This paper discusses scalable cooperative maneuvering using conflict analysis, where conflicts are managed between multiple vehicles in mixed-autonomy environments. Two different classes of cooperation, enabled by vehicle-to-everything (V2X) communication, are considered–status sharing and intent sharing. In status sharing, connected vehicles share their current states, such as position and velocity, while in intent sharing the information about vehicles’ future trajectories is exchanged. The scalability of conflict analysis is studied through traffic scenarios where an ego vehicle interacts with multiple remote vehicles in a safety- and time-critical manner. We show thatpairwiseconflict analysis, which decomposes a large-scale conflict management problem to multiple sequentially solvable smaller-scale problems, is a key component of scalability. Conflict analysis, while being scaled up to more complex traffic scenarios, preserves the efficient consideration of different types of V2X information, time delay effects, and flexible control design. These results are demonstrated using simulations with real traffic data.

Low latency in establishing a communication link is a key to maximize the performance of vehicle-to-vehicle wireless LANs (V2V WLANs), as contact duration of vehicles is often limited due to their fast mobility. However, previous works have mainly addressed Vehicle-to-Infrastructure (V2I) link setup latency on legacy Wi-Fi standards (IEEE 802.11b/g). Moreover, there has been a lack of performance evaluation regarding V2V link setup latency with current Wi-Fi technology and IEEE 802.11ai standard defines Fast Initial Link Setup (FILS), which simplifies the link setup process to reduce the latency. In this paper, we investigate the characteristics of V2V link setup latency with regular Extensible Authentication Protocol (EAP) authentication (EAP-TLS) and FILS with current Wi-Fi technology. The evaluation was conducted in general vehicular scenarios with different combinations of inter-vehicle distance, vehicle speed and type of roads (i.e., urban roads vs highways). FILS achieved the average link setup latency of 0.32 seconds, outperforming EAP-TLS that resulted in the average latency of 2.35 seconds. The link setup latency was stable in most of the scenarios we tested regardless of inter-vehicle distance, speed and Wi-Fi signal strength. The only exception was on highways, where significantly longer link setup latency was observed when the relative speed and distance changed rapidly, line-of-sight (LoS) was fully blocked by other vehicles and there were no other objects (e.g., buildings and vegetation) that form indirect signal propagation paths.

Hardware-Aware Neural Architecture Search (HW-NAS) has emerged as a powerful tool for designing efficient deep neural networks (DNNs) tailored to edge devices. However, existing methods remain largely impractical for real-world deployment due to their high time cost, extensive manual profiling, and poor scalability across diverse hardware platforms with complex, device-specific energy behavior.

In this study, we propose a novel negotiation-based cooperative maneuvering strategy to assist connected automated vehicles (CAVs) in resolving conflicts under different traffic scenarios. We introduce conflict charts to determine when negotiation is necessary, along with a request and response protocol to facilitate traffic conflict resolution. Additionally, we propose an easy-to-implement controller that allows CAVs to resolve conflicts based on the agreement reached through negotiation. Simulation results using real vehicle data are used to demonstrate that the proposed negotiation protocol helps to ensure safety while improving time efficiency compared to cooperations that rely on other communication strategies.

In this paper, we propose models with explicit trainable delays for learning teleoperated driving (ToD) behavior from limited vehicle trajectory data. The data-driven model is integrated with physics-based nonlinear vehicle dynamics and formulated as a neural delay differential equation (NDDE). The model can be analyzed using the same tools as developed for classical delay differential equations. The physics-based nonlinearity built into the data-driven model reduces the model complexity, enables training with limited data, and provides good generalizations. An overall latency in the loop is learned and a generic steering controller that characterizes the remote operator is identified at the same time through the training process. This information could be used to evaluate the performance of ToD in the presence of communication latency. We provide examples of learning from simulation data generated by a kinematic vehicle model and from experimental data generated by a human operator driving in a high-fidelity simulation environment. The same data-driven model and training algorithm is used in both cases, which demonstrates the generalizability of the proposed approach.

Connected vehicles use Vehicle-to-Everything (V2X) communication to improve situational awareness. However, transmitting raw or irrelevant data may congest the communication channel, and limit the scalability and effectiveness of safety applications. This paper introduces the Context-Aware V2X concept, which assesses message relevance and adds contextual meaning before transmission, ensuring that only essential information reaches relevant receivers. We demonstrate the effectiveness of the Context-Aware V2X concept through simulations of the tailgating scenario, where results show that Context-Aware V2X can improve driver awareness and reduce risks associated with tailgating.

This paper examines the critical role of intent-sharing in enabling effective maneuver coordination for connected and automated vehicles (CAVs). Successful maneuver coordinations require vehicles to accurately know other vehicles' driving intentions. Intent-sharing can be achieved by the remote vehicles directly communicating their plans with the ego vehicle, as opposed to the ego vehicle predicting the trajectory on the remote vehicles’ behalf. In this paper, we investigate the potential of intent-sharing on maneuver coordination effectiveness by quantifying the percentage of successful coordinations. We analyze the potential of intent-sharing by comparing its effectiveness for coordinated lane changes in a highway scenario with the effectiveness of a trajectory prediction method based on current kinematic data. Our analysis demonstrates in two scenarios substantial improvements in maneuver coordination when CAVs have direct access to the nearby vehicles’ driving intentions through intent sharing. These findings highlight the importance of including intent-sharing in the maneuver coordination protocol.

Edge computing is becoming ever more relevant to offload compute-heavy tasks in vehicular networks. In this context, the concept of vehicular micro clouds (VMCs) has been proposed to use compute and storage resources on nearby vehicles to complete computational tasks. As many tasks in this application domain are time critical, offloading to the cloud is prohibitive. Additionally, task deadlines have to be dealt with. This paper addresses two main challenges. First, we present a task migration algorithm supporting deadlines in vehicular edge computing. The algorithm is following the earliest deadline first model but in presence of dynamic processing resources, i.e., vehicles joining and leaving a VMC. This task offloading is very sensitive to the mobility of vehicles in a VMC, i.e., the so-called dwell time a vehicles spends in the VMC. Thus, secondly, we propose a machine learning-based solution for dwell time prediction. Our dwell time prediction model uses a random forest approach to estimate how long a vehicle will stay in a VMC. Our approach is evaluated using mobility traces of an artificial simple intersection scenario as well as of real urban traffic in cities of Luxembourg and Nagoya. Our proposed approach is able to realize low-delay and low-failure task migration in dynamic vehicular conditions, advancing the state of the art in vehicular edge computing.

Decentralized content distribution over vehicle-to-vehicle (V2V) WLANs holds promise to mitigate load on cellular networks. The delay in establishing a communication link is a key to maximize V2V data transfer opportunity, as contact duration of vehicles is often limited due to their fast mobility. IEEE 802.11ai standard defines Fast Initial Link Setup (FILS), which simplifies the link setup process to reduce latency. In this paper, we investigate the benefit and limitations of FILS in V2V WLANs. We develop a testbed involving multiple vehicles, and compare the link setup latency of FILS with regular EAP authentication. Counterintuitively, regular EAP achieved equivalent performance in V2V WLANs due to architectural constraints of FILS.

In this paper, we demonstrate how the network latency, the longitudinal velocity and the path curvature affect performance of the teleoperated driving (ToD). The performance of a ToD system is studied analytically through stability analysis of a dimensionless vehicle dynamics model with a scaled delay, which integrates the end-to-end (E2E) latency and the longitudinal velocity of the vehicle. We also establish a numerical simulation framework for ToD while incorporating a stochastic latency in the control loop arising from vehicle-to-network-to-vehicle (V2N2V) communication through a wireless network. The stochasticity of the latency mostly comes from the network scalability challenges to support high video bitrates, which also leads to packet drops. We provide simulation results of teleoperating a vehicle in a realistic parking lot scenario and demonstrate the effects of speed, curvature and stochastic latency on the maneuver performance.

