Li Dapeng

Resource Management in Vehicular Ad Hoc Networks: Multi-parameter Fuzzy Optimization Scheme

Large amount of information like text, audio and video exists in vehicular ad hoc networks(VANETs). However, the highly dynamic network feature and the limited memory of the local server pose challenges to resource management, which not only leads to the failure of resource presentations to users, but the transmission of the invalid fragment data would also result in the significant waste of precious bandwidth and memory. To solve this problem, this paper improved the fuzzy logic resource management (FLRM)--multi-parameter fuzzy logic resource management (MP-FLRM). In the scheme, we first gather and record the request time, download time and upload time for each resource by the designed Vehicle to Infrastructure (V2I) communication mode. Then survival time can be reached after defuzzifier of the three kinds of parameters, which can update the resource list in real time. Simulation results show that MP-FLRM can improve system throughput. In this way system performance was enhanced. Copyright

Research on Q-Learning Based Channel Access Control Algorithm for Internet of Vehicles

A Q-Learning based back-off algorithm was proposed in this paper because the traditional DCF approach used for IEEE 802.11p MAC protocol to access the channel has some problems of the low packet delivery rate, high delay and the poor scalability in VANETs. The proposed algorithm which is quite different from the traditional BEB algorithm was adopted by the nodes(agents) to interact with surroundings continuously and learn from each other. The vehicle nodes adjust the size of CW(Contention Window) dynamically according to the results learned from the surroundings so that the nodes can access the channel with the optimal CW eventually minimizing the packet collisions and end-to-end delay. The simulation results show that the communication nodes using the proposed algorithm can adapt to the unknown vehicular environment rapidly, and simultaneously the high packet delivery ratio, low end-to-end delay and high fairness can be achieved for vehicular network with various load.

A multi-lane network optimization scheme based on connectivity probability perception in vehicular network

In order to improve the resource utilization of wireless environment in vehicle networks, this paper focuses on connectivity probability of various road segments and proposed a multi-lane connectivity probability analysis model based on vehicle speed under free flow state with the consideration of vehicle communication environment. In the scheme, in order to improve the efficiency of data delivery, the neighbor information based multi-hop broadcasting is proceeded to quickly transmit packets from the selected segments to another one. Moreover, the best road segment with high connectivity probability is selected to forward packets. NS2 simulation results showed that the proposed resource optimization algorithm can improve the efficiency of forwarding packets.