Traffic congestion in road networks has been studied as the congestion game in game theory. In the existing work, the road usage by each agent was assumed to be static during the whole time horizon of the agent's travel, as in the classical congestion game. This assumption, however, should be reconsidered because each agent sequentially uses roads composing the route. In this paper, we propose a multi-agent distributed route selection scheme based on a gradient descent method considering the time-dependency among agents' road usage for vehicular networks. The proposed scheme first estimates the time-dependent flow on each road by considering the agents' probabilistic occupation under the first-in-first-out (FIFO) policy. Then, it calculates the optimal route choice probability of each route candidate using the gradient descent method and the estimated time-dependent flow. Each agent finally selects one route according to the optimal route choice probabilities. We first prove that the proposed scheme can exponentially converge to the steady-state at the convergence rate inversely proportional to the product of the number of agents and that of individual route candidates. Through simulations under a grid-like network and a real road network, we show that the proposed scheme can improve the actual travel time by 5.1% and 2.5% compared with the conventional static-flow based approach, respectively. In addition, we demonstrate that the proposed scheme is robust against incomplete information sharing among agents, which would be caused by its low penetration ratio or limited transmission range of wireless communications.

In millimeter wave (mmWave) vehicular communications, multi-hop relay disconnection by line-of-sight (LOS) blockage is a critical problem, particularly in the early diffusion phase of mmWave-available vehicles, where not all vehicles have mmWave communication devices. This paper proposes a distributed position control method to establish long relay paths through road side units (RSUs). This is realized by a scheme via which autonomous vehicles change their relative positions to communicate with each other via LOS paths. Even though vehicles with the proposed method do not use all the information of the environment and do not cooperate with each other, they can decide their action (e.g., lane change and overtaking) and form long relays only using information of their surroundings (e.g., surrounding vehicle positions). The decision-making problem is formulated as a Markov decision process such that autonomous vehicles can learn a practical movement strategy for making long relays by a reinforcement learning (RL) algorithm. This paper designs a learning algorithm based on a sophisticated deep reinforcement learning algorithm, asynchronous advantage actor-critic (A3C), which enables vehicles to learn a complex movement strategy quickly through its deep-neural-network architecture and multi-agent-learning mechanism. Once the strategy is well trained, vehicles can move independently to establish long relays and connect to the RSUs via the relays. Simulation results confirm that the proposed method can increase the relay length and coverage even if the traffic conditions and penetration ratio of mmWave communication devices in the learning and operation phases are different.

Dedicated Short Range Communication (DSRC) is currently standardized as a leading technology for the implementation of Vehicular Networks. Non-safety application in DSRC is emerging beyond the initial safety application. However, it suffers from a typical issue of low data delivery ratio in urban environments, where static and moving obstacles block or attenuate the radio propagation, as well as other technical issues such as temporal-spatial restriction, capital cost for infrastructure deployments and limited radio coverage range. On the other hand, Content-Centric Networking (CCN) advocates ubiquitous in-network caching to enhance content distribution. The major characteristics of CCN are compatible with the requirements of vehicular networks so that CCN could be available by vehicular networks. In this paper, we propose a CCN-based vehicle-to-vehicle (V2V) communication scheme on the top of DSRC standard for content dissemination, while demonstrate its feasibility by analyzing the frame format of Beacon and WAVE service advertisement (WSA) messages of DSRC specifications. The simulation-based validations derived from our software platform with OMNeT++, Veins and SUMO in realistic traffic environments are supplied to evaluate the proposed scheme. We expect our research could provide references for future more substantial revision of DSRC standardization for CCN-based V2V communication.

Wireless caching is one of the promising technologies to mitigate the traffic burden of cellular networks and the large cost of deploying a higher volume of wired backhaul by introducing caching storage. In the manner of “cutting” wired equipments, all types of vehicles can be readily leveraged as serving access points with caching storage, where their moving nature should be taken into account to improve latency and data throughput. In this paper, we consider a mobility-aware vehicular caching which has a role in offloading delay-tolerable contents from cellular networks. We first clarify the influence of mobility in cellular caching networks, then set the mobility-aware optimization problem of vehicular caching to carry on delay-tolerable contents. Trace-driven numerical results based on rural and urban topographies show that, in presence of individual demand for delay-tolerable contents, the proposed vehicular caching scheme enhances the quality-of-service (QoS) (maximally twofold) relying on the contents delivery being centrally or distributedly controlled.

