The Research On Node Mobility In Vehicular Ad Hoc Network

In modern society, network-oriented services and applications have been an essential part of our life. Traditional wired network can not satisfy the people's requirement for establishing communication, anytime and anywhere, which launches and promotes the development of wireless network technology. Vehicular ad hoc network (VANET) is such a rising wireless network tightly related to people's daily life. It is a network which consists of vehicle nodes that equipped with wireless communication device, and the nodes in a network of this kind can deliver packet data among each other in an ad hoc manner, can interact with various infrastructure nodes, and also can collectively provide safety and communication services and applications for drivers and passengers. Different from other kinds of wireless networks, VANET belongs to self-organizing, self-managing ad hoc network where vehicle nodes both serve as routers and hosts. Comparing to common ad hoc networks, VANET has some special characteristics, such as high-speed node movement, node movement relaying on road topologies, vast node numbers and so on, which show particular network features and make VANET researches very challenging. Node mobility is the prime problem and key feature in VANET, and constructs the basis of vehicular routing, data delivering, services and applications. Although there are some preliminary research results about vehicular node mobility, no perfect, integrative mobility model or description is found. Thus, there is a vast space for further study in vehicular node mobility researches.Based on a systematical summary of relevant mobility works on VANET, this dissertation focuses on vehicular network characteristics, data delivery, transportation and other aspects affected by long-time parking and regular movement of vehicles, and gains several achievements on some sub-topics. The major contributions of this dissertation are as below:1. Based on long-time parking of vehicles, we propose the idea of Parked Vehicle Assistance (PVA) in VANET, which exploits parked vehicles to communicate with moving vehicles as roadside infrastructure nodes for collecting, delivering, processing, and distributing vehicular information. In urban areas, parked vehicles have extensive numbers, wide distributions and long average parking time. These parked vehicles have wireless device and high volume battery, and can easily participate in vehicular communications and serve as stationary routers to store and forward data packets. Moreover, as naturally assembled communities, the parked vehicles at different parking lots can be clustered into Parking Lot Clusters (PLC) as stable backbones and data centers with considerable storage and processing abilities. After integrating the parked vehicles and the moving ones, we establish PLC-based distributed database systems to sense event, collect information, process data, disseminate message and response query for vehicle users and outside users. It makes PVA-based vehicular networks can improve current vehicular applications and afford a wide spectrum of new applications without or only with a few infrastructure nodes. PVA discovers neglected resources for vehicular communication, enhances vehicular network from down to top, and promotes vehicular research in depth and scope. Survey and simulation results indicate that PVA has good performance improvement on typical vehicular unicast and broadcast routing schemes.2. Based on regular movement of vehicles, we propose the idea of Trip History Model (THM) in VANET, which records vehicle's trips with onboard device, predicts vehicle moving destination in driving through trip history learning automatically, and then enables vehicle to have the knowledge of self mobility. Current random mobility models in VANET come from the abstraction of substantive vehicle moving, and do not have any node-specific mobility knowledge. THM utilizes the regularities in individual or household daily activities, establishes model on single vehicle's mobility characteristics, and make mobility prediction through machine learning methods as decision trees according to temporal and spatial features in vehicle movement. At the same time, trip history data with temporal scales remedies the limitation of lacking temporal features in trajectory history data, and greatly promotes the prediction accuracy. Survey and simulation results indicate that THM achieves accurate prediction in vehicle moving.3. Aiming at inefficiency in vehicle transportation, we propose the idea of Vehicle-to-Passenger communication (V2P), which achieves direct, fast and flexible interactions between moving vehicles and roadside passengers over VANET and promotes the efficiency in passenger and goods transportation. Passengers equipped with packet wireless device can easily join VANET as roadside nodes, launch vehicle calling queries and distribute their travel demands (e.g., taking a free-ride, calling a taxi). Once matched vehicles are found through the opportunistically disseminated queries, the drivers can decide whether to provide corresponding services, especially the carrying of passengers and goods. Through mobility prediction based on trip history model, V2P supports transparent matching between vehicles and passenger demands. Thus, V2P over VANET enables open and efficient transportation information interacting, and benefits from the relevant efficiency improvement. simulation results indicate that vehicle calling achieves good performance in casual carpooling and taxi-calling.