Negotiation is a class of cooperation enabled by vehicle-to-everything (V2X) communication, which involves the exchange of maneuver requests and responses between road users. In this paper, we develop criteria for request initiation and response generation under a unified conflict analysis framework. This leads to guaranteed maneuver feasibility in request and response that satisfy user-based behavior preferences. We implement negotiation via commercially available V2X devices, and experimentally evaluate the benefits of negotiation in conflict resolution. We demonstrate that negotiation can significantly benefit time efficiency of maneuvers while ensuring safety, compared to lower levels of cooperation such as status-sharing and intent-sharing. These benefits and their degradation under communication delays are quantified.

We present the performance analysis of TCP CUBIC/BBR over V2V Wi-Fi (IEEE 802.11ac). Our measurements focus on three static parking scenarios with different distances at the office area. The results reveal the impact of TCP CUBIC and BBR on data transfer time and TCP metrics. (i) There are two major reasons of fluctuated TCP throughput. The first reason is narrow available bandwidth over V2V Wi-Fi. The second reason is delayed TCP connection establishment due to delayed SYN+ACK and SYN packet retransmission. (ii) The bytes in-flight of TCP CUBIC are dynamically changed by packet retransmission events on V2V Wi-Fi. The loss-based congestion control is not promising the high throughput. We believe that our analysis results provide implications for efficient data transfer over V2V Wi-Fi.

Vehicular Micro Cloud (VMC) is a group of connected vehicles where vehicles collaborate on a task over the vehicular network. A potential use case of VMC is that micro cloud members transfer data to each other via Vehicle-to-Vehicle (V2V) links, and the data is collaboratively uploaded to remote server (e.g., data center) when the connected vehicles are connected to a Wi-Fi network. In this paper, we focus on this use case and propose collaborative upload by VMC. We demonstrated the feasibility of the proposed method through the large-scale urban simulation (Los Angeles downtown traffic model). Our simulation results showed that the proposed method can reduce the upload data of traditional cellular network-based data upload by 50%.

Teleoperated driving (ToD) enables the remote driving or control of vehicles. For this purpose, vehicles must transmit video feeds to the ToD control center so that the remote operator is fully aware of the driving conditions and can safely control the vehicle. 5G (and beyond) networks are fundamental for the deployment of ToD as they can provide the low latency, reliable and broadband connection necessary to connect the vehicle and ToD control center. However, it is unclear whether common 5G network architectures and configurations are well-suited to support the simultaneous teleoperation of multiple vehicles with demanding uplink bandwidth, as current networks are mainly configured to support mobile broadband services. This paper demonstrates that MEC or edge-based 5G networks are better suited to support and scale the ToD service than centralized networks, and quantifies the bandwidth required to simultaneously teleoperate multiple vehicles under various 5G network architectures and configurations, including different duplexing modes and TDD frame structures. Finally, the study shows that the configuration of the control channels can help mitigate the impact that the processing time of the video feeds has on the capacity to support and scale the ToD service.

Connected and automated vehicles can leverage V2X communications to coordinate their maneuvers. Maneuver coordination is expected to improve traffic efficiency and safety, but the design of maneuver coordination is a challenging task in complex traffic scenarios, as maneuvers affect not only the involved vehicles but also nearby traffic. This study introduces a reference state machine for the design of maneuver coordination. Furthermore, we identify and analyze the challenges that maneuver coordination may encounter. We quantify the relevance of each challenge and propose a set of countermeasures to enhance the robustness and effectiveness of maneuver coordination.

Connected Automated Vehicles (CAVs) will use multiple V2X services to support connected and automated driving functions. The bandwidth required to support such services will augment as CAVs are gradually deployed. It is therefore important to accurately estimate the spectrum requirements to anticipate possible scalability challenges ahead. Current estimations consider a simplified modeling of the transmitter as well as context factors such as the number of vehicles in the communication range. Moreover, they do not accurately model if the Quality of Service (QoS) of the considered V2X services is satisfied or not. This study progresses the state of the art with a novel analytical model that quantifies the bandwidth required to support multiple V2X services. The model considers the impact of the vehicular context, the transmission parameters and the communication requirements to take into account the QoS at the receiver. This is important since adapting the transmission parameters can reduce the channel load but also impacts the probability to correctly receive each packet and therefore the bandwidth required to guarantee a target QoS at the receiver. The proposed model can be adapted to different wireless technologies and messages, but is applied in this study to quantify the bandwidth required by LTE-V2X to support the transmission of CAMs, CPMs and MCMs. The study demonstrates the scalability challenges ahead to support multiple V2X services.

Intent sharing is a class of cooperation enabled by vehicle-to-everything (V2X) communication, which allows for information exchange between road users about their intended future behaviors. In this paper, we propose a generalized representation of vehicles’ motion intent from a dynamical systems viewpoint. Based on this, we extend the framework of conflict analysis such that intent information can be interpreted in real time to assist the decision-making of intent-receiving vehicles and ensure conflict-free maneuvers. We create intent messages using commercially available V2X radios, and demonstrate experimentally the benefits of sharing intent in cooperative maneuvering. Experiments are performed on a test track where intent-based on-board decision assistance is provided to human drivers in merge scenarios. The experimental results reveal significant benefits of intent sharing in enhancing vehicle safety and time efficiency. Furthermore, we test intent messages on public roads and evaluate the performance in terms of packet delivery ratio. The data collected on public highways are fed into numerical simulations to investigate the effects of intent transmission conditions on conflict resolution.

As the demand for vehicle-to-everything communication (V2X) band in the 5.9 GHz increases, the millimeter-wave spectrum offers alternative options in unlicensed or radar-dedicated bands with wider bandwidth. Joint radar-communication (JRC) systems emerge as a comprehensive solution to effectively utilize these bands by integrating both functions within the same waveform and hardware. In this work, we present a multiple-input and multiple-output (MIMO) JRC testbed, operating in the 24 GHz mmWave band, utilizing orthogonal frequency division multiplexing (OFDM) waveform that simultaneously carries data across all subcarriers. In particular, we develop a real-time operating, full-duplex JRC prototype with a fully-digital front-end and software-defined radios, providing enhanced flexibility and capability. Additionally, for systems with high computational power, we introduce a high-resolution range-angle processing method based on the MUSIC algorithm. Through mobile experiments with multiple targets, we showcase simultaneous data transmission and high-resolution radar processing capabilities enabled by the fully-digital MIMO architecture. By leveraging radar’s tracking capability, we propose a radar-assisted precoding approach, offering a low-complexity beamforming solution with reduced feedback overhead. Our experimental results demonstrate that the proposed precoding method achieves comparable performance compared to the conventional precoding method.

A Vehicular Micro Cloud (VMC) is a group of connected vehicles where vehicles collaborate on tasks through vehicular networks. VMCs may overlap with other vehicular micro clouds in certain regions (e.g., intersections), known as overlapping zones. In overlapping zones, micro cloud members may have to switch between multiple VMCs back and forth, which degrades the functionality of the VMC. In this paper, we address this problem and propose to regroup micro cloud members when VMCs overlap. VMCs share their information with a remote server. The remote server controls and swaps members among VMCs in overlapping zones. We test the feasibility of the proposed approach through a simulation study. Extensive simulations in different settings demonstrate that controlled membership swaps can reduce the member switches among VMCs in overlapping zones by about 85%.

