Research For Infomation Fusion Algorithm Based On The Internet Of Things In Forest Fire Monitoring

Forest resources are important natural resources, the occurrence of forest fire will cause serious resources and economic losses for the state, so, using advanced technology for intelligent monitoring of forest fires, is very important to protect the forest resources against the loss. In recent years, as constantly-developing the Internet of Things technology, it also makes application filed to expand, and one of the application is intelligent monitoring of forest fire.Wireless Sensor Network(WSN) and Wireless Multimedia Sensor Network(WMSN) as the underlying of the Internet of things, they own some characteristics, for example wide coverage, low cost, high accuracy of data, and can effectively apply in the intelligent monitoring of forest fire. But, the WSN and WMSN belong to resource-constrained network, the power the power supply equipment, computing power and bandwidth of them are limited. However, in the fire monitoring system based on the Internet of Things, there are a large number of redundant data in the underlying network, sensors can produce a large number of monitoring data, and the data transmission can consume vast network and computing resources, it seriously influence the service life of the sensor network.In this paper, aiming at the problems of WSN and WMSN, a valid information fusion algorithm was proposed, and it can reduce the transmission of data in the underlying network, save energy and improve the efficiency of monitoring and early-warning.1) The data fusion algorithm of scalar sensor based on WSNAccording to the characteristics of forest fire, we proposed a data fusion algorithm based on the transmission probability threshold. In the underlying network of WSN, it uses temperature sensor, humidity sensor, infrared sensor and smoke sensor as forest fire monitoring sensors. First, this algorithm uses the weighted average algorithm to calculate the weight coefficient of the sensor’s measurement. Second, the algorithm uses logistic regression model to get fire probability of this node. Third, compare the fire probability of this node and the threshold, if the fire probability is greater than the threshold, sent to the cluster node. Otherwise, not sent. Thus, it effectively reduces the transmission of the invalid data in the underlying network.2) The feature fusion of multimedia image based on WMSNIn the underlying network based on WMSN, it uses multimedia sensors such as image, video or audio induction as forest fire monitoring equipment. In this paper, we use the video as the image data. Aiming at the problem of image transmission energy consumption, we proposed image feature fusion algorithm based hash code, for flame image recognition. On this basis, the invalid and not flame images are discarded, in order to reduce data transmission and save energy. First, this algorithm needs to offline learning, using the test dataset, establishes the database of image hash code as the basis of image retrieval. Second, the features of the image are extracted and hash-coded. Third, we calculate the hamming distance between images’ hash code, then based on the hamming distance, the image is retrieved and identified.The experimental results show that: based on the data transmission probability threshold of the data fusion algorithm, it can reduce about 34% of the transmission energy consumption. The image feature fusion algorithm based on hash coding, the accuracy of flame recognition of this algorithm is 94.12%, and it is higher than other flame recognition algorithms, based on the SVM, BP-neural network and sparse representation. In this paper, based on the information fusion algorithm, it can effectively reduce the transmission of the invalid data in the underlying network, reduce the energy consumption of the network transmission, increase the utilization of the network, prolong the network lifetime, on the basis of guaranteeing the accuracy and timeliness of the fire monitoring.