans les réseaux dits hétérogènes (HetNets), comme une solution clé pour désengorger le trafic au niveau des macrocellules et améliorer la capacité et la couverture du réseau d’accès. Cependant, bien que l’utilisation des petites cellules soit une solution intéressante pour les communications V2I, étant donné leur faible portée, cela provoque des relèves fréquentes qui mènent à une surcharge élevée de signalisation vers le réseau cœur. De plus, étant donné que les petites cellules sont généralement connectées au réseau cœur via une connexion Internet, celle-ci devient le goulot d'étranglement pour la relève et le transfert de données. D’où l’importance de compléter un maximum de relève localement. Cette thèse s'inscrit dans le cadre de l’étude de l’intégration véhicule infrastructure. L'objectif général est de proposer une architecture pour véhicule connecté basée sur la localisation coopérative, la communication V2I et la gestion de relève pour une meilleure intégration VANET – réseau mobile. Cette thèse fait état de trois principales contributions. 1. La première contribution concerne la proposition d’algorithmes de localisation coopérative, basés sur une approche ensembliste, qui permettent d’améliorer la précision de localisation. Le premier algorithme appelé (CLES) est un algorithme générique pour la localisation coopérative basée sur une approche ensembliste. Le deuxième algorithme, appelé (CLEF), est une application de l’algorithme CLES à la localisation par approche des signatures. De plus, nous caractérisons leur précision en évaluant la réduction du diamètre maximal et l’aire du polygone en fonction de différents paramètres tels que le nombre de polygones, la configuration géométrique, la proximité du nœud par rapport à la frontière de son polygone, et l’incertitude sur les mesures de distances. 2. La deuxième contribution porte sur la sélection des passerelles mobiles pour connecter efficacement les véhicules aux petites cellules du réseau mobile. Nous formulons le problème de sélection des passerelles mobiles sous forme d’un problème de programmation linéaire binaire multi-objectif (MO-BIP). Ensuite, nous évaluons l’efficacité de l’algorithme au niveau du temps de calcul pour différents degrés de connectivité et un nombre variable de véhicules. 3. La troisième contribution concerne la gestion de la relève dans les petites cellules du réseau mobile (LTE-A) afin de supporter efficacement les communications des véhicules connectés et réduire la surcharge de signalisation vers le réseau cœur. Pour ce faire, nous proposons un nouveau schéma basé sur le transfert local de trafic en utilisant les liens X2 et les nœuds d’ancrage. Trois procédures sont proposées, à savoir: 1) intra-domaine, 2) inter-domaines, 3) et K-sauts inter-domaines. Ensuite, en utilisant un modèle analytique, nous évaluons l’efficacité du schéma proposé.----------ABSTRACT Connected Vehicles are a new intelligent transportation paradigm that uses wireless communications to improve traffic safety and efficiency. It has received a great deal of attention in recent years, across many communities. While the DSRC is widely recognized as the de facto standard for V2V, other wireless technologies are required for large-scale deployment of V2I communications. Thanks to its high data rates and large scale deployment, the LTE-A enhanced by small cells densification, is positioned as one of the major candidate technologies for V2I communications. However, using LTE-A small cells for V2I communications is challenging due to their small coverage which lead to frequent handoffs and more signaling overhead. Thanks to recent advances in LTE-A Releases-10/11/12, the 4G LTE-Advanced (LTE-A) mobile network appears as one of the major candidate technologies for V2I communications. In fact, the LTE-A promises to deliver reduced connection setup time and lower latency (10ms) and higher data rates (up to 1Gbps) by using new physical layer technologies and new network elements and functions such as, network densification using Small Cells (SCs), Dual Connectivity (DC), Relaying functionality, Carrier Aggregation (CA), Device to Device (D2D) communication, etc.; developments that pave the way to 5G in the Horizon 2020, with the promise of an even higher data rates (more than 10 Gbps) and even much lower latency (1ms), which reinforces the trend for future integration between VANET and mobile networks for V2I communications. Although the macrocell will remain the major Radio Access Network (RAN) element for wide-area coverage and high-mobility users, it is no longer sufficient to meet user's demand in many high-density areas. Indeed, due to the proliferation of mobile devices and applications, mobile data demand continues to grow exponentially. Small cells, which include microcells, picocells, and femtocells, are widely recognized as a key solution for enhancing RAN capacity and coverage. They are increasingly used by mobile operators, in the so-called Heterogeneous Network (HetNet), to offload traffic from their macrocells. A HetNet is typically composed of several layers (macrocells, small cells), and in some cases different access technologies (e.g., LTE-A, UMTS, WiFi). SCs densification involves deploying more small coverage base stations in high demand areas to bring higher spectral efficiency per coverage area. Nevertheless, the SCs deployment faces a number of problems relevant to mobility handling that have to be addressed. More specifically, the use of SCs with limited coverage causes frequent handovers that lead to high signaling overhead toward the core network. In addition, since the small cells are generally connected to the EPC via a network Internet connection, this one becomes the bottleneck for handovers and data forwarding, hence the importance of completing a maximum of handover locally. This thesis therefore aims to propose solutions for VANETs and mobile networks integration. The main contributions of this thesis are summarized as follows: The first contribution concerns the proposed cooperative localization algorithms, based on a set-membership approach which improves the location accuracy. The first algorithm called (CLES) is a generic algorithm for cooperative localization based on a set-membership approach. The second algorithm called (CLEF) is an application of CLES algorithm to fingerprinting localization. In addition, we characterize their accuracy by evaluating the reduction of the maximum diameter and the area of the polygon depending on various parameters such as the number of polygons, the geometric configuration, the nearest node in relation to the boundary of the polygon, and the uncertainty of distance measurements. The second contribution concerns the selection of mobile gateways to effectively connect vehicles to small cells of the mobile network. In fact, while each vehicle may directly uses its LTE-A interface for V2I communications, we argue that by selecting a limited number of GWs, we can effectively reduce the mobility signaling overhead. Hence, we propose a new network-based mobile gateway selection scheme with one-hop clustering to efficiently relay the traffic from neighbouring vehicles toward the serving SC. The selection problem is formulated as a multi-objective binary linear programming problem. Using linear programming solver, we show that, for realistic number of vehicles per small cell and GW connectivity degree, the execution time is relatively short. As a third contribution of this thesis, we focus on challenges relevant to mobility for VANETs using LTE-A network. Specifically, a novel architecture that integrates VANET and 4G LTE-A Heterogeneous Network for enhanced mobility in LTE-A small cells is introduced. First, we propose a new network-based mobile gateway selection scheme with one-hop clustering to efficiently relay traffic from neighbouring vehicles toward the serving SC. The problem is formulated as a multi-objective binary programming problem. Then, for seamless mobility of connected vehicles, we propose a local k-hops anchor-based mobility scheme with three procedures, namely intra-domain, k-hops inter-domain and inter-domain procedures. Numerical results show the effectiveness of the proposed mobility schemes for reducing the generated signaling load towards the core network

