Infrastructure Wi-Fi for connected autonomous vehicle positioning : a review of the state-of-the-art

In order to realize intelligent vehicular transport networks and self driving cars, connected autonomous vehicles (CAVs) are required to be able to estimate their position to the nearest centimeter. Traditional positioning in CAVs is realized by using a global navigation satellite system (GNSS) such as global positioning system (GPS) or by fusing weighted location parameters from a GNSS with an inertial navigation systems (INSs). In urban environments where Wi-Fi coverage is ubiquitous and GNSS signals experience signal blockage, multipath or non line-of-sight (NLOS) propagation, enterprise or carrier-grade Wi-Fi networks can be opportunistically used for localization or “fused” with GNSS to improve the localization accuracy and precision. While GNSS-free localization systems are in the literature, a survey of vehicle localization from the perspective of a Wi-Fi anchor/infrastructure is limited. Consequently, this review seeks to investigate recent technological advances relating to positioning techniques between an ego vehicle and a vehicular network infrastructure. Also discussed in this paper is an analysis of the location accuracy, complexity and applicability of surveyed literature with respect to intelligent transportation system requirements for CAVs. It is envisaged that hybrid vehicular localization systems will enable pervasive localization services for CAVs as they travel through urban canyons, dense foliage or multi-story car parks

Radio Frequency-Based Indoor Localization in Ad-Hoc Networks

The increasing importance of location‐aware computing and context‐dependent information has led to a growing interest in low‐cost indoor positioning with submeter accuracy. Localization algorithms can be classified into range‐based and range‐free techniques. Additionally, localization algorithms are heavily influenced by the technology and network architecture utilized. Availability, cost, reliability and accuracy of localization are the most important parameters when selecting a localization method. In this chapter, we introduce basic localization techniques, discuss how they are implemented with radio frequency devices and then characterize the localization techniques based on the network architecture, utilized technologies and application of localization. We then investigate and address localization in indoor environments where the absence of global positioning system (GPS) and the presence of unique radio propagation properties make this problem one of the most challenging topics of localization in wireless networks. In particular, we study and review the previous work for indoor localization based on radio frequency (RF) signaling (like Bluetooth‐based localization) to illustrate localization challenges and how some of them can be overcome

Development and evaluation of localization techniques for vehicular ad hoc networks and intelligent transportation systems

