Architecture, Protocols, and Algorithms for Location-Aware Services in Beyond 5G Networks

Lane-level vehicle self-localization is a challenging and significant issue arising in autonomous driving and driver-assistance systems. The Global Navigation Satellite System (GNSS) and onboard inertial sensor integration are among the solutions for vehicle self-localization. However, as the main source in the integration, GNSS positioning performance is severely degraded in urban canyons because of the effects of multipath and non-line-of-sight (NLOS) propagations. These GNSS positioning errors also decrease the performance of the integration. To reduce the negative effects caused by GNSS positioning error, this paper proposes to employ an innovative GNSS positioning technique with the aid of a 3-D building map in the integration. The GNSS positioning result is used as an observation, and this is integrated with the information from the onboard inertial sensor and vehicle speedometer in a Kalman filter framework. To achieve stable performance, this paper proposes to evaluate and consider the accuracy of the employed GNSS positioning method in dynamic integration. A series of experiments in different scenarios is conducted in an urban canyon, which can demonstrate the effectiveness of the proposed method using various evaluation and comparison processes.

The use of third party assessment for software intensive safety related systems is often suggested by standards for higher integrity level systems. A recent European Esprit project, CASCADE, (Certification and Assessment of Safety-Critical Application Development) has devised a new assessment method addressing the certification and assessment of software intensive safety critical systems in the railway and automotive industries. While there has been much interest from the railway industry in the new method, this has not been the case within the automotive industry. This paper describes the automotive environment with regard to market, legislation, safety and validation and explains how these factors affect the use of third party validation for software based systems.

The coexistence of human type communications (HTC) with an ever-increasing number of machine type communications (MTC) is a challenge that the forthcoming Fifth Generation (5G) network is claimed to tackle. Ultra-Reliable and Low Latency Communications (URLLC) are among the most demanding services that 5G network will support. Also referred to as mission-critical services, URLLC often requires group communications. The evolved Multimedia Broadcast and Multicast Services (eMBMS) architecture, currently specified by 3GPP to support group-based services in cellular networks, needs a revision to deliver future mission-critical services. In this paper, we analyze the performance of different machine-type multicast service (MtMS) scenarios and we compare three subgroup based approaches for multicast data delivery to MTC devices with limited channel bandwidth. Achieved simulation results provide useful guidelines for the design of multicasting schemes able to support delay-sensitive applications in 5G networks.

GPS related ITS applications can perform adequately in even the toughest of conditions such as dense foliage and urban canyons. Current receiver designs draw approximately 150 ma (nominal), but in power saving modes this can be reduced significantly (less than 25 ma) for intermittent updates allowing ultra low power (Trickle Power) applications. This is possible by exploiting the 100 ms signal re-acquisition capability unique to the SiRFstar/LX chipset allowing the GPS receiver to turn off key components once they have completed their task. This ultra low power operation ushers in new opportunities for applications such as asset tracking and fleet management as well as the possibility of long term battery powered applications such as containers, trailers, road side traffic monitoring systems etc, where periodic position information is required. (A) For the covering abstract see IRRD E102826.

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

Reliable and precise information pertaining to the position, velocity, and attitude is essential for automated driving. This paper proposes FGO-MFI, a cost-effective and robust multi-sensor fusion and integration localization framework that utilizes factor graph optimization. Firstly, a tightly coupled Global Navigation Satellite Systems (GNSS)/on-board sensor fusion localization framework is established to estimate vehicle states, including position, velocity, and attitude. To address the large drift rate of the Inertial Measurement Unit (IMU), this study introduces a novel IMU/Dynamics pre-integration method based on the vehicle dynamics model. We establish a two-degree-of-freedom vehicle dynamics model utilizing measurements from the wheel speed sensor and steering wheel angle sensor. The IMU/Dynamics factor is devised through a close integration of the model output and IMU pre-integration, enabling the construction of precise odometry with low-cost on-board sensors. Then, to address the issue of the non-Gaussian distribution of GNSS pseudorange error, this paper employs a Gaussian Mixture Model (GMM) to characterize the pseudorange noise, which is then applied to further sensor fusion. Given the time-varying nature of pseudorange noise, the expectation maximization algorithm is utilized to estimate GMM parameters online, leveraging the pseudorange residuals within a sliding window. Comprehensive experiments, inclusive of challenging scenarios such as urban canyons, tunnels, and wooded areas, have been carried out. They affirm the superior performance of the proposed method. Experiments have shown that our method demonstrates reliable localization across different statuses of GNSS signal, exhibiting a 39.4% improvement in the root mean square error of position error when compared to the state-of-the-art. Additionally, this FGO-MFI is a general sensor data fusion framework and is able to incorporate diverse sensor measurements, for example, from cameras and LiDARs to provide more reliable and accurate localization information.

