48 research outputs found
Robust distributed resource allocation for cellular vehicle-to-vehicle communication
Mit Release 14 des LTE Standards unterstützt dieser die direkte Fahrzeug-zu-Fahrzeug-Kommunikation über den Sidelink. Diese Dissertation beschäftigt sich mit dem Scheduling Modus 4, einem verteilten MAC-Protokoll ohne Involvierung der Basisstation, das auf periodischer Wiederverwendung von Funkressourcen aufbaut. Der Stand der Technik und eine eigene Analyse des Protokolls decken verschiedene Probleme auf. So wiederholen sich Kollisionen von Paketen, wodurch manche Fahrzeuge für längere Zeit keine sicherheitskritischen Informationen verbreiten können. Kollisionen entstehen vermehrt auch dadurch, dass Hidden-Terminal-Probleme in Kauf genommen werden oder veränderliche Paketgrößen und -raten schlecht unterstützt werden. Deshalb wird ein Ansatz namens "Scheduling based on Acknowledgement Feedback Exchange" vorgeschlagen. Zunächst wird eine Funkreservierung in mehrere ineinander verschachtelte Unter-Reservierungen mit verschiedenen Funkressourcen unterteilt, was die Robustheit gegenüber wiederholenden Kollisionen erhöht. Dies ist die Grundlage für eine verteilte Staukontrolle, die die Periodizitätseigenschaft nicht verletzt. Außerdem können so veränderliche Paketgrößen oder -raten besser abgebildet werden. Durch die periodische Wiederverwendung können Acknowledgements für Funkressourcen statt für Pakete ausgesendet werden. Diese können in einer Bitmap in den Padding-Bits übertragen werden. Mittels der Einbeziehung dieser Informationen bei der Auswahl von Funkressourcen können Hidden-Terminal-Probleme effizient vermieden werden, da die Acknowledgements auch eine Verwendung dieser Funkressource ankündigen. Kollisionen können nun entdeckt und eine Wiederholung vermieden werden. Die Evaluierung des neuen MAC-Protokolls wurde zum großen Teil mittels diskreter-Event-Simulationen durchgeführt, wobei die Bewegung jedes einzelnen Fahrzeuges simuliert wurde. Der vorgeschlagene Ansatz führt zu einer deutlich erhöhten Paketzustellrate. Die Verwendung einer anwendungsbezogenen Awareness-Metrik zeigt, dass die Zuverlässigkeit der Kommunikation durch den Ansatz deutlich verbessert werden kann. Somit zeigt sich der präsentierte Ansatz als vielversprechende Lösung für die erheblichen Probleme, die der LTE Modus 4 mit sich bringt.The LTE Standard added support for a direct vehicle-to-vehicle communication via the Sidelink with Release 14. This dissertation focuses on the scheduling Mode 4, a distributed MAC protocol without involvement of the base station, which requires the periodic reuse of radio resources. The state of the art and a own analysis of this protocol unveil multiple problems. For example, packet collisions repeat in time, so that some vehicles are unable to distribute safety-critical information for extended periods of time. Collisions also arise due to the hidden-terminal problem, which is simply put up with in Mode 4. Additionally, varying packet sizes or rates can hardly be supported. Consequently, an approach called "Scheduling based on Acknowledgement Feedback Exchange" is proposed. Firstly, a reservation of radio resources is split into multiple, interleaved sub-reservations that use different radio resources. This increases the robustness against repeating collisions. It is also the basis for a distributed congestion control that does not violate the periodicity. Moreover, different packet rates or sizes can be supported. The periodic reuse of radio resources enables the transmission of acknowledgements for radio resources instead of packets. These can be transmitted in a bitmap inside the padding bits. Hidden-terminal problems can be mitigated by considering the acknowledgements when selecting radio resources as they announce the use of these radio resources. Collisions can also be detected and prevented from re-occurring. The evaluation of the MAC protocol is mostly performed using discrete-event simulations, which model the movement of every single vehicle. The presented approach leads to a clear improvement of the packet delivery rate. The use of an application-oriented metric shows that the communication robustness can be improved distinctly. The proposed approach hence presents itself as a promising solution for the considerable problems of LTE Mode 4
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Intelligent and bandwidth-efficient medium access control protocols for IEEE 802.11p-based Vehicular Ad hoc Networks
Vehicle-to-Vehicle (V2V) technology aims to enable safer and more sophisticated transportation via the spontaneous formation of Vehicular Ad hoc Networks (VANETs). This type of wireless networks allows the exchange of kinematic and other data among vehicles, for the primary purpose of safer and more efficient driving, as well as efficient traffic management and other third-party services. Their infrastructure-less, unbounded nature allows the formation of dense networks that present a channel sharing issue, which is harder to tackle than in conventional WLANs.
