WSN Data Fusion Algorithm Based On Ant Colony Algorithm And BP Neural Network

Wireless sensor networks converges embedded computing technology, sensor technology, distributed information processing technology and wireless communication technology.It is a new type of sensor network model, has lower cost and a wide range of monitoring. In recent years, along with the technology development, energy problem has become one of the most important factors which limit the development of the premise, how to meet the performance of wireless sensor work, reduce the energy consumption has become an important subject of current.In the process of reducing energy consumption in wireless sensor network, ant colony algorithm has very strong feasibility, and using BP neural network is able to achieve optimization for the ant colony algorithm. In this paper, in order to achieve the minimum energy consumption of network as the goal, based on ant colony algorithm (ACO) and BP neural network algorithm (BPNN) was studied by ACOBP algorithm of data fusion, data fusion and routing problem into a NPC problem, and the use of complementary ACO and BPNN WSN to achieve balanced energy consumption of the cluster nodes. The main contents of this paper can be divided into the following several aspects:1.Have a main analysis of WSN architecture, system structure and the composition of the node. Also describes the key technology of WSN and its characteristics,application. Followed by a detailed analysis of the definition and model of data fusion, data fusion classification.2. Based on the concept of WSN and data fusion, has a detailed analysis of the basic principles of the ACO, the core issue, the method, the advantages and disadvantages as well as application in WSN data fusion. Researchs a typical model based on ACO data fusion, according to the ACO algorithm, selecting the cluster head in this model and improving the efficiency of data fusion.3.Have a detailed analysis of the basic principles of the BPNN, key issues, strengths and weaknesses as well as the application in WSN data fusion. Combining BP neural network with data fusion,get data fusion model based on the BPNN. Ant colony algorithm is applied into BP neural network, the optimization of the structure and parameters.Neural network based on ant colony optimization has faster convergence speed,also overcomes the shortcomings of BP algorithm falling into local optimal solution easily. And then using of the BP neural network optimization, effectively extract the wireless sensor network data fusion raw data into a small amount of characteristic data, then send characteristic data to the sink node, to enhance the efficiency of data collection, to extend the network lifetime. According to the complementary of ACO and BPNN algorithms,proposed the model of the data fusion ACOBP algorithm, and detailed description of the process of its implementation. Finally, by MATLAB simulation, has performance comparison analysis with LEACH, BP and GABP algorithm. the reduction of energy consumption in the network is verified,but also the algorithms and GABP algorithm evolution algebraic optimization contrast, founding that the convergence of the algorithm is improved,network energy consumption is significantly reduced, the network lifetime has been effectively extend.