AFMR Routing Algorithm in WSN

The AFMR routing algorithm in WSNs solves the challenge of how mobile agents can fuse and collect correlated data in an energy-efficient manner by constructing optimized routes and adaptively determining whether to perform data fusion at each node. The algorithm comprehensively considers both transmission energy consumption and fusion energy costs during the mobile agent's routing process. Based on the energy overhead of data fusion algorithms and potential energy savings, it adaptively adjusts whether to execute data fusion operations when mobile agents migrate to different nodes, thereby optimizing mobile agent energy expenditure across various application scenarios. From an implementation perspective, the algorithm typically incorporates energy threshold calculations and cost-benefit analysis functions to make real-time fusion decisions.

One key advantage of the AFMR algorithm is its flexibility in choosing whether to perform data fusion operations during the mobile agent's routing process. This adaptive capability allows the system to decide on data fusion execution based on current energy overhead and energy-saving gains, achieving optimal energy management and data collection efficiency in different application scenarios. The decision-making logic can be implemented through conditional statements that compare energy consumption metrics with predefined thresholds.

Furthermore, the AFMR algorithm accounts for mobile agent energy consumption and transmission capabilities to ensure efficient utilization of energy resources during data fusion processes. By comprehensively considering these factors, AFMR can optimize mobile agent energy expenditure in diverse environments, thereby enhancing overall system performance and data collection efficiency. The implementation often includes energy-aware routing tables and dynamic power management modules to track residual energy levels at sensor nodes.

In summary, AFMR is an efficient routing algorithm that addresses mobile agent energy management and data collection challenges. Through adaptive decisions on data fusion operations and consideration of mobile agent energy consumption and transmission capabilities, it achieves optimized energy expenditure across various application scenarios. The algorithm's architecture typically features modular components for route calculation, energy assessment, and fusion decision-making that work cohesively to minimize total energy consumption while maintaining data quality.