This thesis focuses on optimising channel access strategies, which is important for the efficient usage of the available wireless bandwidth and the successful deployment of VANETs. To start with, the default channel access control method for V2V is evaluated hardware via modifying the appropriate wireless interface Linux driver to enable finer on-the-fly control of IEEE 802.11p access control layer parameters. More complex channel sharing scenarios are evaluated via simulations and findings on the behaviour of the access control mechanism are presented. A complete channel sharing efficiency assessment is conducted, including throughput, fairness and latency measurements. A new IEEE 802.11p-compatible Q-Learning-based access control approach that improves upon the studied protocol is presented. The stations feature algorithms that “learn” how to act optimally in VANETs in order to maximise their achieved packet delivery and minimise bandwidth wastage. The feasibility of Q-Learning to be used as the base of selflearning protocols for IEEE 802.11p-based V2V communication access control in dense environments is investigated in terms of parameter tuning, necessary time of exploration, achieving latency requirements, scaling, multi-hop and accommodation of simultaneous applications. Additionally, the novel Collection Contention Estimation (CCE) mechanism for Q-Learning-based access control is presented. By embedding it on the Q-Learning agents, faster convergence, higher throughput, better service separation and short-term fairness are achieved in simulated network deployments.
The acquired new insights on the network performance of the proposed algorithms can provide precise guidelines for efficient designs of practical, reliable, fair and ultra-low latency V2V communication systems for dense topologies. These results can potentially have an impact across a range of related areas, including various types of wireless networks and resource allocation for these, network protocol and transceiver design as well as QLearning applicability and considerations for correct use
An efficient cluster-based service model for vehicular ad-hoc networks on motorways
Vehicular Ad-Hoc Networks (VANET) can, but not limited to provide users with useful traffic and environmental information services to improve travelling efficiency and road safety. The communications systems used in VANET include vehicle-to-vehicle communications (V2V) and vehicle-to-infrastructure communications (V2I). The transmission delay and the energy consumption cost for maintaining good-quality communications vary depending on the transmission distance and transmission power, especially on motorways where vehicles are moving at higher speeds. In addition, in modern transportation systems, electric vehicles are becoming more and more popular, which require a more efficient battery management, this also call for an efficient way of vehicular transmission. In this project, a cluster-based two-way data service model to provide real-time data services for vehicles on motorways is designed. The design promotes efficient cooperation between V2V and V2I, or namely V2X, with the objective of improving both service and energy performance for vehicular networks with traffic in the same direction. Clustering is an effective way of applying V2X in VANET systems, where the cluster head will take the main responsibility of exchanging data with Road Side Units (RSU) and other cluster members. The model includes local service data collection, data aggregation, and service data downloading. We use SUMO and OMNET++ to simulate the traffic scenarios and the network communications. Two different models (V2X and V2I) are compared to evaluate the performance of the proposed model under different flow speeds. From the results, we conclude that the cluster-based service model outperforms the non-clustered model in terms of service successful ratio, network throughput and energy consumption
Kollektive Perzeption in fahrzeugbasierten Ad-hoc Netzwerken
In combination with the current developments in the area of automatically driving vehicles, the introduction of inter-vehicle communication plays a crucial role for realising the long-term objective of what is known as cooperative driving.
A cornerstone for the expansion of automated vehicles is their thorough understanding of the current driving environment.
For this purpose, each vehicle generates an environment model containing information about other perceived traffic participants and objects.
Local perception sensors are important data providers for this model, as they contribute implicit knowledge about the environment.
