In wireless sensor networks, sensor nodes have the characteristics of dense deployment and collaborative perception. The information collected by adjacent nodes during data acquisition has great similarity. In specific applications, only the monitoring results are of concern, and there is no need to collect a large amount of raw data. Therefore, in the process of collecting data through the wireless module, the local computing power and storage capacity of nodes should be fully utilized to process and combine multiple data or information to generate more effective and user-oriented data. This approach is called data fusion. Among them, the multi-sensor system is the "hardware" basis for data fusion, and multi-source information is the processing object for data fusion.

Data fusion of the wireless module has the following four significant characteristics

• Redundancy of information: a same signal may be captured by different sensors. It is necessary to remove unnecessary duplicate information.
• Complementarity of information: one sensor captures one kind of feature, and the combination of multiple features can obtain more comprehensive information.
• Timeliness of information processing: parallel acquisition and processing of multiple sensors.
• Low cost of information processing: multiple cheap sensors can cooperate to replace a single powerful but expensive sensor to obtain accurate information.

Unlike traditional wireless networks that focus on high service quality and efficient bandwidth utilization, the battery energy, computing power, storage capacity, and communication bandwidth of wireless sensor network nodes are extremely limited. Therefore, energy saving is the primary consideration in their design.

The large-scale and dense deployment of wireless sensor networks results in a lot of redundant data. Therefore, data fusion techniques can be used to process data during transmission. Data fusion technology is an important means to achieve this goal.

The research on wireless module network data fusion technology

In the application of wireless sensor networks, the research on data fusion technology mainly focuses on three aspects: saving the energy of the entire network, enhancing the accuracy of obtaining information, and improving the efficiency of data collection.

The information in data fusion processing mainly comes from the data of the same type of sensor. Usually, complementary optimization of the data characteristics of different spaces at the same time can lead to a relatively ideal result.

For example, in person positioning using the wireless module, multiple known node coordinate data need to be fused; in forest fire prevention applications, temperature data detected by multiple temperature sensors need to be fused.

In the actual application of sensor networks, data fusion needs to be combined with routing algorithms. The received information is fused through intermediate nodes for data optimization.

By coordinating and optimizing the information from multiple sensors, data fusion technology can effectively reduce unnecessary communication overhead in the entire network, improve information accuracy and data collection efficiency. Therefore, transmitting fused information saves more energy than transmitting unprocessed data, which extends the life of the network.

However, data fusion technology in wireless sensor networks also faces many challenges such as limited node energy, synchronization of multiple data streams, time-sensitive data, limited network bandwidth, unreliable and dynamic wireless communication.