Today, deep learning optimization is primarily driven by research focused on achieving high inference accuracy and reducing latency. However, the energy efficiency aspect is often overlooked, possibly due to a lack of sustainability mindset in the field and the absence of a holistic energy dataset. In this paper, we conduct a threefold study, including energy measurement, prediction, and efficiency scoring, with an objective to foster transparency in power and energy consumption within deep learning across various edge devices. Firstly, we present a detailed, first-of-its-kind measurement study that uncovers the energy consumption characteristics of on-device deep learning. This study results in the creation of three extensive energy datasets for edge devices, covering a wide range of kernels, state-of-the-art DNN models, and popular AI applications. Secondly, we design and implement the first kernel-level energy predictors for edge devices based on our kernel-level energy dataset. Evaluation results demonstrate the ability of our predictors to provide consistent and accurate energy estimations on unseen DNN models. Lastly, we introduce two scoring metrics, PCS and IECS, developed to convert complex power and energy consumption data of an edge device into an easily understandable manner for edge device end-users. We hope our work can help shift the mindset of both end-users and the research community towards sustainability in edge computing, a principle that drives our research. Find data, code, and more up-to-date information at https://amai-gsu.github.io/DeepEn2023.

Intent sharing is an emerging type of vehicle-to-everything (V2X) communication where vehicles share information about their intended future trajectories. In this study, we implement intent sharing via commercially available V2X devices, and experimentally demonstrate its benefits in resolving conflicts arising in cooperative maneuvering. An extended framework of conflict analysis is used to provide decision-making assistance via on-board warnings to a human-driven vehicle in highway merge scenario. We show that intent information can significantly benefit safety and time efficiency. Using the experimental data, we also evaluate the effects of communication conditions (e.g., sending rate and intent horizon) on the gained benefits.

Teleoperated driving (ToD) can support autonomous driving under complex or unexpected traffic scenarios that an autonomous vehicle may not understand or be able to handle. In ToD, autonomous vehicles transmit video feeds and perception data to the remote control center. The operator uses this data to understand the driving environment and remotely control the vehicle that can take over the control once the scenario is resolved. ToD requires reliable and low latency communications between the vehicle and the ToD control center. This study analyzes the feasibility to support ToD with 5G networks. The study demonstrates that the feasibility strongly depends on the bandwidth and the Time Division Duplexing (TDD) frame structure that conditions how the bandwidth is distributed between uplink and downlink transmissions. The study also shows that scaling the number of 5G-supported ToD vehicles requires the vehicles to reduce the video bitrates. The study also shows that traditional centralized 5G network deployments may be challenged by some of the most stringent ToD latency requirements due to the latency introduced by the Internet connection to the ToD control center.

In the field of vehicular networks, multiple approaches have been proposed to share and reuse data acquired by participating hosts. In this context, vehicular micro clouds extend the concept of Mobile Edge Computing (MEC) and bring data storage and processing to the vehicles, solving application tasks that need to be done in real-time. A critical point in shared computing tasks and storage is to keep all nodes synchronized and to maintain consistency. For the first time, we study the relevance of data versions in micro clouds offering intersection management service at four-way stop intersections and we investigate how different versions of data affect both road traffic and wireless communications. Our results validate the intuition that an increase in the amount of different data versions in the micro cloud has negative effects on both road traffic and wireless channel usage. Yet, the choice of the data sharing algorithm can make a huge difference and reduce these effects on a large scale. We found that synchronicity of data versions can be increased by up to 20% through small changes, e.g., keeping small amounts of data history in the applied algorithm.

It is estimated that the data volume between connected vehicles and edge/cloud server(s) will be about 100 petabytes per month by 2025. The networking framework we have, on the other hand, is the existing cellular network in which the most connected vehicles function today. However, such a network suffers from several issues and may not work under this predicted data demand. To address such a dilemma, a new paradigm, Vehicular Knowledge Networking (VKN), is recently introduced. In VKN, the data is transformed into knowledge and it is distributed with various lifetimes/relevance. To benefit from the knowledge, on the other hand, it should be placed intelligently such that a high number of vehicles can access and consume it. In this paper, we tackle this issue and propose mobility-aware smart knowledge placement. In the proposed method, vehicle mobility is analyzed to measure the centrality degree of a region. The computed centrality degrees are then further analyzed to identify the most central zones. The knowledge is placed on these zones to increase availability. We demonstrate the benefits of the proposed method through a simulation. Our preliminary result has shown that the mobility-aware smart knowledge placement makes knowledge accessible from vehicles over short range communication. Through such short-range availability of knowledge, vehicles can use the free spectrum to download it which decreases the cellular communication cost significantly.

In this paper, we extend the conflict analysis framework to resolve conflicts between multiple vehicles with different levels of automation, while utilizing status-sharing and intent-sharing enabled by vehicle-to-everything (V2X) communication. In status-sharing a connected vehicle shares its current state (e.g., position, velocity) with other connected vehicles, whereas in intent-sharing a vehicle shares information about its future trajectory (e.g., velocity bounds). Our conflict analysis framework uses reachability theory to interpret the information contained in status-sharing and intent-sharing messages through conflict charts. These charts enable real-time decision making and control of a connected automated vehicle interacting with multiple remote connected vehicles. Using numerical simulations and real highway traffic data, we demonstrate the effectiveness of the proposed conflict resolution strategies, and reveal the benefits of intent sharing in mixed-autonomy environments.

Vehicular Micro Clouds (VMCs) are an emerging development in the domain of vehicular networks posed to provide local services to users without the need for external infrastructure. This can significantly improve the user experience, in particular due to the low latencies that such systems can achieve. Due to the distributed nature of such a VMC, effective local coordination is important while using minimal communication resources. To this end, it is important to know, how long vehicles will be participating in, and contributing to a VMC. In this work, we investigate, how previous, heuristic-based approaches can be improved by incorporating local, learning-based techniques. Our analysis indicates a potential improvement of the accuracy of the prediction, and resulted in an improved simulation environment within which the learning-based approach can be deployed.

Cellular networks usually support non-safety-critical V2X services using Vehicle-to-Network (V2N) connections. However, the flexibility and capabilities of 5G have triggered interest in analyzing whether 5G could also support advanced V2X services using Vehicle-to-Network-to-Vehicle (V2N2V) connections instead of direct Vehicle-to-Vehicle (V2V) connections. V2N2V requires the integration of the 5G network with computing platforms for processing the V2X packets. The flexibility introduced by 5G facilitates the integration with multiple computing platforms such as Multi-access Edge Computing (MEC), edge cloud, shared data center or central cloud. This results in alternative 5G network deployments with the computing platform installed at different locations between the base station and the Internet. These deployments can have important technical implications for supporting V2X services. In this study, we analyze the impact of different 5G and computing platform deployments on the end-to-end (E2E) latency of V2N2V communications under multi-MNO (Mobile Network Operator) scenarios since vehicles may be served by different operators. We also identify which deployment strategies are more suitable to meet the latency requirements of V2X services for connected and automated driving.

In this paper, we use the Perceived Safety Analysis Framework (PSAF) to assess the awareness of vehicles performing an unprotected left turn at unsignalized intersections. PSAF is an analytical method developed to quantify the awareness of vehicles to surrounding safety-critical road users in traffic. We derive safety conditions for unprotected left turns using surrogate safety measures and right of way rules, and determine which road users are safety-critical to the left-turning ego vehicle. Then, we evaluate the Perceived Safety Error based on whether the ego vehicle can detect critical road users (CRUs) using sensors (such as camera or radar) and via vehicle-to-everything (V2X) communication. We demonstrate that for intersections with sparse traffic, vehicle-to-vehicle (V2V) communication may be insufficient for left-turning vehicles to get full awareness of CRUs, and vehicle-to-infrastructure (V2I) communication helps to eliminate awareness gaps.

5G and multi-access edge computing (MEC) are being considered to support V2X services demanding low latency and highly reliable communications using V2N2V (Vehicle-to-Network-to-Vehicles) communications instead of direct or sidelink V2V (Vehicle-to-Vehicle). Guaranteeing V2X service continuity using V2N2V is a challenging task in multi-Mobile Network Operator (MNO) deployments where vehicles are supported by different MNOs. MEC federations have been proposed to address some of these challenges. A MEC federation is a federated model of MEC systems enabling shared usage of MEC services and applications. Through MEC federations, vehicles can seamlessly access V2X applications independently of whether they are hosted on their MNO’s MEC, or on the MEC of a different (but federated) MNO. This paper presents the first study that analyses the impact of MEC federation on the end-to-end (E2E) latency when supporting V2X services using 5G V2N2V in multi-MNO scenarios. The paper also evaluates the feasibility to support the latency requirements of advanced V2X services in these scenarios, and the benefits introduced by MEC federation. This study considers the V2Xbased cooperative lane merge service as a case study.