In combination with a direct communication link between traffic participants, explicit knowledge can be added to the environment model as well.
The key concept developed within this thesis is called Collective Perception:
it focuses on sharing data gathered by local perception sensors of one vehicle with other traffic participants by means of inter-vehicle communication.
As a result of this concept, future applications relying on a comprehensive understanding of the current driving environment are made feasible.
The analyses presented in this thesis employ a vehicular ad-hoc network (VANET) based on the standardised framework of the European IEEE 802.11p-based ITS G5 protocol stack for inter-vehicle communication.
The effectiveness of the technology relies on an existing communication link between a sufficient number of communication partners - the critical mass.
The expansion of inter-vehicle communication, however, can be supported by capacitating indirect effects.
Collective Perception is one representative of these effects, as the information density within the network between the vehicles is increased, even at low market penetration rates.
At the core of Collective Perception stands the introduction of a message format which serves as a vehicle for the exchange of sensor data within a VANET.
The development of the message is influenced by two perspectives:
First, the vehicle perspective affects the relevant contents of the message required by data-fusion processes and application algorithms.
Second, from the network perspective, constraints resulting from the network stack and effects caused by congestion control mechanisms have to be considered.
This thesis addresses both perspectives to develop a holistic concept for exchanging sensor data within a VANET.Im Zusammenhang mit den aktuellen Entwicklungen im Themenbereich automatisch fahrender Fahrzeuge spielt die Einführung der Fahrzeug-zu-Fahrzeug-Kommunikation eine zunehmend wichtige Rolle, um langfristig kooperatives Fahren zu realisieren.
Eine Voraussetzung für dessen Umsetzung ist dabei die umfassende Wahrnehmung der aktuellen Fahrumgebung.
Jedes Fahrzeug erstellt dafür ein sogenanntes Umfeldmodell, welches Informationen über andere Verkehrsteilnehmer und Objekte beinhaltet.
Eine wichtige Datenquelle für dieses Modell sind zum einen lokale Umfeldsensoren, welche implizites Wissen über die aktuelle Fahrumgebung beisteuern.
Zum anderen kann dem Umfeldmodell bei einer direkten Kommunikationsverbindung mit anderen Verkehrsteilnehmern auch explizites Wissen hinzugefügt werden.
Im Rahmen dieser Arbeit wird ein Konzept zur Realisierung der sogenannten kollektiven Wahrnehmung entwickelt:
Hierbei wird Fahrzeugen der Austausch lokaler Sensordaten mit anderen Verkehrsteilnehmern unter Verwendung der Fahrzeug-zu-Fahrzeug-Kommunikation ermöglicht.
Somit können zukünftige Fahrerassistenzfunktionen auf ein umfassenderes Umfeldmodell zugreifen.
Den im Rahmen der Arbeit durchgeführten Analysen liegt ein fahrzeugbasiertes Ad-hoc Netzwerk zugrunde, welches auf dem europäischen IEEE 802.11p basierten ITS G5 Protokollstapel beruht.
Die Effektivität der Technologie fußt hierbei auf der Existenz der sogenannten kritischen Masse:
Eine ausreichende Anzahl an Kommunikationspartnern muss zugegen sein, damit der Technologie ein Nutzen zugemessen werden kann.
Die Verbreitung der Technologie kann jedoch durch indirekte Effekte unterstützt werden.
Die kollektive Wahrnehmung ist ein Repräsentant dieser indirekten Effekte, da die Informationsdichte in dem zwischen den Fahrzeugen bestehenden Netzwerk selbst bei niedrigen Marktausstattungsraten erhöht wird.
Im Rahmen der Arbeit wird daher ein neues Nachrichtenformat entwickelt, welches von zwei Perspektiven beeinflusst:
Die Sicht der fahrzeugseitigen Assistenzsysteme und deren Datenfusionsalgorithmen beeinflusst die notwendigen Inhalte der Nachricht.
Weiterhin werden aus der Netzwerksicht durch Mechanismen wie denen der Lastkontrolle und den bestehenden Nachrichtengrößenbeschränkungen spezifische Anforderungen gestellt.
