## 1 Introduction  
In the past, sensors were manually installed in fixed positions. Over the years, the use of sensors has expanded with the use of mobile sensors with wireless connections in Wireless Sensor Networks (WSNs). Combining the mobility with wireless technology has created new challenges. Connections are temporary and more fragile, and mobile sensors have limited energy storage. Mobile Agents (MAs) can alleviate these challenges. A Mobile Agent is a computer program with autonomous mobility function [1], which can gather data while moving in Wireless Sensor Networks [2; 3; 4; 5; 6; 7; 8]. This special software saves its own state, transfers its state to a new host, and resumes its task at the new host. It carries its own routing information, interacts with the environment, and completes its task autonomously [9]. The path can be static using predefined information, or dynamic based on autonomous routing decisions. Agents can process data independently. Mobile agents allow for effective use of local resources, reduced network load, improved network scalability, and increased reliability and real-time performance [10]. Eventually, central network points collect the data. If sensor nodes send data directly to a sink node, the sink can become a bottleneck. Data fusion at the sink can integrate multi-source data to increase its accuracy, completeness, and dependability. Data fusion by mobile agents can decrease energy consumption, increase efficiency, improve precision, and help meet application layer requirements [11]. Movement can interfere with this process when transmission of routing information [12] and data [13] is interrupted. In that case, data fusion in Mobile Wireless Sensor Networks (MWSNs) will suffer from network delays, high and uneven energy consumption, poor scalability, and increased demands on the sensors. Obviously, sensors must use their limited energy independently and effectively. However, just focusing on energy conservation can result in an ineffective network structure if the conservation efforts do not realize the benefits. Collaborative data mining and information fusion may require sensor interaction to complete tasks or respond to queries. If too many sensors are involved, merging of mined data leads to increased energy consumption and competition for communication resources. On the other hand, decentralization to limit energy consumption and communication may be inaccurate and ineffective due to lack of coordination. Balancing communication requirements and energy consumption with coordination is necessary to maximize use of network information. Our data fusion algorithm for multi-protocol mobile agents in wireless networks contributes to the solution of this dilemma. It is based on the Agent-based Model in cooperative industrial networks [14], which shows the impact of business interoperability on the performance of networked companies. This theoretical agent-based model was confirmed in a dam construction project, where a cooperative information system platform with Radio Frequency Identification (RFI) technology reduced the time for analysis of testing results for concrete up to 98%. Like the earlier study, we developed a simulation model and confirmed the model experimentally. Sending data by mobile agents in fused format produces reliable data while lowering energy consumption and network delays, especially in larger networks. These results are potentially important in emerging large-scale mobile wireless networks. We organize the rest of this paper as follows: Section 2 discusses the related literature and briefly reviews the theoretical framework. Section 3 presents our Improved Multi Mobile Agent Data Fusion scheme. This is a network simulation comparing energy requirements and network delays between fused and unfused data, and with our improved routing decision making. Section 4 discusses the experimental environment where we check if fused data can give reliable measurements of events.