Research On Traffic Abnormal Information Detection And Transmission Technology In Vehicular Sensor Network

With the in-depth promotion of wireless sensor networks in the field of vehicles, ve-hicular sensor networks(VSNs) has attracted more attention as a new generation of network technology. Its application into the urban road condition monitoring, traffic abnormal event detection and other aspects would be hopefully promising. To solve the problem of traffic jam and secondary accident caused by the abnormal traffic event, it is essential to know the road condition accurately and timely, and provide the feasible traffic information release ser-vice and subscription service to mitigate the serious impact caused by the abnormal event on urban traffic through VSNs. This is very crucial in the construction of a smart city.The dissertation researches on the perspective of traffic abnormal event detection and information transmission protocols. Under the premise of discovering road congestion, it discusses the issues of multi-target geocasting and deals with the inter-region information subscription problem based on the collected data of urban road condition. The main contri-butions of this dissertation can be summarized as follows:(1) Due to the drivers’energy-saving need and the unfair energy consumption among nodes, the existing data collection mechanisms are unreliable. To address this issue, this dissertation proposes an energy-aware socially-based routing protocol based on the DTN routing and stimulation ideas, and designs a new data collection framework by taking the public transport vehicles as the main part. Firstly, this protocol prorates the limited replication tokens by sensing the residual energy and speed of vehicle nodes. It not only can avoid blind data spraying, but also equalize the whole energy consumption level. So, it indirectly achieves the purpose of encouraging selfish nodes to participate the data forwarding. Meanwhile, this protocol evaluate the social rela-tionship ability of nodes according to the difference of their history encounters, and designs a socially-based focus algorithm. Amount of simulation experiment results show that, under scenarios closing to the real environment, the probability of data suc-cessful delivery to the sink nodes of this protocol is about10%higher than SF, and20%higher than SW and EBR.(2) To deal with the incompleteness of abnormal information detection and the limitation of single-level information fusion, from the perspective of comprehensive optimiza-tion, this dissertation proposes a multi-level information fusion mechanism to detect the road congestion information, by combining the feature level information fusion with the decision-level information fusion. Firstly, this mechanism implements the fuzzy-clustering-based message aggregation method to remove the inaccurate and re-dundant atomic messages. Then, it selects congestion feature information utilizing the custom event prediction function and message credibility assignment strategy, and proposes an anti-jamming congestion decision method based on the Dempster-Shafer evidence theory, so as to avoid the false congestion evidences generated by the long-time traffic lights. The experimental results show that the average message aggregation ratio of this mechanism can reach to98%, which is only2%higher than RSMA, but it can extract more subtle congestion feature information from the neighbor lanes ac-curately. Also, the theoretical analysis shows that it can ensure the consistency and accuracy of event detection.(3) To address the non-uniqueness of the target regions where disseminating abnormal information, the repeatability of message transmission paths, and the mobility of mes-sage receiving nodes in each target region, this dissertation proposes a smart geo-casting protocol. It guides the message broadcast behaviors and controls the mes-sage transmission direction, by deploying some virtual landmarks like lighthouses and buoys at cross-road center and in each target region, and embedding their coor-dinates information in each broadcast message. To reduce the routing overhead, the message firstly builds the shared multicast path using the lighthouse. After the path splitting, the message arrives at the entrance of the target region. Then, the initial message broadcast is completed based on buoys in each target region, and the mes-sage rebroadcast maintenance mechanism is implemented by cutting a small unit area near the exit of the target region and predicting the next optimal rebroadcast time. The experimental results show that, this protocol can greatly reduce the total message rebroadcast times, and minimize the message repeatedly receiving probability while limiting the message missing probability in a tolerant range.(4) Due to the cross-regional feature between the subscribing node and the publishing node, the less and uneven distribution of RSUs, the abnormal information reply based on the vehicle-to-vehicle communication has the problems of a high transmission cost and low successful subscribing probability. To this, this dissertation proposes an op-portunistic routing protocol. It designs the quota-style message replication procedure based on the link stability, to avoid the limited number of message copies are easily aborted. Meanwhile, with the sociality and regularity of vehicles’mobile behaviors, this protocol proposes a method to construct the vehicle community structure based on the visiting similarity degree, and designs a community-aware message forward-ing algorithm by integrating "the message moving tendency" with "the social rela-tionship ability". The amount experiment results show that, in obvious cross-region scenario, the proposed protocol can reach the same high successful subscribing prob-ability like Epidemic with lower transmission cost, and its value is about8%higher than ProPHET.