Orientadores: Leandro Aparecido Villas, Daniel Ludovico GuidoniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Devido aos diversos problemas nos sistemas de tráfego ocasionados pela evolução dos grandes centros urbanos, existe o campo de estudo dos Sistemas de Transportes Inteligentes (Intelligent Transportation Systems, ITS), que visa prover metodologias de comunicação, processamento e armazenamento de dados voltados para o segmento de transportes e comutação de pessoas nas cidades. Neste contexto, o advento das tecnologias de comunicação sem fio, sobretudo das tecnologias de comunicação sem fio dedicadas e de curto alcance, culminaram no surgimento do padrão de comunicação sem fio IEEE 802.11p para as redes veiculares (Vehicular Ad Hoc Netowrks, VANETs). Nos últimos anos, uma miríade de protocolos, aplicações e serviços vêm sendo desenvolvidos com os mais diversos objetivos, variando desde conforto a segurança. Muitos destes serviços confiam em algum sistema de localização, e necessitam de diferentes níveis de acurácia para seu pleno funcionamento. A solução imediata para localização em VANETS e ITS são os Sistemas de Navegação Global via Satélite (Global Navigation Satellite System, GNSS). No entanto, os sistemas GNSS sofrem problemas de inacurácia e indisponibilidade em zonas urbanas densas, rodovias multinível e túneis, o que representa um desafio para os protocolos, aplicações e serviços que confiam em localização. Com esta motivação, primeiramente foi realizada uma caracterização dos problemas de inacurácia e indisponibilidade dos sistemas GPS a partir de datasets reais. Foram selecionadas regiões no entorno de túneis. Uma vez que os nós da rede veicular são dotados de capacidade de comunicação sem-fio, processamento e armazenamento. Foram desenvolvidas e avaliadas as técnicas de localização Dead Reckoning e uma abordagem Cooperative Positioning onde os veículos compartilham suas estimativas de localização por meio da rede veicular com o objetivo de melhorar suas estimativas de localizações. As situações de indisponibilidade caracterizadas nos datasets foram reproduzidas em ambiente de simulação para validação das soluções de localização propostas. Resultados de simulação apresentam um ganho médio de 60% a 80% da solução Dead Reckoning em termos do Erro Médio Quadrático (Root Mean Square Erro, RMSE), se comparados com os resultados da solução stand alone GPS. Os resultados da solução Cooperative Positioning apresentam um ganho médio entre 80% e 92% no RMSE em relação a solução GPS stand alone, e entre 23% a 74% em relação a solução Dead Reckoning. Além disso, as soluções conseguem cobrir 100% das zonas de indisponibilidade do GPS nos cenários avaliadosAbstract: Due to the many problems in the traffic systems caused by the evolution of the large urban centers, there is the field of study of Intelligent Transportation Systems (ITS), which aims to provide communication, data processing and storage methodologies for the transport of people, assets and services in the cities. In this context, the advent of the wireless communications technologies especially the Dedicated Short Range Communications (DSRC), culminated in the development of the IEEE 802.11p standard for Vehicular Ad Hoc Networks (VANETs). In recent years, a myriad of protocols, applications, and services have been developed with a wide range of objectives, ranging from comfort to security. Many of these services rely on some location system, and require different levels of accuracy for their full operation. The Global Navigation Satellite Systems (GNSSs) are an off-the-shelf solution for localization in VANETS and ITS. However, GNSS systems suffer from problems of inaccuracy and unavailability in dense urban areas, multilevel roads and tunnels, posing a challenge for protocols, applications and services that rely on localization. With this motivation, we first carried out a characterization of the problems of inaccuracy and unavailability of GPS systems from real datasets. Regions were selected around tunnels. Since the nodes of the vehicular network are endowed with wireless communication, processing and storage capacbilities. A Dead Reckoning technique and a Cooperative Positioning approach were developed and evaluated. Vehicles share their location estimates using the vehicular network in order to improve their locations. The unavailability situations characterized in the data sets were reproduced in a simulation environment to validate the proposed localization solutions. Simulation results show an average gain of 60% to 80% of the Dead Reckoning solution in terms of RMSE, when compared to the results of the stand alone GPS solution. The results of the Cooperative Positioning solution show an average gain between 80% and 92% in the RMSE compared to the stand alone GPS solution, and between 23% and 74% in relation to the Dead Reckoning solution. In addition, the solutions can support 100% of the GPS unavailability zones on the evaluated cenariosMestradoCiência da ComputaçãoMestre em Ciência da Computação132244/2016-0CNP

FedVCP: A Federated-Learning-Based Cooperative Positioning Scheme for Social Internet of Vehicles

Intelligent vehicle applications, such as autonomous driving and collision avoidance, put forward a higher demand for precise positioning of vehicles. The current widely used global navigation satellite systems (GNSS) cannot meet the precision requirements of the submeter level. Due to the development of sensing techniques and vehicle-to-infrastructure (V2I) communications, some vehicles can interact with surrounding landmarks to achieve precise positioning. Existing work aims to realize the positioning correction of common vehicles by sharing the positioning data of sensor-rich vehicles. However, the privacy of trajectory data makes it difficult to collect and train data centrally. Moreover, uploading vehicle location data wastes network resources. To fill these gaps, this article proposes a vehicle cooperative positioning (CP) system based on federated learning (FedVCP), which makes full use of the potential of social Internet of Things (IoT) and collaborative edge computing (CEC) to provide high-precision positioning correction while ensuring user privacy. To the best of our knowledge, this article is the first attempt to solve the privacy of CP from a perspective of federated learning. In addition, we take the advantages of local cooperation through vehicle-to-vehicle (V2V) communications in data augmentation. For individual differences in vehicle positioning, we utilize transfer learning to eliminate the impact of such differences. Extensive experiments on real data demonstrate that our proposed model is superior to the baseline method in terms of effectiveness and convergence speed