Recently, edge computing and data analytics have been recognized as some of the key technologies to deliver low latency, high data rate services using the 5G networks. The 5G edge infrastructure is likely to cater to a mix of cellular and non-cellular devices mainly covering the Internet of things (IoT) and miniature sensor devices. The network vulnerability and threat mitigation at the edge become utmost important to protect the network from upstream attacks originating from the sensors. In mobile network deployments, the app data from multiple user devices can be collected at the mobile edge analytics server residing on the network side. These app data feeds can be processed at the edge server to dynamically build network quality maps. The quality analysis can be utilized to precisely monitor the experience of the users. Further, the serving cells or base stations serving those users within the edge can also be identified on the maps. This can help to pinpoint the real-time traffic variations per user in each cell. The user traffic trends, derived from the app-based quality analysis, can also help to identify security anomalies that may be taking place at the edge. In this paper, a novel framework to couple the traffic analysis and security monitoring at the mobile edge has been proposed with an example manual case study. With the increasing use of artificial intelligence (AI)/machine learning (ML) in automatic traffic management and optimization in 5G, such app-based framework is likely to play a key role to harden the 5G edge fabric in the future deployments. Keywords5G networksSecurity threatsData optimizationMobile edge

As vehicle weight and dimension (VWD) regulations governing the Canadian trucking industry are relaxed, the need to understand the competitive relationship between trucking and the nation's railways becomes acute. The 1990s will see the highway freight industry adapt to larger allowable vehicle weights and dimensions, strengthening its competitive position in the freight service marketplace. This paper predicts the impacts of trucking weights and dimensions reform on the railway industry in Canada. Commodity sections moved by the two main modes, rail and truck, are studied. The annual tonnage of freight for the four. largest commodity sections are identified, where the competition between the railway and trucking industries is very high.The national and western vehicle weight and dimension regulations agreements and the Roads and Transportation Association of Canada report entitled Economics of Truck Sizes and Weights in Canada, as well as the writers' recent research to model Canadian interprovincial freight movement, are applied. The impacts on the rail transportation industry, including the necessary improvements in order to retain its existing market share, the link‐by‐link railway revenue loss for each freight section, and the total loss in railway revenue, are presented.

At present, autonomous driving vehicles are designed in an ego-vehicle manner. The vehicles gather information from their on-board sensors, build an environment model from it and plan their movement based on this model. Mobile network connections are used for non-mission-critical tasks and maintenance only. In this paper, we propose a connected autonomous driving system, where self-driving vehicles exchange data with a so-called road supervisor. All vehicles under supervision provide their current position, velocity and other valuable data. Using the received information, the supervisor provides a recommended trajectory for every vehicle, coordinated with all other vehicles. Since the supervisor has a much better overview of the situation on the road, more elaborate decisions, compared to each individual autonomous vehicle planning for itself, are possible. Experiments show that our approach works efficiently and safely when running our road supervisor on top of a popular traffic simulator. Furthermore, we show the feasibility of offloading the trajectory planning task into the network when using ultra-low-latency 5G networks.

This chapter describes the support for IP based multimedia services on Third Generation (3G) wireless cellular networks. While earlier cellular networks offered only basic IP connectivity on a best-effort basis, 3G networks provide explicit support for real time multimedia services with guaranteed Quality of Service (QoS). As a result, they can offer traditional voice telephony, rich telephony services and multimedia streaming over IP, both inside a 3G network and in conjunction with external networks such as the Internet, the Public Switched Telephone Network (PSTN) and the Integrated Services Digital Network (ISDN). In fact, 3G networks will deploy for the first time in a large scale technologies such as IP version 6 (IPv6) and policy based IP QoS, spearheading the introduction of such technologies into the Internet. The two most important aspects of 3G networks with respect to IP based multimedia services are the IP Multimedia Subsystem (IMS) and the Multimedia Broadcast / Multicast Service (MBMS). The IMS enables complex IP based multimedia sessions to be created with guaranteed QoS for each media component. Example applications include voice telephony and video conferencing. The IMS interoperates with both traditional telephony services and external IP based multimedia services. The MBMS provides native IP broadcast and multicast support in 3G networks, allowing high bandwidth services to be economically offered to multiple users. Example applications include video streaming via multicast and location based services via broadcast. The MBMS interoperates directly with IP multicasting. While both the IMS and the MBMS are IP based, their standardization is proceeding independently. It is however clear that their combination would allow numerous new services to be provided. The outline of this chapter is as follows. In Section II we give an overview of cellular networks and their evolution, while in Section III we describe in detail a specific 3G network, the Universal Mobile Telecommunications System (UMTS). Section IV presents an introductory description of the features and services provided by the IMS and the MBMS. In Section V we provide the details of the IMS, including service architecture, session setup and control and interworking issues. Section VI describes the MBMS in the same manner as for the IMS. Finally, Section VII discusses the QoS issues for IP based multimedia services, describing the overall QoS concept, the policy based QoS control scheme and its application to IMS sessions. At the end of the chapter, Tables II and III list the acronyms used. An extensive UMTS vocabulary is provided in [1].