Connected Automated Vehicles (CAVs) can utilize V2X communications to exchange their driving intentions and coordinate maneuvers. Previous studies have shown that maneuver coordination can improve traffic efficiency and safety. However, these studies focus on specific scenarios with a limited number of vehicles. Large-scale evaluations with complex interactions among vehicles are necessary to fully recognize the impact of maneuver coordination and to understand how to effectively design and conFigure maneuver coordination. This study progresses the state-of-the-art with a large-scale evaluation of lane change maneuver coordination in a highway scenario. We show how maneuver coordination impacts the traffic distribution and improves traffic fairness. Our study also highlights the need to consider safety when designing maneuver coordination and demonstrates that the coordination triggering conditions impact the execution of the coordination and their spatiotemporal distribution.

The riskiness of the roadway environment needs to be known in advance to improve driving safety. Such knowledge brings strong benefit to drivers and could be used to reduce the risk of collision. For example, vehicles can support a driver with guidance before arriving at the risky zones. In this paper, we focus on this use case. We propose risk avoidance by Vehicular Knowledge Networking (VKN). The proposed method mines the maneuver conflicts to determine risky zones. According to identified zones, guidance (e.g., speed and lane change suggestions) is shared with vehicles to help drivers pass these risky regions smoothly. Extensive simulations in different settings have shown that risk avoidance by VKN could decrease the collision risk by approximately 50%.

Large-scale deployment of connected vehicles with cooperative sensing technologies increases the demand on the vehicular communication spectrum in 5.9 GHz allocated for the exchange of safety messages. To support the high data rates needed by such applications, the millimeter-wave (mmWave) automotive radar spectrum at 76–81 GHz can be utilized for wideband communication as well. For this purpose, various joint automotive radar-communication (JARC) systems have been proposed in the literature to perform both functions using the same wideband waveform. However, the wideband joint waveforms encounter frequency-selectivity in both radar and communication channels due to multi-path propagation. In this paper, we address the optimal joint waveform design problem to exploit the frequency-selectivity for wideband JARC operations via orthogonal frequency-division multiplexing (OFDM) wherein subcarrier coefficients are designed for optimal power allocation and phase coding. We show that the problem is a non-convex quadratically constrained quadratic programming (QCQP) problem which is known to be NP-hard. Existing approaches to solve QCQP include semidefinite relaxation (SDR) which incurs high time complexity. Instead, we propose approximation methods to solve QCQP more efficiently by leveraging structured matrices and using convex approximations. Finally, we demonstrate the efficacy of the proposed approaches through numerical simulations.

In this paper, we propose an optimization-based strategy that utilizes vehicle-to-everything (V2X) communication in order to resolve conflicts between vehicles of different automation levels. The strategy consists of a decision checking mechanism and a control law to adjust the decision of an ego vehicle in a certain maneuver based on status update messages received from a remote vehicle involved in that maneuver. Using numerical simulations with real highway data, we demonstrate the proposed opportunistic strategy and show how it improves safety and maximizes the time efficiency of the ego vehicle. We also highlight the benefits of the strategy by comparing the results with an existing conservative strategy.

Connected and automated vehicles (CAVs) are expected to make use of Vehicle-to-Everything (V2X) communication to exchange sensor and trajectory data. Using this data, CAVs can coordinate their maneuvers for safer and more efficient driving. ETSI and SAE are currently working to define standards for maneuver coordination and cooperative driving. The current approach at ETSI is based on a distributed solution where vehicles use Vehicle-to-Vehicle (V2V) communication to exchange their planned and desired trajectories. This study evaluates the potential benefits of the ETSI Maneuver Coordination Service to improve the traffic speed using a unique simulation tool. To do so, we first evaluate the impact of maneuver coordination on the vehicles involved in a coordination process. We also evaluate the effects of maneuver coordination on the overall traffic compared to scenarios without coordination. Our study shows that maneuver coordination can yield significant benefits to traffic mobility, however these improvements are intimately linked to the surrounding traffic environment and the specifications of the coordinated maneuver. This highlights the need for more detailed studies on the design of maneuver coordination protocols that should consider the vehicular context when executing and configuring the coordination process.

Vehicular edge computing is enabling a variety of new services that better assist safety and comfort of driving. However, vehicles cannot offload an unlimited amount of computational tasks and input sensor data to a remote edge server because of the limitations in network bandwidth. In this paper, we design a learning-based task of floading mechanism that selects a small subset of input sensor data, which are expected to improve the application performance if processed by a rich and resource-intensive algorithm, hosted by the edge server. As a case study, we apply this framework to a vision-based object tracking application. The simulation results show that the proposed solution significantly improves object tracking accuracy with the same amount of resource consumption.

5G offers high flexibility at the radio, transport and core networks to support various services of critical verticals such as connected and automated driving. At the Radio Access Network (RAN), 5G defines a New Radio (NR). 5G NR utilizes different subcarrier spacing, slot durations, modulations and channel coding schemes. This flexibility offers the possibility to support automotive services with different and demanding requirements, such as Advanced Driver-Assistance System (ADAS), cooperative driving, and remote driving. Previous studies showed that 5G NR can be configured to achieve latencies below 2 ms. However, existing studies are generally restricted to scenarios with a limited number of users and unlimited bandwidth. Therefore, it is important to analyze whether 5G NR can effectively support these services as the network scales under limited spectrum allocations. This study advances the current state of the art to demonstrate that the capability of 5G NR RAN to support advanced V2X services depends on the RAN configuration (subcarrier spacing, slot duration and error protection) and network load.

Large-scale deployment of connected vehicles equipped with multiple automotive radar systems increases the demand on both the millimeter-wave (mmWave) automotive radar spectrum in 76–81 GHz and the vehicle-to-everything (V2X) communication spectrum in 5.9 GHz that is mainly allocated for the exchange of safety messages. To supplement V2X communication and support high data rates needed by broadband applications, the automotive radar spectrum with up to 4 GHz of contiguous bandwidth can be leveraged. For this purpose, various joint automotive radar-communication (JARC) systems have been proposed in the literature to perform both functions using the same radio-frequency (RF) signal and transceiver hardware. Combined with the high mobility in traffic, the directionality of RF transmission in mmWave spectrum and interference from other systems prevent JARC systems to achieve optimal communication and radar performance. In this work, we propose a dedicated neighbor discovery and medium access control (MAC) protocol for JARC systems to establish reliable communication links and improve the robustness of radar functionalities without requiring a separate control channel.

Vehicular micro cloud is a promising solution in which connected vehicles offer their resources as services to others and collaborate on tasks through vehicular networks. The properties of the vehicular micro cloud are generally set when it is formed at the core area and they remain static. However, a change in the environment could leave the micro cloud unable to respond adequately as its static properties become obsolete. Reacting to changes in the environment, on the other hand, requires dynamic updates on the properties of the vehicular micro cloud. In this paper, we address this dilemma and propose a chain of interdependent vehicular micro clouds. To respond to the changes in the environment, interdependent micro clouds are formed around the first vehicular micro cloud. Guidance information is transmitted before members of the dependent micro clouds arriving at the core area of the first micro cloud. Extensive simulations in different vehicular micro cloud settings show that the chain of interdependent vehicular micro clouds significantly improves the ability to respond to changes within the first micro cloud. Through the provided guidance information, members of dependent micro clouds are collectively prepared before they arrive at the core area.

