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

Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage

Radio resources in vehicle-to-vehicle (V2V) communication can be scheduled either by a centralized scheduler residing in the network (e.g., a base station in case of cellular systems) or a distributed scheduler, where the resources are autonomously selected by the vehicles. The former approach yields a considerably higher resource utilization in case the network coverage is uninterrupted. However, in case of intermittent or out-of-coverage, due to not having input from centralized scheduler, vehicles need to revert to distributed scheduling. Motivated by recent advances in reinforcement learning (RL), we investigate whether a centralized learning scheduler can be taught to efficiently pre-assign the resources to vehicles for out-of-coverage V2V communication. Specifically, we use the actor-critic RL algorithm to train the centralized scheduler to provide non-interfering resources to vehicles before they enter the out-of-coverage area. Our initial results show that a RL-based scheduler can achieve performance as good as or better than the state-of-art distributed scheduler, often outperforming it. Furthermore, the learning process completes within a reasonable time (ranging from a few hundred to a few thousand epochs), thus making the RL-based scheduler a promising solution for V2V communications with intermittent network coverage.Comment: Article published in IEEE VNC 201

Optimization of vehicular networks in smart cities: from agile optimization to learnheuristics and simheuristics

Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an ‘Internet of vehicles’ with the potential to significantly enhance citizens’ mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities.Peer ReviewedPostprint (published version

Efficient Message Dissemination on Curve Road in Vehicular Networks

Effective emergency message dissemination is a great importance on a specific road in vehicular networks (VN). The existing methods are not most efficient solutions for message dissemination on the curve road, which primarily focus on highway and urban road. In order to improve the efficiency of message dissemination on the curved road, the paper proposed a message dissemination method based on bidirectional relay nodes. The message can be disseminated in two directions simultaneously. The paper designed a relay node selection method based on the neighbor nodes’ coverage length of the road. Different waiting delays are assigned to the neighbor nodes according to the cover capability of the road in which the message has not arrived. Simulation results demonstrated that the efficiency of the proposed method is superior to the common solutions in terms of the contention delay and the propagation velocity

Vehicular Dynamic Spectrum Access: Using Cognitive Radio for Automobile Networks

Vehicular Dynamic Spectrum Access (VDSA) combines the advantages of dynamic spectrum access to achieve higher spectrum efficiency and the special mobility pattern of vehicle fleets. This dissertation presents several noval contributions with respect to vehicular communications, especially vehicle-to-vehicle communications. Starting from a system engineering aspect, this dissertation will present several promising future directions for vehicle communications, taking into consideration both the theoretical and practical aspects of wireless communication deployment. This dissertation starts with presenting a feasibility analysis using queueing theory to model and estimate the performance of VDSA within a TV whitespace environment. The analytical tool uses spectrum measurement data and vehicle density to find upper bounds of several performance metrics for a VDSA scenario in TVWS. Then, a framework for optimizing VDSA via artificial intelligence and learning, as well as simulation testbeds that reflect realistic spectrum sharing scenarios between vehicle networks and heterogeneous wireless networks including wireless local area networks and wireless regional area networks. Detailed experimental results justify the testbed for emulating a mobile dynamic spectrum access environment composed of heterogeneous networks with four dimensional mutual interference. Vehicular cooperative communication is the other proposed technique that combines the cooperative communication technology and vehicle platooning, an emerging concept that is expected to both increase highway utilization and enhance both driver experience and safety. This dissertation will focus on the coexistence of multiple vehicle groups in shared spectrum, where intra-group cooperation and inter-group competition are investigated in the aspect of channel access. Finally, a testbed implementation VDSA is presented and a few applications are developed within a VDSA environment, demonstrating the feasibility and benefits of some features in a future transportation system

Survey on Congestion Detection and Control in Connected Vehicles

The dynamic nature of vehicular ad hoc network (VANET) induced by frequent topology changes and node mobility, imposes critical challenges for vehicular communications. Aggravated by the high volume of information dissemination among vehicles over limited bandwidth, the topological dynamics of VANET causes congestion in the communication channel, which is the primary cause of problems such as message drop, delay, and degraded quality of service. To mitigate these problems, congestion detection, and control techniques are needed to be incorporated in a vehicular network. Congestion control approaches can be either open-loop or closed loop based on pre-congestion or post congestion strategies. We present a general architecture of vehicular communication in urban and highway environment as well as a state-of-the-art survey of recent congestion detection and control techniques. We also identify the drawbacks of existing approaches and classify them according to different hierarchical schemes. Through an extensive literature review, we recommend solution approaches and future directions for handling congestion in vehicular communications

UAV-assisted data dissemination based on network coding in vehicular networks

Efficient and emergency data dissemination service in vehicular networks (VN) is very important in some situations, such as earthquakes, maritime rescue, and serious traffic accidents. Data loss frequently occurs in the data transition due to the unreliability of the wireless channel and there are no enough available UAVs providing data dissemination service for the large disaster areas. UAV with an adjustable active antenna can be used in light of the situation. However, data dissemination assisted by UAV with the adjustable active antenna needs corresponding effective data dissemination framework. A UAV-assisted data dissemination method based on network coding is proposed. First, the graph theory to model the state of the data loss of the vehicles is used; the data dissemination problem is transformed as the maximum clique problem of the graph. With the coverage of the directional antenna being limited, a parallel method to find the maximum clique based on the region division is proposed. Lastly, the method\u27s effectiveness is demonstrated by the simulation; the results show that the solution proposed can accelerate the solving process of finding the maximum clique and reduce the number of UAV broadcasts. This manuscript designs a novel scheme for the UAV-assisted data dissemination in vehicular networks based on network coding. The graph theory is used to model the state of the data loss of the vehicles. With the coverage of the directional antenna being limited, then a parallel method is proposed to find the maximum clique of the graph based on the region division. The effectiveness of the method is demonstrated by the simulation

A Q-Learning approach with collective contention estimation for bandwidth-efficient and fair access control in IEEE 802.11p vehicular networks

Vehicular Ad hoc Networks (VANETs) are wireless networks formed of moving vehicle-stations, that enable safety-related packet exchanges among them. 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, due to fundamental differences of the protocol stack. Optimising channel access strategies is important for the efficient usage of the available wireless bandwidth and the successful deployment of VANETs. We present a Q-Learning-based approach to wirelessly network a big number of vehicles and enable the efficient exchange of data packets among them. More specifically, this work focuses on a IEEE 802.11p-compatible contention-based Medium Access Control (MAC) protocol for efficiently sharing the wireless channel among multiple vehicular stations. The stations feature algorithms that "learn" how to act optimally in a network in order to maximise their achieved packet delivery and minimise bandwidth wastage. Additionally, via a Collective Contention Estimation (CCE) mechanism which we embed on the Q-Learning agent, faster convergence, higher throughput and short-term fairness are achieved