Geolocation of mobile nodes with non‐orthogonal multiple access (NOMA) is a crucial process for enabling advanced location‐based services (LBSs) in 5G networks. In this study, the authors investigate the theoretical limits of received‐signal‐strength (RSS)‐based positioning for uplink cooperative NOMA under different network conditions with special emphasis on the user fairness feature. In addition, they compare the performance of the RSS‐based positioning of cooperative NOMA with that of orthogonal multiple access (OMA). The Cramer‐Rao lower bounds are derived in order to show the fundamental limits of position estimation error by taking amplify‐and‐forward and decode‐and‐forward relaying into account. The numerical results for both schemes are presented and compared considering the effects of number of relays and user equipments, different relaying on the performance of RSS positioning. The effects of power allocation on the user fairness of cooperative NOMA are also investigated. The results show that the cooperative NOMA outperforms cooperative OMA in terms of positioning. It provides high precision position accuracy and user fairness under different network conditions. Hence, the results suggest that the RSS‐based positioning for uplink cooperative NOMA is a promising technique for achieving user fairness and supporting diversified low‐cost LBSs in 5G networks.

One of the key targets of the upcoming 5G system is to build a mobile network architecture that supports not only classical mobile broadband applications (i.e., Internet and IMS), but also vertical industry services, such as those of automotive systems, e-health, public safety, and smart grid. Vertical industry is known to have specific needs that cannot be sustained by the current cellular networks. More notably, automotive systems require strict quality of service in terms of ultrashort latency for vehicle-to-infrastructure/network (V2I/N) communications. In this paper, we introduce the Follow Me edge-Cloud (FMeC) concept, leveraging the mobile edge computing (MEC) architecture to sustain requirements of the 5G automotive systems. Assuming that automotive services are deployed on MEC entities, FMeC ensures low-latency access to these services by guaranteeing that vehicles (i.e., as well as user equipment on board vehicles) always connect to nearest automotive service. Besides the FMeC architecture, our contribution in this paper consists in presenting a projection of the FMeC solution on an automated driving use case that integrates automotive and Telco infrastructures, to realize the vision of future 5G automotive systems. We introduce the envisioned software defined networking/OpenFlow-based architecture and our mobility-aware framework based on a set of building blocks that permit achieving the automated driving requirements within 5G network. The evaluation results, obtained conjointly through theoretical analysis and computer simulation, show that our proposed solution outperforms baseline approaches in meeting the automated driving latency requirement and minimizing the incurred global cost.

Conceptual Model of Management in Automotive Projects

Blockchain has presented its benefits in real world in many industries. This technology is now expanding in financial sector. The automobile industry is technology-intensive, and has been from its beginning. The 21st-century cars are moving data centers with on-board sensors and computers that capture information about the vehicle. Here we examined the current situations in the automobile industry. We contrived various surveys in hunt for various issues faced by manufacturers and the customers in the automotive industry. The issues highlighted in this research papers can be overcome using the perquisite of blockchain technology.

User-centric wireless access virtualization (WAV) allows each user to be served by a set of carefully selected transmission points (TP)s forming a user-specific virtual basestation (uVBS) adapted to its environment and quality of service (QoS) requirement. In this way, this new concept breaks away from the conventional cell-centric architecture to provide boundaryless communications in future 5G networks. This fundamental structural 5G evolution alongside the ultra-dense multi-tier heterogenous context foreseen in such networks require an inevitable rethinking of efficient scalable TPs clustering. As such, this paper proposes three innovative low-cost clustering approaches that enable user-centric WAV and provide dynamic, adaptive, and overlapping TPs clusters while requiring not only negligible overhead and power costs, but also minimum signaling changes at both network and user sides. In contrast to existing clustering techniques, the new ones we propose better leverage 5G features such as extreme densification and massive connectivity as well as new concepts such as mmWave spectrum and massive MIMO. Furthermore, these approaches are flexible enough to be adapted to different network dimensions (i.e., space, time, etc.), thereby paving the way for achieving the dramatic performance improvements required by 5G networks to cope with the upcoming mobile data deluge.