Wireless Sensor Network for Community Intrusion Detection System Based on Improved Genetic Algorithm Neural Network

A community intrusion detection system based on improved genetic algorithm neural network (IGANN) is presented in this paper. This system is composed of ARM (advanced RISC machines) data acquisition nodes, wireless mesh network and control centre. The data acquisition node uses sensors to collect information and processes them by image detection algorithm, and then transmits information to control centre with wireless mesh network. When there is abnormal phenomenon, the system starts the camera and the IGANN is used to recognize the face image. The improved genetic algorithm neural network is combined the adaptive and floating-point code genetic algorithm with BP network which has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. With the ability of strong self-learning and pattern classification and fast convergence of IGANN, the recognition method can truly classify the face. This system resolves the defect and improves the intelligence and alleviates workerpsilas working stress.