Entwicklung und Implementierung eines Peer-to-Peer Kalman Filters für Fußgänger- und Indoor-Navigation

Smartphones are an integral part of our society by now. They are used for messaging, searching the Internet, working on documents, and of course for navigation. Although smartphones are also used for car navigation their main area of application is pedestrian navigation. Almost all smartphones sold today comprise a GPS L1 receiver which provides position computation with accuracy between 1 and 10 m as long as the environment in beneficial, i.e. the line-of-sight to satellites is not obstructed by trees or high buildings. But this is often the case in areas where smartphones are used primarily for navigation. Users walk in narrow streets with high density, in city centers, enter, and leave buildings and the smartphone is not able to follow their movement because it loses satellite signals. The approach presented in this thesis addresses the problem to enable seamless navigation for the user independently of the current environment and based on cooperative positioning and inertial navigation. It is intended to realize location-based services in areas and buildings with limited or no access to satellite data and a large amount of users like e.g. shopping malls, city centers, airports, railway stations and similar environments. The idea of this concept was for a start based on cooperative positioning between users’ devices denoted here as peers moving within an area with only limited access to satellite signals at certain places (windows, doors) or no access at all. The devices are therefore not able to provide a position by means of satellite signals. Instead of deploying solutions based on infrastructure, surveying, and centralized computations like range measurements, individual signal strength, and similar approaches a decentralized concept was developed. This concept suggests that the smartphone automatically detects if no satellite signals are available and uses its already integrated inertial sensors like magnetic field sensor, accelerometer, and gyroscope for seamless navigation. Since the quality of those sensors is very low the accuracy of the position estimation decreases with each step of the user. To avoid a continuously growing bias between real position and estimated position an update has to be performed to stabilize the position estimate. This update is either provided by the computation of a position based on satellite signals or if signals are not available by the exchange of position data with another peer in the near vicinity using peer-to-peer ad-hoc networks. The received and the own position are processed in a Kalman Filter algorithm and the result is then used as new position estimate and new start position for further navigation based on inertial sensors. The here presented concept is therefore denoted as Peer-to-Peer Kalman Filter (P2PKF)

Improved Indoor Location Systems in a Controlled Environments

The precise localization by using Wi-Fi Access Point (AP) has become a very important issue for indoor location based services such as marketing, patient follow up and so on. Present AP localization systems are working on specially designed Wi-Fi units, and their algorithms using radio signal strength (RSS) exhibit (relatively) high errors, so industry looks more precise and fast adaptable methods. A new model considering/eliminating strong RSS levels in addition to close distance error elimination algorithm (CDEEA) combined with median filters has been proposed in order to increase the performance of conventional RSS based location systems. Collecting local signal strengths by means of an ordinary WiFi units present on any laptop as a receiver is followed by the application of CDEEA to eliminate strong RSS levels. Median filter is then applied to those eliminated values, and AP based path loss model is generated, adaptivelly. Finally, the proposed algorithm predicts locations within a maximum mean error of 2.96m for 90% precision level. This achievement with an ordinary wifi units present on any commercial laptop is comparably at very good level in literature

Delimitated Anti Jammer Scheme for Internet of Vehicle:Machine Learning based Security Approach

