Research And Simulation Evaluation Of Collaborative Optimization Mechanism In Vehicular Networking Communications

As the key technology for the development of intelligent transportation systems,vehicular networking has received extensive attention from both academia and industry.The highly dynamic network topology and diverse QoS demands for VANET applications pose challenges to vehicular networking communication,and a single wireless communication technology cannot guarantee reliable and timely message delivery.With the increase of network scale,the delivery performance of message delivery is also constrained by the limited spectrum resources.In addition,the static infrastructures in the vehicular networking lack flexibility to adapt to the dynamic access demands.In view of the above issues,we proposed an aerial-ground cooperative heterogeneous vehicular networking architecture,based on which the collaborative optimization mechanisms in vehicular networking communications are studied.The main contents of this paper are as follows:(1)A centralized clustering algorithm named as CC-HVNA under hybrid vehicle infrastructure cooperative architecture is designed,in which the collaborative control between IEEE 802.11 p and LTE is realized to achieve clustering and to coordinate message delivery.In this architecture,vehicles are capable of selecting communication modes according to network connectivity and the infrastructures utilize the gathered vehicle info to participate clustering.The modified Kmeans algorithm and relative mobility metrics based cluster head election are adopted to perform size-limited clusters partition and a specific cluster update strategy is designed based on the cluster info table.In addition,we leverage a control center to aggregate the distributed clustering info from road side units and base station to schedule message forwarding.The proposed algorithm is effective to offload base station,reduce the control overhead of clustering algorithm and improve the delivery performance of safety data.(2)The dynamic optimization of drone small cells under the distributed aerial-ground cooperation architecture is studied.A distributed drone small cells dynamic planning algorithm named as ODDP-ASYNC,which based on markov approximation,is proposed to serve data traffic demand in hot spots and offload the macro base station.Multi drone small cells are deployed in the task area to serve uplink data demand and offload the macro base station.After the initialization,drone small cells make state transitions based on real-time throughput measurement and limited message passing.Without the communications between control center,drone small cells are able to select deployment location and operating channel autonomously.The system throughput in this scheme can coverage to the optimal value asymptotically through the dynamic planning of drone small cells.(3)An interactive simulation platform for collaborative optimization in vehicular networking which based on SUMO and NS3 is established.The road topology and vehicle mobility model are configured in the traffic simulation tool SUMO,while the node communication protocol and channel model are configured in the network simulation tool NS3.The traffic control interface(TraCI)in SUMO are utilized to realize the coupling between simulation tools.The interaction between traffic flow and information flow are used to simulate the information acquisition of vehicular sensors and to control vehicles.The platform can support the verification of applications in vehicular networking and provide reference for the actual operation of the vehicular networking systems.Finally,the proposed algorithms in this paper are simulated in the interactive simulation platform and compared with other algorithms.The centralized clustering scheme CC-HVNA is compared with 802.11p-only and LTE-only to evaluate the end-to-end delay and packet delivery ratio of safety data,while the drone small cell deployment scheme ODDP-ASYNC is compared with the random state transition(RST)and the minimum throughput based state transition(MTST)to evaluate the system throughput.The results show that the proposed collaborative optimization algorithms are efficient to improve the safety data dissemination and offload the macro base station.This research will be helpful to design more optimized system architectures for future vehicular networking.