Simulation of Monitoring Multiagent Systems
Keywords
Abstract
The article considers the aspects of simulation of monitoring multi-agent systems (MAS) that combine mobile unmanned aerial vehicles (UAVs) and stationary wireless sensor networks (WSNs). The relevance of the work is determined by the fact that UAV–WSN systems simultaneously involve the physical dynamics of mobile agents, network delays, packet losses, energy constraints of sensor nodes, and inter-agent coordination tasks, which complicates the use of a single universal simulation environment. The aim of the work is to systematize the approaches to the simulation of such systems and to form a criteria-based scheme for selecting a software environment depending on the level of model detail, target metrics, computational costs, and the possibility of transferring results to real-world conditions. The paper distinguishes low, medium, high, and multi-agent levels of modeling, for which the main simulation objects, typical tasks, and key metrics are defined. A multi-criteria scheme for evaluating the suitability of a simulation platform is formed, taking into account the accuracy of reproducing target processes, scalability, suitability for the Sim-to-Real transition, computational costs, and integration complexity. Based on a variant comparison of software environments, appropriate areas of their application for different stages of research are identified: abstract or specialized environments for high-level and multi-agent experiments, robotic 3D simulators for engineering verification of nodes, and discrete-event simulators for the analysis of network processes. For UAV–WSN systems, a hybrid modeling organization scheme is formulated, which involves the use of Python or Mesa/NumPy for testing high-level algorithms, Gazebo or Webots for engineering verification of mobile nodes, and ns-3 or OMNeT++ for detailed analysis of network processes. The practical significance of the work lies in the formation of a methodological framework for coordinating different levels of simulation and reducing the gap between abstract modeling and further engineering verification of UAV–WSN monitoring systems.