Large-scale deployment of connected vehicles with cooperative awareness technologies increases the demand for vehicle-to-everything (V2X) communication spectrum in 5.9 GHz that is mainly allocated for the exchange of safety messages. To supplement V2X communication and support the high data rates needed by broadband applications, the millimeter-wave (mmWave) automotive radar spectrum at 76-81 GHz can be utilized. For this purpose, joint radar-communication systems have been proposed in the literature to perform both functions using the same waveform and hardware. While multiple-input and multiple-output (MIMO) communication with multiple users enables independent data streaming for high throughput, MIMO radar processing provides high-resolution imaging that is crucial for safety-critical systems. However, employing conventional precoding methods designed for communication generates directional beams that impair MIMO radar imaging and target tracking capabilities during data streaming. In this paper, we propose a MIMO joint automotive radar-communication (JARC) framework based on orthogonal frequency division multiplexing (OFDM) waveform. First, we show that the MIMO-OFDM preamble can be exploited for both MIMO radar processing and estimation of the communication channel. Then, we propose an optimal precoder design method that enables high accuracy target tracking while transmitting independent data streams to multiple receivers. The proposed methods provide high-resolution radar imaging and high throughput capabilities for MIMO JARC networks. Finally, we evaluate the efficacy of the proposed methods through numerical simulations.

In this work, we propose the use of hybrid offloading of computing tasks simultaneously to edge servers (vertical offloading) via LTE communication and to nearby cars (horizontal offloading) via V2V communication, in order to increase the rate at which tasks are processed compared to local processing. Our main contribution is an optimized resource assignment and scheduling framework for hybrid offloading of computing tasks. The framework optimally utilizes the computational resources in the edge and in the micro cloud, while taking into account communication constraints and task requirements. While cooperative perception is the primary use case of our framework, the framework is applicable to other cooperative vehicular applications with high computing demand and significant transmission overhead. The framework is tested in a simulated environment built on top of car traces and communication rates exported from the Veins vehicular networking simulator. We observe a significant increase in the processing rate of cooperative perception sensor frames when hybrid offloading with optimized resource assignment is adopted. Furthermore, the processing rate increases with V2V connectivity as more computing tasks can be offloaded horizontally.

Signal Phase and Timing (SPaT) refers to the current signal state of an intersection and the time duration that the state will last for each lane. Learning SPaT information constitutes a building block for many connected vehicle applications such as light duration advisory and start-stop control. However, retrieving SPaT information is not an easy task. One alternative approach to learn SPaT information could be the analysis of vehicles' mobility patterns. This paper investigates the design and feasibility of such an approach and proposes a system namely Virtual SPaT (V-SPaT). In V-SPaT, a group of connected vehicles form a Vehicular Cloud (VC) and collaborate to act as a virtual infrastructure at an intersection. Cloud members not only analyze their mobility patterns to predict the current phase and a residual time of that phase but also keep the SPaT information through collaborative data storage so that vehicles approaching the intersection can obtain this information over Vehicle-to-Vehicle (V2V) networks. The simulation results show that V-SPaT can identify the current phase and estimate the residual time by about 85% accuracy under a certain degree of V2V communications penetration rates.

Large scale deployment of connected vehicles with cooperative sensing technologies increases the demand on the vehicular communication spectrum in 5.9 GHz allocated for the exchange of safety messages. To support high data rates needed by such applications, the millimeter-wave (mmWave) automotive radar spectrum at 76-81 GHz can be utilized for communication. For this purpose, joint automotive radar-communication (JARC) system designs have been proposed in the literature to perform both functions using the same waveform. However, employing a large band in the mmWave spectrum deteriorates the performance of both radar and communication functions due to frequency-selectivity. In this paper, we address the optimal joint waveform design problem for wideband JARC systems via Orthogonal Frequency-Division Multiplexing (OFDM). We show that the problem is a non-convex quadratically constrained quadratic fractional programming (QCQFP) problem, which is known to be NP-hard. Existing approaches to solve QCQFP include Semidefinite Relaxation (SDR) and randomization approaches, which have high time complexity. Instead, we propose an approximation method to solve QCQFP more efficiently by leveraging structured matrices in the quadratic fractional objective function. Finally, we evaluate the efficacy of the proposed approach through numerical results.

Sensor-based perception on vehicles are becoming prevalent and important to enhance road safety. Autonomous driving systems use cameras, LiDAR and radar to detect surrounding objects, while human-driven vehicles use them to assist the driver. However, the environmental perception by individual vehicles has the limitations on coverage and/or detection accuracy. For example, a vehicle cannot detect objects occluded by other moving/static obstacles. In this paper, we present a cooperative perception scheme with deep reinforcement learning to enhance the detection accuracy for the surrounding objects. By using deep reinforcement learning to select the data to transmit, our scheme mitigates the network load in vehicular networks and enhances the communication reliability. To design, test and verify the practical and resource-efficient cooperative perception framework, we develop a Cooperative & Intelligent Vehicle Simulation (CIVS) Platform where we integrate three software components: a traffic simulator, a vehicle simulator, and an object classifier. The simulation platform constitutes a unified framework to evaluate a traffic model, vehicle model, communication model, and object classification model. Simulation results show that our scheme decreases packet loss and thereby increases the detection accuracy by up to 12%, compared to the baseline protocol.

In this paper we investigate the problem of a vehicle merging to a main road while another vehicle is approaching on that road. We utilize conflict analysis to help the decision making and control for vehicles of different automation levels. We demonstrate that using vehicle-to-everything (V2X) communication, e.g., basic safety message (BSM), we are able to prevent conflict between the two vehicles. We design a longitudinal controller for the merging vehicle and show that V2X communication is also beneficial in improving the time efficiency of the merge. The results are demonstrated by performing simulations based on real highway data.

Vehicles are expected to generate and consume an increasing amount of data, but how to perform risk reasoning over relevant data is still not yet solved. Location, time of day and driver behavior change the risk dynamically and make risk assessment challenging. This paper introduces a new paradigm, transferring information from raw sensed data to knowledge and explores the knowledge of risk reasoning through vehicular maneuver conflicts. In particular, we conduct a simulation study to analyze the driving data and extract the knowledge of risky road users and risky locations. We use knowledge to facilitate reduced volume and share it through a Vehicular Knowledge Network (VKN) for better traffic planning and safer driving.

The growing penetration of on-board communication units is enabling intelligent vehicles to share their sensor data with cloud computing platforms as well as with other vehicles. Although this unlocks the possibility of a variety of emerging applications, the massive amount of data traffic in vehicular networks is expected to pose a big challenge in the long term. In this paper, we shed light on the potential of value-anticipating networking to tackle this issue. A vehicle sending a piece of information first anticipates the value of that information for potential receivers. When the network is congested, the sender may defer or even cancel transmissions of less valuable information, so that important information can be delivered to receivers more reliably. We investigate the applicability of this concept to cooperative perception, where vehicles exchange processed sensor data over vehicle-to-vehicle (V2V) networks to collaboratively improve coverage and accuracy of environmental perception. Through simulations based on realistic road traffic, we show that value-anticipating V2V communications can significantly improve the performance of cooperative perception under heavy network load.

This paper investigates the design and feasibility of Co-Park, where a group of connected vehicles in a parking facility collaborate with each other to find available parking spots in a timely fashion. Vehicles measure occupancy of the surrounding parking spots by their on-board sensors, and share the occupancy information with other group members over vehicular networks. Based on prediction of the short-term occupancy variation and coordination among group members, the vehicles strategically plan efficient paths to search for an available spot. Simulation results show that the system can significantly reduce the trip time in a parking facility.

