Research Of The Key Technologies On Distributed Self-Adaptive Traffical Monitoring System Based On WSN

Automobile brings great convenience to human’s travel, but rapid increment of the automobile results in severe traffic congestion as well as a series of social and economic problems. Although construction of super highway, elevated loop and widening road makes some improvement for the traffic, but the physical resources are limited, especially in cities, where infrastructure like road area can’t be extended without limit. In order to solve this conflict, the governments and research institutions strive to develop "Intelligent Traffic System", improving efficiency of transportation system by improving utilization and management level of information. Highway Traffic Monitoring System is the main part of the Intelligent Traffic System. Highway Traffic Monitoring System is required to collect various information of traffic flow, traffic environment and the status of main traffic facilities. The typical technology used for construction of traffic monitoring system such as electromagnetic induction loop coil detection, radar wave detection and image processing and computer visibility, detecting speed, type, number of cars and whether they over speed or go in the wrong direction. But limited by the technology itself or specific environment factors, there exist the disadvantages such as failing to identify type of car, locate the position and get feedback in time, etc. While the Wireless Sensor Networks as a brand new information acquisition and processing technology, the problems above can be solved to a large extent.Wireless Sensor Networks is a brand new information acquisition and information processing technology, integrating sensor technology, MEMS technology and network technology, with self-organizing and self-adaption characteristics. Wireless Sensor Network is a sensor group composed of multiple sensor nodes. Every node in the sensor network is composed of one or multiple sensor units. After the information collected on some node is converted into wireless electronic signal, it is transmitted in these nodes by means of some route, and processed by sink nodes at last. Because of the simple structure, low cost of the individual sensor of the Wireless Sensor Networks, the self-organizing network and other visible advantages, we consider applying the Wireless Sensor Networks as the core technology to construct the modern Highway Traffic Monitoring System. But we should solve a series of technical problems before deploy the Highway Traffic Monitoring System.The work presented in this thesis focuses on the study of the sensor network energy management, underlying structure distribution and density optimization, location and speed testing. Main contributions of this thesis are as follows:(1) In the distributed and adaptive Highway Traffic Monitoring System based on the WSN, according to the traffic flow and the physical characteristics of the highway itself, by combining characteristics of the wireless sensor network routing protocol and Power Manageable Component (PMC) in the sensor node, we have presented an energy consumption model TTL (Timeout Threshold Leach) for the Highway Traffic Monitoring System. The model is improved based on LEACH (Low Energy Adaptive Clustering Hierarchy) routing protocol, capable of increasing the life circle of the network. In addition to this, the model can also perform further calculation of TT (Timeout Threshold) of every sensor node, and dynamically set PMC, comparing the sum of the free time with the timeout threshold to go into different stages of dormancy, with the view to further bringing down the energy consumption of the sensor node.(2) We optimize the coverage and the connectivity of the network through optimizing the position of the sensor nodes of the WSN. According to the physical characteristics of the highway, in light of the effect the sense coverage and the communication ability of the sensor node on the highway, we construct the multi-objective constrained optimization mathematical model, and convert it into the single constrained optimization problem by means of Geometric Weighted Method. The CRO (Chemical Reaction Optimization) algorithm is applied to solve the problem. The reasonable distribution of the wireless sensor network has improved dramatically the signal collection, maintenance and extension of the system.(3) In the distributed and adaptive Highway Traffic Monitoring System based on WSN, DV-Hop algorithm is often applied in locating the unknown node of the network, but in most cases, the accuracy of the unknown node location is very low. Therefore, we have proposed a new algorithm applying PSO (Particle Swarm Optimization) and SA (Simulated Annealing) to improve DV-Hop algorithm. Simulation results show that the new algorithm can increase the accuracy of the unknown node’s location and reduce the deviation of the unknown node’s location greatly. The new algorithm is more suitable to the Highway Traffic Monitoring System.(4) The thesis proposes the Safety Distance Monitoring Model between the automobiles combining with the road visibility, traffic flow and other detailed data. According to the weather information and traffic flow information which are collected by the Highway Traffic Monitoring System, the model can calculate the appropriate traveling distance between the automobiles. When the traveling distance between the automobiles is less than the safe distance, the model can sent the alert singal to the drivers through the vehicle’s radio, the vehicle’s GPS or RFID. The Safety Distance Monitoring Model can avoid the traffic accidents and the rear-end collision effectivly.The related research achievement of the thesis is practical to be implemented and has a reference value in construction of the Highway Traffic Monitoring System based on WSN. Especially research results have important practical value and economic value for improving the life circle of the wireless sensor networks in the highway environment, improving the network deployment and density optimization of the sensors and improving the location accuracy of the automobiles.