Research On Street Lighting Control System Based On Wireless Sensor Network

As an important part of urban road lighting,the number of street lamps is increasing year by year with the rapid development of urban construction in our country.The large amount of waste of power resources caused by traditional lighting control methods runs counter to the energy-saving and emission-reduction policies promoted by the state.For this reason,domestic and foreign scholars have combined road environment monitoring and lighting control,and conducted many researches on street lighting control systems,but the communication management technology integrated with monitoring is relatively lagging.In response to the above problems,this article applies wireless sensor networks to road environment monitoring and lighting control,taking the street lamp lighting control based on wireless sensor network as the research direction.The system is analyzed from five aspects:network architecture,wireless communication protocol,dimming classification model establishment,and node software and hardware design.First of all,the paper puts forward the network architecture of street lights monitoring system,according to the characteristics of the ribbon distribution of street lamps combined with the actual transmission distance of the network and the efficiency of data transmission,the multi-channel ribbon cluster network topology is designed,and the data transmission mechanism is studied based on this.The nodes in the cluster adopt TDMA to avoid data collision,the cluster head node enjoys a channel of the base station node,and introduces the CSMA/CA protocol to study the data escape strategy in case of suddenness,so as to realize the node’s collaborative work and efficient transmission.In the experimental part,the test results of network communication show that the network has good data transmission characteristics in single-channel and multi-channel mode,and the performance indicators such as throughput and packet loss rate meet the design requirements.In view of the single dimming level of traditional lighting control methods,which causes power waste to a certain extent,use the characteristics of BP neural network nonlinearity and self-learning to establish a street lamp dimming classification model,and combine the road environment monitoring data to classify the lights.The sample set processing,model parameter design and model construction processes are analyzed in detail.The verification experiment results show that the model classification results have a high correct rate and can be used for classification prediction of actual lighting adjustment.According to the overall system structure and main functional indicators,a sensor node with the Mini STM32 development board as the main board,CC2630 wireless communication module,HB100 microwave radar sensor module,BH1750 FVI light intensity sensor,etc.as peripheral devices,and a base station equipped with 4G modules and multiple CC2630 s were built.The system lighting control algorithm is designed based on the hardware platform,the algorithm combines the BP neural network dimming classification model with the traditional lighting threshold control,decides whether to invoke the dimming classification model according to the lighting threshold to reduces the network burden to a certain extent.In addition,an algorithm for waking up when there is no car for a long time at night is designed to solve the problem of untimely dimming of the incoming car and the adverse effect of the light jump on the pedestrian or driver’s vision.Finally,a complete intelligent street lamp monitoring system for data collection,processing and transmission is formed.In the end,the system built in this paper is tested for lighting and energy saving effect,and the feasibility of the algorithm and the good energy saving effect of the system are verified.The research results provide theoretical basis for the design of urban street lighting control system,and are of great significance for promoting the development of "smart city".