Beide Untersuchungen werden dabei in der Arbeit zur Erstellung eines ganzheitlichen Konzeptes für die kollektive Wahrnehmung verbunden
APPLYING COLLABORATIVE ONLINE ACTIVE LEARNING IN VEHICULAR NETWORKS FOR FUTURE CONNECTED AND AUTONOMOUS VEHICLES
The main objective of this thesis is to provide a framework for, and proof of concept of, collaborative online active learning in vehicular networks. Another objective is to advance the state of the art in simulation-based evaluation and validation of connected intelligent vehicle applications. With advancements in machine learning and artificial intelligence, connected autonomous vehicles (CAVs) have begun to migrate from laboratory development and testing conditions to driving on public roads. Their deployment in our environmental landscape offers potential for decreases in road accidents and traffic congestion, as well as improved mobility in overcrowded cities. Although common driving scenarios can be relatively easily solved with classic perception, path planning, and motion control methods, the remaining unsolved scenarios are corner cases in which traditional methods fail. These unsolved cases are the keys to deploying CAVs safely on the road, but they require an enormous amount of data collection and high-quality human annotation, which are very cost-ineffective considering the ever-changing real-world scenarios and highly diverse road/weather conditions. Additionally, evaluating and testing applications for CAVs in real testbeds are extremely expensive, as obvious failures like crashes tend to be rare events and can hardly be captured through predefined test scenarios. Therefore, realistic simulation tools with the benefit of lower cost as well as generating reproducible experiment results are needed to complement the real testbeds in validating applications for CAVs. Therefore, in this thesis, we address the challenges therein and establish the fundamentals of the collaborative online active learning framework in vehicular network for future connected and autonomous vehicles.Ph.D
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Blockchain based secure message dissemination in vehicular networks
Vehicular ad-hoc networks (VANETs) are one of the key elements in Intelligent Transportation System (ITS) to enable information exchange among vehicles and Roadside Units (RSUs) via vehicle-to-vehicle (V2V) and vehicle-to- nfrastructure (V2I) communications. With continuously increasing number of vehicles on road, there are numerous security and privacy challenges associated with VANETs. Communication among vehicles is needed to be secure and bandwidth efficient. Also, the messages exchanged between vehicles must be authentic so as to maintain a trusted network in a privacy-preserving manner. Furthermore, a sustainable economic model is required to incentivise honest and cooperative vehicles. Traditional security and privacy solutions in centralised networks are not applicable to VANETs due to its distributed nature, heterogeneity, high mobility and low latency requirements. Meanwhile, the new development of blockchain has been attracting significant interests due to its key features including consensus to evaluate message credibility and immutable storage in distributed ledger, which provides an alternative solution to the security and privacy challenges in VANETs.
This thesis aims to present blockchain solutions for the security and privacy of VANETs meeting the stringent requirements of low latency and bandwidth-efficient message dissemination. VANETs are simulated in OMNeT++ to validate the proposed solutions. Specifically, two novel blockchain consensus algorithms have been developed for message authentication and relay selection in presence of malicious vehicles. The first employs a voting based message validation and relay selection, which reduces the failure rate in message validation by 11% as compared to reputation based consensus. The second utilises federated learning supported by blockchain as a better privacy-preserving solution, which is 65.2% faster than the first voting based solution. Both approaches include blockchain-based incentive mechanisms and game theory analysis to observe strategic behaviour of honest and malicious vehicles. To further study the privacy aspect of vehicular networks, the integration of blockchain with physical layer security is also theoretically analysed in Vehicle-to-Everything (V2X) communications scenarios. The integration results in 8.2 Mbps increased goodput as compared to the blockchain solution alone.
In essence, our research work shows that blockchain can offer better control and security, as compared to centralised solutions, if properly adjusted according to the application and network requirements. Thus, the proposed solutions can provide guidelines for practically feasible application of blockchain in vehicular networks
5G and beyond networks
This chapter investigates the Network Layer aspects that will characterize the merger of the cellular paradigm and the IoT architectures, in the context of the evolution towards 5G-and-beyond, including some promising emerging services as Unmanned Aerial Vehicles or Base Stations, and V2X communications