Vehicular micro clouds are considered a prime building block for next generation Intelligent Transportation Systems (ITS)also supporting a variety of Information and Communication Systems (ICT)applications in smart cities. Such micro clouds are established by multiple cars equipped with communication, storage, and computational resources. We recently presented the concept of hierarchical vehicular cloud computing, which is meant to extend on Mobile Edge Computing (MEC). Based on clustering algorithms, we can set up and maintain such micro clouds and eventually make use of the distributed resources. Looking at the high mobility of cars, it is very difficult to reliably maintain data collected by cars at a given location in space that is geographically relevant, e.g., at intersections. In this paper, we propose a new protocol which encourages coordination between neighboring micro clouds to help keeping local data current, i.e., cars moving out of the micro cloud may take data to neighboring clouds, hand data over to cars moving towards the original micro cloud, and, thus, returning the data to its original geographical location. We evaluate the performance of the protocol with different vehicle densities in a Manhattan Grid scenario and our results show the benefits of our proposed inter micro cloud coordination protocol.

Recently, the millimeter wave (mmWave) band has been investigated as a means to support the foreseen extreme data rate demands of emerging automotive applications, which go beyond the capabilities of existing technologies for vehicular communications. However, this potential is hindered by the severe isotropic path loss and the harsh propagation of high-frequency channels. Moreover, mmWave signals are typically directional, to benefit from beamforming gain, and require frequent realignment of the beams to maintain connectivity. These limitations are particularly challenging when considering vehicle-to-vehicle (V2V) transmissions, because of the highly mobile nature of the vehicular scenarios, and pose new challenges for proper vehicular communication design. In this paper, we conduct simulations to compare the performance of IEEE 802.11p and the mmWave technology to support V2V networking, aiming at providing insights on how both technologies can complement each other to meet the requirements of future automotive services. The results show that mmWave-based strategies support ultra-high transmission speeds, and IEEE 802.11p systems have the ability to guarantee reliable and robust communications.

The emerging concept of vehicle cloudification is a promising solution to deal with ever-growing computational and communication demands of connected vehicles. A key idea is to have connected vehicles in the vicinity form a cluster, called vehicular micro cloud, and collaborate with other cluster members over vehicle-to-vehicle (V2V) networks to offer data processing, data storage, sensing and communication services. It allows us to use vehicles as virtual edge servers that complement traditional cloud and physical edge servers in the backbone network. In this paper, we design a mechanism to intelligently schedule where and when to form such vehicular micro clouds. A remote server maintains statistics of the amount of available on-board computational resources spatio-temporally, and analyzes these statistics to identify the locations where vehicles can consistently offer a sufficient amount of resources for service provisioning. The results from our proof-of-concept simulations show that our system can significantly reduce the risk of resource scarcity in vehicular micro clouds.

One of the most recent applications in the vehicular networking domain is distributed data processing using cars as sensors of information. In recent work, the concept of vehicular cloud computing has been explored to provide the necessary scalability and to improve the communication between clusters of cars being called vehicular micro clouds and other participants (cars, bicyclists, pedestrians). In order to provide a bigger picture and also to interconnect such micro clouds, data centers or cloud servers are considered bridging the gap. We study the uplink capabilities from connected cars to such data centers. Options include direct LTE uplinks from all cars, selected use of Roadside Units (RSUs) with back-end connectivity or LTE uplinks from the vehicular micro clouds, and finally hybrid solutions taking network quality and available channel resources into account. Our findings clearly show the advantages of such hybrid solutions both in terms of throughput as well as of optimizing operational costs.

The four papers in this special section provide deep research results to report the advance in mobile and cloud computing services. In recent years, cloud computing has become a scalable services consumption and delivery platform in the field of Services Computing. The technical foundations of cloud computing include Service-Oriented Architecture (SOA) and virtualizations of hardware and software. The goal of cloud computing is to share resources among the cloud service consumers, the cloud service providers, and the cloud vendors in the cloud value chain.

Many studies show that the dedicated short range communication band allocated to vehicular communications is insufficient to carry the wireless traffic generated by emerging vehicular applications. A promising bandwidth expansion possibility presents itself through the release of large TV band spectra (i.e., the TV white space spectrum) by the Federal Communications Commission for cognitive access. One primary challenge of the so-called TV white space (TVWS) spectrum access in vehicular networks is the design of efficient channel allocation mechanisms in face of spatial-temporal variations of TVWS channels. In this paper, we address the channel allocation problem for multi-channel cognitive vehicular networks with the objective of system-wide throughput maximization. We show that the problem is an NP-hard non-linear integer programming problem, to which we present three efficient algorithms. We first propose a probabilistic polynomial-time (1-1/e)-approximation algorithm based on linear programming. Next, we prove that the objective function can be written as a submodular set function, based on which we develop a deterministic constant-factor approximation algorithm with a more favorable time complexity. Then, we further modify the second algorithm to improve its approximation ratio without increasing its time complexity. Finally, we show the efficacy of our algorithms through numerical examples.

Many studies show that the dedicated short range communication (DSRC) band is insufficient to carry increasing wireless traffic demands in vehicular networks. The release of TV white space band by the Federal Communications Commission (FCC) for cognitive access provides additional bandwidth to solve the DSRC spectrum scarcity problem. However, FCC requires portable devices to use significantly lower transmitting power than fixed devices, which creates a challenging coexistence environment for portable (e.g., vehicular) and fixed (e.g., IEEE 802.22) networks. In this paper, we address the coexistence problem between a vehicular and an 802.22 network via resource allocation. We first formulate the coexistence problem as a mixed-integer nonlinear programming (MINLP) problem, to which three algorithms are developed. The first algorithm converts the MINLP into a convex program and obtains a near-optimal solution to the initial MINLP. In the other two algorithms, we first convert the MINLP into an integer programming (IP) problem. Then, we solve the linear program relaxation of the IP and obtain a fractional solution. Thereafter, two rounding algorithms are developed to round the fractional solution based on column-sparse packing and dependent rounding techniques, respectively. Finally, we compare the performance of the proposed algorithms with an optimal MINLP solver through numerical examples.

In this paper, we analyze mixed traffic environments consisting of fully autonomous vehicles, vehicles capable of communication only, and manually driven vehicles to determine what self-generated content should be shared among peer vehicles for increased traffic intelligence. For this purpose, we present information sharing utility-cost tables for a variety of communication strategies. These tables are used to determine communication requirements in terms of bandwidth, distance, packet delay and loss rate tolerance. We specifically evaluate vehicle lane change events due to their role as foundational building blocks in most other traffic scenarios. The presented work demonstrates requirements for the communication systems in mixed-traffic environments based on sharing and fusing necessary sensor information using occupancy grid mapping.

Vehicular networks need to make optimal use of their limited radio resources to achieve sufficient performance and reliability. However, the scale and dynamicity make it very challenging to optimize this use and to meet the strict requirements. In this paper, we propose a centralized scheme that allocates resources to geographic locations to address this problem. The scheme can improve the overall network performance by leveraging wide-scale information instead of the restricted local views used in distributed approaches.

Due to rapid growth of wireless traffic demands in vehicular networks, spectrum scarcity is becoming urgent in the Dedicated Short Range Communication (DSRC) band. One solution is reusing automotive radar bands without degrading radar performance. Despite having massive bandwidths, imaging accuracy of automotive radars is still low due to correlations between sequential target observations of single radar. A solution is that vehicles exchange imaging information through vehicle-to-vehicle communications. Since observations of different vehicles are less correlated, Joint Automotive Radar and Communication (JARC) network is able to improve imaging accuracy. More importantly, some spectrum resources can be left to alleviate the DSRC spectrum scarcity problem. In this paper, we derive the Cramer-Rao bound for parameter estimation in JARC networks. Then, we formulate the spectrum utilization problem as an NP-complete integer quadratic program, to which we propose an optimal (in expectation) algorithm with low complexity. Finally, efficacy of the algorithm is illustrated through numerical results.

We propose and demonstrate a framework for statistical modeling of propagation using spectrum sensors mounted on moving vehicles. To illustrate the concept, we deploy sensors on four vehicles to collect signal strength measurements together with corresponding locations on a suburban road. From the data we build a model for large scale fading, that is, the path loss and shadowing, as well as the shadowing correlation of a TV transmitter on two UHF channels. Depending on a specific band to which it is applied, such a model can be used for different purposes like coverage planning, to improve accuracy of a white space spectrum database, for selection of relay nodes in an ad- hoc or a mesh network, selection of spectrum sensors with significant diversity gain for cooperative sensing, etc. For instance, obtained numerical results imply that sufficient decorrelation of signal strength can be observed in the experimental area if the sensors are separated by more than 20m.