How to manage the video streaming in future networks is becoming a more and more challenging issue. Recent studies on vehicular networks depict a new picture of the next generation Intelligent Transport System (ITS), with high level road safety and more comfortable driving experience. To cope with the heterogeneous network development for the next generation cellular network, centralized medium control is promising to be employed upon Road Side Unit (RSU). To accommodate the QoS constraints posed by video services in vehicular networks, the scalable video coding (SVC) scheme in H.264/AVC standard family offers spatial and temporal scalabilities in the video dissemination. In this paper, we target the resource allocation and layer selection problem for the multi-user video streaming over highway scenario, by employing SVC coding scheme for the video contents. We propose a Resource Allocation and Layer Selection (RALS) algorithm, which explicitly takes account of the utility value of each Group Of Picture (GOP) among all the vehicular users. Simulation results show that our proposed RALS algorithm outperforms the comparison schemes in typical scenarios.

The past decade has witnessed a growing interest in vehicular networking. Initially motivated by traffic safety, vehicles equipped with computing, communication and sensing capabilities will be organized into ubiquitous and pervasive networks with a significant Internet presence while on the move. Large amount of data can be generated, collected, and processed on the vehicular networks. Big data on vehicular networks include useful and sensitive information which could be exploited by malicious intruders. But intrusion detection in vehicular networks is challenging because of its unique features of vehicular networks: short range wireless communication, large amount of nodes, and high mobility of nodes. Traditional methods are hard to detect intrusion in such sophisticated environment, especially when the attack pattern is unknown, therefore, it can result unacceptable false negative error rates. As a novel attempt, the main goal of this research is to apply data mining methodology to recognize known attacks and uncover unknown attacks in vehicular networks. We are the first to attempt to adapt data mining method for intrusion detection in vehicular networks. The main contributions include: 1) specially design a decentralized vehicle networks that provide scalable communication and data availability about network status; 2) applying two data mining models to show feasibility of automated intrusion detection system in vehicular networks; 3) find the detection patterns of unknown intrusions.

Reliable broadcasting in vehicular ad-hoc networks is challenging due to their unique characteristics including intermittent connectivity and various vehicular scenarios. Applications and services in intelligent transportation systems need an efficient, fast and reliable broadcasting protocol especially for safety and emergency applications. In our previous work, we have proposed DECA, a new reliable broadcasting protocol which suits such characteristics. To address the issue of various vehicular scenarios, our protocol performs beaconing to gather local density information of 1-hop neighbors and uses such information to adapt its broadcasting decision dynamically. Specifically, before broadcasting a message, a node selects a neighbor with the highest density and adds the selected neighbor's identifier to the message. Upon receiving of a broadcast message, each node checks whether or not it is the selected neighbor. If so, it is responsible for rebroadcasting the message immediately. This mechanism can raise the data dissemination speed of our protocol so that it is as fast as simple flooding. To address the issue of intermittent connectivity, identifiers of broadcast messages are added into beacons. This helps nodes to check if there are any broadcast messages that have not yet been received. In this paper, we propose DECA with a new beaconing algorithm and a new waiting timeout calculation, so-called DECA-bewa. Our new protocol can reduce redundant retransmissions and overall overhead in high density areas. The protocol performance is evaluated on the network simulator (NS-2). Simulation results show that DECA-bewa outperforms existing protocols in terms of reliability, overhead and speed of data dissemination.

Most of the IP mobility management schemes based on the IETF's MIPv6 may not be suitable for delay-sensitive vehicular applications since there will be frequent service disruptions as the moving vehicles frequently change their points of wireless network attachment. This paper presents a fast IP mobility management scheme for vehicular networks where multiple wireless network interfaces are used to perform fast handovers without handover latency or packet loss. In order to do this, the IETF standard HMIPv6 has been extended, where multiple simultaneous tunnels between the HMIPv6 mobility anchor point (MAP) and the mobile gateway are dynamically constructed. We have designed the architecture for a mobile gateway for supporting multiple tunnels, the structure of the extension MAP (E-MAP), and the signaling procedure to achieve fast IP handover in vehicular networks. Both mathematical analysis and simulation have been done for performance evaluation. The results show that the proposed scheme is superior to HMIPv6 and MIPv6 with regard to handover latency and packet loss as the vehicle moves between different wireless network cells at high speed.

Handoff plays an important role in vehicular networks due to high movement of vehicles. To provide seamless connectivity under Access Points (AP), this paper proposes an adaptive handoff triggering method to minimize communication time for a vehicle with an AP switch (i.e., whether and when to trigger a handoff process). In the proposed method, combined with an improved data transmission rate based trigger, handoff triggering decision is executed based on three different communication methods (called C-Dire, C-Relay and C-ALLRelay) to minimize the transmission delay when a vehicle moves from an AP to another. Transmission delay is derived through considering vehicle mobility and transmission rate diversity. The simulation results show that the proposed method is proven to be adaptive to vehicular networks.