Showing papers by "Yunpeng Wang published in 2010"

A VANETs routing algorithm based on Euclidean distance clustering

Abstract: Routing is a challenging task in the ad hoc networks, especially in vehicular ad hoc networks (VANETs) where the network topology changes fast and frequently. Since the nodes in VANETs are vehicles, which can easily provide the required power to run GPS receiver to get the accurate information of their position, the position-based routing is found to be a very promising routing strategy for VANETs. In this paper we present a clustering routing algorithm for VANETs. The clustering method is based on the Euclidean distance, which uses the position information to divide the vehicles into clusters. Furthermore, only the same direction vehicles can be divided into the same cluster. To reduce the flooding of the routing control message and increase the stability of the route, the routing discovery is also restricted by the vehicle's driving direction. We implement the routing algorithm in NS2 and compare it with AODV, the simulation results show that in the same VANETs environment, the algorithm not only generate fewer routing control overhead, but also maintain stable route to transfmit more data packets.

A vehicular ad hoc networks intrusion detection system based on BUSNet

Abstract: The open medium, dynamic topology, and multi-hop cooperative routing of vehicular ad hoc networks (VANETs) make it facing more security challenge than wired networks. In this paper, a hierarchical VANETs intrusion detection system based on BUSNet is present. BUSNet is basically a virtual mobile backbone infrastructure that is constructed using public buses. We use the bus nodes as the cluster-heads to gather the routing control messages and data packets transmitted among the vehicles. The bus nodes first transmit the original network behavior information to the access points deployed along the road sides. Then the access points can get a global view of the VANETs, and we can detect anomaly behaviors through analyzing the data. The anomaly detection method is based on the neural network which can build the normal network behavior model through learning process. After the trained neural network is stable, it can monitor the VANETs security by detecting the network control message and data packet in real time and alarm immediately if there is anomaly behavior. The experiments in NS2 show that the detection method can detect anomaly behavior with low false alarm rate.

Safety message transmitting method for vehicle infrastructure integration

Abstract: Summary form only given. Recent years, as one of the application scenario of MANETs, the vehicular ad hoc networks (VANETs) attracted great interest in the field of intelligent transportation systems (ITS). There are some properties in VANET which make it different from MANET, for example high speeds vehicles result in frequent and fast changes in the network topologies, network topologies and the node movements are constrained by physical factors as vehicles can only move along road ways. With the help of VANETs, many kinds of safety and traffic efficiency related applications can be realized, such as cooperative collision avoiding: the high speed of wireless communication let drivers have enough time to stop the vehicle before crashes happening; dynamic traffic assignment: the traffic conditions can be transmitted to the drivers directly with wireless data communication. These applications, particularly safety applications, require a high availability of the communication system and low delivery latency for data packets.