Existing rate adaptation protocols have advocated training to establish the relationship between channel conditions and the optimal modulation and coding scheme. However, wireless devices for outdoor and vehicular communications frequently enter environments they have not yet encountered and therefore, have insufficient training for rate adaptation decisions. In addition, protocols are often optimally tuned for indoor environments but, when taken outdoors, perform poorly. In both cases, the decision structure formed offline lacks the ability to acclimate to a new situation on the fly. The diverse and ever-changing environments of increasingly mobile wireless devices call for a rate adaption scheme that can quickly adjust accordingly to form a unique environment set established by the user. In this paper, we propose an on-the-fly, in-situ training (FIT) mechanism which addresses the challenges of making rate decisions with unpredictable fluctuation and lack of repeatability of real wireless channels. We design and conduct extensive experiments on emulated and in-field wireless channels to evaluate the in-situ training process, showing that the rate decision structure can be updated as channel conditions change using existing traffic flows.

Many studies show that the Dedicated Short Range Communication band is insufficient to carry increasing wireless data traffic in vehicular networks. The release of large TV spectra by FCC for cognitive access provides additional spectrum resources to solve the spectrum scarcity problem. However, FCC allows fixed devices to use high transmitting powers, while requiring portable devices to use significantly lower powers. This power asymmetry policy leads to a challenging coexistence environment for portable (e.g., vehicular) and fixed (e.g., IEEE 802.22) networks. In this paper, we address the coexistence problem between vehicular and 802.22 networks via resource allocation. We show that the problem is an NP-hard mixed-integer nonlinear programming problem, to which we propose two algorithms. First, we convert it to a convex programming problem, and propose a near-optimal primal-dual algorithm. Next, we reformulate the problem as a packing problem, and present a constant-factor approximation algorithm. Finally, we evaluate the algorithms through numerical examples.

This paper presents design and experimental evaluation of a distributed autonomous multi-hop vehicle-to-vehicle (V2V) communication system over TV white space performed in Japan. We propose the two-layer control channel model, which consists of the Zone Aware Control Channel (ZACC) and the Swarm Aware Control Channel (SACC), to establish the multi-hop network. Several vehicles construct a swarm using location information shared through ZACC, and share route and channel information, and available white space information through SACC. To evaluate the system we carried out field experiments with swarm made of three vehicles in a convoy. The vehicles observe channel occupancy via energy detection and agree on the control and the data channels autonomously. For coarse synchronization of quiet periods for sensing we use GPS driven oscillators, and introduce a time margin to accommodate for remaining drift. When a primary user is detected in any of the borrowed channels, the vehicles switch to a vacant channel without disrupting the ongoing multi-hop communication. We present the experimental results in terms of the time to establish control channel, channel switching time, delivery ratio of control message exchange, and throughput. As a result, we showed that our implementation can provide efficient and stable multi-hop V2V communication by using dynamic spectrum access (DSA) techniques.

Recent studies show that the Dedicated Short Range Communication (DSRC) band allocated to vehicular networks is insufficient to carry the wireless traffic load generated by emerging applications for vehicular systems. A promising bandwidth expansion possibility presents itself through the release of large TV band spectra by FCC for cognitive access. One of the primary challenges of the so-called TV White Space (TVWS) access in vehicular networks is the design of efficient channel allocation mechanisms in face of high vehicular mobility and spatial-temporal variations of TVWS. In this paper, we address the channel allocation problem for multi-channel cognitive vehicular networks with the objective of system-wide throughput maximization. We show that the problem is a NP-hard combinatorial optimization problem, to which we present two solution approaches. We first propose a probabilistic polynomial-time (1 - 1/e)-approximation algorithm based on linear programming. Next, we prove that our objective function can be written as a submodular set function, based on which we develop a deterministic polynomial-time constant-factor approximation algorithm with a more favorable time complexity. Finally, we show the efficacy of our algorithms through numerical examples.

We present the results of TV band sensing experiments performed in a suburban environment in Iizuka, Japan. This work adds to our previous work in which we proposed to exploit dynamic spectrum access techniques to facilitate use of the white space to solve shortage of spectrum for vehicular networks. Here, we focus on the terrestrial TV band spectrum sensing, which in addition to the "whitespace database", is one of the methods envisioned to identify vacant channels. A convoy of four vehicles senses multiple TV channels using energy detection. Local signal strength measurements collected from the USRP software defined radios (SDRs) are combined at the trailing vehicle which acts as the fusion center. We post- processed collected measurements to improve accuracy of local sensing outcomes by removing temporal drift and differences between the sensors remaining after calibration. We tested three conventional fusion algorithms: 1) selection combining; 2) simple averaging; and 3) weighted averaging. The results show that collaboration provides a significant improvement in the sensing performance. We observed minor differences in performance among used fusion algorithms.

This paper presents design and experimental evaluation of a distributed autonomous multi-hop vehicle-to-vehicle (V2V) communication system over TV white space performed in Japan. We propose the two-layer control channel model, which consists of the Zone Aware Control Channel (ZACC) and the Swarm Aware Control Channel (SACC), to establish the multi-hop network. Several vehicles construct a swarm using location and direction information shared through ZACC, and share route and channel information, and available white space information through SACC. To evaluate the system we carried out field experiments with swarm made of three vehicles in a convoy. As the test case application, the leading vehicle sends real-time speed and sudden break information to the rear vehicle, while the vehicle in between acts as the relay. The vehicles observe channel occupancy via energy detection and agree on the control and the data channels autonomously. For coarse synchronization of quiet periods for sensing we use GPS driven oscillators, and introduce a time margin to accommodate for remaining drift. When a primary user is detected in any of the borrowed channels, the vehicles switch to a vacant channel without disrupting the ongoing multi-hop communication. We present results of the experiments in terms of the time to establish control channel, channel switching time, and throughput.

Context awareness has received increasing attention with the proliferation of various types of sensors on mobile devices. However, while wireless performance is known to be highly correlated with environmental settings, mobile devices have yet to fully exploit the awareness of context to improve wireless performance. In this paper, we leverage available context information to improve link-level adaptation via decision-tree classifiers and extensively evaluate its performance over emulated channels as well as with in-field trials. We first propose a classification method based on decision trees to select the optimal transmission parameters such as modulation, coding rate and packet size. We then quantify the throughput improvement using the proposed scheme and show that in some scenarios the throughput increases by over 100% compared to traditional SNR-based rate adaptation protocols. Second, we analyze the amount of training to assess the classification scheme. Third, we validate classification-based method by implementation on two different test platforms for extensive experimentation. We reveal the importance of the various contextual attributes used and identify channel type as a key parameter that affects classification performance. Finally, we study and quantify the use of context information across multiple different frequency bands and demonstrate the significant throughput gains that can be obtained.

Vehicular networking has significant potential to enable diverse range of applications, including safety and convenience. As the number of vehicles and applications using the specially designated wireless spectrum grow, one can expect substantial increase in the bandwidth requirements. In this paper and demonstration, we advocate that dynamic spectrum access techniques facilitating the utilization of white spaces for vehicles will be the first step towards solving the expected spectrum shortage. In the demonstration, we will introduce field tests of a multi-hop inter-vehicle communication system with distributed and autonomous TV white space channel selection, performed in Japan from January to February 2012. Furthermore, in addition to the field test videos, we will show an indoor emulation of the entire system used during the field tests in the vehicles.