Algorithms for Positioning with Nonlinear Measurement Models and Heavy-tailed and Asymmetric Distributed Additive Noise

Determining the unknown position of a user equipment using measurements obtained from transmitters with known locations generally results in a nonlinear measurement function. The measurement errors can have a heavy-tailed and/ or skewed distribution, and the likelihood function can be multimodal.A positioning problem with a nonlinear measurement function is often solved by a nonlinear least squares (NLS) method or, when filtering is desired, by an extended Kalman filter (EKF). However, these methods are unable to capture multiple peaks of the likelihood function and do not address heavy-tailedness or skewness. Approximating the likelihood by a Gaussian mixture (GM) and using a GM filter (GMF) solves the problem. The drawback is that the approximation requires a large number of components in the GM for a precise approximation, which makes it unsuitable for real-time positioning on small mobile devices.This thesis studies a generalised version of Gaussian mixtures, which is called GGM, to capture multiple peaks. It relaxes the GM’s restriction to non-negative component weights. The analysis shows that the GGM allows a significant reduction of the number of required Gaussian components when applied for approximating the measurement likelihood of a transmitter with an isotropic antenna, compared with the GM. Therefore, the GGM facilitates real-time positioning in small mobile devices. In tests for a cellular telephone network and for an ultra-wideband network the GGM and its filter provide significantly better positioning accuracy than the NLS and the EKF.For positioning with nonlinear measurement models, and heavytailed and skewed distributed measurement errors, an Expectation Maximisation (EM) algorithm is studied. The EM algorithm is compared with a standard NLS algorithm in simulations and tests with realistic emulated data from a long term evolution network. The EM algorithm is more robust to measurement outliers. If the errors in training and positioning data are similar distributed, then the EM algorithm yields significantly better position estimates than the NLS method. The improvement in accuracy and precision comes at the cost of moderately higher computational demand and higher vulnerability to changing patterns in the error distribution (of training and positioning data). This vulnerability is caused by the fact that the skew-t distribution (used in EM) has 4 parameters while the normal distribution (used in NLS) has only 2. Hence the skew-t yields a closer fit than the normal distribution of the pattern in the training data. However, on the downside if patterns in training and positioning data vary than the skew-t fit is not necessarily a better fit than the normal fit, which weakens the EM algorithm’s positioning accuracy and precision. This concept of reduced generalisability due to overfitting is a basic rule of machine learning.This thesis additionally shows how parameters of heavy-tailed and skewed error distributions can be fitted to training data. It furthermore gives an overview on other parametric methods for solving the positioning method, how training data is handled and summarised for them, how positioning is done by them, and how they compare with nonparametric methods. These methods are analysed by extensive tests in a wireless area network, which shows the strength and weaknesses of each method

Image Based Indoor Navigation

Over the last years researchers proposed numerous indoor localization and navigation systems. However, solutions that use WiFi or Radio Frequency Identification require infrastructure to be deployed in the navigation area and infrastructure less techniques, e.g. the ones based on mobile cell ID or dead reckoning suffer from large accuracy errors. In this Thesis, we present a novel approach of infrastructure-less indoor navigation system based on computer vision Structure from Motion techniques. We implemented a prototype localization and navigation system which can build a navigation map using area photos as input and accurately locate a user in the map. In our client-server architecture based system, a client is a mobile application, which allows a user to locate her or his position by simply taking a photo. The server handles map creation, localization queries and path finding. After the implementation, we evaluated the localization accuracy and latency of the system by benchmarking navigation queries and the model creation algorithm. The system is capable of successfully navigating in Aalto University computer science department library. We were able to achieve an average error of 0.26 metres for successfully localised photos. In the Thesis, we also present challenges that we solved to adapt computer vision techniques for localisation purposes. Finally we observe the possible future work topics to adapt the system to a wide use

Understanding mobile network quality and infrastructure with user-side measurements

Measurement collection is a primary step towards analyzing and optimizing performance of a telecommunication service. With an Mobile Broadband (MBB) network, the measurement process has not only to track the network’s Quality of Service (QoS) features but also to asses a user’s perspective about its service performance. The later requirement leads to “user-side measurements” which assist in discovery of performance issues that makes a user of a service unsatisfied and finally switch to another network. User-side measurements also serve as first-hand survey of the problem domain. In this thesis, we exhibit the potential in the measurements collected at network edge by considering two well-known approaches namely crowdsourced and distributed testbed-based measurements. Primary focus is on exploiting crowdsourced measurements while dealing with the challenges associated with it. These challenges consist of differences in sampling densities at different parts of the region, skewed and non-uniform measurement layouts, inaccuracy in sampling locations, differences in RSS readings due to device-diversity and other non-ideal measurement sampling characteristics. In presence of heterogeneous characteristics of the user-side measurements we propose how to accurately detect mobile coverage holes, to devise sample selection process so to generate a reliable radio map with reduced sample cost, and to identify cellular infrastructure at places where the information is not public. Finally, the thesis unveils potential of a distributed measurement test-bed in retrieving performance features from domains including user’s context, service content and network features, and understanding impact from these features upon the MBB service at the application layer. By taking web-browsing as a case study, it further presents an objective web-browsing Quality of Experience (QoE) model

Planificación y Optimización Automática de Redes Móviles LTE

Osa Ginés, V. (2013). Planificación y Optimización Automática de Redes Móviles LTE [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/29755TESI