Recently, Internet of vehicles (IoV) has witnessed significant research and development attention in both academia and industries due to the potential towards addressing traffic incidences and supporting green mobility. With the growing vehicular network density, jamming signal centric security issues have become challenging task for IoV network designers and traffic applications developers. Global positioning system (GPS) and roadside unit (RSU) centric related literature on location-based security approaches lacks signal characteristics consideration for identifying vehicular network intruders or jammers. In this context, this paper proposes a machine learning oriented as Delimitated Anti Jamming protocol for vehicular traffic environments. It focuses on jamming vehicle's discriminated signal detection and filtration for revealing precise location of jamming effected vehicles. In particular, a vehicular jamming system model is presented focusing on localization of vehicles in delimitated jamming environments. A foster rationalizer is employed to examine the frequency changes caused in signal strength due to the jamming or external attacks. A machine learning open-sourced algorithm namely, CatBoost has been utilized focusing on decision tree relied algorithm to predict the locations of jamming vehicle. The performance of the proposed anti jammer scheme is comparatively evaluated with the state of the art techniques. The evaluation attests the resistive characteristics of the anti-jammer technique considering precision, recall, F1 score and delivery accuracy metrics

Wireless Location Verification and Acquisition Using Machine Learning

Traditional wireless location verification (authentication) is only feasible under the assumption that radio propagation is described by simple time-independent mathematical models. A similar situation applies to location acquisition, albeit to a lesser extent. However, in real-world situations, channel conditions are rarely well-described by simple mathematical models. In this thesis, novel location verification and acquisition techniques that integrate machine learning algorithms into the decision process are designed, analysed, and tested. Through the use of both simulated and experimental data, it is shown how the novel solutions developed remain operational in unknown time-varying channel conditions, thus making them superior to existing solutions, and more importantly, deployable in real-world scenarios. Location verification will be of growing importance for a host of emerging wireless applications in which location information plays a pivotal role. The location verification solutions offered in this thesis are the first to be tested against experimental data and the first to invoke machine learning algorithms. As such, they likely form the foundation for all future verification algorithms

Secure Localization Topology and Methodology for a Dedicated Automated Highway System

Localization of nodes is an important aspect in a vehicular ad-hoc network (VANET). Research has been done on various localization methods. Some are more apt for a specific purpose than others. To begin with, we give an overview of a vehicular ad-hoc network, localization methods, and how they can be classified. The distance bounding and verifiable trilateration methods are explained further with their corresponding algorithms and steps used for localization. Distance bounding is a range-based distance estimation algorithm. Verifiable trilateration is a popular geometric method of localization. A dedicated automated highway infrastructure can use distance bounding and/or trilateration to localize an automated vehicle on the highway. We describe a highway infrastructure for our analysis and test how well each of the methods performs, according to a security measure defined as spoofing probability. The spoofing probability is, simply put, the probability that a given point on the highway will be successfully spoofed by an attacker that is located at any random position along the highway. Spoofing probability depends on different quantities depending on the method of localization used. We compare the distance bounding and trilateration methods to a novel method using friendly jamming for localization. Friendly jamming works by creating an interference around the region whenever communication takes place between a vehicle and a verifier (belonging to the highway infrastructure, which is involved in the localization process using a given algorithm and localization method). In case of friendly jamming, the spoofing probability depends both on the position and velocity of the attacker and those of the target vehicle (which the attacker aims to spoof). This makes the spoofing probability much less for friendly jamming. On the other hand, the distance bounding and trilateration methods have spoofing probabilities depending only on their position. The results are summarized at the end of the last chapter to give an idea about how the three localization methods, i.e. distance bounding, verifiable trilateration, and friendly jamming, compare against each other for a dedicated automated highway infrastructure. We observe that the spoofing probability of the friendly jamming infrastructure is less than 2% while the spoofing probabilities of distance bounding and trilateration are 25% and 11%, respectively. This means that the friendly jamming method is more secure for the corresponding automated transportation system (ATS) infrastructure than distance bounding and trilateration. However, one drawback of friendly jamming is that it has a high standard deviation because the range of positions that are most vulnerable is high. Even though the spoofing probability is much less, the friendly jamming method is vulnerable to an attack over a large range of distances along the highway. This can be overcome by defining a more robust infrastructure and using the infrastructure\u27s resources judiciously. This can be the future scope of our research. Infrastructures that use the radio resources in a cost effective manner to reduce the vulnerability of the friendly jamming method are a promising choice for the localization of vehicles on an ATS highway