In this paper, we propose a reinforcement learning-based approach for enabling vehicles to make intelligent channel selection choices across TV whitespace spectrum. In order for vehicle communication networks to dynamically access TV whitespace in a secondary manner, it is imperative that these communication systems be capable of coexisting with other types of secondary wireless networks operating within the same frequency range. Consequently, we first propose a TV whitespace channel sharing scheme that would facilitate the coexistence between WLAN, WRAN, and vehicular communication networks. Using the channel utilization variations observed by a collection of mobile vehicular communication systems, we then devised a reinforcement learning-based adaptive channel selection algorithm that employs channel utilization sensing in order to reinforce the decisions made by the vehicular communication system. Moreover, the parameters of the proposed learning approach are adaptively tuned in order to achieve better adaptation to a particular environment. A computer emulation environment composed of actual real-world sensing measurement data and a simulated TV whitespace network is created in order to accurately model the characteristics of future wireless environment, as well as to test the proposed learning-based channel access approach. Experimental results show a significant performance improvement with respect to vehicle communication.

Future vehicular communications systems are expected to utilize the vacant channels (white spaces) of the spectrum, otherwise allocated for specific designated use. One such candidate of white space comes from the TV broadcast band. In this demonstration, we will first present animated results of a TV spectrum measurement campaign along the entire portion of Interstate I-90 located in the US state of Massachusetts. Next, we will demonstrate a cyber-physical proof-of-concept lab implementation of our previously developed control and data channel assignment schemes for vehicle-to-vehicle communications over (TV) white space. Finally we will show a video of actual vehicle to vehicle communications field tests conducted in Japan using TV white space.

Vehicular networking has significant potential to enable diverse range of applications, including safety and convenience. As the number of vehicles and applications using wireless spectrum grow, one can expect to see a shortage of either spatially or temporally available spectrum. In this paper, we advocate that dynamic spectrum access for vehicles be the first step towards solving the spectrum shortage. For this, vehicles must be able to sense the availability of spectrum before attempting to transmit. The existence of other transmitters should be detected in order not to cause or experience interference. However, spectrum sensing in vehicular environments is a challenging task due to mobility, shadowing and other factors that govern vehicular environments. Therefore, spectrum sensing by a single vehicle may not be able to provide accurate information about the spectrum vacancies. Cooperative spectrum sensing, on the other hand, uses spatial diversity and can be employed to overcome the limitations associated with a single sensor/vehicle. In this paper, we investigate cooperative spectrum sensing performance in a vehicular environment for sensing signals transmitted from i) a roadside infrastructure and ii) radios located on other vehicles, by using energy-based detection of a transmitted pilot tone as an example. Our goal is to characterize the limits on detection speed and reliability of simple hard and soft cooperative energy-based schemes for this environment. We show how cooperation reduces sensing time by a factor of five in an AWGN channel. The cooperative sensing time reduction is far more significant in a vehicular environment with fading and shadowing. Finally, we illustrate how infrastructure-to-vehicle scenario favors soft equal gain combining while vehicle-to-vehicle scenario favors hard fusion OR rule.

The use of dynamic spectrum access techniques has a great potential in future inter-vehicle communications, while it must cope with (i) temporal and spatial spectrum utilization changes introduced by the primary and secondary users (environmental changes); and (ii) topology changes due to movement of vehicles (spatial movement). In the present paper, along this line, dynamic per-hop channel switching in multi-hop vehicular ad hoc networking is investigated. After defining a set of simple metric-based dynamic channel selection schemes with/without spatial-awareness, the basic performance (the total communication duration and the amount of transmitted data) are evaluated. These spatially-aware schemes estimate the maximum communication period, and tend to select a channel with a large amount of possible data transmission within the period. The simulation results demonstrate the advantages of the proposed spatial-awareness, especially in the multi-hop and highly congested environments with high-speed mobility.

Inter-vehicle communications is a topic of growing interest, with a number of target applications under consideration. One significant application for inter-vehicle communications is dissemination of vehicle safety information such as road and traffic-related events and conditions. To support vehicle safety information, a reliable and efficient inter-vehicle communications system which can meet stringent safety application performance requirements is needed. This paper reviews developments in routing schemes targeted for ad hoc vehicle networks and considers them in the context of safety communications

Load balancing for distributed servers is a common issue in many applications and has been extensively studied. Several distributed load balancing schemes have been proposed that proactively route individual requests to appropriate servers to best balance the load and shorten request response time. These schemes do not require a centralized load balancer. Instead, each server is responsible for determining, for each request it receives from a client, to which server in the pool the request should be forwarded for processing. We propose a new request routing scheme that is more scalable to increasing number of servers and request load than the existing schemes. The method combines random server selection and next-neighbor load sharing techniques that together prevent the staleness of load information from building up when the number of servers increases. Our simulation shows that it outperforms existing schemes under a piggyback-based load update model.

In mobile wireless environments, bandwidth fluctuations are unpredictable, and the throughput experienced by mobile user applications is dynamic. Real-time tracking of bandwidth variations is desirable to effectively manage the throughput available to a user application at any given time. We propose a throughput control mechanism for applicability over wireless networks, transparent to the underlying transport and network technologies. To improve the utilization of the available throughput, we develop a two-component application-layer, real-time control technique for mobile users traversing diverse networks with varying service rates. We first calculate the weighted average throughput experienced by the user's application. Secondly, we appropriately adjust the download rate to the user based on the real-time average throughput information. In this paper, we detail the throughput control algorithm and provide some initial simulation results.

In order to ensure proper quality of service for real-time communication in a mobile wireless Internet environment it is essential to minimize the transient packet loss when the mobile is moving between different cells (subnets) within a domain. Network layer mobility management schemes have been proposed to provide optimized fast-handoff for multimedia streams during a client's frequent movement within a domain. This paper introduces application layer techniques to achieve fast-handoff for real-time RTP/UDP based multimedia traffic in a SIP signaling environment. These techniques are based on standard SIP components such as user agent and proxy which usually participate to set up and tear down the multimedia sessions between the mobiles. Unlike network layer techniques, application layer techniques do not have to depend upon any additional components such as home agent and foreign agent. It thus provides a network access independent solution suitable for application service providers.

As multicast services become prevalent, it is important to find viable solutions for multicasting to mobile nodes. This problem is complicated by the necessity to support multicast services over existing backbone and access networks which may have varying network and/or link layer multicasting capabilities. While most work on supporting multicast services focuses on the IP layer solution, we propose an application-layer approach for providing multicast services to mobile users traversing networks with diverse multicast capabilities. We propose placing multicast proxies in the backbone and access networks to support several multicast-related functions at the application layer including the creation of virtual networks for dynamically tunneling through non-multicast-capable networks. We describe our proposed application-layer multicast architecture and its advantages to third-party service providers for multicasting to mobile users in diverse networks.

Packet scheduling is one of the key mechanisms that will be employed in the network nodes (routers and switches) for supporting multiple quality of services. We propose a new packet scheduling algorithm called urgency-based round robin which computes an index for flows in order to keep track of instantaneous bursts. With this approach, flows which might be in need of momentary service can be detected. Also, we propose a novel weight allocation scheme to be used together with the scheduler with the aim of preventing network under-utilization. Our algorithm can be considered as a version of the weighted round robin (WRR) with improved delay characteristics. After an introduction about the necessity of a scheduler, we describe related work and necessary background for the rest of the paper. Next we describe the operation of our algorithm. We then give some simulation results showing the delay performance of the proposed algorithm comparing it to that of the WRR. Also, we remark on the issue of necessary bandwidth reservation. We conclude by describing future extensions to the algorithm.

An approximation heuristic is proposed for solving the heterogeneous multi-server queueing problem associated with the analysis of resequencing of packets travelling over multiple physical links in a packet-switching network. Even though a method for obtaining the exact solution exists, its processing time and memory requirements vary exponentially in terms of the number of servers and render it infeasible even for moderately-sized systems. Precision of the proposed approximation which has linear time complexity is demonstrated. The approximation is recommendable in cases when system population and resequencing delays, rather than individual link utilizations, have to be calculated.>